Calculating Betting User LTV: A Publisher's Framework

The Hidden Metric That Changes Everything

Your betting vertical is live. Users are wagering. Revenue is flowing. But are you extracting the full value from each user over their lifetime?

Most publishers measure success in weekly or monthly metrics: session volume, active users, revenue per session. These are useful vanity metrics. But they miss the question that matters most to CFOs, boards, and investors: What is each betting user worth to us over their entire relationship with our platform?

That number is Lifetime Value (LTV), and it's the single most important metric you'll calculate as your betting vertical matures. Why? Because LTV drives every strategic decision you'll make—from which commercial model you choose (CPA, revenue share, fixed fee) to where you allocate marketing spend to why certain cohorts of users matter more than others.

The problem: most publishers haven't calculated their LTV properly. They lack the framework, the data infrastructure, or the discipline to model it correctly. Result? They leave money on the table, make poor commercial decisions, and can't articulate the value of their betting vertical to stakeholders.

This guide fixes that. We'll walk you through a battle-tested LTV framework, show you the formulas, provide worked examples for different publisher sizes, and explain how to use cohort analysis to drive real business decisions.

What Is Betting User LTV? And Why It Matters

Lifetime Value is the total revenue a single user generates from the moment they first place a bet on your platform until they stop.

For a betting vertical, LTV typically accounts for:

Direct betting revenues (your share of wins, rake, or revenue share)
Session-based bonuses (deposit matching, reload offers, promotions)
Cross-vertical spillover (increased sports content consumption, sponsorship engagement)
Referral value (users who bring friends to your platform)

In practice, most publishers focus on direct betting revenue because it's measurable and attribution is clear. That's fine. Start there.

LTV matters because it tells you:

Which revenue model to choose. A publisher with 8-month cohort retention and £15 average LTV per user should choose revenue share (flexible, no upfront risk). A publisher with 2-week retention and £50 LTV should choose CPA (monetise quickly, assume users churn fast).
How much you can spend to acquire a user. If your LTV is £50 and you operate with a 3:1 LTV-to-CAC ratio, you can spend £16.67 to acquire a user. That drives your marketing budget and channel mix.
Which cohorts to double down on. Desktop users might have 60% longer retention and 2x higher average bet size than mobile-only users. LTV analysis reveals these patterns and drives product investment decisions.
Whether your vertical is actually profitable. You can be generating £500K in monthly revenue and still destroying shareholder value if LTV < CAC. Most publishers never realise this until they run the numbers.

The LTV Formula: Simple Version

At its core, betting user LTV is this:

LTV = (ARPU × Average Lifespan in Months) × Retention Adjusted Multiplier

Where:

ARPU (Average Revenue Per User) = total revenue in month 1 ÷ active users that month
Average Lifespan = median months before a user stops wagering (typically 4–12 months for sports betting)
Retention Adjusted Multiplier = accounts for the fact that revenue declines each month (users churn, engagement drops)

Let's use a concrete example:

Scenario: Mid-sized UK publisher

Month 1 ARPU: £2.50
Median user lifespan: 8 months
Month-over-month retention rate: 45% (45% of users who bet in month 1 return in month 2)

Naive calculation: £2.50 × 8 = £20. But this assumes revenue stays flat. It doesn't.

The realistic calculation:

With 45% retention, here's what revenue actually looks like:

Month	Cohort Remaining	Revenue per User	Cumulative Revenue
1	100%	£2.50	£2.50
2	45%	£2.25	£4.75
3	20%	£1.80	£6.55
4	9%	£1.35	£7.90
5	4%	£0.90	£8.80
6	2%	£0.45	£9.25

LTV = £9.25

Notice the difference: naive formula says £20, reality says £9.25. That's a 54% error, which cascades through every decision you make.

The Advanced Formula: Cohort Analysis Approach

To calculate LTV properly, use cohort analysis. Here's how:

LTV = Σ (Month Revenue per User × Cohort Retention Rate)

Sum this across every month until retention hits zero.

Step 1: Build your cohort table

Take a specific cohort (e.g., all users who placed their first bet in January 2026) and track their revenue month-by-month.

Example: 1,000 users from January 2026 cohort

Month	Active Users	Total Revenue	Revenue/User	Retention %
Jan	1,000	£2,500	£2.50	100%
Feb	420	£945	£2.25	42%
Mar	168	£302	£1.80	16.8%
Apr	67	£91	£1.35	6.7%
May	27	£24	£0.90	2.7%
Jun	11	£5	£0.45	1.1%
Jul	4	£1	£0.25	0.4%

LTV for this cohort = £2.50 + £2.25 + £1.80 + £1.35 + £0.90 + £0.45 + £0.25 = £9.50

Step 2: Compare across cohorts

Now repeat for all monthly cohorts. You'll notice patterns:

Seasonal cohorts: January cohort (New Year's Resolution period) may have higher Month 1 revenue but lower retention than October cohort.
Product changes: A cohort that joined after you launched live in-play betting may show 30% higher LTV than pre-launch cohorts.
Marketing channel: Organic search cohorts often show 2–3x higher LTV than paid social cohorts (lower acquisition cost, more intentional users).

Worked Examples: Three Publisher Sizes

Small Publisher (Under 100K Monthly Betters)

Profile:

Premium sports editorial site with growing betting audience
Partner: revenue share deal with major sportsbook
Marketing: mostly organic, some sponsorship activation

Month 1 ARPU: £1.80 Median retention at Month 2: 38% Estimated LTV:

Month	Retention	Revenue/User
1	100%	£1.80
2	38%	£0.68
3	14%	£0.25
4	5%	£0.09

LTV = £2.82

Implications:

With a CPA model at £0.50 per user, you'd operate at 5.6:1 LTV-to-CAC (excellent).
With a 3% deposit bonus cost, you'd break even at Month 2.
Total addressable revenue: if you have 80K monthly actives, that's 80K × £2.82 = £226K annual user LTV.

Mid-Sized Publisher (100K–500K Monthly Betters)

Profile:

Regional sports media business with strong mobile presence
Partner: white-label deal with 50/50 revenue share
Marketing: mix of organic, paid social, affiliate partnerships

Month 1 ARPU: £3.20 Median retention at Month 2: 52% Estimated LTV:

Month	Retention	Revenue/User
1	100%	£3.20
2	52%	£1.66
3	27%	£0.86
4	14%	£0.45
5	7%	£0.22
6	4%	£0.13

LTV = £6.52

Implications:

If you acquire at £1.50 per user (paid social + affiliate), you're at 4.3:1 LTV-to-CAC.
Revenue share deal: with 250K monthly betters, that's 250K × £6.52 × 12 months (but accounting for churn, actual annualized revenue is lower—see below).
Estimated annual betting revenue contribution: £1.9M (conservative estimate accounting for cohort rollover and churn).

Large Publisher (500K+ Monthly Betters)

Profile:

National broadcast/digital network with dedicated betting platform
Partner: 60/40 revenue share with major European sportsbook
Marketing: owned channels, sponsorship integration, premium content

Month 1 ARPU: £5.50 Median retention at Month 2: 68% Estimated LTV:

Month	Retention	Revenue/User
1	100%	£5.50
2	68%	£3.74
3	46%	£2.53
4	31%	£1.71
5	21%	£1.16
6	14%	£0.77
7	10%	£0.55
8	7%	£0.39
9	5%	£0.28
10	3%	£0.17

LTV = £16.80

Implications:

With CAC of £2.50 (owned channels, lower cost), you're at 6.7:1 LTV-to-CAC (excellent).
With 500K monthly betters and 60/40 rev share: estimated annual betting revenue = £25M–£30M (assuming 60% of 500K are sustainable monthly cohorts).
This is the scale at which betting becomes a material business unit for a large publisher.

Cohort Analysis Deep Dive: Retention Curves

Here's where LTV analysis gets strategic. Look at your retention curves by segment:

Key insight: Shape matters more than absolute numbers.

A "cliff" retention curve (steep drop in Month 2, then flat) indicates that most users are one-time bettors. Your LTV is front-loaded, and you should optimise for maximizing Month 1 revenue and referrals.

A "smile" retention curve (steep Month 2 drop, then stabilization) is typical for casual bettors. Small core of habitual users (5–10% of cohort) drives long-tail LTV.

A "smooth decay" curve (steady decline each month) indicates consistent user engagement. You have both casual and regular bettors, and product improvements across the board will lift LTV.

How to use this:

Cliff curves: focus on onboarding friction reduction, aggressive Day 1 promotions, and referral mechanics.
Smile curves: build loyalty programs, VIP tiers, and live betting features to retain the 5–10% core.
Smooth decay: invest in content (analysis, live commentary) that justifies repeated visits.

Your LTV directly determines which commercial model makes sense.

CPA (Cost Per Acquisition):

Best for: LTV < £10, short retention windows (2–4 weeks), unpredictable user base
Rationale: You monetise quickly and avoid downside risk from churned users

Revenue Share (most common):

Best for: LTV £8–£25, retention 6+ months, predictable engagement
Rationale: Aligns incentives with your platform's ability to drive long-term user value
Typical split: 50/50 to 70/30 (your favour), depending on traffic volume and retention profile

Fixed Fee (Hybrid):

Best for: LTV > £20, high predictability, large user bases (1M+ monthly)
Rationale: Guarantees minimum revenue, reduces operator risk

Example decision tree:

LTV = £3, retention = 6 weeks → CPA model
LTV = £7, retention = 5 months → Revenue share (50/50)
LTV = £15, retention = 10 months → Revenue share (60/40 your favour) or hybrid fixed fee
LTV = £25+, retention = 12+ months → Hybrid or fixed fee

Building Your LTV Model: Key Metrics to Track

To calculate LTV properly, you need:

Cohort acquisition date (when user first bet)
Monthly active status (did they bet in that month? yes/no)
Monthly revenue attribution (how much did they win/lose)
Churn definition (e.g., no bet in 90 days = churned)

Track these in a simple spreadsheet or analytics tool. Update monthly. Use Excel formulas or SQL to calculate retention curves automatically.

Minimal viable tracking:

Cohort Month	Total Users	Month 1 Revenue	Month 2 Active	Month 2 Revenue	...	LTV
Jan 2026	1,200	£3,000	480	£1,080	...	£8.50
Feb 2026	950	£2,850	370	£833	...	£7.80

Once you have 6+ months of cohort data, you can reliably project LTV forward.

Advanced: LTV by Segment

Don't stop at overall LTV. Break it down:

By geography:

UK users: £11 LTV, 9-month retention
EU users: £8 LTV, 7-month retention
US users: £15 LTV, 10-month retention

By traffic source:

Organic search: £13 LTV, 8-month retention
Paid social: £6 LTV, 5-month retention
Affiliate: £4 LTV, 4-month retention

By device:

Desktop: £12 LTV, 9-month retention
Mobile: £7 LTV, 6-month retention

By user type:

Daily active: £28 LTV, 12+ month retention
Weekly active: £14 LTV, 8-month retention
Casual: £4 LTV, 3-month retention

These segments inform everything: where to market, what product to build, who to target for loyalty programs.

Common Pitfalls (And How to Avoid Them)

Pitfall 1: Confusing revenue with LTV

Wrong: "My betting vertical generated £500K last month, so LTV = £500K ÷ users."
Right: Track individual user revenue from first bet to churn, then sum.

Pitfall 2: Ignoring cohort effects

Wrong: Using Year 1 average LTV to forecast Year 2 revenue.
Right: Model cohorts separately; account for seasonality, product changes, and marketing channel mix.

Pitfall 3: Setting retention too aggressively

Wrong: Assuming 100% of users in Month 2 who place a bet are "retained."
Right: Define retention as consistent monthly activity (e.g., 2+ bets per month). One-off bettors don't count.

Pitfall 4: Forgetting downstream revenue

Wrong: Only counting direct betting revenue.
Right: Add referral value, sponsorship lift, content upsells. Betting is an engagement driver.

Investor Perspective: Why VCs Care About LTV

If you're raising capital for your betting vertical, expect investors to ask:

"What's your LTV by cohort?"
"How does LTV compare to CAC?"
"What's your LTV-to-CAC trend (improving or declining)?"
"Is LTV sustainable or inflated by promotional spend?"

Your answer matters. A publisher with improving LTV-to-CAC ratios is a growth business. A publisher with declining LTV is a warning sign (product fatigue, market saturation, or poor retention mechanics).

Before you lock in your commercial model, read these companion pieces:

ROI of BetTech: A Business Model Comparison (1.11) — See how LTV drives ROI calculations across CPA, revenue share, and fixed-fee models.
Revenue Per Session: The Key Metric You're Missing (3.6) — Understand how RPS aggregates into LTV.
CPA vs Revenue Share: Which Model Fits Your Publisher (3.11) — Use LTV to decide between models.
Case Study: premium US sports publishers — $5M+ Betting Revenue (3.3) — See how a large publisher's LTV translated to commercial success.
Publisher Revenue Benchmarks: What's Normal? (3.13) — Compare your LTV to peers.

Your Next Steps

Audit your data infrastructure. Do you have user-level acquisition dates, monthly revenue attribution, and churn definitions in your analytics platform? If not, build this first.
Calculate your baseline LTV. Using the cohort formula above, model your last 6 months of data. You may discover your LTV is 50% lower (or higher) than you thought.
Segment your cohorts. Break LTV down by geography, traffic source, and device. Find the high-LTV segments and double down.
Run commercial model scenarios. If CPA offer is £0.75 per user and your LTV is £8, you're leaving £7.25 on the table. Does revenue share make sense? Model it out.
Track forward. Update your cohort table monthly. Use the trends to forecast annual revenue and inform board conversations.

Call to Action

Betting user LTV is the foundation of a sustainable, high-ROI betting vertical. Without it, you're flying blind—guessing at commercial models, marketing budgets, and profitability.

If you're already live with a betting partner and haven't calculated LTV yet, start this week. It's a 2–3 hour project with a spreadsheet and your analytics tool. The insights will reshape how you think about your vertical.

Need help modelling LTV for your specific publisher profile? Our team has worked through LTV calculations for 20+ publishers across the UK, EU, and US markets. We can help you build the framework, identify optimisation levers, and pressure-test your commercial assumptions.

Schedule a 30-minute LTV workshop with our team →

Last updated: March 2026 | Evidence base: FairPlay publisher cohort data, 125M+ price change dataset, 1.1B prediction model | Compliance: UKGC, MGA, state gaming commissions