Use Case: How Ad blocker - Adblock Optimize App Marketing Campaigns With Funnel Analysis

Adblock grow MRR over 30% per month. See how they did it!

Ad blocker - Adblock is an ad-blocking iOS app that provides users with a better browsing experience by preventing them from being tracked and annoyed by dangerous ads. With Ad blocker - Adblock, users can also adjust filter settings to block certain ads. The app uses a mixed monetization approach with both free and in-app subscription options. Like many iOS apps, Ad blocker - Adblock grows its user base through organic and paid user acquisition channels, like Apple Search Ads and third-party ads agents.adblock-app-store-screenshotThis article is going to share the success story of Ad blocker - Adblock: how they optimize app marketing campaigns with Appflow.ai funnel analysis and grow their MRR at an amazing rate of over 30% per month in the past 3 consecutive months.

monthly-recurring-money-growth-graph

What Ad blocker - Adblock Needs from Appflow.ai

When they choose Appflow.ai as their in-app purchase backend infrastructure, Ad blocker - Adblock wants more than subscription revenue monitoring, they need Appflow.ai to answer:

  • Post-install measuring: how many installs, trials, subscriptions, and even renewals off of each channel, each campaign;
  • What is the trial activation rate for installs in specific countries?
  • Which acquisition campaign is giving them the best trial rate?
  • Which keywords from their Apple Search Ads campaigns are giving them the highest trial conversion rate?
  • Which acquisition channel is giving them the best return in terms of investment ROI;
  • How are the product SKUs selling in all marketing channels/countries;
  • How is the conversion funnel of paid users from different channels;
  • Which stage along the conversion funnel is causing the highest churn rate? from installs to trials? Or from trials to subscriptions?

How Ad blocker - Adblock Optimize App Campaign Performance With Appflow.ai Funnel Analysis

Cost-effective user acquisition is always the cornerstone of an app’s sustainable development, as paid traffic getting increasingly expensive. Thus, many app companies need to keep a close eye on their advertising user acquisition channels, measure, evaluate and adjust app campaigns in a prompt and data-based manner, and so does Ad blocker - Adblock.

Here are some specific ways Ad blocker - Adblock uses Appflow.ai to improve their app marketing campaigns:

Synthesizing all app campaign channel data in one place
Ad blocker - Adblock uses a combination of self-attributed networks like Apple Search Ads and third-party attribution platforms like Adjust to track user installs. With Appflow.ai low-code integration solution, they are able to aggregate all of these data in one place, and get a unified overview of their campaign performance across all user acquisition channels.

Tracking and measuring post-install campaign performance in real-time
Appflow.ai allows Ad blocker - Adblock to track the installs, trials, subscriptions, and renewals of each campaign, and even drill down to compare the subscription performance of each creative. This helps them identify the most effective keywords and campaign assets and make the most out of their creatives.

Also, by tracking the whole conversion funnel of each app campaign, Ad blocker - Adblock is able to understand if the app campaign meets its investment goals and where they need to adapt right away, without wasting the marketing budget. Furthermore, they can track custom in-app events in the funnel to identify the critical point along the whole user lifecycle.

Focusing on conversion before scaling up
Growing your app with increasing advertising costs is no longer the right way. Ad blocker - Adblock knows that customer acquisition cost is constantly increasing, so they focus on retention and conversion rates before boosting their marketing budget. By tracking their conversion funnel, they are able to understand where they need to adapt their campaigns and improve their in-app user experience to promote conversion.

Ad blocker - Adblock integrated ASA attribution and Adjust to Appflow.ai in September, and then they started paid app campaigns to grow their user base. They managed to improve the conversion rate from install to trials by 86% in one month by continuing to measure subscription conversions from each ad campaigns at Appflow.ai Funnel. So, when they scale up their marketing budget, their conversion gets better.

conversion-funnel-example-created-by-appflow.aiconversion-funnel-example-created-by-appflow.ai-with-suggestion

Dynamically adjust campaign strategy and marketing budget
As market and campaign strategies from your competitors are changing all the time, it is crucial to establish a dynamic app campaign strategy, making better marketing budget allocation.

The more subscription data you have for your app users, the more effectively you could identify the valuable users; The more flexible your paid app campaign strategy, the better you improve your return on advertising spent.


Appflow.ai funnel analysis is available in free plan!

You paid us until you make your conversion right to scale! We believe that we should get paid by our clients only after we help them grow. Ideally, 100 conversions will provide app marketers with enough data to evaluate the effectiveness of their performance marketing campaigns. So, Appflow.ai free plan (up to $10K/MTR) is enough for app companies to start optimizing their paid marketing campaigns.

Besides, aggregating your attribution data in Appflow.ai is easy, only a few lines of code are required to get the most accurate overview of your paid campaigns.

Low-code Integrationswift-code-for-integrating-appflow.ai-with-asaCheck ASA documentation

Unified UA Channels Performance Overview, Drill Down to Creativesuser-acquisition-funnel-by-channelsCustomizable Conversion Funnel for UA Channelscustomizable-conversion-funnel-by-appflow.ai

Start Growing with Free Funnel Analysis        

Use Case: How Ad blocker - Adblock Optimize App Marketing Campaigns With Funnel Analysis

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