{"id":803,"date":"2026-01-11T09:56:15","date_gmt":"2026-01-11T09:56:15","guid":{"rendered":"https:\/\/gtaroads.com\/blog\/?p=803"},"modified":"2026-04-01T09:56:52","modified_gmt":"2026-04-01T09:56:52","slug":"cohort-analysis-and-ltv-key-upgrade-for-arbitrage-and-performance-marketers","status":"publish","type":"post","link":"https:\/\/gtaroads.com\/blog\/cohort-analysis-and-ltv-key-upgrade-for-arbitrage-and-performance-marketers\/","title":{"rendered":"Cohort Analysis and LTV: the Key Upgrade for Arbitrage and Performance Marketers"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>Cohort analysis in arbitrage and performance marketing: how to stop looking only at clicks and start seeing LTV<\/em><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Why arbitrage and performance marketing need cohort analysis<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The classic approach in traffic arbitrage:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>we look at the cost per click (CPC), cost per lead\/deposit (CPA),<\/li>\n\n\n\n<li>calculate ROI\/ROAS on fresh traffic,<\/li>\n\n\n\n<li>cut sources and creatives based on top-level metrics.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The problem: <strong>the money often doesn\u2019t come on day one.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Especially in niches like:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>gambling \/ betting \u2013 getting users to the 2nd+ deposit;<\/li>\n\n\n\n<li>subscriptions \/ apps \u2013 renewals, upsells;<\/li>\n\n\n\n<li>SaaS and B2B services \u2013 recurring payments;<\/li>\n\n\n\n<li>e-commerce \u2013 repeat purchases.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">A lead looks \u201cexpensive\u201d on the first deposit, but after 30\u201390 days it might pay back 2\u20133x. If you kill setups based only on short-term ROI, you\u2019re cutting off your own scale.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Cohort analysis<\/strong> is exactly about this: grouping users into cohorts and tracking <strong>how they pay over time<\/strong> so you can see:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>real LTV by sources \/ creatives;<\/li>\n\n\n\n<li>payback not only on day 0, but also at 7\/30\/60\/90-day horizons;<\/li>\n\n\n\n<li>\u201clong\u201d setups that look like trash by click metrics, but bring cash in the long run.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">What is a cohort in simple terms<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A <strong>cohort<\/strong> is a group of users who share the same \u201cstart\u201d attribute.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Most often in arbitrage and performance marketing a cohort = users who:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>performed their <strong>first target action in the same period<\/strong> (day \/ week \/ month);<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">and\/or:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>came from the <strong>same source \/ campaign \/ creative<\/strong>;<\/li>\n\n\n\n<li>belong to the same <strong>geo<\/strong>, <strong>platform<\/strong>, <strong>device type<\/strong>, etc.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Examples of cohorts:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>all users who made their first deposit between 1\u20137 November from Facebook campaign X, geo DE;<\/li>\n\n\n\n<li>all installs from TikTok campaign Y in October, iOS platform;<\/li>\n\n\n\n<li>all registrations from teaser network Z for a week, segment \u201cmobile\/Android\/BR\u201d.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9-1024x576.jpg\" alt=\"\" class=\"wp-image-254\" srcset=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9-1024x576.jpg 1024w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9-300x169.jpg 300w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9-768x432.jpg 768w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9-1536x864.jpg 1536w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9-370x208.jpg 370w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9-1170x658.jpg 1170w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9-760x428.jpg 760w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9-270x152.jpg 270w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Using-Cohort-Analysis-to-Double-Your-Customer-LTV-16_9.jpg 1600w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">The key difference: \u201cclick analysis\u201d vs \u201ccohort LTV analysis\u201d<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">If you look only at clicks \/ day 1<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">You see:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>CTR,<\/li>\n\n\n\n<li>CPC,<\/li>\n\n\n\n<li>CR (registration\/deposit),<\/li>\n\n\n\n<li>CPA,<\/li>\n\n\n\n<li>ROI\/ROAS on the launch day.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">And you conclude: \u201cthe campaign is in the red, cut it.\u201d<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">If you look at cohorts by LTV<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">You take, for example, a cohort of users who made their first deposit in October:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>see how much they brought on days 0, 7, 30, 60, 90;<\/li>\n\n\n\n<li>compare that with the costs for the same cohort;<\/li>\n\n\n\n<li>calculate not only \u201ctoday\u2019s\u201d ROI, but <strong>cumulative<\/strong> ROI.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Suddenly it turns out:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Day 0: ROI \u221230%<\/li>\n\n\n\n<li>Day 30: ROI +15%<\/li>\n\n\n\n<li>Day 90: ROI +70%<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This setup, in a \u201cclick-thinking\u201d mindset, would look like a loss. In cohort analysis, it\u2019s a gold mine \u2014 just with a long payback tail.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">How to choose a cohort axis in arbitrage and performance marketing<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Usually there are two dimensions:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Time axis (start point)<\/strong>\n<ul class=\"wp-block-list\">\n<li>date of first click,<\/li>\n\n\n\n<li>date of registration,<\/li>\n\n\n\n<li>date of first deposit \/ first purchase,<\/li>\n\n\n\n<li>date of app install.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Segmentation axis (who they are)<\/strong>\n<ul class=\"wp-block-list\">\n<li>traffic source (FB, TikTok, Google Ads, DSP, push, etc.),<\/li>\n\n\n\n<li>specific campaign \/ ad set \/ creative,<\/li>\n\n\n\n<li>geo, language,<\/li>\n\n\n\n<li>device (iOS\/Android, mobile\/desktop),<\/li>\n\n\n\n<li>type of landing, funnel (with\/without pre-lander, quiz \/ direct offer).<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">The most practical option for arbitrage<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Cohort = (date of first deposit\/purchase) \u00d7 (campaign\/creative)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This way you:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>detach from random registrations that never deposit;<\/li>\n\n\n\n<li>compare only people who have already brought money;<\/li>\n\n\n\n<li>see which campaigns drive more repeat payments and upsells.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Which metrics to look at in cohort analysis<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">1. LTV (Lifetime Value)<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">LTV is calculated as the total revenue from a user or group over a period.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>LTV D0 \u2014 revenue on the day of the first action (deposit, purchase).<\/li>\n\n\n\n<li>LTV D7 \u2014 cumulative revenue over 7 days.<\/li>\n\n\n\n<li>LTV D30, D60, D90 \u2014 and so on.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">For a cohort:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><code>LTV_cohort_D30 = (total revenue from the cohort over 30 days) \/ (number of users in the cohort)<\/code><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. CAC \/ CPA (Cost per Acquisition)<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">How much it cost to bring a user who became part of the cohort:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>CAC per deposit,<\/li>\n\n\n\n<li>CAC per purchase,<\/li>\n\n\n\n<li>CAC per install (if you monetize further inside the app).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3. Cohort ROI \/ ROAS<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><code>ROI_D30 = (cohort revenue over 30 days \u2212 traffic cost for this cohort) \/ traffic cost \u00d7 100%<\/code><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><code>ROAS_D30 = cohort revenue over 30 days \/ traffic cost \u00d7 100%<\/code><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Calculated for different horizons: D0, D7, D30, D90.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. Retention and repeat actions<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Especially important for gambling, apps, subscriptions:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>share of users who reach the second deposit,<\/li>\n\n\n\n<li>average number of deposits\/purchases per user,<\/li>\n\n\n\n<li>retention D1\/D7\/D30 (user activity).<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"605\" src=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Customer-Lifetime-Value-CLV.webp\" alt=\"\" class=\"wp-image-255\" srcset=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Customer-Lifetime-Value-CLV.webp 1000w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Customer-Lifetime-Value-CLV-300x182.webp 300w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Customer-Lifetime-Value-CLV-768x465.webp 768w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Customer-Lifetime-Value-CLV-370x224.webp 370w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/Customer-Lifetime-Value-CLV-760x460.webp 760w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">A simple \u201cback-of-the-envelope\u201d cohort analysis example<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Let\u2019s say you\u2019re driving traffic to a gambling offer.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Campaign A (Facebook)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>100 deposits in October<\/li>\n\n\n\n<li>CAC = $25<\/li>\n\n\n\n<li>Spend = $2,500<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Campaign B (TikTok)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>100 deposits in October<\/li>\n\n\n\n<li>CAC = $25<\/li>\n\n\n\n<li>Spend = $2,500<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">By the first deposit (day 0):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A: average deposit $25 \u2192 revenue = $2,500 \u2192 ROI D0 = 0%<\/li>\n\n\n\n<li>B: average deposit $20 \u2192 revenue = $2,000 \u2192 ROI D0 = \u221220%<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">If you look only at D0 \u2014 it\u2019s logical to kill campaign B.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Now look at cohorts and LTV:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>On day 30:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A: LTV D30 = $35 \u2192 revenue = $3,500 \u2192 ROI D30 = (3,500\u22122,500)\/2,500 = +40%<\/li>\n\n\n\n<li>B: LTV D30 = $50 \u2192 revenue = $5,000 \u2192 ROI D30 = (5,000\u22122,500)\/2,500 = +100%<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Conclusion:<\/strong><br>The Facebook campaign A works okay, but TikTok campaign B is <strong>even more profitable in the long run<\/strong> \u2014 its users just warm up slower and bring more repeat deposits.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Without cohort analysis you would have turned it off.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Where and how to collect data for cohort analysis<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Data sources<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Tracker \/ analytics<\/strong> (Keitaro, Binom, Voluum, RedTrack, Bemob, etc.)\n<ul class=\"wp-block-list\">\n<li>clicks, visits, partner postbacks, spend;<\/li>\n\n\n\n<li>you can pass values (deposits, amounts) and build reports.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>CRM \/ billing \/ affiliate stats<\/strong>\n<ul class=\"wp-block-list\">\n<li>real deposits, amounts, native LTV;<\/li>\n\n\n\n<li>user statuses.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>BI tools and spreadsheets<\/strong>\n<ul class=\"wp-block-list\">\n<li>Google Sheets \/ Excel,<\/li>\n\n\n\n<li>Power BI, Tableau, Metabase, Looker Studio, Redash, etc.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">Common flow:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Tracker \u2192 export clicks\/campaigns\/spend.<\/li>\n\n\n\n<li>Affiliate program\/CRM \u2192 export user-level data (ID, deposit dates, amounts).<\/li>\n\n\n\n<li>Join by user_id \/ click_id \/ sub_id \u2192 master table.<\/li>\n\n\n\n<li>On top of that \u2014 cohort reports.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">How to build a cohort report (basic flow)<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Define what the cohort \u201cstart\u201d is.<\/strong><br>For example: date of first deposit.<\/li>\n\n\n\n<li><strong>Create a user table<\/strong> with fields:\n<ul class=\"wp-block-list\">\n<li>user_id,<\/li>\n\n\n\n<li>date of first deposit (cohort_date),<\/li>\n\n\n\n<li>source \/ campaign \/ creative,<\/li>\n\n\n\n<li>acquisition cost (you can use average CAC if you can\u2019t match it exactly),<\/li>\n\n\n\n<li>all subsequent transactions with dates and amounts (can be a separate table).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Calculate LTV by days<\/strong><br>For each user:\n<ul class=\"wp-block-list\">\n<li>Day 0 revenue \u2014 all transactions on the day of first deposit;<\/li>\n\n\n\n<li>Day 1 revenue \u2014 next day, and so on;<\/li>\n\n\n\n<li>or directly calculate cumulative LTV by periods (D7, D30, D60).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Group by cohort<\/strong><br>For example, by month of first deposit and campaign:\n<ul class=\"wp-block-list\">\n<li>Cohort = (month(first_deposit_date), campaign_id)<\/li>\n\n\n\n<li>Sum revenue by days from all users in the cohort;<\/li>\n\n\n\n<li>Divide by number of users \u2192 LTV by days\/weeks.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Build a cohort \u201cheat map\u201d<\/strong><br>Often it\u2019s a table:\n<ul class=\"wp-block-list\">\n<li>rows \u2014 cohorts (e.g. months),<\/li>\n\n\n\n<li>columns \u2014 days since start (D0, D7, D30, D60),<\/li>\n\n\n\n<li>values \u2014 LTV or ROAS \/ ROI.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">This table shows:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>which cohorts (months, sources) are degrading or growing;<\/li>\n\n\n\n<li>after how many days traffic pays back on average;<\/li>\n\n\n\n<li>how traffic quality changes after product\/flow changes (new landers, pre-landers, creatives).<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"537\" src=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/1680988188754-1024x537.png\" alt=\"\" class=\"wp-image-256\" srcset=\"https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/1680988188754-1024x537.png 1024w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/1680988188754-300x157.png 300w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/1680988188754-768x403.png 768w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/1680988188754-370x194.png 370w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/1680988188754-760x399.png 760w, https:\/\/gtaroads.com\/blog\/wp-content\/uploads\/2025\/11\/1680988188754.png 1250w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">How to use cohort analysis results in practice<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">1. Planning acceptable CPA \/ CAC<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Knowing that:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>average LTV D30 = $80,<\/li>\n\n\n\n<li>you want ROI D30 = 30%,<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">you can calculate target CAC:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><code>CAC_target = LTV_D30 \/ (1 + ROI_target) = 80 \/ 1.3 \u2248 61.5$<\/code><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This means that even <strong>CAC $50\u201360 can be totally fine<\/strong> if long-term LTV is high, and you shouldn\u2019t panic over \u201cexpensive\u201d deposits in the moment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. Determining the payback period<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">With cohorts you can see:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>after how many days each cohort breaks even (payback period);<\/li>\n\n\n\n<li>how fast and how far LTV \u201ccatches up\u201d.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This matters if you buy traffic on credit, use borrowed money, or work with investors.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Prioritizing sources and creatives<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Not all clicks are equally valuable:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>a source with cheap clicks but weak LTV \u2192 \u201cjunk\u201d traffic;<\/li>\n\n\n\n<li>a source with expensive clicks but strong LTV \u2192 \u201cpremium\u201d traffic.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Cohort analysis lets you:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>objectively compare sources and campaigns by LTV and ROI over time;<\/li>\n\n\n\n<li>bid more aggressively where LTV is high;<\/li>\n\n\n\n<li>cut where users don\u2019t stick and don\u2019t pay.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">4. Testing changes in product \/ funnel<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">You change:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>landing \/ pre-lander;<\/li>\n\n\n\n<li>bonus mechanics;<\/li>\n\n\n\n<li>welcome pack (free spins, discounts);<\/li>\n\n\n\n<li>call center scripts, etc.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">You then look at:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>LTV of new cohorts vs old;<\/li>\n\n\n\n<li>payback periods;<\/li>\n\n\n\n<li>conversion to second\/third payment.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Common mistakes in cohort analysis<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Mixing periods and cohorts.<\/strong><br>You can\u2019t compare LTV of users who came in different months as if they were the same group. That\u2019s what cohorts are for.<\/li>\n\n\n\n<li><strong>Focusing only on D0\u2013D1.<\/strong><br>The most common arbitrage mistake: turning off \u201clong\u201d setups.<\/li>\n\n\n\n<li><strong>Ignoring traffic costs.<\/strong><br>Looking only at LTV isn\u2019t enough \u2014 you must calculate cohort ROI\/ROAS considering spend.<\/li>\n\n\n\n<li><strong>Over-segmentation.<\/strong><br>If you slice cohorts by too many parameters, you end up with 10\u201320 users per segment \u2014 just noise.<br>Start with:\n<ul class=\"wp-block-list\">\n<li>month (or week) of start,<\/li>\n\n\n\n<li>source\/campaign,<\/li>\n\n\n\n<li>major geos.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>No link between traffic and revenue.<\/strong><br>If you can\u2019t match user_id\/subs\/clicks between tracker and affiliate\/CRM, cohort analysis becomes much less accurate. You need to think about tracking and IDs from day one.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">How to implement cohort analysis \u201cthe simple way\u201d right now<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">If you don\u2019t have fancy BI and a data team, you can start with a minimal setup:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Export from the affiliate program \/ CRM:\n<ul class=\"wp-block-list\">\n<li>user_id,<\/li>\n\n\n\n<li>date of first deposit,<\/li>\n\n\n\n<li>amount of first deposit,<\/li>\n\n\n\n<li>amounts of subsequent payments with dates.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Export from the tracker:\n<ul class=\"wp-block-list\">\n<li>user_id or click_id \/ sub_id,<\/li>\n\n\n\n<li>source, campaign, creative,<\/li>\n\n\n\n<li>traffic cost by these IDs.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Join in Google Sheets \/ Excel by ID.<br>In the worst case \u2014 match \u201con average\u201d (CAC = spend \/ number of first deposits per campaign).<\/li>\n\n\n\n<li>Build a simple pivot table:\n<ul class=\"wp-block-list\">\n<li>rows: month of first deposit \u00d7 campaign,<\/li>\n\n\n\n<li>columns: D0, D7, D30, D60 (LTV, ROAS),<\/li>\n\n\n\n<li>a separate table with cumulative ROI.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Based on these numbers:\n<ul class=\"wp-block-list\">\n<li>recalculate acceptable CAC,<\/li>\n\n\n\n<li>understand which campaigns to scale and which really don\u2019t perform.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Cohort analysis in arbitrage and performance marketing is a shift:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>from \u201cclick-thinking\u201d<\/strong> (CTR, CPC, CPA, ROI on launch day)<\/li>\n\n\n\n<li><strong>to \u201cmoney-over-time thinking\u201d<\/strong> (LTV, cohort ROAS\/ROI, payback period).<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">It allows you to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>avoid killing profitable setups because of weak day-one metrics;<\/li>\n\n\n\n<li>more accurately plan bids, budgets, and acceptable CAC;<\/li>\n\n\n\n<li>understand the real quality of traffic, not just its price;<\/li>\n\n\n\n<li>scale arbitrage and performance channels systematically instead of living \u201cfrom creative to creative\u201d.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">In short: <strong>cohort analysis is a way to see the true value of your traffic, not just its cost.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cohort analysis in arbitrage and performance marketing: how to stop looking only at clicks and start seeing LTV Why arbitrage and performance&hellip;<\/p>\n","protected":false},"author":3,"featured_media":253,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[127,23],"tags":[143,145,66,65,103,144,117,54],"class_list":["post-803","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-affiliate","category-marketing","tag-arbitrage","tag-cohort","tag-cpa","tag-cpc","tag-ltv","tag-performance","tag-roas","tag-roi"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - 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