01 / Engagement
Are shoppers using WardrobeIt meaningfully?
Review conversations, questions, feature adoption, session depth, and repeat usage where permitted.
Usage & Outcome Analytics
Measure what the assistant influences and converts.
Connect shopper conversations with product discovery, Virtual Try-On, cart activity, completed purchases, and assisted revenue through one clear ecommerce analytics experience.
- Full-funnel reporting
- Feature-level outcomes
- Transparent attribution
Shopper activity
Conversations, questions, discovery, comparison, try-on, and product engagement.
Conversion outcomes
Variant selection, cart additions, checkout progression, and completed orders.
Assisted revenue
Completed order value connected with qualifying WardrobeIt interactions.
Full-funnel analytics
A journey, not a pile of events.
Follow the shopper from the first question to the completed purchase. Each stage connects the event, product context, next action, and merchant question it can answer.
Analytics by business outcome
Four lenses. One assisted shopping journey.
Organize reporting around the decisions your team needs to make, from shopper engagement to completed order value. Each lens preserves its own definition.
02 / Product discovery
Which recommendations create real product interest?
Connect impressions with clicks, product views, comparisons, alternative-product selection, and no-match demand.
Discovery mix
03 / Conversion
Which assisted journeys move into cart and checkout?
Measure chat-to-cart, recommendation-to-cart, try-on-to-cart, complete-look additions, and checkout progression.
04 / Revenue
Which completed orders include a qualifying WardrobeIt interaction?
Review assisted revenue, average assisted order value, and outcome reporting by feature, product, collection, or source.
RecommendationsLinked
Virtual Try-OnLinked
Product Q&ALinked
Complete LookLinked
Feature-level performance
Know what shoppers used. Then see what happened next.
Compare each WardrobeIt capability through the same outcome structure: usage, product engagement, cart activity, and completed order reporting.
AI Shopping Assistant
Conversations and guided journeys
SessionsEngaged conversations
SelectionsProduct engagement
Assisted ordersCompleted outcomes
View details
Conversational Discovery
Intent-led product finding
Discovery requestsSuccessful catalog matches
Product opensRecommendation engagement
Discovery-to-cartCart progression
View details
Catalog Recommendations
Similar products and alternatives
ImpressionsRecommendations shown
ClicksProduct comparison
Recommendation-to-cartSelected products
View details
Virtual Try-On
Visual evaluation journeys
Completed previewsProducts and colors tried
Product revisitsAfter preview
Try-on-to-cartSelected variants
View details
Complete the Look
Multi-product styling journeys
Looks generatedRefinements and replacements
Item engagementComplementary products
Full-look additionsMulti-product carts
View details
Product Q&A
Questions, answers, and handoffs
QuestionsAnswered and unresolved
Next actionsSize guides and comparisons
Q&A-to-cartResolved hesitation
View details
Add to Cart
Variant-confirmed cart actions
Cart requestsSuccessful additions
VariantsSelections and alternatives
Checkout progressionAssisted carts
View details
Product and catalog performance
Turn product activity into merchandising intelligence.
Review recommendation interest, try-on behavior, cart contribution, completed outcomes, comparison patterns, and catalog demand together.
Product intelligence board
Product
Recommendation
Evaluation
Cart
Outcome signal
Sage Pleated Midi Dress
Occasionwear · Relaxed fit
Strong
Compared often
Variant selected
Positive progression
Black Satin Wrap Dress
Evening · Structured
Strong
High try-on
Weak progression
Review hesitation
Champagne Clutch
Accessory · Complete Look
Contextual
Often paired
Cross-sell added
Useful pairing
Neutral Strappy Heels
Footwear · Occasion
Frequent
Size questions
Alt requested
Variant demand
Soft Green Wrap Dress
Occasionwear · Regular fit
Alternative
Lower price
Recovery add
Dead-end recovery
Shopper intent and question analytics
See the demand standard analytics cannot hear.
Turn shopper language into structured patterns across goals, styles, budgets, sizes, colors, objections, delivery urgency, and unmet catalog demand.
Most common goals
Wedding looks, workwear, gifting, budget-led discovery.
Create guided entry pointsSizes and colors
Unavailable Medium variants and repeated sage requests.
Review demand gapsBudget language
Fixed product limits and complete-look budgets.
Strengthen price pathwaysLive shopper intent
Structured from conversationI need a minimal sage dress for an outdoor wedding under $200, but I am unsure about the fit.
I can narrow the catalog by occasion, color, budget, available size, and fit preference.
Requested styles
Minimal, relaxed, modest, formal, premium.
Improve style taxonomyBuying objections
Fit, material, delivery timing, price, and returns.
Prioritize content fixesUnmet catalog demand
Missing styles, sizes, colors, price points, and combinations.
Inform merchandisingFunnel and drop-off analysis
See where progress stops. Then inspect the signals around it.
Drop-offs become useful when they connect to product interest, unanswered questions, missing variants, cart friction, and checkout boundaries.
Assisted Journey Observatory
Recommendation shown
ReviewProducts are shown, but shoppers rarely open them.
- Check product relevance and catalog attributes
- Review images, prices, and recommendation reasons
Merchant action: improve ranking and explanation quality.
Product evaluated
High signalShoppers compare several options without selecting one.
- Inspect fit, material, delivery, and return questions
- Clarify differences between frequently compared products
Merchant action: resolve the highest-frequency objection.
Cart created
ReviewAssisted carts do not consistently continue into checkout.
- Review delivery costs and offer eligibility
- Inspect cart composition and product substitutions
Merchant action: investigate cart and checkout friction.
Checkout started
BoundaryCheckout drop-off may involve factors outside the assistant.
- Review merchant checkout and payment experience
- Do not assign every checkout failure to WardrobeIt
Merchant action: combine assistant and checkout evidence.
Segments, filters, and comparison views
Find the products, audiences, and journeys behind each outcome.
Slice supported reporting by time, product, collection, feature, device, source, campaign, market, and journey stage. Save repeatable views only when the required data is active.
Compare assisted progression
Active period
Comparison period
Attribution, reporting, and data quality
Make the measurement rules visible, not hidden.
Define the qualifying event, attribution window, order source, feature overlap, refunds, currency, time zone, and known limitations before the metric informs a commercial decision.
Attribution contract
Definition required- Qualifying interaction
- Engaged conversation, recommendation click, completed try-on, Q&A interaction, or assistant-driven cart action.
- Attribution window
- The approved time period between the qualifying WardrobeIt interaction and the completed order.
- Feature overlap
- Define how one order that uses discovery, Q&A, try-on, and cart actions is counted across feature reports.
- Order treatment
- Specify refunds, cancellations, partial returns, discounts, shipping, taxes, currency conversion, and time zone.
Connected reporting pipeline
Show integrations only when their data flow is active and supported for the merchant workspace.
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Shopify commerce events Product, cart, checkout, and order data.Connected
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WardrobeIt event stream Conversation, recommendation, try-on, Q&A, cart.Tracked
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GA4 event path Available only when implemented and enabled.Conditional
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Exports and scheduled reports Plan and workspace dependent.Conditional
Merchant actions
Turn performance patterns into clear ecommerce decisions.
WardrobeIt highlights the pattern, explains why it may matter, and proposes a reviewable merchant action. Product, pricing, inventory, policy, and campaign changes remain under merchant control.
Conversion opportunity
High product interest. Weak cart progression.
What happened
Recommendation clicks and comparisons are strong, but variant confirmation remains weak.
Review
Fit questions, unavailable sizes, delivery concerns, and product-page clarity.
Next action: improve fit notes and alternative-product recovery.
Merchandising opportunity
The same product pair is compared repeatedly.
Why it matters
Shoppers may not understand which option suits the occasion, fit, or formality.
Review
Comparison content, recommendation reasons, and product differentiation.
Next action: create a clearer side-by-side product story.
Experience improvement
A recurring product question remains unanswered.
What happened
WardrobeIt cannot answer from approved product or policy information.
Review
Missing attributes, fit notes, care guidance, approved answers, or policy content.
Frequently asked questions
Clear definitions before commercial conclusions.
Every metric should separate engagement, cart value, completed order value, attributed revenue, and incremental impact.
Book an Analytics DemoWhat is ecommerce assistant analytics?
It measures how shoppers use an AI shopping assistant and what happens after each interaction, including discovery, recommendations, try-on, questions, cart activity, checkout, and completed purchases.
What does WardrobeIt Analytics track?
Depending on the active configuration, WardrobeIt can track conversations, intent signals, recommendations, product clicks, comparisons, Virtual Try-On, complete looks, variant selections, cart additions, checkout starts, assisted purchases, and assisted revenue.
What is assistant-attributed revenue?
Completed order value connected with a qualifying WardrobeIt interaction under a defined attribution method and time window.
Is attributed revenue the same as incremental revenue?
No. Attribution shows participation in the recorded journey. Incrementality requires an approved causal-measurement method, such as a controlled experiment.
What is chat-to-cart rate?
The percentage of eligible assisted conversations that produce at least one supported cart addition, using a consistent qualifying-session definition.
Can one purchase involve several WardrobeIt features?
Yes. Feature-level reports must handle overlap so the same order is not incorrectly added several times in an overall total.
Does WardrobeIt Analytics work with Shopify?
WardrobeIt is Shopify-first and can use supported store, catalog, product, cart, checkout, and order information from the active merchant connection.
Can WardrobeIt connect with GA4?
GA4 connection is conditional. WardrobeIt should claim only the GA4 events, reports, and data flows that are actively implemented and enabled for the merchant.
Can WardrobeIt identify why shoppers leave?
It can show the tracked stage where progress stopped and surface connected signals. It should not present an inferred reason as a confirmed shopper explanation unless the shopper stated it.
Connect every assisted journey to a measurable outcome.
Track ecommerce assistant analytics across conversations, recommendations, Virtual Try-On, product questions, cart activity, checkout progression, completed purchases, and merchant improvement opportunities.