There are a lot of factors that influence app’s performance. To get a clear picture of what’s going on with your app you have to measure your KPIs (A Key Performance Indicator). But how many are there? And what’s the overall situation: is the number of users growing, the profit they bring? Behind these numbers, there are a set of paradigms which lead modern businesses to dedicate a massive amount of resources to user acquisition – finding and onboarding new users to the product or service.
In the past 20-25 years, many argued against the growing acquisition efforts trend while chanting numbers that familiar to anyone who had already experienced the great debate of acquisition vs. retention:
- It costs five times less to retain a customer than it does to acquire a new one.
- Existing customers are more profitable than new ones, and a 5% increase in retention can translate to a 25-85% increase in profit.
- The probability of selling to an existing customer is 60-70%, but to a new one is only 5-20%.
The problem is – first two points are from an article written in 1990 – long before the app market existed. The article refers to loyalty programs of retail chains.
And so, many of those who have tried prioritizing user retention over user acquisition ended up dissatisfied: the results were far less impressive than promised. There’s a good reason for that, but more importantly, there’s now a good reason to steer the focus towards retention again.
Satisfaction Is Better than Curiosity
There’s almost always a cost attached to acquiring a new user and profit attached to retaining the user. When a user LTV is bigger than CAC or CPI, retaining a user brings more money than that user originally cost which means your ROI is positive. When it’s not, you’re in the red.
However, there’s a significant difference in the type of effort required to optimize each part of the equation. UA and ASO are marketing strategies – their purpose is to make it cheaper to get a new user in and turn him into a loyal user. Focusing on UA or ASO will do nothing for the product itself. No wonder, then, that 21% of apps are opened just once, and only 38% are launched more than 11 times in total (Nearly 1 in 4 people abandon mobile apps after only one use, 2016).
Retention optimization, on the other hand, is usually a company-wide strategy that helps improve the product or service itself. Retention, after all, is driven by satisfaction, whereas acquisition is driven by curiosity. And when the user is loyal, they’re much more reliable in terms of revenue.
Loyal users also drive down the acquisition cost through virality, word of mouth, and glowing reviews. Focusing on an acquisition, again, has close to zero impact on retention.
The Problem with User Retention
So, if focusing on retention is better for the product and has the added benefit of helping acquisition in the process, why isn’t this the default strategy for improving the CLV / CAC ratio and ROI? Simply put, because it’s much harder. No actionable tools were available up until now to help solve the retention challenge. This is why, despite much better UA optimization methods, better audience and lookalike capabilities on FB and Google, top UA channels, and a growing ASO expertise, app retention is in an all-time low. AppsFlyer data is pretty clear about that:
To meaningfully improve user retention, companies need to truly understand what their users want and need. Gaining this knowledge is a herculean task and implementing it to improve the product is even harder. It’s why retention efforts often misfire – they’re based on assumptions and guesses, not knowledge. Even analytics suites are not always helpful, as copious amounts of data do not always lead to actionable insights.
From Knowledge to LTV
Lately, some important strides have been made on the way to making sense of usage data. The rise of AI technology and machine learning opened the door for personal behavioral analytics services – designed to provide businesses with the precise knowledge needed to improve and personalize products.
In the app industry, knowing your customer deeply and tending to their needs in their time seems to be most effective for maintaining long-term, quality relationships. This can be achieved by gaining real-world contextual user data – understanding who your users are and what they do in the real world, not their phones – and applying it to revolutionize engagement strategies.
A task of this magnitude requires a great deal of data science resources and time. Apps that do utilize this technology, usually with the help of a third party vendor such as Neura, save both money and resources by shifting focus from large scale UA to existing users’ interactions, thus optimizing user engagement and increasing customer satisfaction and LTV. With real-world context and truly personalized engagement, the UA / ASO vs. Retention equation has an entirely different outcome.