The ARM funnel shows a typical customer lifecycle, which is comprised of three stages: acquisition, retention and monetization.

Mobile app developers and marketers can gain a huge advantage by looking at the way social app developers scientifically build and optimize their games around customer data on social platforms. They use that data to learn about what is and isn’t working in their applications, and iterate quickly for increasingly stronger returns. From Acquisition to Retention to Monetization–the three stages in the ARM funnel–successful developers have created entire organizations around data-driven design at each level and across all facets of their businesses.

At Kontagent, we’ve witnessed firsthand the ARM funnel’s crucial role in the success of a large majority of studios that publish games on Facebook–and now on iOS and Android as well. Insights garnered from this model help to focus resources and marketing investment. And, having a perpetual flow of data and a supporting data science infrastructure (with the expertise to act on it), has enabled game studies to acquire users more profitably. Teams of data scientists and developers at Zynga, Popcap, Crowdstar and Gaia, for example, have measured, iterated, tested and optimized relentlessly to acquire more users at better customer acquisition cost and with higher yield ratios than most. It’s no coincidence these are the same guys succeeding on mobile platforms as well.

I’ve broken down each branch of the ARM funnel at a very basic level below.

Acquisition: Figure out what audience performs best in your game and how to acquire members of that audience in the most effective, lowest-cost manner possible; from ads, to cross-promotions with other games, to install exchange networks, to viral channels. KPIs = Cohort Customer Acquisition Cost (Cohort CAC); Cohort ARPU and Retention by Channel; Cohort ARPU and Retention by Campaign; Acquisition Conversion Funnel

Retention: Keep that audience engaged for as long as possible. Find fall-off points where users leave. Learn how to encourage sharing with friends. KPIs = Avg. Session Length; Day 1 Retention, Day 7 Retention, Day 30 Retention; Custom Event Funnels; User Lifetime Event Distribution; k-factor (virality)

Monetization: Get these users to pay and to keep paying over time, optimizing their lifetime values (LTV) to the greatest degree possible. KPIs = % Paying Users; Avg. Revenue per User (ARPU); Avg. Revenue per Paying User (ARPPU); Purchase Conversion Event Funnel

As the free-to-play model continues to grow and dominate in mobile, just as it does in social, it’s no surprise that the ARM funnel is the right framework for measuring customer economics and per-user profitability in mobile as well. Just like social app developers, mobile app developers must understand and optimize acquisition, retention and monetization if they are going to succeed.

Still, mobile developers have a long way to go. As more social companies (including Zynga) move aggressively to mobile, getting smart about data is increasingly important. Kontagent believes those mobile developers who recognize the importance of data-driven app development throughout the ARM funnel will be well-positioned to lead the space.

I’ve outlined the biggest challenges and opportunities facing mobile game developers, relative to measuring and optimizing around the tried-and-true ARM funnel:

Discovery & Acquisition

Biggest Challenges

• The biggest hurdle is getting discovered and competing against companies with deep pockets who can muscle their way up the App Store rankings.
• Mobile ad networks are massively fragmented and difficult to target high-yielding cohorts of users (either through behavioral or demographic segmentation).
• Understanding ROI (on iOS in particular, tracking from ad/source to install to monetization used to be impossible).

Biggest Opportunities

• Reach large audiences at a relatively inexpensive cost-basis by advertising on cross-promotional networks and partnering with other developers with high daily active users (DAU) counts.
• Segment users by acquisition channel, and optimize around those producing the highest per-user ROI. Analytics make it possible to see differences between each source: organic, viral, cross-promotion and advertising.
• Build a great, highly engaging game! The better the experience, the more players will tell their friends. Word-of-mouth is a huge organic driver of installs for mobile devs right now, so focus on gameplay, and reap the benefits. Use analytics to tell you where users are losing interest in your app.

Engagement & Retention

Biggest Challenges

• Although testing and iterating is how social devs have found success on Facebook, rapid iteration is not so easy or fluid on mobile platforms like iOS. It can take from three days to two weeks to receive approval for each new version from the App Store, making it difficult to fine-tune to optimize on the fly.
• Many users don’t update their apps regularly, resulting in outdated, or “dirty,” data from the combination of “old” and “new” app version users. This makes it challenging to iterate and improve your app based on user data without being misled, unless your data tools easily filter users by app version.

Biggest Opportunities

• Design the app to use data-driven back-end systems to control user experience in-app when a connection is available on the device. This way, developers can dynamically tweak play balance, economy balance, etc., and then perform split-testing to further optimize customer economics.
• Use an analytics solution that allows filtering data by app version, location and device type (for Android). This is very important in addressing the dirty data issue, and in determining which platforms and devices in which to commit time, effort and resources.
• The free-to-use model proliferating on the App Store provides customers a risk-free opportunity to experience an application, which in turn provides app developers with free insights into their virtual economies and player incentives to gain the most effective conversion rates.

Monetization

Biggest Challenges

• Android users are still experiencing more friction in payments, resulting in lower average revenue per user (ARPU) and lower monetization rates in general.
• To gauge the quality of users, developers need insight into the in-app behavior of their paying users, which marketing channel they came in from, and ultimately how profitable their customers are by each marketing channel on an ROI basis.
• Developers need tools to monetize players that may not make in-app purchases.

Biggest Opportunities

• Optimize game mechanics that encourage purchasing, while balancing engagement. Use analytics to see how the two play out using custom event tracking and funnels.
• Individually track each and every revenue channel, from in-app purchase to advertising yields, understanding the best price points, for-sale item mix and conversion funnels–ultimately using data to understand and optimize the economy underlying the game.
• Employ messaging tactics to remain connected with users on new features/offers, e.g., push notifications and email. Oftentimes, re-engaged users will monetize at a higher rate if the user experience is tweaked and re-targeted to them.

To conclude, remember this: While social and mobile platforms have their differences, user behavior in-app is very much the same. To that end, using the ARM funnel to measure and optimize your mobile application is just as applicable as it is for social app developers. At Kontagent, we are strongly encouraging mobile app developers to not start from square one. Indeed, take advantage of the important lessons we’ve all learned on Facebook and other social platforms to improve the acquisition, retention and monetization of your game, and ultimately your bottom-line.

Click on the image to register.

If you’d like to learn more on how using the lessons learned in social can work for you in mobile, sign up for next week’s webinar, From Social to Mobile: A Playbook for Building Successful Games. We have an expert panel lined up, and will discuss the challenges of developing games and applications in mobile that didn’t exist in social, as well as provide tips and tricks for monetizing that you can use to improve the profitability of your mobile apps.

Republished from:
Social Times: How Lessons Learned in Social Will Give You a Head-Start in Mobile
Gamasutra: Applying Lessons Learned on Facebook to Mobile App Development

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About the author: Josh Williams is president and chief science officer at Kontagent, where he oversees strategic revenue projects, data science and R&D. A serial entrepreneur, Josh has led two successful start-ups to large exits (one in big data, one in gaming). Josh is an investor and loves collaborating with other entrepreneurs.

Be the Brad Pitt Billy Beane of the Social and Mobile Web

What do Kontagent data scientist Martin Colaco and Billy Beane have in common?

On the surface, probably not much. But, when it comes to analytics, Beane changed the game for baseball. Colaco could be doing the same for business.

Moneyball is based on Michael Lewis’s book, Moneyball: The Art of Winning an Unfair Game. Faced with a relatively meager budget, Oakland Athletics manager Billy Beane (played by Brad Pitt) uses a groundbreaking, sabermetric approach—the specialized analysis of baseball through objective, empirical evidence that measures in-game activity—to recruit a competitive baseball team. (Read more on how sabermetrics works.)

Beane goes against convention, taking advantage of more empirical gauges of player performance to build a team that could successfully compete in Major League Baseball. He analyzes stats that are not generally considered top priority in the traditional scouting process. While most managers pored over stats like running speed, stolen bases and batting averages, the sabermetric approach, developed by baseball statistician Bill James, predicts other factors are better indicators of a player’s offensive success, e.g., on-base and slugging percentages.

Beane basically stared the “old guard” of baseball recruiting in the face and said, “Suck it.” And it worked.

Using sabermetrics, the Oakland A’s were able to put together a competitive team on a $41 million salary; by comparison, the New York Yankees spent more than $125 million in payroll in the same 2002 season. It paid off: the Athletics led a 20-game winning streak, and Beane shifted the paradigm of baseball scouting forever.

When it comes to Web business analytics, it’s not a bad idea to take a page out of the Moneyball book. Sometimes you have to look at data through a different lens in order to better make predictions and optimize opportunities. In baseball, Beane approached player stats differently and got different results. In business, data scientists like Colaco say there’s a similar shift happening in the world of Web 3.0.

Moneyball lesson #1: Challenge the status quo.
There’s a scene in Moneyball where Beane and his scouts are arguing over which players to recruit. His scouts want the highly sought-after players. Beane can’t afford them. Frustrated, he says, “You guys are talking the same non-sense. We’ve got to think differently.” Thinking differently, it turned out, made all the difference in the world.

It’s a good lesson to remember when it comes to business. With the explosion of social and mobile applications, we have to move away from the “old guard” of analytics and begin measuring KPIs around virality, engagement, retention, and monetization. It’s no longer about page views; it’s about users. What are these users doing within your applications? Every click of every action of every second. How can we use that information to win? We’ve got to think differently.

Moneyball lesson #2: You can compete with the New York Yankees.
Companies like Zynga, Playdom and CrowdStar aren’t wildly successful just because they’re in the social gaming space. No, these companies are behemoths when it comes to data. And, they’ve figured out how to collect, store, analyze and optimize all this data to succeed.

But what about the little guys who are trying to compete in this space? They may not have the infrastructure to support such massive amounts of data (or the hundreds of thousands of dollars needed to get that infrastructure in place). How do they (think: Oakland A’s) go up against Zynga (think: New York Yankees with the deep pockets)?! Luckily, analytics platforms like kSuite by Kontagent now provide the infrastructure needed to access data for organizations of all sizes.

Okay, great—you have access, but now what?

Moneyball lesson #3: Look at the data that really matters; it may not be obvious at first.
In Moneyball, Beane relied on Yale economics graduate Peter Brand (Jonah Hill) to help him make sense of all of the stats. It was Brand who took the core data and interpreted it so that Beane could make better scouting decisions.“Your goal shouldn’t be to buy players; your goal should be to buy wins,” he says.

The lesson here? Good data is useless if it’s mismanaged.

The right analytics solution needs to give you not only access to big-data analytics, but also an easy-to-understand dashboard and support from data scientists (your own team of Peter Brands!).

Data scientists can help you understand what the patterns of data are telling you to find the users with the greatest potential for monetization and lifetime value. What might appear obvious could be deceiving when you slice the information differently. And it’s these users that you want to understand and develop. It’s these users you need to understand. In. Order. To. Win.

Call it leveling the playing field.

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About the author: Catherine Mylinh is a member of Kontagent’s storytelling team, where she is head of content marketing. She credits her journalism and computer science roots—she was once a programmer!—for her love of learning and writing about all things high tech. You can contact Catherine at @cat_mylinh.