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[Study] How Much Can You Save by Turning Off an Ad Campaign Based on LTV Predictions

The best way to evaluate the effectiveness of an advertising campaign is by using ROI.

ROI is the return on investment made in a product, ad campaign, or other activity. Any ROI value greater than zero reflects net profitability, while a negative value is a clear sign of a loss-making ad campaign.

The return on investment can be calculated using the following formula: 

ROI = (Income – Advertising Costs) / Advertising Costs * 100%, where income is the difference between revenue and the cost of making a product or providing a service.

What if your ad campaign is already running and you're spending money on it, but your profit won’t be calculated until after users make purchases?

In this case, you can use LTV predictions to forecast how much a particular user will spend in your app or on your website over their lifetime with your brand.

This way, you can estimate the revenue that users attracted by the ad campaign will bring, and consequently calculate the ROI and effectiveness of an ad campaign that is already running.

First, let’s take a closer look at how LTV predictions work.

How LTV predictions work

LTV predictions are based on data about purchase history and user behavior in an application or on a website.

The longer an ad campaign runs, the more events take place, enabling more accurate LTV predictions. Go to our documentation to learn more about the accuracy of LTV predictions in various scenarios.

To generate an LTV prediction, seven days of running an ad campaign are enough. During this period, sufficient data is accumulated to decide on the campaign’s effectiveness without spending a large portion of the budget.

But is this data enough to resolve that the ad campaign must be disabled, and how much money can be saved by doing so on the seventh day? These are the questions we seek to answer in our study.



Objective 1. Understand the feasibility of using LTV predictions to make decisions regarding the effectiveness of ad campaigns.

Objective 2. Identify potential cost savings that can be achieved by disabling an ineffective ad campaign at the right time based on LTV predictions.

To achieve our study objectives, we analyzed two metrics:

For Objective 1, we looked at the variance between the predicted LTV and the actual LTV.

For Objective 2, we measured the potential cost savings that could be achieved by utilizing LTV prediction data about expected losses to disable the ad campaign.

What ad campaigns did we study? 

We selected seven ad campaigns across four major gaming projects. We chose these projects because they get the most value out of LTV predictions due to a large number of ad campaigns with varying budgets, creatives, and targeted territories.

Timing of ad campaigns 

2021–2022. We selected this timeframe to ensure a significant time gap between the ad campaigns and our analysis, enabling us to make a more robust comparison between the predicted LTV and the actual LTV.


Results of the study

Ad campaign 1 – App 1

The predicted ROI of this ad campaign was negative right from its launch in December 2021. Over the following months, it continued to deteriorate, yet the marketing team failed to decide to disable the campaign, thereby generating losses.

MonthAd costs (US$)

Predicted 6M LTV (US$)

Actual 6M LTV (US$)Predicted ROI Actual ROI 
Dec 202116009,1413569,0311407,33-15%-29%
Jan 202254566,1644464,5836785,46-19%-33%
Feb 202268364,7839486,4848920,15-42%-28%
Mar 202245520,4628017,4622094,54-38%-51%
Apr 202229478,3219265,1312793,36-35%-57%
May 202233551,533705,1720332,330%-39%
Jun 202213233,756652,495112,55-50%-61%

Ad campaign performance

Ad campaign 2 – App 1

Another ad campaign for App 1 also showed negative ROI from its very outset, with further deterioration over time. This campaign was not disabled either, resulting in multi-million dollar losses.

MonthAd costs (US$)

Predicted 6M LTV (US$)

Actual 6M LTV (US$)Predicted ROIActual ROI 
Nov 202110934,234875,895702,57-55%-48%
Dec 202123266,0918746,4819614,26-19%-16%
Jan 202239885,8432903,7220536,6-18%-49%
Feb 202223497,3514277,8212042,86-39%-49%
Mar 202219886,214376,869485,77-28%-52%
Apr 202234002,8618352,2814647,58-46%-57%
May 202214418,48708,025207,01-40%-64%
Jun 202222860,416801,5316841,74-27%-26%
Jul 202226614,3224080,4315884,56-10%-40%

Ad campaign performance

Ad campaign 3 – App 2

Unlike previous cases, this ad campaign for App 2 showed promising results in March 2022.

MonthAd costs (US$)

Predicted 6M LTV (US$)

Actual 6M LTV (US$)Predicted ROI Actual ROI
Jan 202217806,0914953,7114691,91-16%-17%
Feb 202211823,176195,74832,23-48%-59%
Mar 20226135,777548,788064,323%31%
Apr 20224565,381502,272079,9-67%-54%
May 2022157,1230,9626,95-80%-83%

Ad campaign performance

Ad campaign 4 – App 3

The ad campaign for App 3 lasted three months and was also making losses right from the outset.

MonthAd costs (US$)

Predicted 6M LTV (US$)

Actual 6M LTV (US$)Predicted ROI Actual ROI 
Jan 202214698,98011,796908,83-45%-53%
Feb 202225688,5813462,0610741,54-48%-58%
Mar 202211675,082075,862459,91-82%


Ad campaign performance

Ad campaign 5 – App 3

In this case, the marketing team did a better job of scrutinizing the ad campaign’s performance but still failed to leverage LTV predictions. Although they decided to disable the campaign 60 days after its launch, rather than waiting for six months, these 60 days were long enough to generate sizeable losses.

MonthAd costs (US$)

Predicted 6M LTV (US$)

Actual 6M LTV (US$)Predicted ROI Actual ROI 
Jan 20228418,974403,992863,03-48%-66%
Feb 20225100,73936,09621,47-82%-88%
Mar 2022126,4300-100%-100%

Ad campaign performance

Ad campaign 6 – App 4

Similar to the previous case, the decision to disable the ad campaign was made after 60 days, which was too late and also led to losses in the millions.

MonthAd costs (US$)

Predicted 6M LTV (US$)

Actual 6M LTV (US$)Predicted ROI Actual ROI 
Jan 20223230,292483,091766,3-23%-45%
Feb 202221452,5316730,0514271,35-22%-33%

Ad campaign performance

Ad campaign 7 – App 4

The marketing team of this ad campaign run for App 4 also relied on the actual data over a 60-day period to evaluate the campaign’s performance.

MonthAd costs (US$)

Predicted 6M LTV (US$)

Actual 6M LTV (US$)Predicted ROIActual ROI
Feb 20227269,056446,597032,55-11%-3%
Mar 202221039,346743,39216,28-68%-56%

Ad campaign performance


In this study, we reviewed seven ad campaigns with different budgets and approaches to advertising management. All of them show that:

How to predict LTV

Predict on your own

You can make your own prediction using instructions put together by our experts. To do this, download our free Ebook How to Predict a Mobile App’s LTV on Your Own.

Use our future LTV calculator for subscriptions

You can also make use of our free future LTV calculator for subscriptions – just enter your subscription parameters at the campaign launch and get a prediction.

Enable MyTracker’s free prediction of revenue

Use MyTracker’s LTV predictions to avoid wasting your ad budgets. This is what an LTV prediction report by MyTracker looks like:

ad campaign ltv prediction

Connect MyTracker and set LTV predictions using our documentation or request a demo to receive expert guidance from our team.

Tags: LTV predictive analytics