Design a conversational flow that guides a visitor from stated need through product recommendation to checkout.
Marketing & Sales
Conversational Commerce Chatbots for Online Sales
Learn to design, build and optimise conversational commerce chatbots that guide website and app visitors from product discovery through to completed purchase.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Many website chatbots are built as simple FAQ deflection tools and fail as soon as a visitor expects help choosing between products, even as shoppers grow more comfortable asking a conversational assistant instead of browsing categories and filters. This course teaches conversational commerce chatbot design as a sales discipline: mapping a discovery-to-purchase flow that narrows options through clarifying questions, integrating the chatbot with live catalogue, pricing and inventory data, and writing conversational copy and guardrails for a generative AI-based assistant so it never invents pricing or policy detail. You will design escalation rules that hand a conversation to a human sales or support agent at the right moment, with full context passed across so the customer never repeats themselves. The course closes with testing chatbot performance against conversion rate and customer satisfaction rather than deflection rate alone, and using transcript analysis to find where conversations stall and revise the flow accordingly.
Expected Learning Outcomes
Integrate a chatbot with product catalogue, pricing and inventory data so recommendations reflect real availability.
Write conversational copy and response guardrails for a generative AI-based shopping assistant.
Build product recommendation logic that narrows options using clarifying questions rather than overwhelming choice.
Design escalation rules that hand off a conversation to a human sales or support agent at the right moment.
Test chatbot performance using conversion rate and customer satisfaction rather than deflection rate alone.
Iterate chatbot flows and content based on transcript analysis of where conversations stall or fail.
Who Should Attend
E-commerce managers evaluating or improving a chatbot on their website or app.
Conversational design specialists building sales-oriented chatbot flows.
Customer experience leaders responsible for chatbot and live chat channel performance.
Product managers overseeing an AI shopping assistant feature.
Digital marketing managers who need chatbot performance to support conversion goals, not just support deflection.
Customer service teams whose escalation workload is affected by chatbot handoff design.
Course Modules
Select any module to see its sessions and points.
01Designing Conversations That Guide a Purchase
2 sessions · 8 points
Session 1Mapping the Discovery-to-Purchase Flow
- Map the questions a knowledgeable sales assistant would ask to narrow a customer's need before recommending a product.
- Design conversation branches that handle vague opening messages without forcing the customer into rigid menu choices.
- Sequence clarifying questions to avoid asking for information the chatbot could infer from context or account data.
- Design a clear path from product recommendation to cart addition without unnecessary conversational detours.
Session 2Writing Conversational Copy and Guardrails
- Write chatbot responses in a tone consistent with the brand voice used elsewhere in marketing content.
- Set guardrails for a generative AI-based assistant that prevent it from inventing pricing, stock or policy information.
- Define topics the chatbot should decline to answer and redirect towards a human agent instead.
- Test conversational copy for clarity with users unfamiliar with the product category being sold.
02Connecting the Chatbot to Commerce Data
2 sessions · 8 points
Session 1Integrating Catalogue, Pricing and Inventory
- Connect the chatbot to live catalogue data so recommended products reflect current pricing and description content.
- Filter recommendations by real-time inventory so the chatbot never suggests an out-of-stock item without saying so.
- Surface promotions and bundle offers within the conversation when they match the customer's stated need.
- Handle catalogue data gaps gracefully so missing attributes do not break the recommendation logic.
Session 2Building Recommendation Logic
- Design a decision structure that narrows a full catalogue to a small shortlist using two or three clarifying questions.
- Weight recommendation logic using stated preferences ahead of generic best-seller or popularity defaults.
- Present a limited number of recommended options at a time to avoid overwhelming the customer with choice.
- Allow customers to compare recommended products side by side within the conversational interface.
03Escalation and Human Handover Design
2 sessions · 8 points
Session 1Designing Escalation Triggers
- Define conditions, such as repeated failed recommendations or high cart value, that trigger escalation to a human agent.
- Detect frustration signals in customer messages that should prompt an earlier handover than a rules-only trigger would.
- Set escalation paths that differ for a sales enquiry compared with a post-purchase support issue.
- Design a fallback response for questions entirely outside the chatbot's trained scope.
Session 2Handover Experience and Agent Support
- Pass full conversation history and context to the human agent so the customer does not repeat their request.
- Set expectations within the chatbot conversation about likely wait time before a human agent joins.
- Train agents on how to resume a conversation naturally after an automated handover.
- Log escalation reasons to identify recurring gaps the chatbot's automated flow should be extended to cover.
04Testing and Improving Chatbot Performance
2 sessions · 8 points
Session 1Measuring What Matters Beyond Deflection
- Track conversation-to-conversion rate as the primary measure of a sales-oriented chatbot's performance.
- Measure customer satisfaction at the end of a conversation rather than relying on deflection rate alone.
- Compare average order value and product return rate for chatbot-assisted purchases against unassisted purchases.
- Segment performance metrics by conversation type to distinguish discovery, comparison and support-led interactions.
Session 2Iterating Flows From Real Conversations
- Review conversation transcripts to identify where customers abandon the flow or repeat themselves in frustration.
- Test revised conversation branches against the previous version using a controlled traffic split.
- Update recommendation logic and copy on a regular cycle as the product catalogue and customer questions evolve.
- Prioritise chatbot improvements using the volume and commercial value of the conversations affected.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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