App Monetization: Know What You Need to Know

10/28/11

Back in August of 2010, our CEO penned a blog post titled “A Sense of Deja Vu,” in which he outlined his experiences in the early 2000’s, watching numerous web startups focus on the wrong metrics and analyses—often with dire results.

Then, the idea was “get traffic now, monetize later.” Ultimately this misguided strategy was the significant driver of the dot-com bust. What these startups didn’t understand was that simple metrics like pageviews were not an accurate predictor of profitability or success. As a company squarely focused on improving user engagement and monetization in the mobile ecosystem, we cringe when we hear mobile app publishers focus solely on apps downloads. Downloads are no guarantee of business longevity.

In the interest of avoiding history repeating itself, we’re launching a new blog series to help educate mobile app publishers on how to better understand user behavior, and more importantly, use that knowledge to create app experiences that improve retention, enhance engagement and build realistic, sustainable business models.

Some of the topics we’ll be tackling throughout the series include engagement and conversion problems, how to avoid them both online and in mobile, specific tools for analysis, how to obtain actionable insight and what developers need to know to ensure the success of their apps.

Vanity vs. Discovery Analytics

First up: Understanding User Behaviors. Your ability to effectively monetize or engage users is directly correlated with your ability to understand their behaviors. Uncovering patterns of usage through intelligent analysis should be a top priority for any app developer. There are various forms of analyses, and for mobile apps, we divide them into two main categories:

  1. Vanity Metrics
  2. Discovery Analytics

Although each serves a specific purpose, and can provide meaningful insight into the usage of your app, there are important distinctions between the two.

Vanity Metrics

Vanity Metrics are simple sets of usage data focused on quantifying general trends over time. Examples of these include “app downloads,” “total number of sessions,” or “total number of first time users.” These numbers can quickly tell you how great you’re doing (Hooray! We’ve added 5,000 new users this week!) or just as quickly sound the alarm (Why does the total number of sessions keep dropping?!). Are they interesting statistics? Absolutely. Do they provide insightful analysis? Not quite.

More often than not, vanity metrics are used for – you guessed it – vanity purposes. They have a funny way of glossing over the hard truth about user engagement, which we believe to be the most critical indicator of an app’s potential. Let’s look at a simple example:

After tirelessly creating what you believe will be the next great freemium game, Malcontent Birds, you wake up 15 minutes early each morning to check the number of daily active users (DAUs, see below). Because of favorable reviews in the blogosphere, this number quickly takes off. As more and more people download your app, you start to feel confident that your game is, and will continue to be, a fly-away success All of those in-app offers you’ve created have yet to catch on, but it’s only a matter of time before everyone starts buying like crazy, right?

Upon reading the app store reviews, you hatch a new version that addresses some user complaints and even dip into your nest egg to pay for a high-profile download campaign. After a couple of weeks, you continue to see users flock to the game. Life is good.

Then one day your DAUs takes a dramatic nose-dive, spiraling out of control. The users that you thought were going to propel you to app store dominance have all moved on without so much as a thank you. In addition to the bitter taste of an opportunity lost, you’re left without any answers.

Why did users abandon the game all of the sudden?

Why hadn’t these users generated any revenue?

What went wrong?

 

The problem is that Vanity Metrics give you limited insight into actual user behavior. Even worse, at times they can give you a skewed view of how your app is performing. While keeping tabs on DAUs is a worthwhile exercise, does it get you any closer to understanding exactly what actions users are taking when they get into your app? And more importantly, are you any closer to understanding how these actions are affecting engagement?

Instead of focusing only on simple trends like your DAU on a given day or month, we propose taking a much more user-centric approach to understanding how best to analyze, optimize and monetize your app. Only after you know what users do after they launch your app can you make informed decisions to drive up your app’s success. When you want to get serious about the quality and efficacy of the data you are using to improve user engagement within your app, you need to move beyond Vanity Metrics.

Discovery Analytics

Discovery Analytics is a powerful set of tools aimed at understanding user behavior and providing actionable insights that lead to increased retention, engagement and monetization (three important concepts that are critical to the long-term success of your app).

Discovery Analytics give you the knowledge to truly understand your users. You’ll be able to calculate how different groups of users converted on specific goals, how changes to your app affect users’ engagement levels or whether one particular ad spend acquired higher-quality users than another. In more simple terms, you will now be able to identify which users are coming back to the app, interacting with the app more deeply, or monetizing as expected.

The specific analyses to track this user behavior include:

  • Cohort-based analysis – quantify exactly how specific groups of users continue to use and engage with your app and which groups generate revenue over time.
  • User-centric funnel analysis – understand how distinct user segments convert on specific goals.
  • ARPU – calculate the average revenue you are receiving across all, or segments, of your users.

These are just a few examples of what’s possible with Discovery Analytics. In future posts, we’ll dive deeply into these specific tools and analyses we offer to take all of this great data and turn it into actionable insight.

We promise you this: By the end of this series, you will be a master of behavioral insights and user-centric analysis.

Tune in next time as we begin to talk about cohort-based analysis and how it can help you manage user retention, engagement, and monetization.

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