Driving Buyer Behavior with In-App Purchases
Part 3 of 4 of A KPIs Guide for Google Play Apps and Games
Posted by Alyssa Perez, Developer Growth Consultant — Google Play
This piece is part of a larger series of articles and business growth webinars about understanding Google Play app performance based on key metrics. We recommend reading Part 1, “Introduction to Metrics” and Part 2, “Acquiring and Keeping New Users,” or watching our on-demand webinars.
If you are looking for ways to optimize your monetization strategy for your app or game, you may be looking at a handful of different metrics to measure app performance and wondering where to begin. If your business model is built around in-app purchases (IAP) or perhaps a hybrid of IAP with another model as well, this article should provide a foundation for performance analysis with regard to increasing revenue. We’ll walk through some primary and secondary monetization metrics, along with how to calculate them, tips for prioritization, and illustrative examples of how to use our Google Play metrics tree to figure out what levers you can pull to achieve your goals.
Today we’re going to dig into IAP best practices and offer tips for adjusting your design, pricing, and testing to maximize potential with your users. The below guide will help you answer questions like the following:
- “What does driving engagement have to do with revenue?”
- “How do I know which metrics to optimize to drive revenue?”
- “Is the design of my app or game and its in-app assets impacting my monetization?”
Let’s get started.
Monetization is directly linked to engagement
Before we dig deep into the metrics tree, I just want to briefly touch on the importance of thinking about user engagement in the context of monetization.
A strong IAP monetization model should be designed to tie directly to a user’s engagement within your app. In other words, the key activity within your app should be relevant to why a user might want to make an in-app purchase. Let’s look at a few examples.
- Standard gameplay in this genre involves players swapping items on a game board to create rows or columns of three matching items (such as gems, candy, or food). The core action is completing levels and moving on to different challenges. Each game level adds difficulty and sometimes requires the player to achieve a variety of goals in a set number of moves or time. If the player loses, they may have to wait to play again — or pay to recharge and try again immediately. This is a simple example of engagement (completing more levels) driving demand for in-app purchases: the more a user plays, the more they may be willing to spend.
- A key user motivation is to find a mutual connection. The core action might be to swipe or ‘like’ someone, in addition to sending messages. Some apps integrate IAP to enhance a user’s experience by offering them more daily match opportunities or more ‘likes’ to use; this means that the more a user engages with the app and completes this core action, the more incentive a user may have to pay to unlock more, since they’ve seen the value of the activity and want to continue.
Think through how your users’ motivations are satisfied by the engagement with your app, and then see if there’s an opportunity to drive purchases by enhancing that experience. If you’re interested in learning more on this topic, you can read more in my Bridging the Gap medium post.
In this chart, each ‘parent’ metric is a product of the ‘child’ metrics beneath, and all child metrics are multiplied to calculate the parent number (with the only exceptions being those delineated with a dotted line, which are added instead of multiplied). Let’s see this in practice by looking at the Average Revenue Per Daily Active User (ARPDAU) branch of the tree.
- Daily Conversion, or the percentage of active users that see enough value to spend (any) money in a given day, multiplied by the Average Revenue Per Paying User (ARPPU).
- For example, if 3% of your active users spend money, and the average amount a paying user spends is $8, you have a $0.24 ARPDAU (average revenue per daily active user).
Let’s work our way down the Daily Conversion side:
- New Buyer Conversion is the percentage of paying users that spent (any amount of) money for the very first time.
- Repeat Buyer Conversion is the percentage of paying users that spent (any amount of) money who have spent money before.
- We’ve delineated these with dotted lines in the chart for a quick reminder that these numbers are added to calculate Daily Conversion.
Now let’s look at the Average Revenue Per Paying User (ARPPU) side:
- Average Transaction Value (Avg Tx Value) is the dollar amount averaged out from paying users only. (If half your users pay $2 and the other half pay $4, your Avg Tx Value is $3 per paying user.)
- Transactions per buyer (#Tx/Buyer) is the average number of times a paying user made a transaction. (If half your users buy one item and half your users buy three items, your #Tx/Buyer is two.)
- These are multiplied together to get the parent metric, ARPPU.
- Using the examples above, if the Avg Tx Value is $3 and the #Tx/Buyer is two, your ARPPU is $6.
Focus on primary metrics before you optimize secondary metrics
Here on the Google Play business growth expert team, we like to think of Daily Buyer Conversion as a ‘primary’ monetization metric. This is because it helps understand how successful you are at monetizing your entire user base — giving you insight into how well you are converting users into buyers (and driving those new buyers to be recurring buyers). Average Revenue Per Paying User (ARPPU), on the other hand, would be a ‘secondary’ monetization metric. This is because optimizing your ARPPU metric is only addressing the users who have paid in your app or game, which is just a small percentage of your active user base. Optimizing against this metric means trying to get more value from a smaller pool of users, which isn’t as sustainable in the long term.
Focus on going for breadth — how many users can you reach by creating monetization strategies that speak to different segments of your whole active user base — before trying to optimize how much you are getting from your buyers. Think of it as hedging your bets: it’s better to have 100 users spending $1 each than one user spending $100 (if you lose one user in the former scenario, you’ll still have $99, whereas if you lose one user in the latter scenario, you have $0). It’s much easier to lift users up incrementally — get them to notice a little more value, spend a little more — than continuing to get more out of a user who is already paying a large amount. There is definitely an art to finding the right balance between the primary and secondary metrics, but let’s cover some things to consider in order to drive improvements in each.
Considerations for driving primary monetization metrics
In order to drive stronger buyer conversion, you have two levers: increasing the number of first-time buyers or increasing the number of returning buyers. Refer to the metrics tree as a guide.
For New Buyer Conversion, you have a few options to take into consideration:
- Use your existing data to understand expected time to conversion for your new buyers. What is the most common timeframe (in days, or time in app) for a user to convert to a buyer? What was the driver? What offer or SKU was most commonly purchased?
- Introductory pricing or starter packs are common ways to incentivize users to convert. There’s no one-size-fits-all for starter packs, and continual testing and iteration are important. Think about what offerings are most valuable and how you can bundle multiple things together in order to increase the value perception of the opening offer. More importantly, think about how you can discount that offer if a user decides not to convert on it the first time. Is there an opportunity for you to resurface that offer X weeks later at a slightly lower price to make it more enticing?
- A user will need to see clear value in your app or game in order to be incentivized to convert. Therefore, ensure that you are allowing your user enough time to engage with your title before surfacing any type of introductory or starter pack offer.
- Long tenured non-buyer sales or offers. There may be a set of users that continue to engage with your title, but have never converted to a buyer — therefore they’re adding social value, but no monetary value. Consider creating improved valued offers at low price points in order to incentivize these users to convert to a buyer.
- Test your available price points. One thing that can directly impact first time buyers is the available price points in your app. It’s important to test both your starter pack and standard SKU price points to ensure you are not pricing out any new users into your app. What is your starter pack price point? What about the lowest price point in your app? Should it vary my market?
With each of these suggestions, make sure it fits with the user experience you are trying to create. The most important thing before you convert a user is ensuring they are engaged enough to continue coming back and to see the value of making an in-app purchase.
For Repeat Buyer Conversion, take a user’s past purchases into consideration:
- Be ‘predictably unpredictable’ with your sales and content cadence. Sales and content releases are popular ways to drive more buyers, but it is important to not be predictable with any of these offerings. If a user knows that a sale will always occur on Saturdays, it may drive down their likelihood to purchase outside of the expected sale day, so ensure that you are providing an incentive to purchase on any day with new content releases.
- Surface relevant offers based on prior purchases. Take into account the price point and item that a user purchases in order to create a special package offer that is available for a limited time.
- Understand when there is a need for a purchase. As an example, if your user has any type of in-app asset wallet, surface an offer when they’ve fallen below a particular threshold (for example, below the 25th percentile of your paying user’s wallet size) as they likely have a larger need for converting than those with a high asset wallet size. You can also use the recency of purchase as an indicator for surfacing an offer.
- Specialty offers for churned buyers that are still active in your app or game. If a user was previously a buyer and has not purchased in an extended period of time, consider how you can utilize all of these examples to get the buyer back on your targeted purchase cadence.
Considerations for optimizing secondary monetization metrics
To drive average revenue per paying user (ARPPU), you have two available levers to drive in our metrics tree: the transaction value (Avg Tx Value) or the number of transactions a user makes (#Tx / Buyer).
- We have found that the transaction value is linearly and positively correlated with average revenue per paying user. This means if you can get users to pay at a higher price point, you’re likely to get more revenue from them. In games with in-app currency, we find that average transaction value is the main driver for average revenue per paying user.
- Transactions per buyer on the other hand, sees no clear, reliable pattern when plotted against average revenue per paying user. This is likely because this KPI is more directly tied to the engagement and design of your app or game — and how closely it is tied to the monetization drivers.
- If you have in-app currency, consider some of the following questions that can naturally impact the number of transactions a buyer would make:
- How many assets do you sell in your app or game? Multiple offerings available for real money can drive a slightly higher #Tx / Buyer, as opposed to only having a single asset available for sale.
- How quickly are the assets that a user buys consumed? What is the velocity of money? If your app or game incentivizes asset hoarding (where users can buy a single pack with a large set of assets that will last them an extended period of time) this may drive down the number of transactions you see from your buyers as the demand for purchase will go down.
My colleague has a post on signs and signals of sub-optimal monetization with in-app purchases, where you can learn more about this area and see some interesting correlations and benchmarks for secondary monetization metrics.
The optimizations against secondary monetization metrics are likely to be very app- or game- specific, as well as an opportunity to exercise your creativity. Remember, you should be looking for the optimal balance between your buyer conversion and the average revenue per paying user (ARPPU) to try to have as many users convert at the optimal price for your app.
Now that we’ve stepped our way down the IAP version of the monetization branch of the metrics tree, our next article in the series will focus on subscription-based businesses and using similar KPIs for driving strong subscription behaviors.
If you’re interested in seeing what data trends should look like for each of these KPIs and additional practical examples, or if you want to ask our Google Play business growth experts questions related to this tree, be sure to sign up for one of our upcoming webinar dedidated to this topic and follow us on here to catch future articles in this series. Happy analyzing!
What do you think?
What metrics are you most or least familiar with when analyzing app performance? Let us know in the comments below, or tweet using #AskPlayDev and we’ll reply from @GooglePlayDev, where we regularly share news and tips on how to be successful on Google Play.