{"id":1800,"date":"2026-07-20T13:00:00","date_gmt":"2026-07-20T18:00:00","guid":{"rendered":"https:\/\/tolinku.com\/blog\/?p=1800"},"modified":"2026-03-07T03:37:23","modified_gmt":"2026-03-07T08:37:23","slug":"analytics-for-product-teams","status":"publish","type":"post","link":"https:\/\/tolinku.com\/blog\/analytics-for-product-teams\/","title":{"rendered":"Deep Link Analytics for Product Teams"},"content":{"rendered":"\n

Marketing teams use deep link analytics to measure campaigns. Product teams should use the same data to make product decisions: which features are users actually reaching? Where do onboarding flows break? Which entry points produce the most engaged users?<\/p>\n\n\n\n

This guide covers how product teams can use deep link analytics. For marketing-focused analytics, see deep link analytics for marketing teams<\/a>. For the full analytics overview, see deep link analytics: measuring what matters<\/a>.<\/p>\n\n\n\n

\"Tolinku\nThe analytics dashboard with date range selector, filters, charts, and breakdowns.<\/em><\/p>\n\n\n\n

Product Questions Deep Link Data Answers<\/h2>\n\n\n\n

Feature Discovery<\/h3>\n\n\n\n

Deep links are controlled entry points. When you deep link a user to a feature, you know exactly which feature they arrived at and what happened next.<\/p>\n\n\n\n

\n\n\n\n\n\n\n\n
Product Question<\/th>\nDeep Link Data Point<\/th>\n<\/tr>\n<\/thead>\n
Are users finding Feature X?<\/td>\nHow many deep links to Feature X are clicked vs ignored?<\/td>\n<\/tr>\n
Which features drive retention?<\/td>\nDay 7 retention by landing route<\/td>\n<\/tr>\n
Does onboarding improve engagement?<\/td>\nSession depth: onboarding deep link vs generic home screen<\/td>\n<\/tr>\n
Which entry point produces power users?<\/td>\nFeature adoption rate by first deep link route<\/td>\n<\/tr>\n
Where do users get stuck?<\/td>\nFallback rate and error rate by route<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n

Feature Adoption Tracking<\/h3>\n\n\n\n

When you launch a new feature and deep link users to it, you can measure adoption:<\/p>\n\n\n\n

SELECT\n  route,\n  COUNT(DISTINCT click_id) AS deep_link_clicks,\n  COUNT(DISTINCT CASE WHEN outcome = 'app_opened' THEN user_id END) AS users_reached,\n  COUNT(DISTINCT CASE WHEN feature_used THEN user_id END) AS users_adopted,\n  ROUND(\n    COUNT(DISTINCT CASE WHEN feature_used THEN user_id END)::DECIMAL \/\n    NULLIF(COUNT(DISTINCT CASE WHEN outcome = 'app_opened' THEN user_id END), 0) * 100, 1\n  ) AS adoption_rate\nFROM deep_link_clicks dlc\nLEFT JOIN feature_events fe\n  ON dlc.user_id = fe.user_id\n  AND fe.feature = 'new_search'\n  AND fe.timestamp BETWEEN dlc.timestamp AND dlc.timestamp + INTERVAL '7 days'\nWHERE dlc.route = '\/features\/new-search'\n  AND dlc.timestamp >= '2026-07-01'\nGROUP BY route;\n<\/code><\/pre>\n\n\n\n
This tells you: of users who clicked the deep link to the new search feature, what percentage actually used it within 7 days?<\/p>\n\n\n\n
Onboarding Flow Analysis<\/h2>\n\n\n\n
Measuring Onboarding Deep Links<\/h3>\n\n\n\n
Deep links during onboarding (email verification, profile setup, feature tours) should be measured as a funnel:<\/p>\n\n\n\n
SELECT\n  step,\n  COUNT(DISTINCT user_id) AS users,\n  ROUND(\n    COUNT(DISTINCT user_id)::DECIMAL \/\n    FIRST_VALUE(COUNT(DISTINCT user_id)) OVER (ORDER BY step_order) * 100, 1\n  ) AS pct_of_start\nFROM onboarding_funnel\nWHERE cohort_date >= '2026-07-01'\n  AND acquisition_source = 'deep_link'\nGROUP BY step, step_order\nORDER BY step_order;\n<\/code><\/pre>\n\n\n\n
\n\n\n\n\n\n\n\n\n
Step<\/th>\nUsers<\/th>\n% of Start<\/th>\n<\/tr>\n<\/thead>\n
Deep link click<\/td>\n5,000<\/td>\n100%<\/td>\n<\/tr>\n
App opened<\/td>\n3,750<\/td>\n75%<\/td>\n<\/tr>\n
Onboarding screen 1<\/td>\n3,200<\/td>\n64%<\/td>\n<\/tr>\n
Onboarding screen 2<\/td>\n2,400<\/td>\n48%<\/td>\n<\/tr>\n
Onboarding complete<\/td>\n1,800<\/td>\n36%<\/td>\n<\/tr>\n
First key action<\/td>\n1,200<\/td>\n24%<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
If 25% of users drop between "App opened" and "Onboarding screen 1," the transition from deep link landing to onboarding is broken. Maybe the deep link lands on the wrong screen, or there is a loading delay.<\/p>\n\n\n\n
Comparing Onboarding Paths<\/h3>\n\n\n\n
Different deep link entry points may produce different onboarding completion rates:<\/p>\n\n\n\n
\n\n\n\n\n\n\n
Entry Point<\/th>\nOnboarding Completion<\/th>\nFirst Key Action<\/th>\nDay 7 Retention<\/th>\n<\/tr>\n<\/thead>\n
\/welcome<\/code> (generic)<\/td>\n36%<\/td>\n24%<\/td>\n18%<\/td>\n<\/tr>\n
\/products\/:id<\/code> (product)<\/td>\n42%<\/td>\n31%<\/td>\n25%<\/td>\n<\/tr>\n
\/invite\/:code<\/code> (referral)<\/td>\n55%<\/td>\n40%<\/td>\n35%<\/td>\n<\/tr>\n
\/features\/search<\/code> (feature)<\/td>\n38%<\/td>\n28%<\/td>\n22%<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
Referral deep links produce the best onboarding and retention because the user has social context. Product deep links also perform well because the user has immediate value to explore.<\/p>\n\n\n\n
Drop-Off Analysis<\/h2>\n\n\n\n
Identifying Drop-Off Points<\/h3>\n\n\n\n
When a deep link takes a user to a specific screen, product teams want to know what happens next:<\/p>\n\n\n\n
SELECT\n  landing_route,\n  next_action,\n  COUNT(*) AS occurrences,\n  ROUND(COUNT(*)::DECIMAL \/ SUM(COUNT(*)) OVER (PARTITION BY landing_route) * 100, 1) AS pct\nFROM (\n  SELECT\n    dlc.route AS landing_route,\n    COALESCE(\n      FIRST_VALUE(ue.action) OVER (\n        PARTITION BY dlc.click_id ORDER BY ue.timestamp\n      ),\n      'no_action'\n    ) AS next_action\n  FROM deep_link_clicks dlc\n  LEFT JOIN user_events ue\n    ON dlc.user_id = ue.user_id\n    AND ue.timestamp BETWEEN dlc.timestamp AND dlc.timestamp + INTERVAL '5 minutes'\n  WHERE dlc.outcome = 'app_opened'\n) sub\nGROUP BY landing_route, next_action\nORDER BY landing_route, occurrences DESC;\n<\/code><\/pre>\n\n\n\n
\n\n\n\n\n\n\n
Landing Route<\/th>\nNext Action<\/th>\n%<\/th>\n<\/tr>\n<\/thead>\n
\/products\/:id<\/code><\/td>\nview_details<\/td>\n45%<\/td>\n<\/tr>\n
\/products\/:id<\/code><\/td>\nadd_to_cart<\/td>\n22%<\/td>\n<\/tr>\n
\/products\/:id<\/code><\/td>\nback_navigation<\/td>\n18%<\/td>\n<\/tr>\n
\/products\/:id<\/code><\/td>\nno_action<\/td>\n15%<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
15% of users who land on a product page via deep link take no action. This could indicate slow loading, confusing UI, or mismatched expectations between the link and the content.<\/p>\n\n\n\n
Platform-Specific Drop-Offs<\/h3>\n\n\n\n
The same deep link may fail differently on iOS vs Android:<\/p>\n\n\n\n
SELECT\n  platform,\n  route,\n  COUNT(*) AS clicks,\n  ROUND(SUM(CASE WHEN outcome = 'app_opened' THEN 1 ELSE 0 END)::DECIMAL \/ COUNT(*) * 100, 1) AS open_rate,\n  ROUND(SUM(CASE WHEN outcome = 'fallback' THEN 1 ELSE 0 END)::DECIMAL \/ COUNT(*) * 100, 1) AS fallback_rate,\n  ROUND(SUM(CASE WHEN outcome = 'error' THEN 1 ELSE 0 END)::DECIMAL \/ COUNT(*) * 100, 1) AS error_rate\nFROM deep_link_clicks\nWHERE timestamp >= '2026-07-01'\nGROUP BY platform, route\nHAVING COUNT(*) >= 100\nORDER BY error_rate DESC;\n<\/code><\/pre>\n\n\n\n
If a route has a 5% error rate on Android but 0% on iOS, there is likely an Intent filter or App Link configuration issue.<\/p>\n\n\n\n
Measuring User Quality by Entry Point<\/h2>\n\n\n\n
Session Depth<\/h3>\n\n\n\n
How deeply do users engage after arriving via different deep links?<\/p>\n\n\n\n
SELECT\n  dlc.route AS entry_route,\n  COUNT(DISTINCT dlc.user_id) AS users,\n  ROUND(AVG(s.screens_viewed), 1) AS avg_screens,\n  ROUND(AVG(s.session_duration_seconds), 0) AS avg_duration_sec,\n  ROUND(AVG(s.actions_taken), 1) AS avg_actions\nFROM deep_link_clicks dlc\nJOIN sessions s\n  ON dlc.user_id = s.user_id\n  AND s.session_start BETWEEN dlc.timestamp AND dlc.timestamp + INTERVAL '1 hour'\nWHERE dlc.outcome = 'app_opened'\n  AND dlc.timestamp >= '2026-07-01'\nGROUP BY dlc.route\nHAVING COUNT(DISTINCT dlc.user_id) >= 50\nORDER BY avg_actions DESC;\n<\/code><\/pre>\n\n\n\n
\n\n\n\n\n\n\n\n
Entry Route<\/th>\nUsers<\/th>\nAvg Screens<\/th>\nAvg Duration<\/th>\nAvg Actions<\/th>\n<\/tr>\n<\/thead>\n
\/social\/feed<\/code><\/td>\n1,200<\/td>\n8.3<\/td>\n420s<\/td>\n12.5<\/td>\n<\/tr>\n
\/products\/:id<\/code><\/td>\n2,500<\/td>\n5.1<\/td>\n180s<\/td>\n6.2<\/td>\n<\/tr>\n
\/offers\/weekly<\/code><\/td>\n800<\/td>\n4.8<\/td>\n150s<\/td>\n5.0<\/td>\n<\/tr>\n
\/settings<\/code><\/td>\n400<\/td>\n2.1<\/td>\n45s<\/td>\n1.8<\/td>\n<\/tr>\n
\/dashboard<\/code><\/td>\n600<\/td>\n3.5<\/td>\n90s<\/td>\n3.2<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
Social feed deep links produce the longest, most engaged sessions. Settings deep links produce short, utilitarian sessions (which is expected and not a problem).<\/p>\n\n\n\n
Revenue per Entry Point<\/h3>\n\n\n\n
For apps with in-app purchases or transactions:<\/p>\n\n\n\n
SELECT\n  dlc.route AS entry_route,\n  dlc.source,\n  COUNT(DISTINCT dlc.user_id) AS users,\n  ROUND(SUM(COALESCE(t.amount, 0)) \/ COUNT(DISTINCT dlc.user_id), 2) AS revenue_per_user,\n  ROUND(SUM(COALESCE(t.amount, 0)), 2) AS total_revenue\nFROM deep_link_clicks dlc\nLEFT JOIN transactions t\n  ON dlc.user_id = t.user_id\n  AND t.timestamp BETWEEN dlc.timestamp AND dlc.timestamp + INTERVAL '30 days'\nWHERE dlc.outcome = 'app_opened'\nGROUP BY dlc.route, dlc.source\nHAVING COUNT(DISTINCT dlc.user_id) >= 50\nORDER BY revenue_per_user DESC;\n<\/code><\/pre>\n\n\n\n
Product Team Dashboard<\/h2>\n\n\n\n
Key Metrics to Track<\/h3>\n\n\n\n
A product team deep link dashboard should include:<\/p>\n\n\n\n
    \n
  1. Route health:<\/strong> Open rate, error rate, and latency for each deep link route.<\/li>\n
  2. Feature adoption funnel:<\/strong> Deep link click to feature use, by route.<\/li>\n
  3. Session quality:<\/strong> Screens viewed, session duration, and actions taken by entry point.<\/li>\n
  4. Onboarding completion:<\/strong> Funnel from deep link to onboarding complete, segmented by source.<\/li>\n
  5. Retention by entry point:<\/strong> Day 1\/7\/30 retention broken down by first deep link route.<\/li>\n<\/ol>\n\n\n\n
    Alerting for Product Teams<\/h3>\n\n\n\n
    Set alerts for product-relevant issues:<\/p>\n\n\n\n
    \n\n\n\n\n\n\n
    Alert<\/th>\nThreshold<\/th>\nAction<\/th>\n<\/tr>\n<\/thead>\n
    Route error rate > 2%<\/td>\nCheck every 15 minutes<\/td>\nInvestigate broken deep link<\/td>\n<\/tr>\n
    Open rate drops > 10% for a route<\/td>\nCheck hourly<\/td>\nAASA\/assetlinks.json may be misconfigured<\/td>\n<\/tr>\n
    Onboarding completion < 30%<\/td>\nCheck daily<\/td>\nReview onboarding flow for new deep link users<\/td>\n<\/tr>\n
    Feature adoption < 10% (deep-linked)<\/td>\nCheck weekly<\/td>\nFeature may not match user expectations from the link<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    Tolinku for Product Analytics<\/h2>\n\n\n\n
    Tolinku's analytics<\/a> provide route-level performance data, including open rates, fallback rates, and error rates. Product teams can use the Tolinku dashboard<\/a> to monitor deep link health and identify routes that need attention.<\/p>\n\n\n\n
    For marketing analytics, see deep link analytics for marketing teams<\/a>. For growth metrics, see app growth metrics: the 15 KPIs that matter<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"
    Use deep link analytics for product decisions. Track feature adoption via deep links, measure onboarding flows, and identify drop-off points.<\/p>\n","protected":false},"author":2,"featured_media":1799,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"rank_math_title":"Deep Link Analytics for Product Teams","rank_math_description":"Use deep link analytics for product decisions. Track feature adoption via deep links, measure onboarding flows, and identify drop-off points.","rank_math_focus_keyword":"analytics for product teams","rank_math_canonical_url":"","rank_math_facebook_title":"","rank_math_facebook_description":"","rank_math_facebook_image":"https:\/\/tolinku.com\/blog\/wp-content\/uploads\/2026\/03\/og-analytics-for-product-teams.png","rank_math_facebook_image_id":"","rank_math_twitter_title":"","rank_math_twitter_description":"","rank_math_twitter_image":"https:\/\/tolinku.com\/blog\/wp-content\/uploads\/2026\/03\/og-analytics-for-product-teams.png","footnotes":""},"categories":[14],"tags":[37,20,222,113,69,27,545,33],"class_list":["post-1800","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-analytics","tag-deep-linking","tag-feature-adoption","tag-growth","tag-mobile-development","tag-onboarding","tag-product-management","tag-user-experience"],"_links":{"self":[{"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/posts\/1800","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/comments?post=1800"}],"version-history":[{"count":2,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/posts\/1800\/revisions"}],"predecessor-version":[{"id":2419,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/posts\/1800\/revisions\/2419"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/media\/1799"}],"wp:attachment":[{"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/media?parent=1800"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/categories?post=1800"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tolinku.com\/blog\/wp-json\/wp\/v2\/tags?post=1800"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}