The Analyze solution will integrate with your on-chain/ off-chain data, marketing data and game data to act as a single source of truth from which to base decisions on.
The return on ad spend (ROAS) is one of the most important KPIs in mobile / blockchain marketing. Assuming your campaign goal isn’t brand awareness or something similar, when you calculate the ROAS, you can essentially determine the financial success of your campaign.
Taking this a step further, predicted ROAS means gauging how much money you are likely to get back for every dollar spent on your campaigns. Of course, predictive models are never 100% accurate and so you will often be presented with a curve that looks like something similar to the one below. This accounts for variances on either side of the prediction but still acts as a good metric to determine campaign success pre / mid-campaign.
There are several reasons why ROAS measurement and predictive analytics have been impacted in 2022, some of which aren’t the same for mobile and Web3 games.
The key difference with mobile gaming, specifically iOS games, is the lack of data transparency and post-IDFA tracking. Following the 2020 COVID boom of mobile games, Apple introduced much stricter privacy laws on data tracking, leaving game studios left with minimal SKAN (StoreKit Ad Network) data to base marketing decisions on. This has left mobile game marketers in the dark about their campaign results and thus, has led to budget cuts and data discrepancies.
Why does all of this matter? Because it retired all previous predictive analytics models and data analysts had to go back to the drawing board in terms of how they predict future campaign performance. Studios are having to build their own models or outsource to agencies, of which most can’t get the accuracy of data needed for predictive analytics models to be effective.
With blockchain / Web3 games, the landscape is slightly different. Due to their recency in mass adoption (some would argue it still hasn’t been adopted by the masses), there are very few platforms that can offer accurate predictive analytics. In fact, even sourcing an accurate dashboard that is able to pull data from multiple sources can prove to be difficult.
Essentially, what predicted ROAS and predictive analytics in general allow marketers to do is identify early trends and patterns that are indicative of success or failure. Therefore, they can put their ad spend into campaigns that are showing early signs of success and lower or stop the spending on those that aren’t.
What both mobile / blockchain gaming have in common is the need for an accurate predictive analytics platform. Using ROAS as an example, many marketers often look at 3-day ROAS, 7-day ROAS and so on to determine the best performing channels as this is one of the building blocks for ROAS prediction. We will cover this more in the section below but hopefully, it is clear that the absence of the above makes marketers unconfident in spending budget and uncertain about campaign performance.
There is a lot to consider when predicting ROAS. The first is how far in the future the ROAS be predicted with certain confidence (3 months, 1-2 years, etc.). Of course, the longer the timespan, the more difficult it becomes to predict with certainty.
The level of granularity needed for a useful prediction is also important. In some cases, daily cohort analysis may well be okay but in most cases, you will need at least campaign or user-level data – the likes of which can be hard to attain for mobile games post-IDFA. However, if there is any historical data available, the predictive models will be more accurate and therefore, more useful.
Standard modeling approaches to predict ROAS
Option 1 – Take the last available ROAS number (e.g. day 14) and assume that there is a stable ratio between 6-months ROAS and 14-day ROAS. Looking at historical data, you can estimate this ratio and use it to predict 6-month ROAS based on the 14-day ROAS.
Option 2 – Estimate the retention to calculate the lifetime of a cohort and multiply it by average revenue per daily active user (ARPDAU).
Whilst these models are useful, they only work on an aggregated level. On top of this, they are not very helpful when working with smaller campaigns because they require large user samples. So, if you were trying to predict ROAS for a single, smaller campaign, these models wouldn’t give an accurate prediction.
What’s different when SuperScale predicts ROAS?
At SuperScale, we take into account more than just the last available ROAS number. For example, daily growth over previous days is an important signal that is completely ignored if your model takes into account only the last available revenue metric.
We can identify cohorts that stopped growing sooner than the average cohort (because of payer churn) which helps the model to not overestimate the predictions. It is always better to have a conservative prediction that may underestimate the ROAS, than an overly optimistic one that motivates UA managers to spend more than is profitable.
Recognizing the different monetization behaviors from different user segments is also key. If you group players into daily cohorts, you mix all segments together and lose crucial pieces of information. We can calculate separate predictions for different segments giving us better overall accuracy.
Our machine learning (ML) prediction pipeline is built on experience from analyzing dozens of games across different genres. It’s one of the fundamental building blocks that enable us to have confidence in our industry-leading ROAS predictions.
Our Analyze solution offers incomparable data visibility for both mobile and blockchain games. That means all your on-chain data (Ethereum, Solana, Binance SmartChain, Tron, etc.) and off-chain data (Meta, Google UAC, TikTok, etc.) can be pulled into one, ultra-reliable platform.
A staple of the Analyze solution is the industry-leading ROAS prediction model which works on some of the principles previously mentioned. This means confidence in marketers being able to spend budget and quickly be able to see reliable data on a single dashboard. For mobile game marketers specifically, this also means trustworthy post-IDFA predictive analytics. What’s more, our in-house experts will provide ongoing support from inception until the dashboards are completed and beyond. Please click below to get in touch for a 15-minute consultation.