Discovery
Requests, matches, alternatives, and no-match signals
AI Shopping Assistant Attribution
Track Assistant Outcomes
Track what happens after shoppers interact with your AI shopping assistant.
WardrobeIt connects supported conversations, shopper intent, product recommendations, product questions, Virtual Try-On activity, cart actions, checkout progression, and completed orders into one measurable assisted-shopping journey.
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Engagement
Understand how shoppers use WardrobeIt across discovery, evaluation, questions, comparisons, and visual experiences.
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Conversion
Track how qualifying assisted journeys progress into supported cart, checkout, and completed-purchase events.
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Revenue
Connect qualifying interactions with completed order value under documented attribution rules.
Why Assistant Outcomes Matter
Activity metrics alone do not prove business impact.
A shopper opening an assistant is useful, but it does not tell the merchant whether the experience helped move the shopper closer to purchase.
- 01Intent-qualified sessions
From conversations to intent
Ask what shoppers were trying to find, compare, understand, or buy—not only whether they opened the assistant.
- 02Product progression
From recommendations to product action
Review which recommendations were followed by clicks, product views, comparisons, try-ons, or supported cart additions.
- 03Assisted outcomes
From engagement to assisted revenue
Connect qualifying journeys with completed orders under documented rules, while deduplicating overlapping feature involvement.
- 04Controlled measurement
Participation is not causation
A recorded interaction shows participation in the journey. Incremental impact requires a control group, holdout, or other valid causal method.
The Assisted Shopping Funnel
Follow every supported stage from conversation to purchase.
WardrobeIt brings shopper and commerce events into one connected reporting journey. Each stage needs a clear qualification rule.
Stage 1
Conversation started
Qualifying first interaction
Stage 2
Intent captured
Goal and buying preferences
Stage 3
Product recommended
Relevant catalog products shown
Stage 4
Product evaluated
Views, questions, comparisons, try-ons
Stage 5
Added to cart
Valid product or variant action
Stage 6
Purchase completed
Connected completed merchant order
Qualification principle
An assistant open should not automatically count as an engaged conversation. Define the qualifying event, attribution window, exclusions, and reporting period for every stage.
Outcomes by WardrobeIt Feature
Separate usage, cart contribution, and assisted outcomes.
Feature-level analytics help merchants understand which capabilities participate in product decisions without confusing activity with commercial outcome.
Track usage
Observe supported sessions, questions, recommendations, looks, try-ons, and cart-action requests.
Track cart contribution
Review supported product and variant actions that follow qualifying feature interactions.
Track assisted outcome
Connect completed order value under documented rules; do not claim the feature caused the order.
Engagement, Conversion, and Revenue Analytics
Organize analytics around the questions merchants actually ask.
Review activity, conversion movement, and assisted revenue separately so each report says exactly what its evidence supports.
Engagement
Are shoppers using WardrobeIt to explore and evaluate products?
Observed usage
Conversion
Do qualifying interactions progress toward cart and checkout?
Supported events
Revenue
Which qualifying journeys connect with completed order value?
Assisted revenue
Incrementality
What would not have happened without WardrobeIt?
Controlled methodReporting controls
Definitions required- 01
Define every rate
Qualifying event, numerator, denominator, window, and exclusions
- 02
Define order treatment
Taxes, shipping, discounts, refunds, returns, and currencies
- 03
Control feature overlap
Do not add the same completed order value more than once
Important distinction
Assisted revenue shows recorded participation. Incremental revenue requires an experiment, holdout, or valid causal method.
Segmentation and Filtering
Find the products, journeys, and sources behind each result.
Top-level totals can hide differences between products, collections, devices, sources, and features.
Filter 01
Date range
Review whether observed performance changes across a documented reporting period.
Use a consistent time zone and comparison basis.
Filter 1 of 6
From Outcome Data to Merchant Actions
Use reporting to improve products, recommendations, and shopping journeys.
Outcome reporting should help teams identify where shoppers progress and where product, catalog, or journey improvements may be needed.
Observed signal
Merchant review
Strong engagement but weak cart activity
- Review price, availability, and fit information
- Review imagery, descriptions, and buying concerns
- Review delivery, returns, alternatives, and cart errors
Interpretation boundary
ObserveReviewTest
Questions before purchase
Recurring questions can point to product-page or policy information that deserves review.
Observed
Not established
Feature involvement
Review adoption, product involvement, entry points, and collection performance before expanding a pattern.
Feature-overlap warning
A purchase involving several features should not be counted as separate revenue in combined totals.
Repeated journey drop-off
Review recommendation relevance, variant availability, product-page clarity, mobile UX, cart errors, and missing alternatives.
Attribution Rules and Reporting Boundaries
Keep every outcome term distinct.
Credible reporting states what is tracked, how it qualifies, and what it cannot prove.
Value of products added during or after assisted activity.
Cart value
Not the same as a completed merchant order.
Value from completed merchant orders.
Completed order value
Apply documented order-treatment rules.
Completed order value connected with a qualifying WardrobeIt interaction.
Assisted revenue
Shows recorded participation under documented rules.
Revenue assigned under a selected attribution model.
Attributed revenue
Depends on the defined interaction, source, and model.
Estimated revenue that would not have occurred without WardrobeIt.
Incremental revenue
Requires an experiment, holdout, or valid causal method.
Assisted outcomes are observational, not causal proof.
Deduplicate overlapping features and exclude or label unreliable data.
Credible Outcome Reporting
Make the methodology visible before acting on the result.
Definitions, exclusions, order treatment, and data reliability determine what an assisted-outcome report can support.
Define every metric
- QualificationEvent, numerator, and denominator
- Time basisWindow, period, currency, and time zone
- Order treatmentRefunds, cancellations, returns, and discounts
- Overlap treatmentDeduplication or documented allocation
Exclude or label unreliable data
Internal sessionsDo not mix staff activity with shopper reporting
Test traffic and ordersKeep implementation checks out of commerce outcomes
Duplicate eventsPrevent repeated instrumentation from inflating activity
Unsupported identityDo not present anonymous activity as a known shopper
Interpretation boundary
Recorded participation
An interaction before purchase does not establish that it caused the purchase.
Use a suitable controlled method before describing incremental conversion or revenue impact.
Observe the journeyDocument the rulesTest causal impact
High-performing journeys
Review the prompts, products, and supporting content involved.
Weak cart progression
Review price, availability, product information, and UX.
Recurring questions
Improve approved product, size, delivery, and return information.
Repeated drop-off
Investigate relevance, variants, page clarity, and cart errors.
No-match demand
Improve attributes, inventory planning, and approved alternatives.
Assisted Journey Walkthrough
See one assisted journey from question to completed outcome.
Record supported events without inventing causal impact.
- Conversation begins
A qualifying first interaction is recorded.
- Intent is captured
Supported product needs and preferences are recorded.
- Products are recommended
Recommendation impressions and supported product clicks may be recorded.
- Product is evaluated
Supported questions, comparisons, and eligible try-on events may be recorded.
- Commerce progresses
Valid cart, checkout, and completed-order events may be connected.
Attribution summary
Qualifying interaction
Defined by merchant methodology
Connected outcome
Supported merchant commerce data
Incremental impact
Not established without a controlled test
Frequently Asked Questions
Clear answers for evaluating assistant outcomes.
Definitions and reporting boundaries stay visible so observational outcomes are not mistaken for causal impact.
AI shopping assistant attribution connects qualifying assistant interactions with later product, cart, checkout, and purchase events. It helps merchants understand where WardrobeIt participated in the shopping journey under a defined attribution model.
AI can support better assistant outcomes by helping shoppers find relevant products, answer decision-critical questions, compare options, evaluate eligible products visually, choose variants, and move toward cart. Analytics then show which journeys progress and where shoppers stop.
Poor outcomes often come from irrelevant recommendations, incomplete catalog data, missing product information, unavailable variants, weak shopper prompts, poor mobile experience, unclear next actions, cart errors, inaccurate analytics events, or undefined attribution rules.
Begin with a clearly defined funnel. Track each stage consistently, identify the largest drop-off, review the products and shopper concerns involved, improve that part of the journey, and measure the change using the same definitions.
It can when it connects with merchant products, shopper context, product knowledge, cart actions, and measurable events. If it only answers broad support questions, it may create conversation activity without supporting the product or purchase journey.
WardrobeIt connects conversational product discovery, catalog-only recommendations, Product Q&A, Complete the Look, Virtual Try-On, variant selection, Add to Cart, and merchant analytics. This lets merchants evaluate the full journey rather than one isolated interaction.
Relevant metrics include conversations started, intent-qualified sessions, recommendation impressions, recommendation clicks, product questions, try-on completions, chat-to-cart rate, recommendation-to-cart rate, try-on-to-cart rate, checkout progression, assisted conversion rate, assisted orders, average assisted order value, and assisted revenue.
Chat-to-cart rate measures how often eligible assisted conversations result in at least one supported cart addition. The merchant should define the qualifying conversation, cart event, measurement window, and exclusions.
Recommendation-to-cart rate measures how often a qualifying product recommendation leads to the recommended product being added to cart. Reports should clarify whether the denominator uses recommendation impressions, clicks, sessions, or unique shoppers.
Try-on-to-cart rate measures how often eligible completed Virtual Try-On journeys are followed by a supported cart addition.
Assisted revenue is completed order value connected with a qualifying WardrobeIt interaction under documented attribution rules. It does not automatically prove WardrobeIt caused the order.
No. Assisted revenue shows recorded participation in the purchase journey. Incremental revenue estimates what would not have happened without WardrobeIt and requires a suitable causal-measurement method.
Yes. A shopper may use Product Discovery, Product Q&A, Virtual Try-On, Complete the Look, and Add to Cart before one purchase. Feature-level reports must prevent overlapping revenue from being incorrectly added together.
Yes, when a reliable campaign identifier, UTM value, or supported source field is captured and preserved through the assisted journey. This does not automatically mean a direct advertising-platform integration is active.
No. WardrobeIt provides guided-shopping and measurement capabilities designed to support product discovery, shopper confidence, cart progression, and merchant insight. Actual results depend on the merchant’s traffic, products, data, implementation, customer behavior, and testing methodology.
Track Assistant Outcomes
Measure what happens after the conversation.
Track how shoppers use WardrobeIt, which products they discover, and which qualifying journeys progress toward completed purchases under documented rules.