In-App Message Frequency and Channel Coordination
A fintech app uses in-app messages (IAMs) to promote a new budgeting feature. They have three IAMs planned for the same week: a tooltip on the home screen, a modal on the transactions page, and a full-screen takeover on app open. The product team wants maximum exposure. However, user research shows that 40% of users find frequent IAMs annoying, and the app has a 30% weekly active user rate. The marketing team also sends two emails per week and one push per week. The IAMs are currently set to show every time a user visits the relevant screen, with no frequency capping. The goal is to drive feature adoption without increasing uninstalls or email unsubscribes.
1. What changes should be made to the IAM strategy to balance exposure and user experience?
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First, implement frequency capping: limit each user to seeing at most one IAM per day and three per week across all IAMs. Use Braze's frequency capping rules at the campaign level. Second, prioritize IAMs by value: the full-screen takeover is most intrusive, so show it only to users who haven't engaged with the feature after other attempts. Use a Canvas or sequence: start with the tooltip (least intrusive), then modal, then full-screen only if the user hasn't converted. Third, coordinate with other channels: since users receive emails and pushes, ensure IAMs don't repeat the same message on the same day. Use Braze's Intelligent Channel or a cross-channel frequency cap. Fourth, set IAMs to show only once per user per campaign (not every visit). Finally, segment: show IAMs only to users who have used the app at least twice and haven't opted out of marketing. This reduces annoyance while still reaching engaged users.
2. How would you measure the impact of these changes on feature adoption and uninstalls?
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Set up an A/B test: 50% of users get the new capped/sequenced IAM strategy, 50% get the current uncapped strategy. Measure feature adoption (e.g., first use of budgeting feature) within 7 days, uninstall rate within 14 days, and email unsubscribe rate within 30 days. Also track IAM impression frequency per user to ensure caps are working. Use Braze's reporting to compare groups. Since uninstalls are rare, ensure sample size is large enough (at least 10,000 per group). If adoption is similar but uninstalls decrease, the new strategy is better. If adoption drops significantly, adjust the sequence to show the full-screen earlier for high-intent users. The key is to have a control and to monitor both intended and unintended consequences.