Your eCommerce app drove 80,000 installs last quarter. Your analytics dashboard shows impressive session times and product page views. Then your performance marketing lead asks: "What percentage of users who installed last month actually completed a purchase?" You pull the data and discover that only 3,200 users (4%) converted to first purchase. The other 76,800 users are browsing without buying, adding to cart without checking out, or churning before experiencing the core value of your product.

This is the measurement gap that separates profitable eCommerce apps from those that burn acquisition budgets chasing install volume. Generic event tracking (installs, sessions, page views) tells you people are browsing. Purchase funnel tracking tells you whether they're buying, which is the only metric that matters for eCommerce unit economics.

eCommerce apps require specialised event taxonomies because the purchase funnel has distinct stages that generic tracking cannot differentiate. A user who views 50 products but never adds to cart has fundamentally different intent than a user who adds to cart but abandons checkout. Each behaviour requires specific measurement to identify drop-off points, optimise conversion rates, and understand which acquisition sources drive buyers versus browsers.

Why Generic Event Tracking Fails for eCommerce (The Attribution Revenue Gap)

Most eCommerce teams track standard events: product_viewed, app_opened, search_performed. These create a dangerous illusion of engagement. Your dashboard might show 100,000 product views this week, but product views doing what? Are users comparing prices then leaving? Adding items to wishlist for later? Completing purchases?

Generic tracking treats all engagement as equal. A user who opens your app 15 times but never purchases appears "highly engaged" in standard metrics. In reality, they're window shopping without any purchase intent. You're paying acquisition costs for users who generate zero revenue.

The attribution revenue gap emerges when you optimise campaigns for install volume rather than purchase behaviour. A campaign driving 10,000 installs at ₹30 CPI looks efficient. But if only 1% of those installs purchase (100 users) with ₹800 AOV, you've spent ₹3 lakh for ₹80,000 revenue. Meanwhile, a "expensive" campaign at ₹80 CPI that drives 4,000 installs with 6% purchase rate (240 users) generates ₹1.92 lakh revenue for ₹3.2 lakh spend, a far better return.

The 7 events below represent the minimum viable event taxonomy for any eCommerce app. They track the complete purchase funnel from browse intent through repeat purchase and advocacy, revealing exactly where customers drop off and which acquisition sources drive buyers rather than browsers.

Event #1: Product Viewed (Browse Intent Signal)

Event name: product_viewed

When to fire: When user opens a product detail page (not category pages, search results, or product cards in listings)

Why it matters: Product view is the first intent signal in eCommerce. Users who view product detail pages are considering specific items, not just browsing categories. This event enables measurement of browse-to-cart conversion rate and identification of which products attract attention but don't convert to cart additions.

Properties to track:

• Product ID

• Product category (clothing, electronics, home, beauty)

• Product price

• Product availability (in stock, limited stock, out of stock)

• Discovery method (search, category, recommendation, deep link, ad)

• Session number (first session vs return visit)

Benchmark targets: eCommerce apps typically see 8-15% of product views convert to add-to-cart actions. Higher conversion rates (above 18%) suggest strong product-market fit or effective merchandising. Lower rates (below 6%) indicate products don't match customer expectations or pricing misalignment.

Common issues detected:

High product views but low add-to-cart rates on specific categories reveal merchandising or pricing problems. If certain acquisition channels show high product views but dramatically lower add-to-cart rates, those channels are driving browsers with no purchase intent.

Optimisation opportunities:

Analyse which products have highest view-to-cart conversion to feature prominently. Identify products with high views but low cart rates for pricing or description optimisation. Track which discovery methods (search, recommendation, category browse) drive highest conversion from view to cart.

Event #2: Add to Cart (Purchase Intent Signal)

Event name: add_to_cart

When to fire: When user adds any item to their shopping cart (including quantity changes that add new items)

Why it matters: Add-to-cart is the strongest purchase intent signal before checkout. Users who add items to cart have expressed specific interest and are building toward a purchase. This event enables calculation of cart-to-checkout conversion and identification of cart abandonment patterns.

Properties to track:

• Product ID added

• Product category

• Product price

• Quantity added

• Cart total after addition

• Items in cart count

• Source action (product page, wishlist, quick add, recommendation)

Benchmark targets: 30-50% of users who add to cart should proceed to checkout initiation in healthy eCommerce apps. Lower rates indicate checkout friction, unexpected shipping costs, or lack of purchase urgency. Higher rates (above 55%) suggest effective checkout UX and clear pricing.

Common issues detected:

Users adding many items but not proceeding to checkout often discover shipping costs or minimum order requirements at cart review. High cart totals with low checkout rates suggest sticker shock or price comparison behaviour. Single-item carts with low checkout rates might indicate impulse additions without real purchase intent.

Optimisation opportunities:

Implement cart reminder notifications for users who add items but don't checkout within 24 hours. Offer free shipping thresholds based on average cart abandonment totals. Create cross-sell recommendations in cart to increase order value and push customers over free shipping minimums.

Event #3: Checkout Started (High-Intent Milestone)

Event name: checkout_started

When to fire: When user initiates checkout process (clicks "Proceed to Checkout" or similar, enters first checkout step)

Why it matters: Checkout initiation represents the highest intent signal before purchase. Users who start checkout have committed to attempting a purchase. The gap between checkout started and purchase completed reveals checkout funnel friction that's costing you conversions and revenue.

Properties to track:

• Cart total at checkout start

• Items count

• Applied discounts or coupons

• Guest checkout vs logged in

• Payment method availability (COD, UPI, card, wallet)

• Estimated delivery date shown

Benchmark targets: 60-75% of users who start checkout should complete purchase. This is where eCommerce apps lose significant revenue. Every percentage point improvement in checkout completion directly increases revenue without additional acquisition spend. Apps with optimised checkout flows see 75-85% completion rates.

Common issues detected:

Sudden drops between checkout started and purchase completed indicate technical issues, payment failures, or UX friction. If checkout completion rates vary significantly by device type (Android vs iOS) or payment method, specific integrations need investigation.

Optimisation opportunities:

Simplify checkout to minimum steps (address, payment, confirm). Offer multiple payment methods with clear failure recovery. Save addresses and payment methods for returning users. Show order summary clearly without requiring back navigation. Provide real-time delivery estimates to set expectations.

Event #4: First Purchase Completed (Conversion Confirmation)

Event name: first_purchase_completed

When to fire: When user successfully completes their first-ever purchase on your platform (order confirmed, payment successful)

Why it matters: First purchase is eCommerce's true conversion event. Everything before this point is consideration; this is commitment. This event enables calculation of install-to-purchase conversion rate by acquisition channel, which is the fundamental metric for understanding marketing ROI. Users who complete first purchase are 5-8x more likely to make repeat purchases than users who only add to cart.

Properties to track:

• Order value (revenue)

• Items purchased count

• Categories purchased

• Payment method used

• Discount applied (percentage off, flat discount, coupon code)

• Time since install

• Time since first product view

• Delivery option selected (standard, express)

Benchmark targets: 3-8% of installs should convert to first purchase within 30 days for healthy eCommerce apps. This varies by category. Fashion and beauty apps see 5-10% conversion, while electronics and furniture see 2-5% due to higher consideration periods. Optimise acquisition for first purchase rate, not install volume.

Common issues detected:

Very low first purchase rates (below 2%) from specific acquisition channels indicate those channels drive window shoppers, not buyers. Long time-to-first-purchase (over 14 days) suggests consideration friction or lack of urgency. Heavy discounting on first purchases (above 30% average discount) may indicate pricing or value perception issues.

Optimisation opportunities:

Send postback events for first_purchase_completed to ad platforms (Meta, Google, TikTok) to optimise campaigns toward buyers rather than installers. Create first-purchase incentives (discount codes, free shipping) to reduce conversion friction. Analyse which product categories and price points have highest first-purchase conversion to feature in acquisition campaigns.

Event #5: Repeat Purchase (Loyalty Signal)

Event name: repeat_purchase_completed

When to fire: When user completes their second (and subsequent) purchases (track milestones: 2nd, 5th, 10th purchase)

Why it matters: Repeat purchase is the foundation of eCommerce profitability. First purchases often happen at break-even or loss due to acquisition costs. Second and subsequent purchases generate profit because acquisition costs are already paid. This event measures customer lifetime value trajectory and retention effectiveness.

Properties to track:

• Purchase number (2nd, 3rd, 5th, 10th milestone)

• Order value

• Days since previous purchase

• Category consistency (same categories as before vs new categories explored)

• Discount dependency (discount used vs full price)

• Trigger source (push notification, email, organic return, ad retargeting)

Benchmark targets: 25-40% of first-time purchasers should make a second purchase within 60 days. Higher repeat rates indicate strong product satisfaction, effective retention marketing, and product assortment that encourages repeat buying. Lower rates suggest one-time need fulfilment or poor post-purchase experience.

Common issues detected:

Long gaps between purchases (60+ days) indicate your product isn't essential or top-of-mind. Declining order values across repeat purchases suggest discounting dependency or lack of cross-sell effectiveness. Customers who only repeat in one category are underexposed to your broader catalogue.

Optimisation opportunities:

Trigger personalised recommendations based on first purchase to encourage second purchase. Create loyalty programmes that reward repeat purchasing. Send replenishment reminders for consumable products based on estimated usage timelines. Analyse which first-purchase categories have highest repeat purchase rates to optimise acquisition toward those customers.

Event #6: Wishlist Added (Future Intent Indicator)

Event name: wishlist_added

When to fire: When user adds any product to wishlist, save-for-later, or favourites

Why it matters: Wishlist additions indicate strong purchase intent for future consideration. Users who wishlist items are signalling interest but aren't ready to buy immediately. This event enables price-drop notifications, stock alerts, and targeted remarketing to users with known product preferences. Wishlist users convert at 2-4x higher rates than general browsers when triggered appropriately.

Properties to track:

• Product ID

• Product price at time of wishlist addition

• Product category

• User purchase history (first-time vs repeat customer)

• Discovery method

Benchmark targets: 8-15% of users who view products should add at least one item to wishlist within 30 days. This indicates your wishlist feature is discoverable and useful. Very low wishlist rates (below 5%) suggest the feature is hidden or users don't see value in saving items.

Common issues detected:

High wishlist additions but low conversions from wishlist suggest price or availability barriers. If wishlisted items rarely convert even with promotions, users might be using wishlist as aspirational browsing rather than purchase planning. Wishlists with many items but no purchases indicate decision paralysis.

Optimisation opportunities:

Send price-drop notifications when wishlisted items go on sale. Alert users when low-stock wishlisted items might sell out. Create wishlist-specific promotions ("Complete your wishlist for 10% off"). Analyse which wishlisted products have highest conversion rates to feature in remarketing campaigns.

Event #7: Review Submitted (Advocacy and Quality Signal)

Event name: review_submitted

When to fire: When user submits a product review (rating with or without written review)

Why it matters: Review submission indicates customer satisfaction and advocacy willingness. Users who review products are engaged beyond transaction completion. This event measures user-generated content generation, which builds social proof for future customers. Review submission also correlates strongly with repeat purchase likelihood.

Properties to track:

• Rating given (1-5 stars)

• Review length (characters)

• Days since purchase

• Product category reviewed

• Photo included (yes/no)

• Verified purchase (yes/no)

Benchmark targets: 5-15% of purchasers should submit reviews within 30 days of delivery. Higher review rates indicate satisfied customers and effective post-purchase engagement. Very low rates (below 3%) suggest poor post-purchase communication or complicated review process.

Common issues detected:

Low average ratings (below 3.5 stars) indicate product quality or expectation mismatch issues. If reviews concentrate on specific products with negative sentiment, those products need quality investigation. Long delays between delivery and review (14+ days) suggest review prompts aren't timed effectively.

Optimisation opportunities:

Send review request notifications 3-5 days after delivery confirmation. Incentivise reviews with loyalty points or future discounts (while maintaining authenticity). Create easy in-app review flows that minimise friction. Respond to negative reviews promptly to recover customers and demonstrate service quality.

Attribution Windows for eCommerce: 7-Day Click, 1-Day View Standard

eCommerce has faster conversion funnels than EdTech or fintech. Users often convert within hours or days of discovering products. However, different eCommerce categories have varying consideration periods that should influence attribution window configuration.

Standard configuration for most eCommerce apps:

• Click-through attribution: 7 days

• View-through attribution: 1 day

This configuration credits conversions that happen within 7 days of ad click or 1 day of ad view. It captures the typical eCommerce purchase journey without over-attributing to awareness impressions that didn't drive action.

Category-specific adjustments:

• Fashion and beauty: 7-day click, 1-day view (impulse-friendly, fast conversion)

• Electronics: 10-14 day click, 1-day view (comparison shopping, higher consideration)

• Home and furniture: 14-21 day click, 1-day view (high-value, longer research phase)

• Grocery and essentials: 3-day click, 1-day view (immediate need, very fast conversion)

Track time-to-purchase by acquisition channel to validate your attribution windows. If 90% of Meta conversions happen within 4 days but Google Search conversions take 8 days, consider channel-specific windows rather than a single platform-wide setting.

Most importantly, ensure your MMP sends purchase events (not just install events) back to ad platforms. Platforms like Linkrunner allow you to configure postbacks for first_purchase_completed, enabling Meta, Google, and TikTok to optimise campaigns toward buyers rather than installers.

Frequently Asked Questions

Should I track add_to_cart for guest users or only registered users?

Track for all users including guests. Use anonymous device IDs for guest users, then merge with user accounts if they register or log in later. Guest add-to-cart behaviour reveals product interest even if users don't convert to registered accounts. Many eCommerce conversions happen from guest checkouts.

How do I handle purchase tracking for COD (Cash on Delivery) orders?

Fire first_purchase_completed at order placement, not delivery. For attribution purposes, the purchase intent and order commitment matter. Track a separate event (cod_delivered or cod_cancelled) for delivery outcome. Attribute revenue at placement, then reconcile actuals based on delivery rates.

What's the best way to track promotional purchase impact?

Include discount properties in purchase events: coupon_code, discount_percentage, discount_amount. This enables analysis of promotion effectiveness. Compare purchase rates, order values, and repeat rates for discounted vs full-price first purchases to understand whether promotions drive sustainable customer acquisition.

How do I attribute purchases across web and app if users browse on web and buy in app?

Implement cross-platform identity linking. When users log in on both web and app, associate both sessions with the same user ID. Your MMP should support web-to-app attribution for users who click web ads, install the app, and purchase in-app. Linkrunner's web SDK enables this cross-platform journey tracking.

Should I track revenue as gross order value or net revenue after discounts?

Track both. Send net revenue (after discounts, before shipping) in the primary revenue field for accurate ROAS calculation. Include gross order value and discount amount as separate properties for promotion analysis. Ad platform optimisation should target net revenue since that's your actual margin contribution.

How do I measure the impact of push notifications on purchase behaviour?

Include push_campaign_id or trigger_source properties in purchase events when users arrive via push notification deep links. Compare conversion rates and order values for push-triggered sessions vs organic returns. This reveals which push campaigns drive genuine incremental purchases rather than just recapturing users who would have purchased anyway.

If you want to connect purchase behaviour directly to acquisition sources and optimise campaigns toward buyers rather than installers, request a demo from Linkrunner to see how unified attribution and revenue tracking works in practice.