#user acquistion

There are over 1.5 million apps out there, so how do you compete to get more users for your app? Here are a few basic primers for how mobile app user acquisition works. Mobile App marketing is predominantly driven by the Cost per Install model (CPI). You pay for every user that downloads and installs your app, but it can be expensive as this chart from Chartboost shows.

Incentivized vs. Non Incentivized

Incentivized campaigns are ads that give a reward when a user installs an app or completes an action in the newly downloaded app. For example, free virtual coins/gems, coupons or credit for existing retail or ecommerce stores. Non-Incentivized campaigns, are adverts that try to convince you to install an app based on the quality of the advert and content. The users who install apps from these campaigns are usually referred to as low quality users (Incent) vs. Quality users (Non-Incent). This is because typically, incent users are usually not interested in the app they just installed and are downloading it to earn the incentives, whereas quality users have downloaded the app because there have some interest in it, therefore they are more likely to use, refer people and spend money on the app.

So what is the purpose of getting incent users?

Burst Campaigns

It's an app marketing technique used to boost your app rankings in the store. The reason is that by ranking high in the charts, you would gain more organic downloads of the app from people who are browsing the charts. People typically do not scroll down to the lower ranks; therefore a higher placement increases the visibility of your app.

App ranking formulas are quite secret, but in general, the larger number of downloads you receive in a smaller time frame will boost your app ranking higher. Therefore it is important to spend the marketing money quickly to gain the rank boost, otherwise you are wasting money. Burst campaigns are usually performed with a sustain campaign afterwards to try and maintain a high ranking for as long as possible.

Burst Strategies

Usually it makes sense to go with multiple ad networks when performing a burst campaign. This is because, firstly, an ad network will have access to a different number of apps, which increase the overall reach you may have. Secondly, the CPI increases as you attempt to gain more installs from a single network. For example a pricing framework (not real numbers) :-

More downloads = more cost due to the way the bidding algorithms work. By using multiple ad networks, you can increase the total volume of installs whilst maintaining a low CPI, therefore driving down the overall cost of CPI. For example, using 4 networks to get 5,000 downlaods each, you would pay $20,000 vs. using 1 network and paying $40,000.

Consider the timing of the burst campaign. You want to be near the high ranks of the charts when there is more organic traffic available, e.g. weekends and seasonal holidays. This means you need to start your campaign when you are competing against less organic traffic for installs so that you need a lower total volume of incent installs to gain a higher ranking. This should reduce your overall cost for burst campaigns and provide you with a greater upside in organic installs.

Burst Measurement

There are a couple of ways to measure the effectiveness of your burst campaigns. If we discount the value of the incent installs and split the cost amongst the organic and referral based installs, we can determine an effective CPI for those users. You'd want to measure this against the Lifetime Value (CLV) of your app users to determine the ROI if you have that data at hand.

App ranking - Since the main objective is to rank high, keeping track of the app's rank in your target category can help to evaluate the performance. In particular an estimation of the spend required for the day and hour to achieve or maintain the rank can be useful for planning future burst campaign budgets. One thing I did when tracking was looking at the lag in reporting to changes in spend and the number of installs. Understanding this lag and your position will help you to modify your spending to obtain higher ROIs.

Quality Campaigns

These campaigns are focused on attracting high Lifetime Value (LTV) users. People who download apps from high quality campaigns have no other motive other than that they are attracted by the title, advertisement, app description, screenshots and reviews. Therefore they are more likely to stick around play the game, spend money or bring in more players if the app is designed in a way that makes is socially viable in a non-intrusive way for sharing, or has mechanics in the app that are conducive and acceptable to the user to share. The target for these types of ads is that the average LTV of these users (and the users they bring in) is higher than the CPI. This is why the App Store Optimization (ASO) is important because all the marketing materials are part of the conversion criteria for the user.

Video vs. Image

In the past year (2015-2016) as bandwidth and connectivity has improved, there has been a shift in advertising from images to video. Video is considered to attract higher quality users. It also acts as the first interaction between user and app because the video acts as a tutorial. Potential users do not want to go through manuals, or read lots of text. Traditional tutorials can also create high dropout rates, so video has moved upstream in the education process of getting users started. By understanding what they need to do, how to operate, and what value they get from the app, they can "hook" into the app a lot quicker via video. This is the main reason video has become an increasingly large driving force for user acquisition campaigns.

Burst and Quality Combined

It's quite an effective strategy to kick off an app with a burst campaign to get high in the charts, start attracting organic downloads and switch to quality campaigns to sustain the position and drive revenues and profits this way. Since each country is different, it's important that to ensure that the performance for each country is tracked and monitored.

Tracking

Tracking can be quite straight forward. Every phone has an advertiser ID (which can be deleted by the user). This means that it is possible to track users across an ad network/vendor, and to see which games they are acquired from. There are variety of tools out there such as adjust, mobileapptracking, appsflyer, ad-x, tapstream etc. The objective is a platform where you can install one SDK and use this to attribute installs to individual ad networks and even to the individual apps. This allows for deeper analysis such as which type of apps are providing higher quality users for your app allowing you to adjust bidding criteria for particular target segments are even down to the individual app level.

What's still needs to be done

One of the most important things when developing your mobile app (or web app) is determining how many people are returning to use it and how to measure if the changes you are making is really improving it for the users. The number of registered users is not a great measure of how your app is engaging users. You could have 1 million users signing up but none of them may return to use the app after the first time.

A better measure is to see how many people are actively using your app. However, this number can get confused with the number of people who are new users, so you need to work out how many people stop using your app for each group of new users and when they stop using it.  This makes it complicated to forecast the growth (or non-growth) of your app, making it hard to measure if your changes are having the desired  effect of making your app sticky. 

When is it time to get more users?

If your losing lots of users, you may not want to focus on acquiring lots of new users because they will likely only use your app once, so you need a good indication of when to start applying resources to get more users.

Spreadsheet

I've created a basic google spreadsheet that lets you to set up some active user goals and makes some forecasts for you. You can setup some targets for a future number of active users for a certain point in the future and understand how many people you need to acquire to hit your goal. 

This lets you decide if it is an appropriate time to turn on the taps for user acquisition. e.g. if achieving 100,000 active users in 50 weeks means acquiring 10,000,000 new users, it's clearly not the time to plough financial resources into acquiring new users.

The Output

It will produce a chart like the above.

The Redline is the forecast of active users. 

The orange line is your real data. 

The blue bars are the number of new users you are acquiring for each data point (day, week, month.. it's up to you). 

Target Active Users is the number of active users you hope to achieve

Target Period is the time by which you are looking to reach that number of active users

User Acqusition Growth rate is the linear % increase of the number of users you need to acquire for each time period. 

Total Users is the number of overall users you need to have acquired by the final period to hit your target number of active users

What do you need to Input?

You need to type in the Target number of active users and the target period you hope to achieve it by. To generate the graph you need to input into the fields described below

The first 2 fields are your baseline. It's the number of new users you acquired in the last period and the number of active users you had in the last period.

Churn rates

Churn rate is the % of users you lose every period. e.g If you acquired 100 users in a period and in the next period, 30 of them are still active, your churn rate is 70%. However, usually the churn rate is high for the first period and subsequent periods are lower, this is because in the first period people are trying out your app and quite a few of them will decide it is not for them. The subsequent periods tend to have a more 'natural' churn.

So in this chart you need to apply a first period churn rate and a second churn rate for all other periods. You should be able to extrapolate these figures form your analytics tool.

New Users and Active Users

The last things to fill out is the number of new users you get every period. You can start by plugging in old data to see if your churn rate estimates are accurate. Fill in the actual number of active users and you can see how well the prediction is based on your inputted churn rates.

Fill in your new user forecasts and it should give you a forecast on the number of active users. 

Testing your performance

When you get new data for a period, you can input the number of new users and the actual active user number. Look at the numbers and the chart and if it starts diverging upwards, then the changes you have made to your app are having a positive effect on your churn rate.

You can reuse the chart with your new churn rates to re-estimate the targets you are trying to achieve.

If you find this useful, or know someone who may find this useful, spread it around.

The example spreadsheet can be found here