Chip — AI Fitting & Shopping Assistant
Chip is TaylorMade's AI assistant that turns product discovery, club fitting, shopping, and support into one conversational experience.
AI Fitting
In-chat Shopping
Search, reimagined
I led the UX audit and redesign strategy — grounding every recommendation in real behavioral data — to improve the fitting funnel, make the conversation shoppable, and personalize it around each golfer's account and game.
Chip is working — now let's design & test into it
The audit surfaced strong, data-backed recommendations. First, the evidence that Chip already earns its place:
The Gap
- Reach is thin. Chip touches just 0.67% of site sessions vs 1.98% for Traditional Search — a third of the reach on the site's highest-intent surface.
- Mobile carries the volume but underperforms. ~70% of Chip sessions are mobile, yet desktop converts higher (5.99% vs 4.18%) and at a bigger Avg order value ($534 vs $387).
- Shoppable categories lack Chip visibility. Rescue PDPs saw 572 sessions and zero Chip opens — a sign of missing or inconsistent CTAs across some product pages.
- Chip creates intent but still lacking on conversion. 69.94% of sessions reach a PDP, but only 7.51% add to cart — the conversation hands shopping back to the site at the decision point.
- Grow conversion and revenue per Chip session and fitting.
- Increase average order value through guided, confident purchasing.
- Deflect support volume by answering order, stock, subscription, and account questions in the chat.
- Drive loyalty account creation, engagement, and repeat purchase.
- Ultimately, position Chip to replace traditional search.
The Future of Chip
Chip becomes the primary way golfers find, fit, and buy on the site — a single, personalized conversation that replaces search, generic filters, and manual configuration, and knows each golfer's game before they ask.
Competitors using AI shopping assistants well
- Amazon Rufus — shoppable answers that keep the purchase inside the conversation and continue shopping.
- Nike — account-driven personalization and recommendations.
- Sephora — guided product discovery that turns complex catalogs into confident easy choices.
Needs & Pain Points
Three core user behaviors emerged from the fitting and shopping flow data.
1. Input Controls (AI Fitting)
Conversion Impact — Very High
In addition to open-text fitting questions adding tappable options, "Unsure" paths, and a progress indicator.
Fitting is the highest-intent surface — 42% of fittings led to a purchase within 7 days.
More completed fittings ending in a saved, shoppable recommendation and a confident purchase.
Fitting completion above 40%.
Previous Design
Optimized Design
2. Shopping Modules
Conversion Impact — Very High
Product cards, carousels, quick-add, configure CTAs, and save — all inside the chat.
69.94% of sessions reach a PDP but only 7.51% add to cart — there's no shoppable moment at the point of decision.
More add-to-carts and purchases from Chip, and more qualified traffic into club configuration (PDPs).
Lift in-chat add-to-cart to 14% (from 7.51%).
Previous Design
Optimized Design
3. Player Profile (Saved to Account)
Conversion Impact — High
Save hand, height, gender, handicap, and sizes so future fittings pre-fill instead of restarting.
Cuts repetitive input — the top friction point for returning golfers — and builds a reusable player profile.
Faster repeat fittings and recommendations tailored to the golfer's game and size.
Increase returning fitting completions and reduce inputs per returning fittings.
Previous Design
Optimized Design
4. Chip Mimics Search
Conversion Impact — High
Chip replaces traditional search while preserving familiar patterns — product carousels, search-focused prompts, and search terminology.
Chip reaches 0.67% of sessions vs 1.98% for search, but carries a 27% higher AOV.
Captures more discovery volume with product-first results.
Grow Chip's sessions from 0.67% to 3% to replace traditional search at (1.98%).
Optimized Design
Use Of Search Terms.
Optimized Design
Product carousel
5. Quick Start (Personalized Prompts)
Conversion Impact — High
Open with personalized prompt chips based on history and the PDP the user enters from.
Cold-start is a major drop point — golfers who don't know what to ask leave without interacting.
More sessions start with a productive action.
Grow Quick Start click rate; reduce no-interaction exits.
Previous Design
Optimized Design
Default Personalization prompts
Optimized Design
PDP Specific Tailored prompts
6. Consistent Visual System
Conversion Impact — High
Replace long text answers with a consistent card and comparison-chart system across categories.
Text-heavy replies bury the recommendation; comparison drives confident decisions.
Faster comprehension and easier comparison.
Grow comparison engagement; lift conversion once landed on a PDP.
Previous Design
Previous Design
Optimized Design
Product Recommendations
Optimized Design
Product Comparison
7. Use Existing Account & Fitting Modules
Conversion Impact — Medium
Surface order status, loyalty tier/points, and past fittings for signed-in golfers.
Loyalty is the highest-intent audience — leverage what we already know about our members.
Relevant answers on orders and account info; loyalty status motivates the purchase.
Returning-customer CVR & AOV lift; increased account engagement.
Previous Design
Optimized Design
8. Follow-Up (Personalized Prompts & Questions)
Conversion Impact — Medium
End every response with promoted follow-up and suggested-topic chips.
Conversations dead-end today, killing momentum toward purchase. Low effort, high return.
Longer sessions with more turns ending in an action.
Grow follow-up click-through rate; increase engagement time.
Final Design
9. Feedback Module
Conversion Impact — Medium
Lightweight thumbs, optional comment, and confirmation of feedback.
Only thumbs and no confirmation exist today; feedback creates a response-quality signal.
Continuously improving response quality based on how users engage with Chip.
Feedback submission rate.
Prompt
Confirmation
10. New-User Onboarding
Conversion Impact — Medium
Short first-run intro: How Chip AI & fitting works and how the user can use it.
New users underuse Chip because they don't know what it does and fall back on search.
Higher first-session engagement and adoption of Chip.
Onboarding completion rate.
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