By now, you may have heard the term conversational commerce. Aside from some catchy alliteration that, honestly, just makes it fun to say, you may not know much about why everyone’s talking about it. Let’s clear things up a bit.
Conversational commerce refers to the transactions that take place through digital conversations consumers have with brands on messaging apps like web chat, Apple Messages for Business, text messaging, and even Facebook Messenger– often powered by a conversational commerce platform.
The interactions that take place are powered primarily by chatbots that process consumer messages and offer relevant responses. Conversational commerce takes on many forms and can take place across a multitude of channels.
Here are a few conversational commerce examples in real life:
Brands helping consumers shop by find a store location or online promotions and deals
Customer experience agents Interacting with a customer to reschedule an appointment or a delivery
These are just a few ways messaging has greased the commerce wheel, making it easier and faster for business to get done. In this article we’ll take a close look at conversational commerce and what companies need to do to fully leverage its benefits.
How Does Conversation Commerce Work?
Consider the smartphone — you know, the thing that is essentially glued to everyone’s hands now-a-days — and reflect on how much searching, browsing, buying, and texting takes place on any given device on any given day. Even before it was egged on by mandates, when our normal was just normal, consumers leaned on the ease, convenience, and speed that shopping from their smartphone offered.
As the world still works through “new normal” restrictions on physical locations, we’ve seen a surge in use as more consumers have looked to online shopping, delivery, and curbside pickup to get through their days. According to comScore consumers spend 85% of their smartphone time using only 5 apps, which tend to be either social media, messenger or other media apps. It makes sense that businesses would marry the power of messaging with consumers preferences.
He predicted these real-time conversations would facilitate more convenient online sales. Mr. Messina has yet to comment on where he picked up that incredibly accurate crystal ball of his, but looks like he was pretty spot on. Now, let’s talk about how your company can benefit from an investment in conversational commerce.
Why Invest In Conversational Commerce
Consumer expectations of speed and convenience have birthed new innovations that are opening up a seamless communication between brands and customers. Gone are the days where customers were satisfied with dialing an 800 number or having to write an email to get help.
At least 60% of companies will use artificial intelligence to support digital commerce.
30% of digital commerce revenue growth will be attributable to artificial intelligence technologies, such as those that power conversational commerce.
5% of all digital commerce transactions will come from a bot, such as those that power conversational commerce.
All of the major trends in commerce over the past couple of decades have been in moving to where customers are. Rather than forcing customers to come to you, you go to where they are. The next generation of that is conversational commerce.
Investing in conversational commerce also unlocks tangible business benefits that can elevate your customer experience and bottom line:
Improved Customer Experience
With AI and data-driven insights, conversational commerce allows brands to deliver more personalized interactions. These tools can understand customer preferences and intent, allowing for real-time, contextually relevant responses that feel tailored to the individual. Whether it’s personalized product recommendations via a chat widget, a voice assistant helping narrow down options, or live customer support through messaging apps, these conversational commerce examples help deepen engagement and drive loyalty. The interactions feel more natural and human—making the customer feel seen, heard, and supported throughout the buying journey.
24/7 Availability
AI-powered systems and chatbots enable brands to offer round-the-clock support, meeting rising expectations for always-on service. This is especially valuable for global brands, allowing them to deliver consistent, timely assistance across time zones—without the overhead of a 24-hour support team.
Cost Reduction
Conversational commerce solutions are designed not only to improve customer satisfaction but also to reduce operational costs. By automating routine tasks and frequently asked questions, businesses can scale without heavily increasing headcount. Human agents are freed up to handle higher-impact conversations, improving overall efficiency and productivity.
Create A More Natural Brand-Consumer Relationship With Conversational Commerce
Conversational commerce, much like regular conversations, are meant to build relationships. Conversational commerce is an opportunity to move beyond email blasts, promotional posts on social media, and other communication methods that provide just one-way communication. SMS and other messaging apps enable you to keep your customers informed with updates, and allows them to respond on the same message thread. A real-time exchange of information? Now that’s a conversation.
Creating engaging experiences on these channels are better and easier if your agents have a single view of your customers. Quiq makes it easy for companies to manage conversations with an intuitive agent desktop and native integrations with Salesforce.com, Zendesk, Shopify, and Oracle Service Cloud.
Quiq is architected with an “API First” strategy which means we seek to work in harmony with your existing systems. With Quiq’s integration frameworks you can customize our UI to include data from your internal operations systems or synchronize conversation data into your reporting, customer and other backend systems.
Get Started with Conversational Commerce
Getting started with conversational commerce isn’t complicated. Start with one new channel that customers visit every day such as WhatsApp or Facebook Messenger. Better still, start with text messaging, a universally accepted way to engage with consumers.
Artificial intelligence is evolving rapidly, and one area that’s generating excitement is multimodal AI. This powerful innovation allows machines to process and combine multiple types of data, such as text, images, and audio, for a more comprehensive understanding of complex tasks.
Imagine a single AI system that can analyze a photograph, listen to a related audio description, and synthesize this information into actionable insights. That’s the potential of multimodal AI—and its applications are transforming industries as diverse as customer service, healthcare, and retail.
Keep reading to explore how multimodal AI works; its mechanisms, practical uses, and why it matters to businesses looking to stay ahead.
What is multimodal AI?
Multimodal AI refers to artificial intelligence systems capable of integrating and analyzing data from multiple modalities—think text, visuals, audio, and more. By combining these different input types, multimodal AI achieves a richer understanding of information and can produce results that are contextually nuanced and highly reliable.
Unlike traditional or “unimodal” AI, which processes only one type of input (like text in natural language processing), multimodal AI blends data streams for a more comprehensive view. For example, a multimodal model could process an image of a room and a verbal description to identify objects and their spatial arrangement.
Key examples of multimodal AI:
OpenAI’s GPT-4V combines textual and visual inputs, enabling it to generate captions for images or interpret text-based prompts with associated pictures.
Meta’s ImageBind allows integration across six modalities, including text, audio, and thermal imaging, pioneering applications in content creation and environmental sensing.
Google’s Gemini enables seamless understanding and output generation across text, images, and video—raising the bar for multimodal AI capabilities.
This ability to synthesize varied data types positions multimodal AI as a next-generation tool in solving increasingly complex problems.
How does multimodal AI work?
At its core, multimodal AI processes and integrates multiple data types through advanced learning mechanisms. Here’s how it works step by step:
1. Data fusion
Multimodal AI uses data fusion to combine inputs from various modalities into a unified format. This can happen at different stages, such as:
Early fusion: Raw data from different modalities is combined at the input stage (e.g., pairing an image with its caption).
Mid fusion: Modal data is pre-processed and fused during the learning phase.
Late fusion: Each modality is processed individually before outputs are combined.
2. Advanced machine learning techniques
Deep learning techniques like transformers and neural networks play a pivotal role. For example:
Convolutional Neural Networks (CNNs) specialize in extracting features from images.
Natural Language Processing (NLP) models process text data.
By integrating these, multimodal AI creates a shared “embedding space” where connections between text, visuals, and more are understood.
3. Training multimodal models
These models are trained using massive datasets that cross-reference modalities. For instance, a model may learn to associate a spoken word (“orange”) with both an image of the fruit and its written description.
Popular multimodal AI models:
CLIP by OpenAI aligns images with textual captions, enabling applications like visual search.
Runway Gen-2 generates dynamic videos from text prompts, showing the creative possibilities of multimodal AI.
The result? Systems that are both adaptable and intelligent across multiple forms of information.
Key applications of multimodal AI
The versatility of multimodal AI opens doors across industries. Here are five key applications reshaping businesses today.
1. Customer service automation
Multimodal AI enhances AI agents by integrating text, voice, and visual inputs.
Example: A customer can upload a photo of a damaged product while describing the issue through text or voice. The AI agents process all inputs simultaneously for faster issue resolution.
Why it matters: This leads to smoother, more human-like interactions—vital for improving customer satisfaction.
At Quiq, our rapid agentic AI builder, AI Studio, supports multimodal AI models, along with customer model support. We also integrate multimodal AI into solution builds, such as in our Voice AI product. Here’s how that works:
2. Retail
Retailers are leveraging AI to enhance the online shopping experience with multimodal product search.
Examples: Customers can use an app to photograph an item they like, describe it verbally, or type in keywords. The system combines all inputs to suggest similar products. This is just as valuable from a customer service perspective. For example, if a customer receives a damaged product, they can send a picture of it to the company. That company can then use AI to assess the product and damage, and take action from there—like shipping a replacement or issuing a refund.
Result: Faster, more accurate recommendations drive customer loyalty and increase conversions.
3. Healthcare
The medical field benefits immensely from multimodal AI’s ability to synthesize data streams.
Example: AI combines medical imaging (like x-rays) with electronic patient records to diagnose conditions more accurately.
Autonomous vehicles rely heavily on multimodal AI to interpret their surroundings.
How it works: Data from LIDAR sensors, visuals from cameras, and audio cues are fused to make real-time decisions.
Why it’s crucial: This integration ensures safer navigation and reduces the risk of accidents.
5. Content creation
From generating blog posts with matching images to creating videos based on textual prompts, multimodal AI is revolutionizing creativity.
Example: Tools like OpenAI’s DALL-E 3 turn written descriptions into high-quality images, and Runway Gen-2 extends these functionalities to videos.
Impact: Empowers marketers, artists, and content creators to produce engaging multimedia pieces quickly and cost-efficiently.
By streamlining processes and offering richer outputs, multimodal AI redefines customer and employee experiences alike.
Why multimodal AI is the future of intelligent systems
Multimodal AI is a foundational shift in how we approach and solve problems. By integrating diverse data types, this innovation allows businesses to unlock insights, make better decisions, and offer elevated customer experiences.
From self-driving cars to AI-powered agents, the applications of multimodal AI span across industries, demonstrating its versatility and impact. However, this technology is still evolving, with challenges like data alignment and ethical concerns requiring attention. If you’re interested in integrating multimodal AI into your CX solutions, check out what we’re doing here at Quiq.
Agentic AI is poised to have a massive impact on businesses. Unlike traditional AI systems that do simple answer generation from a knowledge base, agentic AI takes things further—it possesses the ability to act autonomously, learn from interactions, and make independent decisions to achieve specified goals.
This advanced form of AI goes beyond basic automation, offering adaptive and intelligent solutions that can improve how organizations operate and deliver value to their customers. Let’s explore how agentic AI is reshaping industries and creating new opportunities for growth.
Agentic AI use cases across industries
There’s a lot of momentum behind agentic AI throughout many industries, with even more use cases therein, so this won’t be an exhaustive list. Still, here’s where I see the most exciting agentic AI use cases right now.
1. Customer service and support
This is one of our main focuses here at Quiq. Agentic AI is improving pre- and post-sale customer service by automating repetitive, time-consuming tasks without losing the human touch that today’s customers demand. Unlike traditional chatbots that follow rigid scripts, these systems understand context and provide natural, human-like responses.
Here’s how:
Customer-facing AI agents
AI agents go beyond FAQs to handle Tier 1 inquiries by offering nuanced, conversational support. They can understand the context of a conversation, the appropriate time to help a customer self solve, and when to escalate to another team member.
Here are customer journey moments across pre- and post-sales service and support that we’re finding most effective to apply agentic AI agents to:
Pre-sale customer service
Product selection (Web/Mobile)
Product or service configuration
Place an order
Purchase and schedule a service
Product selection shopping cart (AI Agent suggests other products that complement products already in a shopping)
Post-sale customer support
Answer a question with information (using knowledge bases, product descriptions, product catalogs, etc.)
Order statuses
Proactive order status notification
Order returns, changes/corrections, and exchanges
Order/service delivery change (Shipper, installation, in person required)
Subscription managements
Loyalty program, points and/or gift card balance
Break fix/troubleshoot issues
Agent-facing and employee-facing AI assistants
At the contact center level, there’s several high-value applications of agentic AI to support human agents, from suggesting responses based on company/user info, to automating routine processes, like checking a bag, to checking for things like professional tone and spelling. By the way, if you’re interested in all the ways you can get started adopting next-gen AI for contact centers, watch our recent webinar on this topic, From Contact Center to Agentic AI Leader: Embracing AI to Upgrade CX.
Agentic AI can also aid other employees, outside of contact center agents. For example, we worked with one office supply retailer to empower their in-store sales associates with an AI assistant that provides fast answers to customer questions. And another, high-profile carpet retailer in Europe uses a Quiq-powered AI assistant to help onboard and train their employees.
Workflow automation
Outside of automating and improving conversations – whether it’s full automation via an AI agent, or whether via augmenting your human agents – there’s a whole host of other business processes and workflows that can benefit from agentic AI and LLMs more broadly.
Think everything from delivering better semantic search to users on your website (either product or knowledge base search), to automatically classifying and grading every customer interaction with your business (measured with metrics like CSAT).
Workflow automations can enable businesses to leverage the power of agentic AI and LLMs on demand to improve processes and customer touch points across their entire organization, not just during a conversation.
The result: Better containment and resolution rates, and customer effort scores (CES) if a customer is escalated to a human agent. Reduced average handle time (AHT), more consistent service quality, satisfied customers, and support teams empowered to focus on complex, high-value tasks.
2. Sales and account prospecting
Traditional sales outreach has always been a numbers game, with teams spending countless hours on manual prospect research and outreach. Agentic AI is changing this landscape by automating the most time-intensive aspects of prospecting while making interactions more personalized and effective than ever before.
Here’s how:
Intelligent lead scoring: Advanced algorithms analyze vast datasets of customer behaviors, interactions, and market signals to automatically identify and prioritize the most promising leads, allowing sales teams to focus their energy where it matters most.
Data-driven personalization: AI agents craft highly tailored outreach campaigns by synthesizing prospect data, past interactions, and industry trends to create messaging that resonates on an individual level.
Automated account management: Proactive monitoring of customer accounts to predict churn risks, identify up-sell opportunities, and maintain engagement through automated but personalized touch points.
Real-time sales intelligence: AI-powered dashboards provide sales representatives with actionable insights about prospect behavior, helping them make informed decisions about when and how to engage.
Multi-channel engagement optimization: Smart analysis of prospect engagement patterns across channels to determine the optimal timing, medium, and message for each interaction.
Predictive pipeline management: Advanced forecasting capabilities that help sales teams anticipate deals at risk and identify which opportunities are most likely to close.
The result: Sales teams can see higher conversion rates, reduced time spent on manual prospecting, and more meaningful customer relationships built on data-driven insights rather than gut feelings. This optimizes sales cycles and leads to increased revenue—and sales representatives who can focus on what they do best: building relationships and closing deals.
3. Supply chain and logistics
Today’s supply chains demand solutions that can process vast amounts of data and make split-second decisions. Agentic AI is improving this space by creating self-optimizing supply chains that can predict, adapt, and respond to changes in real-time, far beyond what traditional automation could achieve.
Here’s how:
Predictive demand analysis: Advanced AI models process historical data, market trends, and external factors (like weather patterns or social media sentiment) to forecast demand with better accuracy, helping businesses stay ahead of market shifts.
Intelligent route optimization: Real-time analysis of traffic patterns, weather conditions, and delivery windows to automatically determine the most efficient delivery routes, reducing both costs and environmental impact.
Dynamic inventory management: AI-powered systems that continuously monitor stock levels across locations, automatically adjusting ordering patterns based on demand fluctuations, and preventing costly stock-outs or overstock situations.
Supplier risk assessment: Continuous monitoring of supplier performance, market conditions, and global events to identify potential disruptions before they impact operations, allowing for proactive mitigation strategies.
Automated procurement intelligence: Smart systems that analyze market prices, supplier performance, and internal needs to automatically trigger purchases at optimal times and prices.
Predictive maintenance scheduling: AI agents that monitor equipment performance and predict maintenance needs before failures occur, minimizing costly downtime.
The result: Companies achieve more efficient supply chains with reduced operational costs, improved delivery times, and enhanced customer satisfaction. Benefits include more resilient operations, better inventory management, and a significant competitive advantage in the market.
4. IT operations and workflow automation
Agentic AI is recasting IT operations by creating systems that can predict, prevent, and resolve issues autonomously, fundamentally changing how organizations manage their technical infrastructure.
Here’s how:
Intelligent system monitoring: AI agents continuously analyze system performance metrics, user behavior patterns, and potential security threats across the entire IT infrastructure, providing insights to employees and automated responses to emerging issues.
Predictive problem resolution: Advanced algorithms identify potential system failures or bottlenecks before they impact operations, automatically implementing fixes or alerting IT teams with detailed solution recommendations.
Automated security management: Real-time threat detection and response capabilities that go beyond traditional rule-based systems, learning from new attack patterns and automatically implementing defensive measures for the team.
Smart resource allocation: Dynamic adjustment of computing resources based on actual usage patterns and predicted demand spikes, ensuring optimal performance while minimizing costs.
Workflow intelligence: AI-powered analysis of business processes to identify bottlenecks, suggest improvements, and automatically implement optimizations where possible.
Self-service enhancement: Intelligent AI assistants that can handle routine IT requests and troubleshooting, learning from each interaction to improve future responses.
The result: Organizations experience significantly reduced system downtime, faster issue resolution, and more efficient resource utilization. IT teams can shift their focus from routine maintenance to strategic initiatives, while employees enjoy more reliable systems and faster support response times.
5. Marketing personalization
Gone are the days of one-size-fits-all marketing campaigns. Agentic AI is enhancing how brands connect with their audiences by enabling true one-to-one personalization at scale, upleveling generic messaging into highly targeted, contextually relevant experiences that evolve in real-time based on customer behavior.
Here’s how:
Cross-channel personalization: Intelligent systems that maintain consistent, personalized messaging across all customer touch points while adapting to channel-specific requirements and user preferences.
Predictive journey mapping: Advanced analytics that anticipate customer needs and automatically adjust marketing touchpoints, ensuring the right message reaches the right person at the optimal moment in their journey.
Campaign optimization: Continuous monitoring and automatic adjustment of campaign parameters, creative elements, and targeting criteria to maximize performance without human intervention.
Smarter budget allocation: AI-driven analysis of campaign performance that automatically redistributes marketing spend to the highest-performing channels and audiences in real-time.
Behavioral intent analysis: Sophisticated processing of customer interactions to predict future behaviors and automatically trigger relevant marketing actions before customers even express their needs.
The result: Marketing teams can achieve higher engagement rates, conversion rates, and better ROI on their marketing investments. Customers receive more relevant, timely communications that actually add value to their experience, leading to increased brand loyalty and CLV (Customer Lifetime Value).
Agentic AI use cases in four key industries
Agentic AI is improving direct-to-consumer interactions by creating personalized, efficient, and seamless experiences across multiple sectors. Here’s a detailed examination of how agentic AI affects four key industries:
Proactive and personalized shopping assistance: AI agents that provide proactive, real-time advice and product recommendations based on individual preferences and past purchases.
Customer service automation: Intelligent AI agents that handle inquiries, returns, and provide product information 24/7.
Cart abandonment prevention: Smart systems that identify and address potential checkout issues before they lead to abandonment.
The result: Higher conversion rates, reduced cart abandonment, better CSAT, enhanced resolution rates, and improved customer loyalty through AI-driven shopping experiences.
Learn how a national furniture retailer reduced escalations to human agents by 33% with Quiq. Get case study >
2. Travel
The travel industry is leveraging agentic AI to create more seamless journeys.
Here’s how:
Real-time travel assistance: Smart systems that provide customers with on-the-go support and recommendations during trips.
Personalized experiences: AI-driven recommendations to customers for activities and experiences at destinations. Intelligent trip planning: AI agents that create customized itineraries based on preferences, budget, and travel history.
Price prediction: Advanced algorithms that forecast flight and hotel prices to recommend optimal booking times to the customers.
Disruption management: Automated systems that predict and respond to customers’ travel disruptions with alternative solutions.
The result: More satisfying and efficient travel experiences, with fewer disruptions and better value for travelers.
3. Hospitality
Agentic AI enables hotels and restaurants to deliver superior service while improving operational efficiency.
Here’s how:
Smart concierge services: AI agents that provide 24/7 guest support and personalized recommendations.
Room customization: Automated systems that adjust room settings based on guest preferences.
Schedule optimization: Intelligent back-of-house systems that manage staffing, inventory, and maintenance schedules.
Guest experience prediction: AI analysis of guest data to anticipate needs and prevent issues.
The result: Enhanced guest experiences, improved operational efficiency, and higher satisfaction rates across all service touchpoints.
Check out how Accor doubled intent-to-book metrics with Quiq’s AI. Read case study >
4. Financial services
AI agents are revitalizing financial services by delivering 1:1 financial guidance and automated wealth management solutions.
Here’s how:
Personal financial management: AI-powered advisors that provide customers with customized investment strategies and budgeting recommendations based on individual financial goals and risk tolerance.
Investment automation: Smart portfolio management systems that automatically rebalance and optimize investments for customers.
Fraud prevention: Intelligent behind-the-scenes systems that detect and prevent unauthorized transactions in real-time.
Credit decisioning: Automated assessment of creditworthiness using alternative data points and behavioral patterns.
The result: More accessible financial services, improved security, and personalized wealth management solutions for consumers at all levels.
Final thoughts on agentic AI use cases
Agentic AI offers exciting opportunities for efficiency, innovation, and growth. Those who embrace agentic AI will find themselves better positioned to meet evolving customer expectations and market demands.
The technology’s ability to automate complex tasks while maintaining a human touch, as demonstrated by what you can build in Quiq’s AI Studio platform, showcases its potential to revitalize business operations across industries. From customer service to sales, from supply chains to marketing, agentic AI is proving its value in driving business success.
To stay competitive and support consumers’ growing preference for quick self-service resolutions, organizations must consider how agentic AI can enhance their operations and drive growth. Get in touch with us today for a demo on how Quiq’s agentic AI can help your business move the CX metrics that matter most to you.
The landscape of artificial intelligence is rapidly evolving, and at the forefront of this evolution is agentic AI. As noted by UiPath, “the convergence of powerful LLMs (large language models), sophisticated machine learning, and seamless enterprise integration has enabled the rise of agentic AI—which is the ‘brainpower’ behind AI agents.” This powerful technology represents a significant leap forward in how AI systems can autonomously operate, make decisions, and execute complex tasks.
While traditional AI and generative AI have made significant strides in automation and content creation, agentic AI addresses the crucial gaps in autonomous decision-making and task execution. It’s becoming increasingly clear that this technology will reshape how businesses operate, particularly in areas requiring sophisticated problem-solving and adaptability.
What is agentic AI?
Agentic AI refers to artificial intelligence systems that can autonomously execute tasks, make decisions, and adapt to real-time changing conditions. Unlike more passive AI systems, agentic AI demonstrates agency—the ability to act independently and make choices based on understanding the environment and objectives.
As a side note here: I led a webinar recently called From Contact Center to Agentic AI Leader: Embracing AI to Upgrade CX. My colleague Quiq VP of EMEA Chris Humphris and I went deep into agentic AI specifically for the contact center. I highly recommend you watch the replay or read the recap if you’re interested in how this technology works within the confines of the contact center—and what’s needed to make it successful at the platform level. Here’s a hint:
Agentic AI operates through a sophisticated combination of technologies and approaches. As IBM explains, “Agentic AI systems provide the best of both worlds: using LLMs to handle tasks that benefit from the flexibility and dynamic responses while combining these AI capabilities with traditional programming for strict rules, logic, and performance. This hybrid approach enables the AI to be both intuitive and precise.”
The system works by integrating multiple components:
Language understanding: Processing and comprehending natural language inputs
Decision making: Analyzing situations and determining appropriate actions
Task execution: Utilizing APIs, IoT devices, and external systems to perform actions
Learning and adaptation: Improving performance based on outcomes and feedback
For example, in customer service, an agentic AI system can:
Understand a customer’s inquiry about a missing delivery
Access order tracking systems to verify shipping status
Identify delivery issues and initiate appropriate actions
Communicate updates to the customer
Automatically schedule redelivery if necessary
This customer service example demonstrates several key advancements over previous generations of AI assistants:
While traditional chatbots could only follow rigid, pre-programmed decision trees and provide templated responses, agentic AI shows true operational autonomy by orchestrating multiple systems and making contextual decisions.
The ability to seamlessly move between understanding natural language queries, accessing real-time shipping databases, evaluating delivery problems, and initiating concrete actions like rescheduling represents a quantum leap in capability.
Last-gen AI would typically need human handoffs at multiple points in this process – for instance, when moving from customer communication to backend systems access or when making judgment calls about appropriate remedial actions.
The agentic system’s ability to maintain context throughout the interaction while independently executing complex tasks showcases how modern AI can function as an independent problem-solver rather than just a conversational interface. This level of end-to-end automation and response was impossible with earlier generations of AI technology.
What is the difference between agentic AI and generative AI?
While both agentic AI and generative AI represent significant advances in artificial intelligence, they serve distinctly different purposes. Generative AI excels at creating content—text, images, code, or other media—based on patterns learned from training data. Agentic AI, however, goes beyond generation to actively make decisions and execute tasks.
These technologies can work together synergistically, with generative AI providing content creation capabilities within an agentic AI’s broader decision-making framework.
Benefits of agentic AI
Key benefits include:
1. Autonomous operation
By eliminating the constraints of human-dependent processes, agentic AI creates a new paradigm of continuous, reliable service delivery that scales effortlessly with business demands. The result is:
Reduced human intervention: AI agents handle complex tasks independently, freeing human workers to focus on high-value activities requiring emotional intelligence and strategic thinking.
Consistent performance: The system maintains uniform quality standards regardless of workload, time of day, or complexity of tasks, eliminating human variability and fatigue-related errors.
24/7 availability: Unlike human operators, AI agents operate continuously without fatigue, ensuring consistent service availability across all time zones.
2. Improved human-AI agent collaboration
Agentic AI changes the relationship between human agents and technology, creating a symbiotic partnership that enhances overall service delivery and job satisfaction. Here’s how.
Ensures consistency: AI agents establish and maintain standard operating procedures across teams, ensuring every customer interaction meets quality benchmarks regardless of which human agent is involved. This standardization helps eliminate variations in service quality, while still allowing for personal touch where needed.
Accelerates learning: New agents benefit from AI-powered guidance that provides suggestions and best practices, significantly reducing the time needed to achieve proficiency. The system learns from top performers and shares these insights across the entire team.
Reduces training time: By providing contextual assistance, agentic AI helps new agents become productive more quickly. Training modules adapt to individual learning patterns, focusing on areas where each agent needs the most support.
Improves agent performance with insights: The system continuously analyzes agent interactions, providing actionable feedback and performance metrics that help identify areas for improvement. These insights enable targeted coaching and development opportunities.
Improves job satisfaction and reduces agent turnover: By handling routine tasks and providing intelligent support, agentic AI allows agents to focus on more engaging, complex work that requires human empathy and problem-solving skills. This role enhancement leads to higher job satisfaction and lower turnover rates.
3. Enhanced efficiency
Through intelligent automation and rapid processing capabilities, agentic AI significantly improves operational performance across organizations, resulting in:
Faster task completion: AI agents process and execute tasks at machine speed, dramatically reducing resolution times compared to manual processes.
Reduced error rates: Systematic processing and built-in validation reduce mistakes common in human-operated systems.
Streamlined workflows: Intelligent routing and automated handoffs eliminate bottlenecks and optimize process flows.
4. Real-time adaptability
The system’s ability to learn and adjust in real time ensures optimal performance in dynamic business environments. It shows this via:
Dynamic response to changing conditions: AI agents automatically adjust their approach based on current conditions and new information.
Continuous learning and improvement: The system learns from each interaction, continuously refining its responses and decision-making processes.
Personalized solutions: Advanced analytics enable tailored responses that account for individual user preferences and historical interactions.
5. Integration capabilities
Agentic AI integrates with existing business systems to create a unified operational environment. Main ways include:
More seamless connection: The technology easily integrates with current business tools and platforms, maximizing existing investments.
Unified data utilization: AI agents can access and analyze data from multiple sources to make informed decisions.
Comprehensive solution delivery: The system coordinates across different platforms and departments to deliver complete solutions.
6. Cost-effectiveness
Implementation of agentic AI leads to significant cost savings and improved resource utilization. Top areas for savings include:
Reduced operational costs: Automation of routine tasks and improved efficiency lead to lower operational expenses.
Intelligent workload distribution: Ensures optimal use of both human and technological resources.
Use cases for agentic AI
Agentic AI’s applications span numerous industries and use cases. Let’s look at the top four industries that are ripest for benefits from our perspective, and the use cases that are best poised for AI.
1. Customer service
In customer service, agentic AI improves support operations from reactive to proactive, enabling intelligent interactions that enhance customer satisfaction while reducing costs. Top use cases include:
Query resolution: Agentic AI systems can understand, process, and resolve customer inquiries in real-time, handling everything from basic FAQ responses to complex problem-solving. For example, an AI agent can troubleshoot technical issues, process refunds, or update account information without a human being involved.
Ticket management: The technology automatically categorizes, prioritizes, and routes support tickets based on urgency and complexity. It can resolve straightforward issues immediately while intelligently escalating more complex cases to appropriate human agents.
Proactive support: AI agents monitor customer behavior patterns and system metrics to identify potential issues before they become problems. They can initiate contact with customers to prevent issues or offer assistance before it’s requested.
Personalized assistance: By analyzing customer history, preferences, and behavior patterns, agentic AI delivers tailored support experiences. This might include offering specific product recommendations or customizing communication styles to match customer preferences—useful in most industries, but especially in travel and hospitality.
2. eCommerce and retail
In retail and eCommerce, agentic AI revolutionizes the retail experience by creating seamless, personalized shopping journeys while optimizing backend operations for maximum efficiency and profitability. Best use cases include:
Inventory management: Agentic AI systems continuously monitor stock levels, analyze sales patterns, and automatically adjust inventory based on real-time demand. They can trigger reorders, predict seasonal fluctuations, and optimize warehouse distribution.
Personalized shopping recommendations: The technology analyzes customer browsing history, purchase patterns, and demographic data to deliver highly relevant product suggestions. These recommendations adapt in real-time based on customer interactions.
Order processing: AI agents handle the entire order lifecycle, from initial purchase to delivery tracking. They can process payments, coordinate with shipping partners, and proactively address potential delivery issues.
Customer engagement: Through sophisticated analysis of customer behavior, agentic AI creates personalized marketing campaigns, timing promotions optimally, and adjusting pricing strategies based on demand and competition.
3. Business automation
By integrating intelligent decision-making with execution capabilities, agentic AI streamlines complex business processes and eliminates operational bottlenecks across organizations. Start automation targeting:
Supply chain optimization: AI agents monitor and adjust supply chain operations in real-time, coordinating with suppliers, managing logistics, and responding to disruptions automatically.
Process automation: The technology streamlines complex workflows by automating repetitive tasks, managing approvals, and coordinating cross-departmental activities.
Resource allocation: Agentic AI optimizes the distribution of human and material resources based on current demands and predicted future needs.
Workflow management: The system orchestrates complex business processes, ensuring tasks are completed in the correct order and by the appropriate parties.
4. Healthcare
Agentic AI enhances patient care and operational efficiency by combining real-time monitoring with intelligent decision support and automated administrative processes. From what we’re seeing, the biggest opportunities to apply agentic AI rest in:
Patient monitoring: AI agents continuously track patient vital signs and health metrics, alerting medical staff to concerning changes and predicting potential complications.
Treatment planning: The technology assists healthcare providers by analyzing patient data, medical history, and current research to suggest optimal treatment approaches.
Diagnostic support: Agentic AI analyzes medical imaging, lab results, and patient symptoms to assist in accurate diagnosis and treatment recommendations.
Administrative tasks: The system streamlines healthcare by managing appointments, processing insurance claims, and maintaining patient records.
Agentic AI challenges
Let’s take a look at the biggest challenges with agentic AI right now.
1. Ethical considerations
The autonomous nature of agentic AI raises ethical concerns that require careful attention. These systems, designed to make independent decisions and take action, must operate within established ethical frameworks to ensure responsible deployment.
Data security represents a critical challenge in agentic AI implementation, as these systems often require access to sensitive information to function effectively. (If you’re curious, you can learn about our approach to security here).
Primary security concerns include:
Protection of training data and model parameters
Secure communication channels for AI agents
Prevention of adversarial attacks
Data privacy compliance (GDPR, CCPA, etc.)
Access control and authentication mechanisms
3. Integration challenges
Incorporating agentic AI into both customer integrations and your own company integrations can mean significant hurdles, like:
Legacy system compatibility
API standardization and communication protocols
Performance optimization
Scalability concerns
Resource allocation and management
4. Regulatory compliance
The evolving regulatory landscape surrounding AI technology presents potential issues, including:
Adherence to emerging AI regulations
Cross-border compliance requirements
Documentation and audit trails
Risk assessment and mitigation
Regular compliance monitoring and updates
5. Performance monitoring
Maintaining and optimizing agentic AI system performance requires continuous monitoring and adjustment:
Real-time performance metrics
Quality assurance processes
System reliability and availability
Error detection and correction
Performance optimization strategies
These challenges highlight the complexity of implementing agentic AI systems and underscore the importance of careful planning and robust risk management strategies. Success in deploying these systems requires a comprehensive approach that addresses technical, ethical, and operational concerns, while maintaining focus on business value and user needs.
The future of agentic AI: Shaping tomorrow’s enterprise workflows
As we stand at the intersection of technological innovation and business transformation, agentic AI emerges as a cornerstone of future enterprise operations. But what’ll follow? Here’s what I think.
Technical evolution and integration
The future of agentic AI lies in its ability to integrate with existing enterprise systems while pushing the boundaries of what’s possible. Advanced API ecosystems and sophisticated middleware solutions are already enabling AI agents to coordinate across previously siloed systems, creating unified workflows that span entire organizations.
As these integration capabilities mature, we’ll see the emergence of truly intelligent enterprises where data flows freely, and decisions are made with remarkable speed and accuracy.
The next generation of agentic AI systems will feature enhanced natural language processing capabilities, enabling a more nuanced understanding of context and intent. This advancement will allow AI agents to handle increasingly complex tasks while maintaining high accuracy levels. We’re moving toward systems that can execute predefined workflows and design and optimize them in real time based on changing business conditions.
Enhancing enterprise workflows
The impact of agentic AI on enterprise workflows will be substantial. I believe future systems will feature the following:
1. Predictive process optimization
AI agents will move beyond reactive process management to predictive optimization. By analyzing patterns across millions of workflow executions, these systems will automatically identify potential bottlenecks before they occur and implement preventive measures. This capability will enable organizations to maintain peak operational efficiency while minimizing disruptions.
2. Dynamic resource allocation
The future workplace will see AI agents dynamically managing both human and technological resources. These systems will understand the strengths and limitations of different resource types, automatically routing work to optimize for efficiency, cost, and quality. This intelligent orchestration will create more flexible, resilient organizations capable of adapting to changing market conditions in real time.
3. Autonomous decision networks
As agentic AI evolves, we’ll see the emergence of decision networks where multiple AI agents collaborate to solve complex business challenges. These networks will coordinate across departments and functions, making decisions that optimize for overall business outcomes rather than departmental metrics.
Enhanced learning and adaptation
The future of agentic AI lies in its ability to learn and adapt at faster paces. Next-generation systems will feature:
1. Collective learning
AI agents will learn not just from their own experiences but from the collective experiences of all instances across an organization or industry. This shared learning will accelerate the development of best practices and enable rapid adaptation to new challenges or opportunities.
2. Contextual understanding
Future systems will demonstrate deeper understanding of business context, enabling them to make more nuanced decisions that account for both explicit and implicit factors. This enhanced contextual awareness will lead to more sophisticated problem-solving capabilities and better alignment with business objectives.
4. Personalization at scale
As AI agents become more sophisticated, they can deliver highly personalized experiences while maintaining operational efficiency. This will enable organizations to provide custom solutions at scale without sacrificing speed or quality.
Creating more resilient organizations
The evolution of agentic AI will contribute to building more resilient organizations through:
1. Adaptive workflows
Future systems will automatically adjust workflows based on changing conditions, ensuring business continuity even during unprecedented events. This adaptability will be key to maintaining operational efficiency in an increasingly volatile business environment.
2. Proactive risk management
AI agents will continuously monitor operations for potential risks, implementing preventive measures before issues arise. This proactive approach will help organizations maintain stability while pursuing innovation.
3. Sustainable scaling
The future of agentic AI will enable organizations to scale operations more sustainably, automatically adjusting processes to maintain efficiency as the organization grows.
Looking ahead
While challenges around data quality, system integration, and ethical considerations persist, the trajectory of agentic AI points toward increasingly sophisticated systems. Organizations that embrace this technology and prepare for its evolution will be better positioned to:
Create more efficient workflows that respond to changing business needs
Deliver personalized experiences at scale
Build more resilient organizations capable of thriving in uncertain conditions
Drive innovation through intelligent process optimization
As we move forward, the key to success will lie not just in implementing agentic AI, but in creating organizational cultures that can effectively leverage its capabilities while maintaining human oversight and strategic direction. The future belongs to organizations that can strike this balance, using agentic AI to enhance human capabilities, rather than replace them.
We’re only beginning to scratch the surface of what’s possible. As the technology continues to evolve, it will enable new forms of business operation that are more resilient than ever before.
I love Bill’s take on this in another clip from his conversation with Kane:
Final thoughts on agentic AI and how to get started with it
Agentic AI represents a significant advancement in artificial intelligence, offering businesses the ability to automate complicated tasks while maintaining intelligence in decision-making. As organizations seek to improve efficiency and customer experience, agentic AI provides a powerful solution that goes beyond traditional automation and generative AI capabilities.
Quiq stands at the forefront of this technology, offering agentic AI solutions that help businesses improve their operations and customer interactions. With a deep understanding of both the technology and business needs, Quiq provides sophisticated AI agents that can handle complex tasks and drive the outcomes your business cares about.
Artificial intelligence (AI) has been making remarkable strides in recent months. Owing to the release of ChatGPT in November of 2022, a huge amount of attention has been on large language models, but the truth is, there have been similar breakthroughs in computer vision, reinforcement learning, robotics, and many other fields.
In this piece, we’re going to focus on how these advances might contribute specifically to the retail sector.
We’ll start with a broader overview of AI, then turn to how AI-based tools are making it easier to make targeted advertisements, personalized offers, hiring decisions, and other parts of retail substantially easier.
What are AI assistants in Retail?
Artificial intelligence is famously difficult to define precisely, but for our purposes, you can think of it as any attempt to get intelligent behavior from a machine. This could involve something relatively straightforward, like building a linear regression model to predict future demand for a product line, or something far more complex, like creating neural networks able to quickly spit out multiple ideas for a logo design based on a verbal description.
AI assistants are a little different and specifically require building agents capable of carrying out sequences of actions in the service of a goal. The field of AI is some 70 years old now and has been making remarkable strides over the past decade, but building robust agents remains a major challenge.
It’s anyone’s guess as to when we’ll have the kinds of agents that could successfully execute an order like “run this e-commerce store for me”, but there’s nevertheless been enough work for us to make a few comments about the state of the art.
What are the Ways of Building AI Assistants?
On its own, a model like ChatGPT can (sometimes) generate working code and (often) generate correct API calls. But as things stand, a human being still needs to utilize this code for it to do anything useful.
Efforts are underway to remedy this situation by making models able to use external tools. Auto-GPT, for example, combines an LLM and a separate bot that repeatedly queries it. Together, they can take high-level tasks and break them down into smaller, achievable steps, checking off each as it works toward achieving the overall objective.
AssistGPT and SuperAGI are similar endeavors, but they’re better able to handle “multimodal” tasks, i.e those that also involve manipulating images or sounds rather than just text.
The above is a fairly cursory examination of building AI agents, but it’s not difficult to see how the retail establishments of the future might use agents. You can imagine agents that track inventory and re-order crucial items when they get low, or that keep an eye on sales figures and create reports based on their findings (perhaps even using voice synthesis to actually deliver those reports), or creating customized marketing campaigns, generating their own text, images, and A/B tests to find the highest-performing strategies.
What are the Advantages of Using AI in Retail Business?
Now that we’ve talked a little bit about how AI and AI assistants can be used in retail, let’s spend some time talking about why you might want to do this in the first place. What, in other words, are the big advantages of using AI in retail?
1. Personalized Marketing with AI
People can’t buy your products if they don’t know what you’re selling, which is why marketing is such a big part of retail. For its part, marketing has long been a future-oriented business, interested in leveraging the latest research from psychology or economics on how people make buying decisions.
A kind of holy grail for marketing is making ultra-precise, bespoke marketing efforts that target specific individuals. The kind of messaging that would speak to a childless lawyer in a big city won’t resonate the same way with a suburban mother of five, and vice versa.
The problem, of course, is that there’s just no good way at present to do this at scale. Even if you had everything you needed to craft the ideal copy for both the lawyer and the mother, it’s exceedingly difficult to have human beings do this work and make sure it ends up in front of the appropriate audience.
AI could, in theory, remedy this situation. With the rise of social media, it has become possible to gather stupendous amounts of information about people, grouping them into precise and fine-grained market segments–and, with platforms like Facebook Ads, you can make really target advertisements for each of these segments.
AI can help with the initial analysis of this data, i.e. looking at how people in different occupations or parts of the country differ in their buying patterns. But with advanced prompt engineering and better LLMs, it could also help in actually writing the copy that induces people to buy your products or services.
And it doesn’t require much imagination to see how AI assistants could take over quite a lot of this process. Much of the required information is already available, meaning that an agent would “just” need to be able to build simple models of different customer segments, and then put together a prompt that generates text that speaks to each segment.
2. Personalized Offerings with AI
A related but distinct possibility is using AI assistants to create bespoke offerings. As with messaging, people will respond to different package deals; if you know how to put one together for each potential customer, there could be billions in profits waiting for you. Companies like Starbucks have been moving towards personalized offerings for a while, but AI will make it much easier for other retailers to jump on this trend.
We’ll illustrate how this might work with a fictional example. Let’s say you’re running a toy company, and you’re looking at data for Angela and Bob. Angela is an occasional customer, mostly making purchases around the holidays. When she created her account she indicated that she doesn’t have children, so you figure she’s probably buying toys for a niece or nephew. She’s not a great target for a personalized offer, unless perhaps it’s a generic 35% discount around Christmas time.
Bob, on the other hand, buys fresh trainsets from you on an almost weekly basis. He more than likely has a son or daughter who’s fascinated by toy machines, and you have customer-recommendation algorithms trained on many purchases indicating that parents who buy the trains also tend to buy certain Lego sets. So, next time Bob visits your site, your AI assistant can offer him a personalized discount on Lego sets.
Maybe he bites this time, maybe he doesn’t, but you can see how being able to dynamically create offerings like this would help you move inventory and boost individual customer satisfaction a great deal. AI can’t yet totally replace humans in this kind of process, but it can go a long way toward reducing the friction involved.
3. Smarter Pricing
The scenario we just walked through is part of a broader phenomenon of smart pricing. In economics, there’s a concept known as “price discrimination”, which involves charging a person roughly what they’re willing to pay for an item. There may be people who are interested in buying your book for $20, for example, but others who are only willing to pay $15 for it. If you had a way of changing the price to match what a potential buyer was willing to pay for it, you could make a lot more money (assuming that you’re always charging a price that at least covers printing and shipping costs).
The issue, of course, is that it’s very difficult to know what people will pay for something–but with more data and smarter AI tools, we can get closer. This will have the effect of simultaneously increasing your market (by bringing in people who weren’t quite willing to make a purchase at a higher price) and increasing your earnings (by facilitating many sales that otherwise wouldn’t have taken place).
More or less the same abilities will also help with inventory more generally. If you sell clothing you probably have a clearance rack for items that are out of season, but how much should you discount these items? Some people might be fine paying almost full price, while others might need to see a “60%” off sticker before moving forward. With AI, it’ll soon be possible to adjust such discounts in real-time to make sure you’re always doing brisk business.
4. AI and Smart Hiring
One place where AI has been making real inroads is in hiring. It seems like we can’t listen to any major podcast today without hearing about some hiring company that makes extensive use of natural language processing and similar tools to find the best employees for a given position.
Our prediction is that this trend will only continue. As AI becomes increasingly capable, eventually it will be better than any but the best hiring managers at picking out talent; retail establishments, therefore, will rely on it more and more to put together their sales force, design and engineering teams, etc.
Is it Worth Using AI in Retail?
Throughout this piece, we’ve sung the praises of AI in retail. But the truth is, there are still questions about how much sense it makes to leverage retail at the moment, given its expense and risks.
In this section, we’ll briefly go over some of the challenges of using AI in retail so you can have a fuller picture of how its advantages compare to its disadvantages, and thereby make a better decision for your situation.
The one that’s on everyone’s minds these days is the tendency of even powerful systems like ChatGPT to hallucinate incorrect information or to generate output that is biased or harmful. Finetuning and techniques like retrieval augmented generation can mitigate this somewhat, but you’ll still have to spend a lot of time monitoring and tinkering with the models to make sure that you don’t end up with a PR disaster on your hands.
Another major factor is the expense involved. Training a model on your own can cost millions of dollars, but even just hiring a team to manage an open-source model will likely set you back a fair bit (engineers aren’t cheap).
By far the safest and easiest way of testing out AI for retail is by using a white glove solution like the Quiq conversational CX platform. You can test out our customer-facing and agent-facing AI tools while leaving the technical details to us, and at far less expense than would be involved in hiring engineering talent.
Set up a demo with us to see what we can do for you.
From computer-generated imagery to futuristic AI-based marketing plans, retail won’t be the same with the advent of AI. This will be especially true once we have robust AI assistants able to answer customer questions, help them find clothes that fit, and offer precision discounts and offerings tailored to each individual shopper.
If you don’t want to get left behind, you’ll need to begin exploring AI as soon as possible, and we can help you do that. Check out our product or find a time to talk with us, today!
Businesses have always turned to the latest and greatest technology to better serve their customers, and retail is no different. From early credit card payment systems to the latest in online advertising, retailers know that they need to take advantage of new tools to boost their profits and keep shoppers happy.
These days, the thing that’s on everyone’s mind is artificial intelligence (AI). AI has had many, many definitions over the years, but in this article, we’ll mainly focus on the machine-learning and deep-learning systems that have captured the popular imagination. These include large language models, recommendation engines, basic AI assistants, etc.
In the world of AI in retail, you can broadly think of these systems as falling into one of two categories: “the ones that customers see”, and “the ones that customers don’t see.” In the former category, you’ll find innovations such as customer-facing chatbots and algorithms that offer hyper-personalized options based on shopping history. In the latter, you’ll find precision fraud detection systems and finely-tuned inventory management platforms, among other things.
We’ll cover each of these categories, in order. By the end of this piece, you’ll have a much better understanding of the ways retailers are using AI assistants and will be better able to think about how you want to use this technology in your retail establishment.
Let’s get going!
Using AI Assistants for Better Customer Experience
First, let’s start with AI that interacts directly with customers. The major ways in which AI is transforming the customer experience are through extreme levels of personalization, more “humanized” algorithms, and shopping assistants.
Personalization in Shopping and Recommendations
One of the most obvious ways of improving the customer experience is by tailoring that experience to each individual shopper. There’s just one problem: this is really difficult to do.
On the one hand, most of your customers will be new to you, people about whom you have very little information and whose preferences you have no good way of discovering. On the other, there are the basic limitations of your inventory. If you’re a brick-and-mortar establishment you have a set number of items you can display, and it’s going to be pretty difficult for you to choose them in a way that speaks to each new customer on a personal level.
For a number of reasons, AI has been changing this state of affairs for a while now, and holds the potential to change it much more in the years ahead.
A key part of this trend is recommendation engines, which have gotten very good over the past decade or so. If you’ve ever been surprised by YouTube’s ability to auto-generate a playlist that you really enjoyed, you’ve seen this in action.
Recommendation engines can only work well when there is a great deal of customer data for them to draw on. As more and more of our interactions, shopping, and general existence have begun to take place online, there has arisen a vast treasure trove of data to be analyzed. In some situations, recommendation engines can utilize decades of shopping experience, public comments, reviews, etc. in making their recommendations, which means a far more personalized shopping experience and an overall better customer experience.
What’s more, advances in AR and VR are making it possible to personalize even more of these experiences. There are platforms now that allow you to upload images of your home to see how different pieces of furniture will look, or to see how clothes fit you without the need to try them on first.
We expect that this will continue, especially when combined with smarter printing technology. Imagine getting a 3D-printed sofa made especially to fit in that tricky corner of your living room, or flipping through a physical magazine with advertisements that are tailored to each individual reader.
Humanizing the Machines
Next, we’ll talk about various techniques for making the algorithms and AI assistants we interact with more convincingly human. Admittedly, this isn’t terribly important at the present moment. But as more of our shopping and online activity comes to be mediated by AI, it’ll be important for them to sound empathic, supportive, and attuned to our emotions.
The two big ways this is being pursued at the moment are chatbots and voice AI.
Chatbots, of course, will probably be familiar to you already. ChatGPT is inarguably the most famous example, but you’ve no doubt interacted with many (much simpler) chatbots via online retailers or contact centers.
In the ancient past, chatbots were largely “rule-based”, meaning they were far less flexible and far less capable of passing as human. With the ascendancy of the deep learning paradigm, however, we now have chatbots that are able to tutor you in chemistry, translate between dozens of languages, help you write code, answer questions about company policies, and even file simple tickets for contact center agents.
Naturally, this same flexibility also means that retail managers must tread lightly. Chatbots are known to confidently hallucinate incorrect information, to become abusive, or to “help” people with malicious projects, like building weapons or computer viruses.
Even leaving aside the technical challenges of implementing a chatbot, you have to carefully monitor your chatbots to make sure they’re performing as expected.
Then, there’s voice-based AI. Computers have been synthesizing speech for many years, but it hasn’t been until recently that they’ve become really good at it. Though you can usually tell that a computer is speaking if you listen very carefully, it’s getting harder and harder all the time. We predict that, in the not-too-distant future, you’ll simply have no idea whether it’s a human or a machine on the other end of the line when you call to return an item or get store hours.
But computers have also gotten much better at the other side of voice-based AI, speech recognition. Software like otter.ai, for example, is astonishingly accurate when generating transcriptions of podcast episodes or conversations, even when unusual words are used.
Taken together, advances in both speech synthesis and speech recognition paint a very compelling picture of how the future of retail might unfold. You can imagine walking into a Barnes & Noble in the year 2035 and having a direct conversation with a smart speaker or AI assistant. You’ll tell it what books you’ve enjoyed in the past, it’ll query its recommendation system to find other books you might like, and it’ll speak to you in a voice that sounds just like a human’s.
You’ll be able to ask detailed questions about the different books’ content, and it’ll be able to provide summaries, discuss details with you, and engage in an unscripted, open-ended conversation. It’ll also learn more about you over time, so that eventually it’ll be as though you have a friend that you go shopping with whenever you buy new books, clothing, etc.
Shopping Assistants and AI Agents
So far, we’ve confined our conversation specifically to technologies like large language models and conversational AI. But one thing we haven’t spent much time on yet is the possibility of creating agents in the future.
An agent is a goal-directed entity, one able to take an instruction like “Make me a reservation at an Italian restaurant” and decompose the goal into discrete steps, performing each one until the task is completed.
With clever enough prompt engineering, you can sort of get agent-y behavior out of ChatGPT, but the truth is, the work of building advanced AI agents has only just begun. Tools like AutoGPT and LangChain have made a lot of progress, but we’re still a ways away from having agents able to reliably do complex tasks.
It’s not hard to see how different retail will be when that day arrives, however. Eventually, you may be outsourcing a lot of your shopping to AI assistants, who will make sure the office has all the pens it needs, you’ve got new science fiction to read, and you’re wearing the latest fashion. Your assistant might generate new patterns for t-shirts and have them custom-printed; if LLMs get good enough, they’ll be able to generate whole books and movies tuned to your specific tastes.
Using AI Assistants to Run A Safer, Leaner Operation
Now that we’ve covered the ways AI assistants will impact the things customers can see, let’s talk about how they’ll change the things customers don’t see.
There are lots of moving parts in running a retail establishment. If you’ve got ~1,000 items on display in the front, there are probably several thousand more items in a warehouse somewhere, and all of that has to be tracked. What’s more, there’s a constant process of replenishing your supply, staying on top of new trends, etc.
All of this will also be transformed by AI, and in the following sections, we’ll talk about a few ways in which this could happen.
Fraud Detection and Prevention
Fraud, unfortunately, is a huge part of modern life. There’s an entire industry of people buying and selling personal information for nefarious purposes, and it’s the responsibility of anyone trafficking in that information to put safeguards in place.
That includes a large number of retail establishments, which might keep data related to a customer’s purchases, their preferences, and (of course) their actual account and credit card numbers.
This isn’t the place to get into a protracted discussion of cybersecurity, but much of fraud detection relies on AI, so it’s fair game. Fraud detection techniques range from the fairly basic (flagging transactions that are much larger than usual or happen in an unusual geographic area) to the incredibly complex (training powerful reinforcement learning agents that constantly monitor network traffic).
As AI becomes more advanced, so will fraud detection. It’ll become progressively more difficult for criminals to steal data, and the world will be safer as a result. Of course, some of these techniques are also ones that can be used by the bad guys to defraud people, but that’s why so much effort is going into putting guardrails on new AI models.
Streamlining Inventory
Inventory management is an obvious place for optimization. Correctly forecasting what you’ll need and thereby reducing waste can have a huge impact on your bottom line, which is why there are complex branches of mathematics aimed at modeling these domains.
And – as you may have guessed – AI can help. With machine learning, extremely accurate forecasts can be made of future inventory requirements, and once better AI agents have been built, they may even be able to automate the process of ordering replacement materials.
Forward-looking retail managers will need to keep an eye on this space to fully utilize its potential.
AI Assistants and the Future of Retail
AI is changing a great many things. It’s already making contact center agents more effective and is being utilized by a wide variety of professionals, ranging from copywriters to computer programmers.
But the space is daunting, and there’s so much to learn about implementing, monitoring, and finetuning AI assistants that it’s hard to know where to start. One way to easily dip your toe in these deep waters is with the Quiq Conversational CX platform.
Our technology makes it easy to create customer-facing AI bots and similar tooling, which will allow you to see how AI can figure into your retail enterprise without hiring engineers and worrying about the technical details.
Retailers have had a tough few years. The pandemic threw businesses into a tailspin. If you’re like the rest of the industry, you either had to build an online shopping experience from scratch or seriously ramp up your web-store capabilities.
This meant your customer satisfaction took a hit.
E-tailers were still struggling in 2021. According to the American Customer Satisfaction Index, customer satisfaction with online retailers dropped 1.3% in 2021—more than double the 0.5% decrease across the retail industry.
At the same time, customer expectations increased. And online retailers have a harder time building brand loyalty. If customer expectations aren’t met, Microsoft reports that 58% of customers show little hesitation in severing the relationship.
So what’s an e-tailer to do to improve online retail customer satisfaction?
Embrace messaging.
Even if you’ve adopted various forms of business messaging, there are many ways to elevate your strategy and improve your customer satisfaction in retail.
Read on to see why messaging has become a vital part of online retail, along with 6 ways to use it to improve customer satisfaction.
Why messaging is essential in online retail.
Messaging is changing the way online retailers do business, but it’s more than a box that needs checking. You shouldn’t just roll out an SMS/text messaging or WhatsApp program and staff it with customer service reps from your contact center. To make the most of it, you need a well-developed strategy.
Text messaging especially has the potential to improve the online shopping experience. Four out of 5 customers send a text message on a daily basis, and nearly half of consumers prefer messaging as a means to connect with businesses. Your customers are telling you that they want to interact via messaging—why not listen?
Here at Quiq, we’ve seen rapid adoption of messaging by online retailers. Brands like Overstock, Pier 1, and Tailor Brands have experienced tangible benefits, including more natural customer engagement, lower service costs, and a reduced workload.
6 ways to improve online retail customer satisfaction with messaging.
E-tailers struggle with customer satisfaction. There are some aspects out of your control (ahem: shipping and manufacturing anytime after March 2020), but there are things you can do to alleviate your customers’ struggles.
Messaging is a big part of that. Having reliable communications—and using them strategically—helps promote customer satisfaction. Here are 6 ways you can use messaging to improve your online shopping experience.
1. Help shoppers find the perfect product.
The biggest argument against online shopping for years has been the lack of personalized customer service. Shoppers can’t ask for recommendations (and algorithms hardly make up for it), sizing help, or general advice.
Messaging helps your team close that gap (along with the support of chatbots and AI). Yes, it’s great for post-purchase interactions. But customers also want help before they checkout. In fact, nearly two-thirds (64%) of customers use messaging when they want to make a purchase or a booking/reservation, according to our Customer Preference for Messaging report.
Quiq lets you help customers when they need it most. You can provide the on-demand service they need while shopping your site, viewing your products on social media, or browsing your app. You’re giving them that in-store, personalized experience while they’re going about their day. And it doesn’t hurt that helping them before they make a purchase can boost sales.
2. Provide transparent interactions.
When customers call your support line, are they greeted with a “This call may be recorded” message? That’s a great tool for your business, but what about the customer? Once they end the conversation, they have no record of the interaction. They can’t refer back to it later, check to make sure they heard everything correctly, or prove that the conversation even existed. Yes, some companies offer a confirmation number, but that does little to help your customer access the information.
Even popular web chat solutions can be session-based—meaning when the session is over, the conversation disappears. Customers can’t refer back or naturally start the conversation back up when a related question pops up.
We know the importance of asynchronous communication. Customers aren’t always available to respond instantly, and sometimes new questions appear once their first ones have been answered. That’s why Quiq’s web chat conversations are persistent—they start right where they left off. Plus, customers can request to have their web chat transcripts emailed to them.
Mobile messaging adds an additional layer of transparency. Message history can stretch back even further than the last conversation on SMS and Facebook Messenger, giving the customer access to older messages and more conversation details.
3. Staff for multiple messaging channels.
An omnichannel messaging strategy can greatly enhance your online customer experience—when it’s done right. Customers frequently ping-pong across platforms. Zendesk’s 2022 CX Trends report found that 73% of customers want the ability to start a conversation on one channel and pick it back up on another.
Yet, it’s all too easy to add messaging channels and hand them over to your call center agents. While it’s feasible to cross-train your customer support team on both phones and messaging, there’s a little more to it than that.
First, you need to ensure you have available staff to cover multiple messaging channels. Asynchronous messagingdoes save time over traditional phone calls. But if your team is already stretched thin, adding additional channels will just feel like a burden. Plus, we all know that customers hate to wait.
Try assigning staff members to your messaging channels. While Quiq clients can serve customers on the platform the customers prefer, it takes a trained and available support team for a great omnichannel experience.
4. Reduce wait times.
Speaking of waiting—customers hate it. While you might think the pandemic has made customers more patient and understanding, the opposite is true. Frustrated customers want things to return to “normal” and have higher expectations of all business—e-tailers included. According to Zendesk, 60% report that they now have higher customer service standards after the pandemic.
And with 61% of customers willing to switch brands after just one bad experience, all it takes is one surge in call traffic to create call center chaos and cause you to lose business.
Messaging helps smooth the peaks of inbound support requests when you need it most. Since agents can respond to messages at different speeds, they can handle multiple inquiries at once. A message doesn’t require their full attention for a fixed amount of time. As a result, Quiq clients report work time is often reduced by 25–50%.
5. Delight your visually-driven audience.
Why spend 5 minutes describing a problem when you can take a picture of it in 5 seconds? Phone calls only give you one way to interact with your customer, and emails are too slow for problems that need immediate attention.
Rich messaging is the next step to improving your customer service experience. Found in Apple Messages for Business, Google’s Business Messaging, and more, rich messaging amplifies your customer conversations. From GIFs to images to videos, there are plenty of features to engage your audience visually.
You can even take it to the next level and build an entire customer experience with rich messaging. Process secure transitions, schedule appointments, and send reminders, all through messaging.
Improve your customer satisfaction and boost engagement with these advanced features that are sure to delight shoppers.
6. Entice customers to come back.
Remember those high customer expectations? Unfortunately, customers are quick to switch brands—which means you need to consistently give them the best online shopping experience.
It doesn’t stop at the sale. A good messaging strategy includes post-purchase engagement to encourage customers to come back. While email is currently the preferred method for online retail, it comes with low open rates and even lower click-through rates.
Instead, lean into outbound text messaging for post-purchase communications. Here are a few easy examples to get started:
Send an order confirmation
Share a shipment tracking link
Ask for a product review
Send a special discount code
Notify them when similar products go on sale
Ask them to join your rewards program
With nearly a 100% read rate, outbound text messaging is a more engaging way to connect with customers.
Messaging is the way to customer satisfaction.
Online retailers face many challenges, but engaging with customers shouldn’t be one. Messaging is already helping many online retailers establish a stronger relationship with their customers by tackling common shopper struggles. For many retailers, adopting a messaging platform gave them a customer-centric way to chat with their shoppers.
Messaging has become a vital part of the online shopping experience, and implementing these smart strategies will help skyrocket customer satisfaction. And Quiq is there to help.
These days, brick-and-mortar retail is quickly giving way to online shopping. By 2022, eCommerce sales are projected to reach over $850 billion — and it doesn’t seem like the growth of online shopping will be slowing down beyond that. Many consumers have a preference for convenient shopping experiences — and what’s easier than shopping from the comfort of home? However, what hasn’t changed is the fact that shoppers want assistance if they have a problem or question. That’s where virtual shopping assistants shine.
Virtual shopping assistants are support bots that can directly support consumers as they browse. While being available around the clock, these assistants can answer questions, resolve problems, and provide suggestions to consumers, all from the convenience of a chat window.
In this guide, we’ll provide all the details on virtual shopping assistants, including the benefits to shoppers and retailers, how retailers are using chatbots as virtual shopping assistants, and how to get started with Quiq.
What Are Virtual Shopping Assistants?
A virtual shopping assistant is essentially a chatbot that converses directly with online consumers. These assistants work by using a combination of machine learning and language processing software to understand shopper queries and match them with appropriate solutions. As these assistants conduct more conversations, they learn more about consumers’ preferences and language patterns, making the bot more effective over time.
Digital shopping assistants are commonly installed on a store’s website. When a new visitor comes to the website, these assistants automatically greet the shopper through a chat window, offering assistance and notifying them of any current promotions. The potential consumer can then type responses into the chat window to ask questions or request recommendations, which the assistant will answer in a matter of seconds.
Why Do Consumers Like Virtual Shopping Assistants?
Virtual shopping assistants are quickly becoming a staple in the retail industry, especially with younger generations. There are many reasons for this preference, ranging from response time to personalized answers. As a result, 65% of American shoppers prefer self-service through tools like chatbots.
Here are a few specific reasons why consumers enjoy communicating with an online shopping assistant:
Quick responses: Digital shopping assistants provide instant answers to all questions a consumer may have. Chatbot assistants can pull information about orders and deliveries, provide recommendations, and even assist with common problems and complaints. If a shopper has a simple question, they won’t have to wait for a response to a customer support ticket, which can possibly deter them from making a purchase altogether.
Round-the-clock assistance: Virtual shopping assistants don’t have to work in a certain timeframe. These bots can provide information around the clock, no matter the time of day. Bots can also handle hundreds of inquiries at once, meaning consumers don’t need to wait for the next available agent to answer their questions. With a virtual assistant, consumers can get the answers they need when they need them.
Personalization: Chatbots can store information based on shoppers’ accounts, allowing for enhanced personalization. These virtual assistants can use data regarding a consumer’s past purchases and behaviors to make informed recommendations.
Faster experiences: Virtual assistants can quickly pull information to answer consumers’ questions, allowing consumers to make faster shopping decisions. Some chatbots even allow consumers to complete purchases from the chat window, making for a speedy shopping experience.
On top of these benefits, younger generations are more open to embracing new technologies like digital shopping assistants and are more excited to use platforms that integrate AI.
Benefits of Virtual Shopping Assistants for Retailers
In addition to benefitting consumers, digital shopping assistants can also give retailers an edge in the market. These platforms help retailers achieve advantages like cost savings and increased sales, so an investment in chatbots makes a real difference. Additionally, retailers that want to be on the cutting edge of eCommerce technology can expect to reach and develop relationships with consumers who enjoy those new features.
Some of the most significant benefits online shopping assistants offer retail businesses include:
Cost savings: Virtual assistants take the workload off live agents by automatically handling the majority of inquiries. Instead of hiring new members for an internal support team, retailers can implement a virtual shopping assistant that handles most questions and complaints. This concept reduces the number of live agents needed, allowing them to focus on the most complex problems and inquiries while the chatbot resolves questions.
Data collection: eCommerce chatbots can easily collect information about consumers and their preferences by saving their behaviors and purchases in a database. This information is invaluable to retailers, providing demographic and preference information that can be used to create powerful targeted marketing campaigns.
Improved consumer relationships: Virtual assistants provide 24/7 support and a personalized experience, which helps to create more positive experiences for shoppers. As a result, retailers have a chance to build relationships with shoppers, encouraging them to come back in the future.
Increased sales: Online shopping assistants can also help generate more sales through upselling and cross-selling, offering upgrades and related purchases at the check-out window. Additionally, online shopping assistants can send out reminders to consumers that have abandoned their carts, encouraging them to follow through with their purchase.
Competitive advantages: All of these benefits equate to an advantage over competitors, especially those that don’t use chatbots. If a shopper sees that a retailer has chatbot capabilities on their website, they’ll be more likely to stay on their website and make a purchase.
Due to the significant benefits of virtual shopping assistants for both retailers and consumers, it’s easy to see why these tools are quickly taking over the retail industry. eCommerce chatbots are expected to increase consumer retail spend to $142 billion by 2024, meaning more retailers will likely adopt chatbots over the next three years. With the continuing boom of eCommerce, retail businesses can use chatbots to meet consumer expectations going forward.
How to Use a Chatbot as a Virtual Shopping Assistant
With all the benefits of digital shopping assistants, many retailers are looking to implement their own within the next few years. While there are many advanced options available, eCommerce chatbots are still the most common type of virtual shopping assistant. These tools are popular due to their versatility and relative ease of implementation.
It’s key for retail leaders to understand how to use a chatbot as a virtual shopping assistant to ensure they maximize their effectiveness. These assistants have several capabilities. As a result, retailers may want to use them differently depending on their unique needs.
Some essential functions chatbots can fulfill to become virtual shopping assistants include:
Engage visitors: Chatbots are a great way to engage new visitors on a site without being intrusive. Shoppers can interact with the chatbot as much or as little as they want, but they’ll always have the option available to them. Retailers can also ensure every chatbot offers the same level and quality of information to consumers, whether that’s information about products and promotions or updates on orders.
Respond to questions: One of the most common ways to use chatbots as virtual shopping assistants is as an interactive FAQ page. Visitors can type in their questions, and the AI will determine the best response. This way, consumers don’t need to get frustrated while hunting down information — their virtual assistant can provide them learn more about products, shipping costs, refund policies, and store hours with a simple query.
Provide product recommendations: Virtual shopping assistants can also be programmed to provide product recommendations. Using data collected about a shopper’s immediate needs and past behavior and preferences, virtual shopping assistants can show one or more product options that would likely suit the consumer. This chatbot feature is a surefire way to enhance consumer experiences, as product recommendations both simplify and speed up the shopping process.
Order information: Every online shopper is excited after they place an order online, and are looking forward to getting confirmation and information about their order’s progress. While consumers often get this information via email, retailers can also set up online shopping assistants to provide this information when prompted. This way, consumers can easily track the progress of their order, get tracking information, and check in on updates.
Promotion and sale information: Virtual shopping assistants can also be used to offer deals and coupons to shoppers. Different coupons and promotions can be offered at different stages of the shopping experience, depending on the consumer in question. For example, first-time shoppers may see a welcome discount to encourage their first purchase, while repeat consumers may see seasonal offers and product-specific discounts based on their previous purchases. If a shopper abandons their cart, the assistant can even offer a discount as an incentive to complete the purchase.
Cart retrieval: Nearly 85% of online shoppers have abandoned a purchase. Virtual shopping assistants can help reduce the rate of cart abandonment by checking in on consumers and answering their questions. If a shopper abandons their cart, online shopping assistants can send reminders when the person revisits the site or social media page. The assistant can even be programmed to offer discounts to encourage consumers to complete their purchases. These types of messages are less likely to be ignored than emails, leading to a higher conversion rate.
Subscription management: Chatbots can also be programmed to help with subscription management for consumers with retail accounts. All shoppers need to do is provide their credentials, and a chatbot with this capability can look up their profile and find member benefits, coupons, and rewards. These chatbots can also be programmed to provide information about points systems, including how many points shoppers have, how many points they need for their next discount, and how many points they’ll earn from their current cart. Chatbots can also be programmed to assist with subscription management, helping shoppers increase or decrease their membership levels and subscription schedules.
Many chatbot programs are available for retailers to explore, and they can customize them to fulfill one or more of these functions.
How to Get Started With Chatbot Implementation
Virtual shopping assistants are invaluable to online retailers and will be a necessary platform for forward-thinking retail businesses. However, each retailer is unique, so it’s essential to understand how to effectively implement eCommerce chatbots for each retail business’s needs.
Some of the key items to consider before pursuing a chatbot as a virtual shopping assistant include:
Functions: First, consider what core functions the chatbot needs to fulfill. Does the retailer have subscriptions or memberships that need to be managed? Is chat-window purchasing a feature that would benefit the retailer? Knowing the most important functions to enable will help inform a retailer’s choices when they start designing an online shopping assistant program.
Integrations: Think about the types of integrations the digital shopping assistant will need. What systems does it need to plug into? Does it need speech-to-text or text-to-speech capabilities down the road? Look for solutions that are flexible enough to take on new integrations as the retail business evolves.
Updates: Consider how frequently the chatbot will need to be updated. Does the retail business’s product line rotate regularly? Are there new promotions that will need to be added to the system? Look for systems that automatically update product information and can easily undergo manual updates. This way, retailers can communicate the most updated information to their consumers.
Start small: Retailers shouldn’t try to implement a robust chatbot system from day one. Instead, focus on the most important functions first, such as a customer support chatbot. From there, the retailer can add more functions over time, testing thoroughly as they go. As a result, they won’t waste time and effort implementing features they don’t need.
On top of these recommendations, retailers should be sure to work with an experienced chatbot provider. An expert provider will help with programming and implementing a virtual shopping assistant, making recommendations for functions and integrations and helping update platforms as the retailer grows.
Demo Quiq Today for Your Retail Business
Virtual shopping assistants are effective tools that help improve consumer experiences and generate sales, all while improving productivity and reducing the strain on retailers’ internal teams. With current generations increasingly favoring online shopping experiences and consumers warming up to AI chatbots, now is the perfect time for retailers to try shopping assistants for their unique needs. There are numerous ways to implement digital shopping assistants in retailers, and various platforms to choose from. If your retail business is looking for a comprehensive solution that can help you get started with chatbots, Quiq is here to help.
Quiq is a conversational customer engagement platform designed for the retail industry. The goal of Quiq is to help retailers deliver exceptional shopping experiences with every interaction, and our chatbot system does just that. The Quiq platform supports messaging across a range of channel types, including text, web chat, social chat, Apple Business Chat, and Google’s Business Messages. Retailers can use as few or as many channels as they need to communicate with consumers effectively.
With multiple chatbot options, rich messaging experiences, and various integration options, Quiq is here to help you build the best virtual shopping assistant for your retail business. Try Quiq for yourself by scheduling a live demo today.
Conversational commerce isn’t just a catchy alliteration — it’s a trend taking the retail world by storm. Conversational commerce refers to transactions that take place between consumers and brands through digital conversations. These conversational commerce platforms can be powerful tools, especially in a market that’s increasingly moving away from in-store shopping.
In this article, we look at how to use conversational commerce to enhance online retail experiences. We’ll dive into the market trends regarding in-store and online shopping, the benefits and uses of commercial commerce for online shopping, and how conversational commerce works so retailers can enhance their consumers’ online retail experiences.
COVID-19’s Impact on Retail Businesses
Retail shopping has primarily relied on in-store experiences in the past. In brick-and-mortar locations, consumers can physically view items and get help from in-store employees. However, COVID-19 has quickly and dramatically changed the retail landscape. With stay-at-home orders and social distancing and sanitation guidelines, new shopping trends have emerged.
As the world has started to heal from the global pandemic, country-wide lockdowns have begun to ease up. However, in the aftermath of lockdown, consumer behaviors and priorities have shifted. People’s income, values, and exposure to alternative shopping methods have shifted their preferences significantly, powering numerous market trends since the beginning of the pandemic.
Some common changes in consumer behavior and preference include the following:
Focus on health and safety: While consumer concerns about health and safety are slowly decreasing, many consumers are still uncomfortable visiting public places, especially stores they visit less frequently. In fact, nearly 70% of consumers in markets where the pandemic is stabilizing are still concerned for the health of others.
Focus on digital experiences: The lockdowns that resulted from the pandemic created a surge of eCommerce, especially from new consumers who’d previously never used online shopping options. Now that more consumers are aware of their online shopping options and how to use them, consumers will likely continue using these services and push toward improved online experiences.
Increased convenience: Even before COVID-19, consumers were moving toward more convenient shopping experiences as a collective. The pandemic rapidly pushed this trend forward. As a result, many retailers had to quickly implement services focused on conveniences, such as online shopping options, curbside pickup options, and delivery services. Even as the world emerges from the pandemic, consumer preferences for convenience will likely push retailers to maintain or expand these services.
The challenge for retailers is that they need to leverage these trends and adjust their practices with changing consumer preferences. Conversational commerce is one way that retailers are working toward these adjustments.
The Benefits of Conversational Commerce For Shoppers
Conversational commerce has risen as a consumer and retailer favorite since the beginning of the pandemic. Conversational commerce works by using chatbots, brand specialists, and messaging platforms to enable conversations between consumers and a brand. These conversations often occur on messaging platforms like web chats, text messaging apps, Apple Business Chat, and even Facebook Messenger. Chatbots power many of these operations, which process messages from consumers to provide relevant responses, involving a live customer service representative if needed.
Conversational commerce platforms provide consumers with an on-demand shopping interface that enables them to engage with a live or virtual representative that can assist them with anything they need. This convenience presents shoppers with a wide range of benefits, including the following:
Enhanced customer support: Conversational commerce platforms give consumers access to a live or virtual assistant at any time, anywhere. The assistant can answer their questions, help them preview products, and even complete transactions using the digital channels consumers prefer, including text, live chat, and social media messaging platforms.
Faster resolution: Chatbots in conversational commerce platforms also help streamline the problem resolution process. Bots can answer the majority of common questions and route consumers to a live specialist if the problem is more complex. This process results in quick resolution times — many people use chatbots for quick problem resolution when they are concerned with their shopping experience.
Less research: Chatbots also help streamline the shopping experience for consumers. Instead of browsing page after page to find what they want, consumers can give a description of the product to a chatbot. The chatbot’s artificial intelligence then uses the description to find a product that best suits the consumers’ needs, which reduces the time they spend browsing for products, making the experience more pleasurable.
Greater personalization: One of the big trends among consumers today is a desire for personalization. Today, nearly 80% of consumers want more personalization from retailers to enhance their shopping experience. Conversational commerce helps with personalization by connecting consumers with support teams that offer specific product recommendations and offers.
More touchpoints: Conversational commerce gives consumers multiple points of communication to reach brands. Retailers can add chatbots and text messaging communication to email and phone calling methods, maximizing the touchpoints available for consumers. This way, consumers can talk with their retailers in the way they’re most comfortable.
These benefits have already significantly impacted the online retail world, especially when considering that more consumers want shopping experiences that offer messaging services for support. In this way, conversational commerce is incredibly helpful for shoppers.
The Benefits of Conversational Commerce for Retailers
In addition to benefitting consumers, conversational commerce helps retailers improve their eCommerce operations. Some of the most significant benefits retailers experience from implementing conversational commerce include:
Improved responsiveness: Conversational commerce presents an effective way to generate engagement with consumers. When retailers send in-app messages to consumers, they can get five times more opens than push notifications. As a result, consumers are more likely to respond to those messages.
Enhanced efficiency: Chatbots are also cost- and time-efficient compared to live chat options. Chatbots can handle the majority of questions quickly, respond to hundreds of consumer questions at once, and are running around the clock. This efficiency saves time and labor for the customer service team, allowing them to direct their efforts toward the most complex tasks and problems.
Enhanced consumer relationships: Because it’s available 24/7 and offers enhanced consumer experiences, conversational commerce helps create more positive user experiences. This feature generates goodwill between retailers and their consumers, making them more likely to be repeat buyers.
Streamlined selling: Chatbots can promote and upsell products based on consumer queries. For example, if a buyer is looking to buy a specific type of shirt, the chatbot can show the consumer potential accessories, pants, and jackets to create a complete outfit.
Reduced abandonment rates: Cart abandonment is a common problem for online retailers — consumers may be distracted and forget to check out, may not find an answer to a question they have about the product, or simply second-guess themselves. Commercial commerce helps reduce cart abandonment by offering responses to questions quickly, reaching out to consumers with unfinished orders, and checking in on consumers as they browse a website.
Abundant data: Commercial commerce platforms are also valuable sources of consumer data. Tracking behaviors, issues, and questions can help strengthen retailers’ ability to create powerful marketing campaigns and improve their online platforms.
With these numerous benefits to both consumers and retailers, it’s no wonder that conversational commerce is booming. The market size of conversational commerce platforms was estimated to be around $5.94 billion in 2019 and is expected to grow to $30.45 billion by 2027.
8 Ways Retailers Can Use Conversational Commerce
Conversational commerce is a powerful way to address the changing priorities of consumers. The question is how to use conversational commerce from a practical perspective, which will be different for every retailer.
There are multiple ways retailers can use chatbots and automated conversations in their retail stores. Below are eight conversational commerce examples retailers can use to help improve various aspects of their stores:
1. Provide Fast Customer Service
One of the common ways retailers use conversational commerce is as a customer service program. Using this approach, retailers automate responses to consumer questions, which can include everything from product questions and sales to order problems. The system can also be set up to route the shopper to a live support specialist if their issue is more complex.
If retailers can provide quick access to reliable, helpful customer support, consumers will be more likely to make a purchase, as they won’t have to wait around for an answer to their question or concern.
2. Share Product Updates
Retailers can also use conversational commerce to share product updates with previous buyers. New models, software updates, and sales can all be shared via SMS or messaging services, keeping consumers informed about their options and encouraging them to buy or upgrade. This feature allows consumers to continue buying from the same retailer rather than seek out another store when they need a new product.
3. Communicate Current Promotions
Many consumers report that discounts affect their decision to complete an online purchase. Retailers can lean into this trend by using conversational commerce to communicate promotions. Send out text messages to consumers notifying them of sales and use a website chatbot to alert visitors when they arrive on the landing page. Retailers can even set it up so consumers at the checkout screen see a notification of applicable discounts when they start the checkout process.
4. Obtain Post-Survey Responses
Every retailer wants feedback from their consumers, especially if they’re looking to make a change. To ensure they get plenty of accurate responses, retailers can set up a conversational commerce platform to reach out to consumers and visitors for surveys. Retailers can send surveys for products after consumers have received them or set up a questionnaire on their website to ask consumers how they can improve.
5. Cross-Sell and Upsell
Upselling and cross-selling strategies help retailers earn more money by selling products to consumers at the checkout page. Retailers can set up a bot to suggest items commonly purchased with the products in a consumers’ cart or notify them of an upgrade option. Shops can also utilize messaging services to notify recent purchasers of accessories they may want for their new product.
6. Automate Cart Recovery
Abandoned carts are a common issue for retailers, but commercial commerce can help. In the event that a shopper abandons their cart, a retailer can set up their system to send out an automated message to the consumer. Retailers can also program commercial bots to offer a cart recovery option, so it’s even easier for the consumer to return to their potential purchase. Discounts can also be effective in this situation, so retailers may want to push discount offers to people who abandon their carts as an incentive to finish buying.
7. Offer Accurate Order Updates
Everyone knows the feeling of anticipation when they place an order. Stores can generate goodwill with their consumers by offering order updates to let them know when their order is processed and sent out. Delivery notifications are also a practical option. Retailers can set this feature up through text, messaging services, or email, so their consumers get the information they need using their preferred communication method.
8. Increase Customer Reviews
Reviews are a powerful marketing tool for consumers — 91% of younger consumers between the age of 18 to 34 rely on online reviews. Therefore, it’s essential to collect reviews for your products. Conversational commerce is a powerful way to automate reaching out for reviews. Set up automated messages after a shopper has received their product asking for quick reviews or surveys. Be sure to ask questions consumers can easily answer from a mobile phone, such as satisfaction ratings, photos of the product, shoutouts on social media, and short written or video reviews.
How to use conversational commerce depends highly on a retailer’s priorities and operations. Before crafting a solution, retailers should consider their store’s goals, identify opportunities for improvement, and look at their current contact options. Some conversational commerce methods will work better than others, so it’s essential for retailers to develop a solution that addresses their unique needs. Testing specific variables will ensure retailers get the most out of their conversational commerce solutions.
Now is the perfect time for retailers to enhance their online stores with conversational commerce. If you’re interested, Quiq is here to help you get started.
Explore Conversational Commerce for Retail With Quiq
Conversational commerce is an ideal way to address the changing needs of consumers in a post-pandemic world. But even beyond COVID-19, your retail business can benefit. This type of platform offers a personalized experience for consumers while improving productivity, supporting customer service, boosting retention, and increasing sales. There are many ways to implement conversational commerce in your retail store, and Quiq is here to help you every step of the way.
Quiq is a conversational consumer engagement platform that helps retailers deliver exceptional customer experiences. Our platform supports messaging across text messaging, web chat, social, Apple Business Chat, and Google’s Business Messages, so you can use as few or as many channels as you need to communicate with your consumer base. With easy integration, multiple bot options, and rich messaging experiences, Quiq has everything retailers need to incorporate conversational commerce into their stores.
From increasing conversion rates to receiving more on-time payments, the results from brands that have embraced conversational commerce speak for themselves. Conversational commerce can help brands resolve inquiries and satisfy customers.
Conversational commerce means using messaging and bots to enable two-way conversations with your customers. You won’t just tell your customers things like “Hey, we’ve got a promotion on these products today.” You can have a conversation in real-time about ways you can help them shop, find what they need, and complete transactions..
Conversational commerce enables you to listen and learn from your customers while building a solid relationship. That relationship means everything to your business.
Here’s a few of the biggest benefits conversational commerce can bring to your business:
Increased sales
Broader reach and engagement
Higher customer satisfaction
In this article, we take a look at how conversational commerce can change your business. We review the solutions that help businesses enable conversational commerce for sales and customer service and the simple steps you can take today to get started.
As a quick refresher, we defined conversational commerce in a previous post as the transactions that take place through digital conversations consumers have with brands. These conversations take place on messaging apps like web chat, Apple Messages for Business, text messaging, and even Facebook Messenger.
Conversational commerce is all about answering customers’ questions and concerns when and where they prefer. Your customers are given hands-on, personalized support, across the digital channels they prefer like SMS/text, live chat, and social media platforms.
Consider how differently the conversational commerce experience is with technology’s help:
With proactive chat on your website, chatbots or human agents can intervene at critical moments, like when a visitor toggles back and forth between two product options or hesitates at checkout to help them make a decision
Rich Communication Services (RCS) like Apple Messages for Business and Google’s Business Messages enables agents to present products in carousels, schedule appointments, and collect payment, all within the messaging conversation.
The possibilities are limitless. Small and large brands alike are using conversational commerce to reach customers, boost sales, and interact in a personal way with consumers. But where do you start?
Where Do You Start With Conversational Commerce?
The answer for this question is the same as others where there is no hard, fast, or linear answer — it depends. Where you start depends on a few key things:
Your business goals and any identified opportunities for improvement
How your customers currently contact you
Pain points within your customer’s experience that you want to address
Let’s say you own retail stores that sell outdoor apparel. In-store sales have dropped dramatically so your company has decided to have a summer sale that is heavily promoted across your website. You’ve also decided to test Facebook ads to reach new customers. A lot of retailers probably can relate to this scenario and understand how important the sale is for year-end results. Every transaction counts.
Now, let’s say you’ve kicked off that sale and are getting inundated with calls. Customers are having trouble with the promo code at checkout. While some may stop and call, others may decide to just abandon their cart never to return again. Since you kicked off Facebook ads, you’re also seeing a huge spike in conversations in Messenger about the promo code and other product questions as well.
This is where conversation commerce changes the outcome. With messaging, customers are able to easily reach you either on your website, text, or Facebook Messenger while shopping or while they are trying to checkout. By bringing all interactions together in one platform your business saves time and your customers receive a faster, more fluid experience.
Now. you may be thinking, “That’s all well and good but I still need agents on the other side of the conversation.” Ah, yes but no one said they all had to be human agents. Remember when we mentioned chatbots before?
In our retail scenario above, a chatbot could greet the customer, ask questions to understand the customer’s intent or issue, and then present information to guide the customer along. For example, after collecting some information, the chatbot learns that the customer needs help with a purchase, specifically, they need to know where they can enter the promo code.
The chatbot can inform the customer that they’ll have the opportunity to enter the promo code after they enter their credit card number and confirm their purchase. The chatbot can also share a screenshot of the screen with the exact field where the promo code can be entered. If the customer is still having trouble, the bot can seamlessly introduce a human agent into the conversation for further assistance if needed.
Quiq clients have seen call volumes drop by 20% as customers switch from phone calls to messaging. At the same time, customer service agents are able to handle 6 – 8 concurrent conversations. This boost in agent productivity translates directly to the bottom line and to customer satisfaction scores.
Elevate The Online Purchasing Experience With Conversational Commerce
Now is not the time to get sweaty palms about a digital-centric approach to your commerce business. Now is the time to use conversational commerce to change your business for the better.
Conversational commerce isn’t just an easier, more useful way for consumers to shop. It’s also a way for brands to continue growing eCommerce revenue.
Ready to support your customers quickly, boost conversions, reduce sales and support costs, and increase customer satisfaction? Contact one of our conversation experts to discuss the future of conversational commerce in your business.
Referrals have always had their place in marketing. It’s just that now, your customers have more reach and influence than ever before. Not only can they tell their closest friends about your product or service, but they can post Google reviews, share a social post, or use any number of methods to amplify their voice.
Nielson showed us that consumers believe recommendations from their friends and family over all forms of advertising. An impressive 92% of consumers who responded to their study agreed. We all have seen the power of word-of-mouth marketing grow.
Reaching out to your customers manually, at just the right time, to encourage or request a referral is possible. It’s just that it’s time-consuming and expensive. That’s why so many companies have turned to business messaging to increase customer referrals.
In this post, we’ll explore some of the nitty-gritty details of how to use business messaging to increase customer referrals. We’ll also give you a great example of how one of our leading clients puts messaging and bots to work in their organization to boost their customer referrals.
Why Referred Customers Are So Valuable
Aside from being a really effective form of marketing and making your business look awesome, referrals contribute directly to your bottom line. Here are 3 reasons why you need to make sure you have a healthy mix of referred customers in your pipeline:
1. Referred customers have a lower acquisition cost
Customer Acquisition Cost (CAC) is the price you pay to obtain a new customer. That number includes your total Sales and Marketing. With other methods, you pay for each click and impression that lead up to converting that customer.
Your CAC is significantly decreased when you recruit your current customers to do the sales and marketing for you. How much it decreases depends on the success of your referral program, if you offer incentives, and if you are manually doing outreach to your customers.
2. Referred customers are more loyal
In an era where consumers have so many readily available choices at their fingertips, it’s not a surprise that brand loyalty has dwindled. It’s more important than ever to reduce your customer churn.
The Journal of Marketing reports that referred customers are 18% more loyal than non-referred customers. When your customers refer you to their family, friends, or extended network, you receive the benefit of the trust and credibility they’ve built up among their community. It’s also likely that referred customers will have a stronger sense of commitment and attachment to your company because someone they know, like, and trust has matched them to your brand.
Customers that refer you also have a higher likelihood of having a long-term relationship with your company. This is why the intention to refer a company is frequently used as an indicator of loyalty to that brand.
3. Referred customers are more profitable
So far, we’ve determined that referred customers are less costly to acquire and have a higher likelihood to be more loyal. But that’s not all. There are also other ways they are generally more profitable for a business. Referred customers are also more likely to refer others to your business. Rinse and repeat #1 and #2.
In addition to that, your referring customer will likely have a deeper relationship with your product or service. That may mean a higher frequency of using your service or a history of exploring more features. These customers are a lot more advanced than a novice customer.
Consistent positive experiences with different products means that these customers are likely to spend more over their entire life time as your customer. Think of it like this. If you’re a clothing brand and your customer loves the new pants they bought from you, it’ll be a lot easier to sell them the shirt and the jacket.
Why You Should Automate Your Customer Referral Program
With these incredible benefits, you’d think every company would have a referral marketing campaign. The honest truth is that asking your customers at the right time to leave a review isn’t always as easy as it seems. And you will have to ask.
According to a marketing survey conducted by Texas Tech, 83% of satisfied customers are willing to refer products and services. But, only 29% actually do. Implementing and managing customer referrals can be difficult to present at the right time if you have to do it manually.
A lot of companies out there know how painful it is to effectively execute a referral program where agents are making outbound calls to request a referral from their customers. Brinks Home Security has been able to automate their referral program using messaging and bots.
In a previous post, we examined how Brinks used messaging and bots to increase their customer retention. Now, let’s take a look at how the company was able to take their customer referral process to the next level with messaging and bots.
The Quiq bot that Brinks created combines their survey platform with their email service provider. The Quiq bot brings those two customer data streams together to invite new referrals to the Brinks family.
Once a current customer submits a survey indicating that they are a happy customer, the Quiq bot will ask for email addresses of anyone they think would enjoy Brinks service as well. The Quiq bot will pass those email addresses to Brinks Email Service Provider.
Prospects are then informed that they were referred and invited to take a look at Brinks products and services. If the prospect signs up, then the Quiq bot will match the newly signed customer with the customer who referred them and send both customers free product options.
All of this is done with no human intervention. The process is streamlined and infinitely repeatable with none of the intense labor and expense of outbound calling.
Don’t Wait To Use Business Messaging To Increase Customer Referrals
It’s great when your customers eagerly act as advocates for your business on their own accord. That may happen now and again, but if you want to grow your business, retain the customers you already have, and reach your revenue goals, you’ll have to be more proactive with your referral program.
Implementing messaging and bots to automate your customer referral program will save time, money, and make the process easier for your company. More importantly, an automated process like the one Brinks uses makes it easier for your customers.
If you’d like to learn more about using bots in your current workflows visit our bot webpage or take a look at one of our bot webinar replays.
While conversion rates are more traditionally tied to eCommerce sites, it’s a metric that every company cares about. A conversion can mean different things for different companies. Websites can measure conversions by the number of visits, form fills, subscriptions or, more commonly the number of sales made. The goal you’re measuring can be a macro goal, like requesting a demo or it could be a micro-conversion such as watching a video. No matter what the conversion is, there’s always one universal thread that binds them all together. Everyone wants more of them.
Your conversion rate is a direct reflection of the number of visitors that find value when they visit your website. For the purpose of this article, we’ll think about a conversion as any action that leads to revenue generation such as a cart checkout or an appointment confirmed. In this article, we’ll share a few customer messaging tips to help you increase your online conversion rate.
Text Messaging Increases Conversions
Driving traffic to your site is only part of the conversion equation. Once you get traffic there, that user will either bounce from your site or engage in some way. Messaging may not be an obvious way to get more conversions, but it’s proven to be an effective one. Again, we’re seeking to move a site visitor to complete a transaction. There may be some barriers you have to remove for that visitor before they complete the transaction, such as finding the right solution on your site or answering questions about security. Messaging is the vehicle to help you do that.
The average conversion rate across for online retailer is just about 2%. While that may seem shockingly low to some, achieving that number takes some serious effort. Optimizing your site with compelling copy, clear call to actions and easy, logical navigation are critical to hit even the average number of conversions. It’s not unrealistic to achieve conversion rates higher than 2%, but you need to employ ways to get your site visitors engaged with your brand.
Quiq clients have realized an increase in conversions when they’ve implemented messaging. Retail clients, like The Laundress for example, use messaging to provide customers with personalized help to find and purchase the right products. Messaging has allowed their agents to provide the same kind of white-glove service to online customers that visitors to the retailer’s trendy SoHo location have come to expect.
At Quiq, we believe any customer should be able to reach any business via messaging. Why? Because interacting via messaging is the fastest and most convenient way to do business. We’ve also found that messaging encourages customers to re-engage with your brand.
Whether you already have messaging available on your site or are considering opening this up as a channel for your customers, there are a few critical things to keep in mind to make the experience truly beneficial for you and your website visitor. Follow these 7 customer messaging tips to increase conversion rates.
7 Customer Messaging Tips to Increase Conversions
1. Make live chat available right on the homepage
On a sales floor, you don’t let customers suffer in silence. At a trade show, you wouldn’t let prospects just wander aimlessly around the booth. It shouldn’t be hard for customers to reach you on your website. Put out the welcome mat and let customers know that you’re available to answer questions right away.
There are a few asynchronous ways your customers can contact you that sit under the Messaging umbrella. Live chat, SMS, MMS, rich messaging, and social platforms like Twitter and Facebook all present opportunities for you to connect, engage, and convert. All of these channels can be easily managed with the Quiq platform.
2. Make text messaging just as easy
If you’ve ever tried live chat on a mobile phone you know that the experience is less than ideal. Considering the number of purchase decisions that begin with and end with search on a mobile phone, it would be wise to make sure your mobile experience rival that of your desktop experience.
While chat may be the answer for desktop customer messaging, it can be clumsy and cumbersome on a mobile device. Instead, enable a “Text Us” option on your mobile optimized site so that your customer can communicate via their native messaging app. Most traditional chat experiences require both agent and customer to maintain a constant presence on chat, otherwise the chat “times out” and closes itself automatically. Because Quiq’s chat and messaging channels are all asynchronous, the conversation is never closed and it never ends. This allows your customer to send and respond to messages when it’s convenient for them.
3. Use inviting imagery
Since we’re visual creatures by nature, the use of well lit, clear images will keep customers engaged in the conversation and provide more context around the conversation. For retailers, this could be just the right enticement for customers to explore more product lines.
Modern messaging apps allow sharing of hi-res images, videos, links, PDFs, and gifs. Include attractive images of products, videos of client testimonies, or even fun emojis depending on the conversation to add that personal touch.
4. Add an element of interactivity
No one likes a one-sided conversation where one side is talking and the other side may, or may not, be listening. People and your customers want to be heard. It is critical to ensure there are open, two-way channels to support this requirement. Some communication channels, like email may feel like you were “talking at” your customers, modern messaging apps, like Apple Business Chat and Google RCS helps you “talk with” your customers.
These messaging platforms, allow consumers to easily interact with rich cards. Now consumers can choose from a list of available times to schedule an appointment or select the color of an item from a list products – all within a messaging conversation with just a few taps.
5. Give an approximate time for a reply
Every channel has certain expectations that come with it. The same goes with messaging. Customers expect ease, convenience and timeliness. If a successful promo or other event spikes your wait times on the messaging channel, let your customers know how long they can expect to wait.
Couple their approximate wait time with a message such as “Our wait times are longer than expected due to our successful winter sale”. This seemingly small tweak not only notifies your customers how long they may wait, but also provides some social proof that affirms your brand. As we’ve come to understand, social proof, like referrals or online reviews can help online conversions.
6. Provide multiple options for communication
Contact center leadership may cringe at the thought of having to manage multiple contact points simultaneously. Omni-channel communications can sound daunting but Quiq Messaging makes it manageable.
You never know where a prospective buyer may see you. An intriguing Tweet, an irresistible photo on Facebook, or a search on Siri could prompt a prospect to reach out to you. With Quiq, you can easily manage every conversation that comes in via text, live chat, app, social profiles, or searches on Google and Apple.
7. Have a real person available to assist with questions
Consumers are turning to messaging because they’ve exhausted their patience on FAQ pages and with email, and they simply don’t want to call for help just to wade through a confusing IVR system to talk to a live agent. They want answers, and they want them now.
Quiq clients have found success in using technology such as chatbots to gather information prior to handing the conversation over to a live agent. The main objective is supporting the agent with enough information to help your customer faster. Use automation to enhance the human touch, not replace it.
Increasing conversion rates, no matter what that may mean for your site is about getting visitors engaged. While messaging have proven over and over again to be the friction-free way for customers to get post-sales service, we’re seeing more evidence everyday that proves it is also providing the pre-sales support that increases conversions. Ready to join the growing list of Quiq clients who are realizing a boost in their conversion rates? Request to see a short demo today.
when our normal was just normal, consumers leaned on the ease, convenience, and speed that shopping from their smartphone offered.
With these incredible benefits, you’d think every company would have a referral marketing campaign. The honest truth is that asking your customers at the right time to leave a review isn’t always as easy as it seems. And you will have to ask. 
