# Lift conversion 12 percent on the same traffic: the AI build for e-commerce stores Source: https://getstartedwithai.net/tutorials/ai-for-ecommerce-conversion-build Site: Get Started With AI Tagline: Build AI automations that actually run your business. Audience: solo professionals, freelancers, consultants and AI implementers in training Contact: hello@getstartedwithai.co.uk This Markdown companion is generated from the prerendered page for AI crawlers. # Lift conversion 12 percent on the same traffic: the AI build for e-commerce stores Most conversion work in e-commerce is wasted on traffic problems when the gap is on the page. The build below rewrites product copy using your actual customer review corpus as the source, triages support tickets by intent before they reach a human, and adds AI search across the catalogue. Three layers, each independent, each shippable in a day. Stores running the build typically see 8 to 15 percent conversion lift inside 30 days, with the larger lifts on the copy layer. Same traffic, more orders, no rebuild. ## What you need before the build starts - A Claude Pro or ChatGPT Plus account at £15 to £20 a month. - At least 100 products and a meaningful review history. Stores under 50 lines see less because the copy layer matters less. - Export access to your reviews: Trustpilot, Yotpo, Judge.me, Reviews.io or the Shopify reviews app. - A Zapier or Make paid plan for the support triage step. ## Step 1: Build the product copy Project (30 minutes) Generic product copy reads as supplier blurb. Copy written from your own reviews reads as the customer talking to the next customer. 1. Create a Claude Project named Product Copy from Reviews. 2. Upload your brand voice notes, the category structure, and 100 customer reviews from across the catalogue. 3. System prompt: write a UK e-commerce product description for the named SKU. UK English. 90 to 130 words. Lead with the use case the reviews surface most often. Include one specific phrase pulled from a review, in quotes. 4. Test on your 20 worst-performing product pages. Most stores see 8 to 12 percent conversion lift on the rewritten lines inside the first month. ## Step 2: Intent-based support triage (25 minutes) For most stores, the bulk of inbound support is order tracking, returns and product fit. Routing those before they hit a human deflects most of the volume. 1. In Zapier or Make, set the trigger on a new ticket from Gorgias, Zendesk or Shopify Inbox. 2. Action 1: send the ticket body to a Claude Project with the prompt to classify intent into one of: order tracking, returns, product fit, billing, complaint, other. 3. Action 2: for the first three categories, draft the reply with the order data attached. The owner approves and sends. 4. Action 3: route everything else to the human queue with a one-paragraph context note. 5. Test on last weeks ticket archive. Typical deflection lands at 60 to 75 percent in the first month. ## Step 3: AI search and recommendations (20 minutes) On-site search is the fastest path to checkout for shoppers who already know what they want. Default Shopify search misses synonyms and intent. 1. Pick an AI search app from the Shopify or BigCommerce store: Searchspring, Klevu or Algolia AI work for most UK stores. 2. Index the catalogue and the review text together. Reviews carry intent language the product titles miss. 3. Configure synonyms from the top 20 search queries that returned zero results last month. 4. Test on the same 20 zero-result queries. Most stores see search-to-purchase rate roughly double after the swap. ## Step 4: Email triage and recovery (15 minutes) Cart-abandonment emails written by the platform read as the platform. Written from the customer review corpus, they read as a fellow buyer. 1. Add a Project for cart-abandonment emails calibrated to your last 20 sent emails plus your top reviews. 2. System prompt: draft a UK e-commerce cart-abandonment email. UK English. 80 to 120 words. Reference one specific use case from the review corpus. One call to action, no discount. 3. Schedule via Klaviyo, Mailchimp or your existing ESP. Most stores see recovery rate lift 2 to 4 percentage points. ## Step 5: First 30 days of operation Run the four layers across one full month. Compare conversion on rewritten product pages against unedited control SKUs, and compare deflection rate before and after the triage. Most stores see 8 to 15 percent conversion lift on the rewritten copy and 60 to 75 percent triage deflection by week four. ## What you ship Product copy rewritten on your 20 worst-performing pages. Support triage running on Zapier with three categories deflected and the rest routed with context. AI search live with synonym overrides for the top zero-result queries. Cart-abandonment emails calibrated to the review corpus. ## What to build next For Shopify stores, follow up with the shopify-ai-conversion-build tutorial which goes deeper on Shopify Magic and Sidekick. For multi-store retailers, run the WIRE Method to package this build as a paid service. ## Related builds More tutorials in Industries. Each one runs in 60 to 90 minutes and ships a finished asset. ### Reclaim 8 hours a week as an accountant: the AI build for practice work 30 to 60 minutes. Solo Pro / Implementer. [Open tutorial](/tutorials/ai-for-accountants-practice-build) ### Cut 12 hours a week of legal admin: the AI build for solicitors and small firms 30 to 60 minutes. Solo Pro / Implementer. [Open tutorial](/tutorials/ai-for-lawyers-firm-build) ### Replace 6 hours a week of session admin: the AI build for coaches and therapists 60 to 90 minutes. Solo Pro. 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The build triages tickets by intent and only routes the AI-handleable categories; everything else goes to a human with context attached. Typical deflection lands at 60 to 75 percent in the first month. ### Will customers spot AI-written copy? Not when the source is the customer review corpus. Generic AI copy reads as AI; copy written from your own reviews reads as the buyer. The voice match comes from the upload, not from the model. ### What about UK GDPR on customer reviews and emails? Reviews already published on your site are public. Customer email addresses go to the ESP under the same consent rules as today; the AI never sees the address itself. The triage Project only ever reads the ticket body, not the customer record.