Mobile marketing technology has come a long ways in a short period of time. The latest technologies allow marketers to pay only when users actually install their app, show ads to a group of new users who “look like” existing customers and even target ads to users who are likely to not just install an app, but actually purchase something afterwards. What innovation could possibly follow these acts?
One of the biggest problems with the latest mobile marketing technology (event-based bidding) is that, even if it enables marketers to target only users likely to take the action that the marketer desires, their app still has to do the work of unlocking that user’s potential. Even acquiring whale users is only half the battle; after all: just because a user may spend hundreds of dollars in your app, doesn’t mean that they will. Indeed, devoting more attention to what happens after acquiring a user is a message urged by some of the industry’s most influential thought leaders, such as Uber’s Andrew Chen and Grow.co co-founder Adam Lovallo.
The answer to “what’s next” is to expand the marketer’s focus from pre-install, to post-launch.
A/B testing an app’s user experience is already a feature available from companies like Apptimize, and companies such as GoPrimer even take this one step further, by pushing content found in an ad into that user’s first in-app experience.
In considering what the next advancement will be, it’s important to realize that:
- Given every user will interact with your app differently, it is inefficient to present the same UX to all users, especially at the first impression, which by definition influences 100% of an app’s users. For instance, some users find onboarding to be a friction point to using the app, while some find value in reading each slide or tooltip.
- There are a multitude of different types of value that users carry, above and beyond an install, registration or purchase. For example, while some users may purchase a premium subscription and disappear after two weeks, other users may be content with the freemium version, yet use it for months on end and will leave a stellar review and tell several of their friends to try the app, too.
It follows, then, that the next step for mobile marketing is to present an experience optimized for that user’s “value affinity,” from the very first experience.
The information needed to build a custom experience for a user can be gathered over time, but by then it may already be too late to form an experience effective enough to unlock a user’s true value. Some information can be inferred on install, based on a user’s location, OS, acquisition source or even what content they were looking at before installing the app, yet this is not sufficient to create a first experience effective enough to unlock that user’s true value.
One solution is to capture, store and pass on each user’s “value affinity” score, in order to identify the optimal experience for that user from the first impression. This information could be passed on through a sort of block chain that updates as the user moves from app to app, which could be used by each sequential app receiving that user. This allows each app to optimize the user’s experience from the get-go, thus more unlocking more of that user’s true potential than is possible today, by diminishing the chances of an ineffective first experience.
Dave McClure’s Startup Metrics for Pirates lays out a user lifecycle framework that is useful in illustrating this value affinity model. Consider Darius Baker: a gamer that decided to re-download our app after hearing about it on TechCrunch. Each bullet is an example of a value affinity data point that could be captured and utilized.
- Types of apps used (of the last 5 apps, Darius has downloaded three games, one productivity app and one utility app)
- Has the user ever launched your app before? (Yes: Darius is not a new user)
- Average time spent in-app on first open (3 minutes: of the last 5 apps that Darius launched, he spent three minutes in the app on first open)
- Signup completion rate (40%: of the last 5 apps that Darius opened, he failed to complete signup for all but two)
- Average days active (3 days: of the last 5 apps launched, Darius used each for an average of three days)
- Average sequential usage (3 days: of the last 5 apps launched, Darius used them on average for three days in a row)
- Average new users referred (20%: of the last 5 apps launched, Darius shared one app)
- Average review prompt accepts (80%: of the last 5 apps launched, Darius accepted the review request prompt four times)
- Average order value ($4.99: the average value of each of Darius’s purchases from the last 5 apps was $4.99)
- Total order value ($14.97: the total value of each of Darius’s purchases from the last 5 apps was $14.97)
This list includes only a few data points in each stage for illustrative purposes, but there are many, many additional data points that could be used. We could also slice the value affinity data into segments (e.g. only data from the category of app receiving the user), but we may encounter a trade-off between gaining more accurate context vs. losing significance by way of too little data.
Here are some ways in which the value affinity score could actually be used to optimize the in-app experience for Darius from the first impression:
Darius is a gamer that has used our app before, but doesn’t like to complete signups, instead preferring to try the app out himself (possibly the reason we lost him the last time). This time we can start by welcoming him back, and then give him some tangible reward for actually finishing the sign up process. Afterwards, we can continue playing to his gamer mentality by assigning him a challenging opportunity to help him get the most out of our app. Also, we know that once Darius gets into an app, he spends several minutes on first open, and while he’s consistent in use, he only uses apps for three days. This data tells us that push permissions will be vital to try to bring him back later, but we can wait to ask until day two or three, when he will probably be more engaged and more likely provide push permission. Given Darius is likely to be an advocate of our app and to spend some money, we can also try encouraging him to refer new users on day 3, and asking him to rate our app after he purchases something.
While the value proposition is deep for this type of technology, there are naturally some major blockers that could stand in the way of enjoying this sky pie.
For example, this model heavily relies on being able to track users, which is a practice currently under siege by a cultural privacy backlash, supported and expanded by Apple and Google with their IDFA-zeroing, limit ad tracking (LAT) technology. Implementing this value affinity model in an age of limited ad tracking would either require a workaround of LAT (e.g. device fingerprinting) or requesting users to disable their LAT feature (similar to websites requesting users to disable their ad blockers).
Additionally, setting up a value affinity model would require advertisers to add more tracking rigor into their apps, as well as to share their data with other apps. Many companies may not like allowing their data to potentially enrich their competitors’ marketing efforts. For companies that have large app portfolios, however, the benefits of this concept could be obtained within those companies’ walled gardens, similar to house ads.
By tackling the current industry-wide problem of user retention with a solution that is customizable from the very first experience, the value affinity model proves utility and innovation; however, before this type of model can be brought to fruition, several sizable challenges must be overcome.
Incipia is a mobile app development and marketing agency that builds and markets apps for companies, with a specialty in high-quality, stable app development and keyword-based marketing strategy, such as App Store Optimization and Apple Search Ads. For post topics, feedback or business inquiries please contact us, or send an inquiry to firstname.lastname@example.org.
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