AMASE Analogy: This Is What a Profitable App Looks like
Monday, January 19, 2015
Mobile-app development, marketing, and monetization are all extremely difficult to do well – it’s why very few mobile apps end up being successful.
In this article from The Cline Group (I’m the President and CEO), my goal is to provide end-to-end insights into the mobile-user lifecycle. Let’s start by defining a successful mobile app as one that has achieved sustained profitability. To reach this stage, the founders of the app have taken an idea, made thousands of decisions, performed countless of actions, and have successfully reached a product and market fit.
In an often-cited pirate acronym, Dave McClure of 500 Startups has grouped these decisions and actions into five buckets, “AARRR.” We give it a slightly Greek twist, “AAERRR” – acquisition, activation, engagement, retention, referral, and revenue.
Engagement is an important addition because engaged users are likely to stick around (retention), tell their friends (referral), and make purchases (revenue). In general, apps generate revenue in five ways, so the last “bucket” can be broken down further into another acronym, “AMASE” – app/in-app purchases, marketplace/e-commerce, advertising, SaaS, and enterprise. AMASE is an important acronym because key metrics and performance indicators must be customized for each business model and sector.
In the beginning, the acquisition of users is primarily a function of marketing, publicity, and/or advertising (based on one’s desired strategy). A marketing team creates and optimizes campaigns across different channels while accurately attributing and leveraging analytics to identify the most-profitable channels in the process, using combinations of paid, earned, and owned media to generate downloads.
This process costs significant time and money, and the customer acquisition cost (CAC) must stabilize at a significantly-lower level than customer lifetime value (LTV) for an app to become a sustainable business.
To achieve optimal profits, first use a data-driven approach to identify and acquire users from among target audiences who have a higher probability of engagement with your app. For example, roughly 75% of mobile app ads promote other apps, indicating that marketers are targeting audiences with well-understood behaviors within their own app portfolios.
Next, identify which channels have the highest user ROI (which can be measured in revenue and/or engagement) and NOT the lowest CAC. If $10,000 in channel A generates $20,000 in revenue and $10,000 in channel B generates $10,000 in revenue, stick with channel A. If $10,000 in channel C leads to 100 users that spend 30 minutes per day in your app and $10,000 in channel D leads to 1,000 users that spend 1 minute per day in your app, stick with channel C.
Acquired users become “activated” users once they have performed at least one predefined action (often called a “conversion”). According to Localytics, 26% of downloaded apps are used only once, a result that suggests a poor user interface (UI) or user experience (UX) within those apps.
High levels of activation (greater than 60% of acquired users) are the results of simple, intuitive, and aesthetic UXs and UIs. Activation also requires an understanding of the user paths and funnels that lead to conversions. Persistent A/B testing alongside the user paths and conversion funnels will help your design team to improve the UX and UI until activation metrics are hit.
Given the high costs of acquiring new downloads, apps must take advantage of advertising networks to retarget inactive users. Once your activation rates have steadily improved with design and feature updates, use retargeting as an inexpensive way to get users to reactivate and engage.
Keep in mind that these metrics are app-dependent. Gaming-app “activation” (or “conversion”) can vary from account creation to level completion, messaging-app activation can vary from entering a phone number to creating a message, and fitness-app activation can vary from connecting a wearable device to entering nutritional data.
With dozens of app categories across five major business models, the CEO, CTO, and product development team must debate and agree on which activation metrics are relevant and appropriate to achieve.
Activated users become “engaged” users when they perform a high number of desired actions (such as spending more time in an app). Engagement decreases or flat-lines when an app is poorly designed and/or engineered. Conversely, highly-engaged users open apps multiple times per day, experiment with less-visible features, and/or create shareable content within the app when the design and engineering is good.
Again, the yardstick varies based on the type of application. Uber’s (marketplace) engagement metrics, for example, track the frequency of transactions while Snapchat (advertising) tracks the frequency and duration of daily sessions.
Cohort analysis, which segments users by timeframe, can explain how changes in an app affect old and new users and inform future product updates to increase engagement, CLV, and Average Revenue Per User (ARPU). To many analysts and investors alike, engagement is a proxy for valuing a mobile app before revenue metrics are available.
Retaining at least 30% - 40% of an app’s users after six weeks requires an application that provides true utility or novelty. As long as Uber’s drivers provide high-quality transportation, the app provides true utility. As long as new levels are built, Candy Crush provides ongoing novelty.
While an app is in the activation and engagement stages, analytics data can frequently reveal UI/UX and software issues that cause churn (the percentage of users who leave within a certain period of time). A two-hour-per-day user suddenly deletes Candy Crush after a month of gameplay, an Uber user has not booked a ride in December after ten rides in November, and an avid Snapchatter has not sent a snap in weeks!
What happened? This stage of the app lifecycle requires open communication across the product, engineering, and marketing teams, actionable customer feedback, relentless cohort analysis, and sharp execution.
The most successful apps acquire new users through both digital and word-of-mouth referrals. To reduce the time it takes for users to refer others and achieve so-called “virality,” the product team must simultaneously run A/B tests, observe customer behavior within and across each cohort, listen to customer feedback, implement changes, and repeat the process so that the UX is optimized and referral metrics are steadily increasing.
Apps with high customer LTV may incentivize referrals with cash, credit, or other rewards while apps with low customer LTV rely on amazing user experience to fuel referrals.
Telling your friend to download Snapchat or Tinder implies you have had a great experience, but from the company’s point of view, it is nearly impossible to discern in analytics that the new users came from a friend’s offline recommendation. Digital referrals are easy to track, but word-of-mouth attribution requires customer surveys.
A “How Did You Hear About Us?” survey is essential to understand virality, but be cognizant of survey biases and tread carefully because such inquiries can be intrusive and negatively affect user experience.
Revenue metrics also heavily depend on an app’s business model. Premium, marketplace, and SaaS applications can generate revenue from the day of release, but an advertising platform or enterprise software app may not generate revenue for months or years. Facebook, Twitter, Tinder, and Snapchat – unlike Uber, Candy Crush, Groupon, and Spotify – did not rely on initial revenue to achieve product and market fit.
Still, tracking and segmenting revenue helps companies to identify “power” users - those who buy ten times as many virtual goods, products, or premium subscriptions. In most companies, the Pareto principle applies - 20% of users account for 80% of revenue.
Identifying, tracking, and delighting this cohort is strategic, profitable, and an enormous competitive advantage.
As in the acquisition phase, the identification and targeting of these types of valuable users before they download an app gives companies an enormous head start. Once the characteristics that are common among the most-valuable users have been identified and confirmed by their purchasing behaviors, analytics and testing can be used to identify similar behaviors among additional potential audiences.
Additionally, analytics can pinpoint the events when users started but failed to complete the purchasing process. By taking further advantage of retargeting tools, companies can increase customer CLV and ARPU by focusing on those users.
The tools listed below provide mobile app companies with robust SDKs (software development tools) and APIs (integrate and share data with other apps) to track, measure, analyze, evaluate, and make decisions based on the events taking place within their applications. They vary in scope, and many of them are freemium products (free up to a certain amount of usage):
- Amazon Mobile Analytics: Collect, visualize, and understand app usage data at scale. 100 million events per month for free.
- Amazon Simple Notification Service: Fast, flexible, and fully-managed push-messaging service. 1 dollar per 1 million messages.
- Google Analytics: Free analytics with Google Play and AdMob integration.
- AppAnnie: Track your app-publisher data across iTunes, Google Play, and Amazon. One dashboard for all your app revenue, downloads, ratings, reviews, and rankings. Freemium, $59/month for pro.
- Appfigures: Reporting platform for mobile-app developers that brings together all of your app store sales, ad data, worldwide reviews, and hourly rank updates into one intuitive and informative reporting solution. Up to 5 apps free.
- HeapAnalytics: Captures user actions including clicks, taps, swipes, form submissions, and page views. 5,000 monthly visits free, $59/month for startups (25k visits).
- Apptrace: Free data about any app. Age, country, overall ratings, global rank, daily rank, and user sentiment.
- AskingPoint: SDK for monetization, cross-promotion, in-app messaging, push messaging, and analytics. Free up to 3 million notifications per month.
- Flurry: Scrapes 150 billion app sessions per month, generating robust insights and analytics for publishers, developers, brands, and marketers. Free at any scale, one lightweight SDK.
- TestFlight: IOS analytics for beta testing.
- Apsalar: Measures marketing ROI and mobile re-engagement attribution to help to optimize mobile spend. Free for marketers and developers to measure and improve engagement. $999 for business.
- Count.ly: Open-sourced analytics that tracks users, sessions, custom events, countries, carriers, platforms, and versions in real-time. Free trial, $125 for 500k events and sessions a month.
- KeenIO: Fully-customizable analytics service for your entire dataset. Essentially DIY analytics with visualization. Free for a developer up to 50k events/month. $20 for 100k, $125 for 1 million events.
- Capptain: Response marketing with a combination of analytics and push in one platform.
- TapStream: Free attribution, Onboarding Links, and Word of Mouth through text and email.
- Woopra: Real-time tracking, customer profiles, triggers, scheduled tasks, and funnel reports. Free up to 30k actions. Greater than 400,000- actions, $80/month. Greater than 1.25m actions, $200/month.
Achieving a product and market fit requires constant observation, analysis, evaluation, decision-making, and action. The tools discussed in this post can help you progress through the early-stages of your mobile-application’s marketing and monetization quickly and accurately, giving you actionable feedback while helping you to gain enough traction to reach sustained profitability.
*Max Marine contributed to this article. Max is Director of Business Development at The Cline Group, and a Junior Associate at Cline Ventures.
This content is made possible by a guest author, or sponsor; it is not written by and does not necessarily reflect the views of App Developer Magazine's editorial staff.
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