Leadership wanted to understand where digital investments could create measurable commercial value—and where to start without over-investing before the opportunity was proven. The starting point was operational efficiency: how could the retailer reduce support pressure and cost-to-serve while giving customers faster access to help? Retail customers do not wait for business hours. They search and compare products, ask service questions, and request support when the need appears. See how the retailer started with a basic website chatbot, validated customer adoption, and evolved it into a 24/7 AI sales and service capability that captured high-intent demand, improved support, and created richer customer intelligence for sales, service, and marketing teams.
- Client: Retail organization serving B2B and B2C customers.
- Project focus: AI chatbot solution evaluation, MVP implementation, measurement & KPI reporting, analytics integration, first-party data capture, help desk and CRM integration and AI architecture of integrated AI LLM with knowledge-grounded AI powered by RAG (Retrieval-Augmented Generation).
- Digital environment: 6 public-facing websites built on Adobe Experience Manager.
- MarTech ecosystem: Adobe (AEM, Analytics, Target), Oracle (Eloqua marketing automation), Salesforce CRM, and Azure integration.
- AI solution: 24/7 AI Chatbot / service agent with integrated AI adn enterprise knowledge AI.
- Primary goal: To support shoppers, leads and customers online, reduce operational (retail and front-line support) pressures, capture first-party data, sales lead and customer profiling and enrichment, and improve sales/service handoff.
- Related application: A similar but lighter AI chatbot solution without a RAG architecture was also delivered for a B2C automotive retail marketplace for 24/7 shopper support. (Future case-study)
- My role: Lead the strategy to delivery through the translation of business priorities and commercial value, team strategist and delivery lead, vendor management and technical AI solution implementation advisor.
Executive Snapshot
Retail customers do not wait for business hours. They often have an immediate need; searching and comparing products, asking service questions, or requesting support on their own time.
The experience needs to meet them in that moment, with speed, accuracy, and the right answer.
For the retailer, this created a clear digital opportunity: how to support high-intent customers online without adding more staff, increasing operating costs, or forcing every customer to call before they were ready.
The project focused on the business case, technology evaluation, build, launch, and ongoing evolution of an AI-driven chatbot designed to act as a 24/7 digital sales and service assistant.
Start Simple. Prove the Behaviour.
The solution started with an out-of-the-box LivePerson chatbot that could guide users through predefined conversations and connect them to a human agent when needed.
The initial chatbot used defined rules, customer intents, and predefined conversation flows rather than a large language model (LLM).
This allowed the business to test customer engagement and adoption with limited upfront investment.
The MVP focused on measuring whether the chatbot would be adopted by shoppers, could reduce inbound support volume and be quantified, capture basic lead information, and identify where customers most often needed help.
The data provided the evidence needed to validate the first thesis: customers would engage with a chatbot in the moment when they were stuck, had a question, or needed immediate help.
From Chatbot to Knowledge-Grounded AI
Backed by that validation, the chatbot evolved into a richer, business-grounded AI experience connected to approved internal knowledge including support content, product information, OEM specifications, FAQs, customer feedback, CRM signals, and anonymous website behaviour.
This is where RAG, or Retrieval-Augmented Generation, becomes relevant.
In simple terms, RAG lets the chatbots AI look up trusted information from the company’s own knowledge base before answering, helping make responses more accurate, relevant, and grounded in approved business information.
The result was a chatbot that could provide more useful answers to specific product, parts, service, and aftermarket questions while giving the business greater control over the information being used.
Turning Conversations Into Customer Intelligence
The chatbot also became an important first-party data capture point.
When an anonymous visitor shared their name, email address, or phone number, their chatbot conversation could be connected with an existing anonymous digital behaviour profile; which was a prior project I led to identify and convert unknown visistors into known shoppers and customers (similar case study here).
This created a richer view of the customer: what they searched for, what they viewed, what support and help they needed, and when they showed intent in the path to purchase (ex. immediate support or just prior to conversion).
That information could then contribute to a broader Customer 360 view (enabled by a CDP) used across Sales, Service, and Marketing, while also enriching CRM profiles and creating new opportunities for segmentation, follow-up, personalization, and digital experience orchestration.
Applying the Model Elsewhere
A separate project and client, a lighter version of an AI chatbot strategy and solution was also applied within an automotive retail marketplace.
In that use case, the chatbot used an industry-specific knowledge base without the deeper RAG architecture described above.
It helped shoppers search inventory, compare listings, ask questions, and signal buying intent before contacting a salesperson.
I may publish a case study on this use case to show how the value of how different approaches and AI models targeting the same problem can still unlock commercial value.
The Goal Was Not to Replace People
The objective was not to replace front-line teams.
It was to lower the cost to serve by shifting common, repetitive questions from call centres and retail locations into a faster self-service experience, while offering a 24/7 support centre.
The chatbot could help customers get answers sooner, reduce repetitive support demand, guide shoppers toward the next best action, capture high-intent leads, and give Sales and Service teams better context before a human follow-up. Also keep in mind, many of the answers and recommendations were tailored by the busines to influence the shoppers next best action.
The broader opportunity was simple: use AI to handle the repeatable interactions, while giving people more time to focus on the conversations where human expertise creates the most value.
KPI Snapshot
- AI chat engagement rate: 3-5% % of visitors on target pages engaged with chat, initially.
- AI-assisted lead capture rate: 65% of AI conversations captured name, email, or phone.
- After-hours engagement: 35–45% of conversations occurred outside business hours.
- Qualified inquiry rate: ~70% of conversations showed product, parts, service, quote, or support intent.
- CDP identity match rate: 32% of known contacts were stitched to prior anonymous shopping behavior.
The Situation
The retail equipment company supported gobal customers across product research, equipment comparison, parts, service, aftermarket support, and sales inquiries.
Its website was becoming known it enabled business value far beyond the early initial early journey stages.
Leads and customers were using the digital experience to search products, compare options, review service information, look for parts, find retail loctions, and decide whether to contact the business.
But the company faced a common operating challenge.
Customers wanted answers quickly, but sales and service teams could not be available at all times.
Call-centre and retail stores volumes were repetaitive, and at times high-volume with limited staff.
Inbound calls were often repetitive questions. Some required routing. Some needed deeper expertise. And many high-intent visitors were still anonymous.
The business wanted to explore if an AI chatbot could help customers immediately while also serving the same, if not better, customer experience, while building better data for sales, service, and marketing engagement.
In a similar project with an automotive B2C retail client, a similar need existed where shoppers search new and used vehicles, compare inventory, and show buying intent before contacting a sales team.
The Challenge
Customers needed 24/7 support
Customers were asking product, parts, service, and support questions outside normal business hours.
Without an always-on support layer, the business risked losing high-intent visitors before they became known leads.
Staff coverage could not scale the coverage
Many questions did not require a senior sales or service expert. The business needed to reduce repetitive support demand and reserve human teams for complex, high-value, or sensitive conversations.
Product and service knowledge was hard to access online
Useful information lived across product content and a PIM, OEM documents, internal knowledge and exepertise, service teams, FAQs, and support workflows.
The challenge was making that knowledge easier to access through a simple conversational experience.
Website visitors stayed anonymous too long
The organization had a solution in place to track anonymous website visitors and build behavioral profiles based on product views, repeat visits, parts/service page views, and high-intent activity.
But until a visitor shared first-party data, the business could not fully connect that behavior to a known customer record.
A generic chatbot was not enough
Equipment customers often need answers tied to product specs, parts, service, availability, compatibility, or technical fit.
A basic chatbot could help with simple questions if an support agent was available. But the business needed the AI experience to become more specific, more trusted, more real-time and more valueable over time.
The Opportunity
The opportunity was to evolve the chatbot into a 24/7 digital sales and service assistant that could answer customer questions with greater speed, accuracy, and technical depth—while also creating a richer voice-of-customer signal for Sales, Service, Marketing, and other operational teams.
Many customer questions were not simple FAQs. They could involve specific products, parts, service requirements, OEM specifications, or technical support needs. To provide answers that were as relevant and accurate as possible, the chatbot needed to be grounded in trusted internal knowledge rather than relying only on a general-purpose AI model.
This became the rationale for a RAG-based architecture. In simple terms, RAG allowed the AI to retrieve the most relevant information from approved internal knowledge sources before generating an answer, giving customers a more informed response while giving the business greater control over accuracy and content.
The AI chatbot was designed to support five key outcomes:
Answer customer questions faster and more accurately: Across products, parts, service, support, and contact needs, including more technical questions where deeper expertise was required.
Reduce operating pressure: Handle repetitive inquiries, supporting self-service, and routing customers more efficiently when human help was needed.
Capture first-party data: Collect customer’s name, email, or phone number at the right point in the interaction.
Enrich customer records to support the Customer 360 CDP strategy: Both the CRM and Azure-based CDP by connecting chatbot intent and conversations with prior anonymous browsing behaviour, known sales leads and contacts and existing customer account records and user profiles.
Improve sales and service follow-up: Give front-line teams a clearer view of what the customer was looking for, asking about, and trying to accomplish before outreach.
The immediate business driver was customer-centric: give customers faster access to useful answers while the commercialization narrative was about reducing the operational cost and workload placed on front-line teams across a large retail footprint.
The broader opportunity was more strategic. By connecting digital behaviour, AI conversations, customer identity, and CRM/CDP data, the chatbot could also contribute to a practical Customer 360 view—supporting better service, more informed sales follow-up, richer voice-of-customer insight, and more relevant sales and marketing activation.
AI Chatbot Vendor Evalution
The AI chatbot solution was evaluated against business, technical, legal, data, and operating needs.
- User-Stories: An accelerated exercise of documenting operational staf user-stories would help ensure the technical solution was the right fit for the buiness
- Total cost of ownership: License fees, implementation cost, integration cost, support effort, usage-based pricing, and long-term operating cost.
- Time to MVP: How quickly the chatbot could launch on high-intent customer pages without a long development cycle.
- LLM flexibility: Ability to start with a pre-set vendor LLM and later evolve into a more controlled, business-grounded AI agent.
- Knowledge grounding: Ability to use approved FAQs, OEM documentation, prior customer chat solutions, product data and internal knowledge that could be supported with a retrieval-augmented generation approach.
- Internal data integration: Ability to connect with product databases, customer data, support systems, CRM records, service content, customer feedback, and internal knowledge sources.
- MarTech and CRM compatibility: Ability to connect with Salesforce (CRM), Oracle (marketing automation), Adobe ecosystem tools (CMS), analytics (Adobe), and digital workflows.
- Analytics and event tracking: Ability to pass chatbot events into Adobe Analytics and reporting tools.
- First-party data capture: Ability to capture name, email, phone number, inquiry type, product interest, consent signals, and customer context.
- Human handoff controls: Ability to escalate to the right internal team or support stakeholder(s) when a question required the human touch or requested by the user.
- Security, privacy, and legal fit: Data retention, transcript ownership, model training restrictions, privacy obligations, vendor terms, and acceptable use of customer data.
- Brand voice and response control: Ability to manage brand tone, approved answers, disclaimers, restricted topics, escalation rules, and confidence thresholds.
The Solution
The AI chatbot implementation followed a practical MVP-to-enrichment model.
The first priority was speed: launch the chatbot, measure usage, learn from real customer behavior, and improve the model over time.
Phase 1: Launch the AI Chatbot MVP
The first version used a third-party chatbot with a human-agent and vendor-defined workflows.
It was deployed as a subtle bottom-right assistant on high-intent website pages, including:
- Product listing pages
- Product detail pages
- Informative and doorway product and service pages.
- Contact us and support pages
- After-hours support entry points
The placement was intentional.
The chatbot did not interrupt the experience. It appeared where customers were already searching, comparing, or looking for help.
The MVP supported the initial implementation on the website, workflow definitions and integration, front-line staff SOPs and knowledge-base to support product, parts, service inquiries, quote requests, team routing and service handoff and after hours support.
The goal was not to solve every technical question on day one, but ensure the foundation was delivered.
The goal was to create a measurable support layer, learn from real conversations, and identify where AI could help versus where a human was still required.
Phase 2: Connect Measurement and Analytics
The chatbot was integrated with Adobe Analytics using structured event tracking.
Chatbot actions were pushed into the website data layer and passed through Adobe Tag Manager into Adobe Analytics. This made chatbot engagement visible inside the broader digital measurement framework.
Tracked events included:
- Chat session views
- Chat engagement (opened)
- First message sent
- Inquiry type selected
- Engagement length
- Question category asked
- Lead captured and conversion
- Timing of support request (ex. after hours or regular business hours)
- Human handoff requested
This allowed the business to see how users expressed interest in a chatbot as a measn to engage the business, how the chatbot engagement could resolve user questions, how it influenced lead capture, sentiment anlaysis of the conversations (ex. quote requests, service inquiries, high-intent conversion paths, etc.).
Phase 3: Connect Anonomyous AI Chat Users to Known Contacts
The chatbot was connected to a prior visitor intelligence solution that identified and tracked anonymous website visitors.
Before a visitor became known, the business could already see behavioral signals such as:
- High-intent enegagement signals and behavior
- Products viewed
- Equipment categories searched
- Parts visited
- Service viewed
- Return visits
- Contact page visits
- Retail location search
- Chatbot engagement
The chatbot created a new first-party data capture point.
When a visitor shared their name, email, or phone number through chat, the business could create a known lead profile.
That known identity could then be stitched to the visitor’s prior anonymous behavior.
The result was a stronger customer record that showed:
- Who the person was
- What they asked
- What they viewed
- What they cared about
- What category or product they showed interest in
- Whether they were ready for sales, service, parts, aftermarket support, or nurturing
This made the chatbot more than a support tool.
It became a lead intelligence mechanism.
Phase 4: Evolve From Generic Chatbot to Knowledge-Grounded and Agentic AI
The first chatbot proved the customer need, improved access and response speed.
The next evolution focused on something harder: giving customers more relevant, accurate, and technically informed answers—especially after hours, when a human expert was not immediately available.
The solution evolved from a largely out-of-the-box chatbot with predefined rules with human support behind it into a more controlled, business-specific AI experience grounded in the company’s own knowledge.
The knowledge base included:
- Internal product knowledge
- OEM specifications
- Product and service documentation
- FAQs
- Customer feedback
- Historical chat transcripts
- Approved sales and service content
- CRM and behavioural intent signals
- Website content
- Product metadata
The solution used a Retrieval-Augmented Generation (RAG) architecture.
In simple terms, RAG allowed the AI to find relevant information from approved internal sources before generating an answer.
Rather than relying only on the general knowledge of an LLM model, responses could be grounded in the retailer’s own product, service, and technical expertise.
This was especially important for questions where customers expected more than a generic response—such as product compatibility, parts, specifications, service requirements, and aftermarket support.
Add an Agentic Layer
The solution could then move beyond answering questions and begin taking action across connected business systems.
This is where the experience becomes agentic.
Instead of only telling a customer what to do next, the AI could help complete the next step by:
- Creating a support ticket for customer support follow up and inform the accounts sales rep. if identified.
- Checking product or parts inventory
- Updating CRM records and upstream MarTechs
- Routing a case to the right sales or service team
- Capturing and attaching customer intent to a contact record
- Triggering follow-up workflows when human support was required
In simple terms, RAG helped the AI know what to say. The agentic layer helped it do something with that knowledge.
This created a more useful end-to-end experience: answer the question, understand the customer’s intent, and help move the request toward resolution without forcing the customer to restart the process with another channel.
The chatbot also became part of a continuous improvement loop. Customer conversations revealed what people were asking, where knowledge was missing, which questions signalled purchase or service intent, and when human support was still required.
Over time, those insights helped improve the knowledge base, conversation design, workflows, and customer experience—making the AI more specific, more useful, and more connected to the business.
Key Learnings
1. Start with the business problem, not the chatbot solution or AI
The strongest use case was operational, not experimental.
The chatbot worked because it addressed real customer and business needs: after-hours demand, repetitive questions, anonymous visitors, limited staff coverage, and inconsistent access to product and technical knowledge.
It was also a opportunity to demonstrate commercial value from digital investments that drove real business value; both lowering operational costs, improving efficiencies and enhancing the customer experience. The former has become even more of a priority as of late due to the commoditization of the businesses products and services. Where branding and premium price positioning is a diminishing competitive edge, then customer experience becomes the only differentiator and competitive edge.
Yes, the technology mattered, but the business case came first.
2. Meet customers in the moment
Chatbot placement had a direct impact on usefulness and engagement.
It performed best when available in high-intent moments such as product search, equipment listings, parts and service pages, contact experiences, and after-hours support.
The chatbot did not need to dominate the digital experience. It needed to appear when the customer was most likely to need help.
3. Start simple, prove adoption, then invest
The initial rules- and intent-based chatbot allowed the business to launch quickly with limited upfront investment.
That MVP created something more valuable than assumptions: real evidence of how customers used conversational support, what they asked, where they became stuck, and when they needed a human.
That evidence helped justify the next level of investment.
4. Internal knowledge and speed of access created the real AI advantage
The experience became significantly more valuable when the AI was grounded in business-specific knowledge rather than relying on general AI alone, and could deliver it to the individual semi real-time.
Product information, OEM specifications, parts and service content, FAQs, customer feedback, historical conversations, and behavioural signals gave the AI the context needed to provide more relevant and technically informed responses.
RAG helped turn general AI capability into business-specific expertise.
5. AI created the most value across repeatable interactions
AI was particularly effective where customer needs were frequent, structured, and repeatable.
This included common product questions, parts inquiries, service FAQs, lead capture, quote intake, routing, support requests, and after-hours assistance.
With connected systems, the agentic layer could go further—checking inventory, creating support tickets, updating CRM records, and routing cases to the right team.
6. Human expertise remained essential
AI did not remove the need for people.
Human support remained important for complex technical diagnosis, safety-sensitive guidance, warranty disputes, financing, negotiation, complaints, and high-value sales conversations.
The better operating model was not AI versus human.
It was AI for speed and scale, with people focused where judgment, expertise, and relationships mattered most.
7. Identity capture unlocked a second layer of value
An anonymous chatbot conversation provided useful intent.
A known customer created much more value.
Once a visitor shared identifying information, the business could connect chatbot intent with previous digital behaviour and existing customer records.
That created richer context for CRM and Customer 360 profiles, stronger marketing segmentation, and more informed sales and service follow-up.
8. Conversational AI became a new voice-of-customer signal
The chatbot was not only a service channel.
Every conversation created insight into what customers were searching for, what they did not understand, where content was missing, which questions indicated purchase or service intent, and when the digital experience was failing to answer a need.
This created a new source of voice-of-customer intelligence that could inform content, product information, service design, digital experience, and operational priorities.
9. The real opportunity was connecting digital and human experiences
The most strategic value came from connecting what happened online with what happened next.
The AI experience could capture intent, provide immediate assistance, enrich customer records, and give Sales and Service teams better context before human follow-up.
The chatbot therefore became more than a support tool. It became a bridge between digital behaviour, customer intelligence, AI-assisted service, and human action.
Why it matters
For retail companies, AI chat should be more than a website widget or a shiny object a leader can showcase for praise. It can unlock true commercial value and become a 24/7 sales and service capability that helps customers get answers faster, captures high-intent demand, and gives sales and service teams better context before human follow-up.
The value goes beyond faster responses. Done well and with the time put in to do it correctly, AI chat can improve lead capture, reduce repetitive support demand, strengthen customer intelligence, and create a more connected path between online behaviour, sales, service, CRM, and marketing.
The right approach is to start small and prove the value with the right measuring sticks in place: launch an MVP, measure customer adoption and demand, capture first-party data, connect the right business systems, and then enrich the AI with trusted internal knowledge.
Over time, the chatbot can evolve from a basic support channel into a knowledge-grounded and agentic sales and service capability—one that can answer more complex questions, take simple actions, capture customer intent, and route people to the right next step.
The goal is not AI for the sake of AI. It is to serve customers better, lower the cost to serve, and turn more digital interactions into measurable business value.