You’ve relied on traditional metrics like retention rate to keep your users engaged and subscribed to your mobile app. But have you considered what machine learning and AI can bring? Meet retention propensity, a forward-looking metric that could redefine how you approach user engagement and subscription renewals.
This article dives into these two metrics, weighs their pros and cons, and explains why a shift toward predictive metrics like retention propensity is beneficial and soon necessary.
What Retention Rate and Retention Propensity Mean for Your Mobile App
Retention rate gives you a snapshot of how many users have stuck around over a specific time frame. It’s a retrospective metric that’s easy to understand and crucial for evaluating the health of your (subscription) app.
Contrastingly, retention propensity uses machine learning to predict how likely a user is to stay engaged with your app or renew their subscription. It allows you to be proactive rather than reactive in your retention efforts.
With this knowledge, you can improve your personalization. Suppose you know a user has a high retention propensity. In that case, you can capitalize on their likely continued engagement by offering them premium features, upsells, or incentives tailored to their usage patterns and preferences.
For users with a low retention propensity, deploy re-engagement strategies like personalized content recommendations or limited-time promotional offers to entice them back into active usage and potentially convert them to more committed users.
Another example is resource allocation. With retention propensity metrics, you can prioritize your marketing resources more efficiently. Users identified as “high-risk” for churn might warrant a more intensive outreach or re-engagement campaign. Users likely to stick around could be targeted for upsells or cross-promotions. This way, you’re not wasting resources on users likely to remain engaged without extra incentives, allowing you to focus on those who genuinely need more attention.
Retention Rate in Your Mobile App
- Simplicity: Easy to measure, even with standard mobile analytics tools.
- Historical Insight is a factual account of how well you’ve engaged users over a certain timeframe.
- Immediate Feedback lets you quickly gauge the effectiveness of new app features or campaigns.
- Reactive Nature: It only tells you what has already happened, not what might happen next.
- Lack of Detail: It doesn’t indicate why users stay or go, just that they do.
Retention Propensity in Your Mobile App
- Predictive Power: Forecasts user behavior, enabling you to act before a user disengages.
- Personalization: Helps tailor the user experience, increasing the odds of subscription renewals.
- Resource Efficiency: Directs your retention efforts where they’re most needed.
- Complexity: Requires a good grasp of machine learning and access to high-quality data.
- Accuracy Risks: The predictions may only sometimes hit the mark.
- Higher Costs: It demands more resources to implement effectively.
Why You Might Still Be Using Retention Rate
You could be sticking with the retention rate because it’s straightforward and well-supported by your current analytics tools. Or you’re hesitant to navigate the bureaucratic challenges of adopting new metrics. Some might also need to be made aware of the capabilities and benefits of predictive analytics. We like to stick to what we know well for many reasons.
Why You Should Consider AI-Driven Metrics
With AI and machine learning evolving rapidly, the advantages of predictive metrics are becoming harder to ignore:
- Stay Ahead: AI gives you foresight, letting you act before users churn. You can reduce the churn rate by acting before users churn and not focusing on reactivating the users you’ve already lost.
- Resource Optimization: You can focus your resources more effectively.
- Outperform Competitors: Being an early adopter of AI-driven metrics can give you a significant edge. Get ahead of your competition.
- Boost User Experience: AI can help you personalize user interactions, making them more likely to stick around.
While the retention rate has its place, the advent of machine learning is making retention propensity an increasingly crucial part of your analytics arsenal. As the competition in the mobile app space intensifies, transitioning from traditional to predictive metrics isn’t just advisable; it’s a strategic imperative. For those who aim to leverage the latest tech for growth, embracing retention propensity could be a game-changing decision for your mobile app’s success.
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