E-Commerce Analytics for Deep Link Campaigns

Deep links generate data at every step of the e-commerce funnel: click, app open, product view, add-to-cart, and purchase. Tracking this data per link and per campaign lets you understand which marketing channels drive revenue, not just traffic. This article covers the analytics framework for measuring deep link campaign performance in e-commerce.

For deep link analytics fundamentals, see deep link analytics: measuring what matters. For revenue attribution, see revenue attribution: connecting deep links to revenue.

The E-Commerce Deep Link Funnel

Every deep link campaign produces a measurable funnel:

Click → App Open → Product View → Add to Cart → Checkout → Purchase

Funnel Stage	Metric	Benchmark
Click → App Open	Deep link activation rate	70-90%
App Open → Product View	Landing success rate	85-95%
Product View → Add to Cart	Add-to-cart rate	8-15%
Add to Cart → Purchase	Cart conversion rate	40-60%
Click → Purchase	End-to-end conversion	3-8%

Track each stage per campaign, channel, and link to identify where users drop off.

Key Metrics for E-Commerce Deep Links

Revenue Metrics

Metric	Formula	Why It Matters
Revenue per click	Total revenue / Total deep link clicks	Value of each click
Revenue per link	Total revenue / Number of unique links	Which links generate the most value
Average order value (AOV)	Total revenue / Number of orders	Purchase size from deep link campaigns
Customer lifetime value (CLV)	12-month revenue per deep-link-acquired customer	Long-term value of deep link campaigns

Efficiency Metrics

Metric	Formula	Why It Matters
Cost per acquisition (CPA)	Campaign cost / Customers acquired	Efficiency of paid deep link campaigns
Return on ad spend (ROAS)	Revenue / Ad spend	Profitability of paid campaigns
Cost per order (CPO)	Campaign cost / Orders	Cost to generate each sale
Blended CPA	Total marketing spend / Total customers	Overall acquisition efficiency

Engagement Metrics

Metric	Formula	Why It Matters
Browse depth	Products viewed per session from deep link	User engagement quality
Session duration	Average time in app from deep link entry	Engagement level
Return visit rate	Users who return within 7 days / Total users	Stickiness of acquired users
Cart abandonment rate	Carts abandoned / Carts created from deep links	Checkout friction

Per-Channel Analytics

Track metrics separately for each channel:

Channel	Deep Link Source	Key Metric to Watch
Email campaigns	ref=email-{campaign}	Revenue per email sent
Push notifications	ref=push-{type}	Conversion rate
Social media ads	ref=meta-{campaign}	ROAS
Influencer links	ref=creator-{handle}	CPA and CLV
SMS campaigns	ref=sms-{campaign}	CTR and conversion
QR codes	ref=qr-{location}	Scans and revenue per location
Retargeting	ref=retarget-{segment}	Incremental revenue

Channel Comparison Dashboard

Build a dashboard comparing channels side by side:

Channel	Clicks	Orders	Revenue	CPA	ROAS	AOV
Email	12,000	840	$42,000	$2.38	21x	$50
Push	8,500	510	$28,050	$0.00	N/A	$55
Meta ads	25,000	750	$37,500	$13.33	3.75x	$50
Influencers	15,000	600	$36,000	$8.33	7.2x	$60
QR codes	3,200	320	$19,200	$1.56	12x	$60

This view reveals that push notifications and QR codes have the highest efficiency (low or zero CPA), while paid channels like Meta require more spend per order but deliver scale.

Attribution Models

Last-Click Attribution

The simplest model: credit goes to the last deep link the user clicked before purchasing.

Pros: Simple, clear. Cons: Ignores earlier touchpoints that influenced the purchase.

Multi-Touch Attribution

Distribute credit across all touchpoints in the user's journey:

Model	Description	Best For
Linear	Equal credit to each touchpoint	General purpose
Time decay	More credit to recent touchpoints	Short purchase cycles
Position-based	40% first, 40% last, 20% middle	Brand + performance campaigns
Data-driven	ML model assigns credit based on impact	Large datasets

Example Multi-Touch Journey

A user's path to purchase:

1. Clicks Instagram ad (ref=meta-summer-sale) on July 1.
2. Clicks email link (ref=email-price-drop) on July 5.
3. Clicks push notification (ref=push-cart-reminder) on July 6. Purchases.

Model	Instagram	Email	Push
Last-click	0%	0%	100%
Linear	33%	33%	33%
Position-based	40%	20%	40%
Time decay	20%	30%	50%

Setting Up Analytics

Event Tracking

Track these events with deep link parameters attached:

Event	Parameters	Purpose
deep_link_click	link_id, campaign, channel	Click tracking
app_open	deep_link_url, source	Attribution
product_view	product_id, category, price	Engagement
add_to_cart	product_id, quantity, price	Purchase intent
purchase	order_id, revenue, items	Revenue tracking
return	order_id, return_reason	Return tracking

Cohort Analysis

Group users by acquisition campaign and track their behavior over time:

Cohort	Day-1 AOV	Day-30 Revenue/User	Day-90 Revenue/User
Email summer sale	$45	$62	$95
Meta retargeting	$38	$48	$72
Influencer: Sarah	$52	$78	$120
Push: price drop	$40	$55	$85

Cohort analysis reveals which campaigns attract the highest-value customers over time, not just the most immediate conversions.

Reporting Cadence

Report	Frequency	Audience	Key Metrics
Campaign performance	Daily	Marketing team	Clicks, conversions, revenue, ROAS
Channel comparison	Weekly	Marketing lead	Per-channel CPA, AOV, ROAS
Attribution analysis	Monthly	Marketing + finance	Multi-touch attribution, incrementality
LTV by acquisition source	Quarterly	Leadership	CLV per channel, long-term ROAS

Common Mistakes

Mistake	Impact	Fix
Only tracking clicks, not revenue	Cannot measure ROI	Track the full funnel from click to purchase
No per-channel attribution	Cannot optimize budget allocation	Tag every deep link with channel and campaign parameters
Ignoring post-purchase behavior	Overvaluing campaigns that drive returns	Track returns and net revenue, not just gross
Last-click only attribution	Undervaluing awareness and consideration campaigns	Implement multi-touch attribution
Not comparing deep-linked vs non-deep-linked campaigns	Cannot prove deep linking adds value	A/B test deep link vs generic link in the same campaign

Tracking E-Commerce Events with the Tolinku SDK

Tolinku's ecommerce analytics goes beyond click tracking. The SDK includes a dedicated ecommerce module that tracks 13 event types covering the full shopping journey:

import { Tolinku } from '@tolinku/web-sdk';

const tolinku = new Tolinku({ apiKey: 'tolk_pub_...' });
tolinku.setUserId('user_123');

// Track the full funnel
await tolinku.ecommerce.viewItem({ items: [{ item_id: 'sku_1', item_name: 'Running Shoes', price: 89.99 }] });
await tolinku.ecommerce.addToCart({ items: [{ item_id: 'sku_1', quantity: 1 }] });
await tolinku.ecommerce.beginCheckout({});
await tolinku.ecommerce.purchase({
  transaction_id: 'order_456',
  revenue: 89.99,
  currency: 'USD',
  items: [{ item_id: 'sku_1', item_name: 'Running Shoes', price: 89.99, quantity: 1 }]
});

Events are batched automatically (10 events or every 5 seconds) and sent to Tolinku's ClickHouse-powered analytics backend. SDKs are available for Web, iOS, Android, React Native, and Flutter.

Revenue Attribution in the Dashboard

The analytics dashboard includes dedicated tabs for ecommerce data:

Ecommerce tab: Revenue overview cards (total revenue, net revenue, orders, AOV, cart abandonment rate), daily revenue chart, ecommerce conversion funnel (view item through purchase), top products by revenue, revenue by campaign/channel, and coupon performance.

Attribution tab: See which campaigns actually drive purchases, not just clicks. Choose between Last Click, Linear, and Time Decay attribution models. Set attribution windows from 7 to 90 days. View attributed revenue, order count, and percentage of total per campaign.

Cohorts tab: Track customer lifetime value over time with a cohort matrix showing revenue by acquisition month. Identify which campaigns attract the highest-value customers over the long term.

All revenue is automatically converted to your base currency using live exchange rates (200+ currencies supported). Transaction deduplication prevents double-counting from network retries. Built-in fraud detection catches bot purchases, impossible revenue amounts, and velocity anomalies.

For the full setup guide, see the ecommerce analytics documentation. For the API reference, see the ecommerce API docs.

For deep link analytics fundamentals, see deep link analytics: measuring what matters. For the complete e-commerce guide, see deep linking for e-commerce apps.