We have a controversial statement to make: MAUs are out. While measuring scale in terms of monthly active users has been the prevailing method of recent years, location data has advanced such that this metric is no longer good enough. As with bidstream data, which was once so popular but was ultimately proven inadequate to be the industry standard in terms of accuracy and privacy, MAUs are now insufficient in producing high-quality location data.
So, MAUs are out — and DAUs are in. Our reasoning is pretty simple: Offline analytics and attribution need to be based on daily measurement. This means you should have the ability to see the behavior of your opted-in users every day, not only a few days a month. After all, if you don’t see your users every day, then what exactly are you measuring?
DAUs: The Key to Measuring the Complete Consumer Journey
Let’s consider the consumer journey. Mapping daily consumer patterns with a future-proof privacy approach is the only safe way to measure the consumer journey effectively. If you’re not able to see consumers every day, then you do not necessarily know if they indeed visited a store after being exposed to an ad. This means you might not be accurately measuring store visits. Also, you may be missing out on a huge part of their buying journey — such as brand affinity, brand loyalty, churn, purchase intent, and more. By understanding the complete journey, you can activate against these insights to target more effective audiences that are more likely to convert, thus driving better-performing ads and an increase in ROAS.
Whether you’re a brand, agency, or publisher, it’s imperative to partner with a company that leverages first-party data, collected on a daily basis — this is the only way to gain meaningful data and insights. Having a panel of DAUs is the best practice for getting data regularly, because it reveals where users shopped before as well as after seeing an ad. This paints a complete picture of the consumer journey — not just a part of it.
New iOS Updates Require a New KPI
What’s more, the iOS updates that went into effect in September give users more control over their data sharing frequency, rendering MAUs no longer a good measure of panel scale and quality. Since MAUs could include users that are only seen once, this KPI is no longer effective.
As follows, it’s important to ask existing and potential partners what their DAU count is. Collecting sparse data points (from MAUs) is no longer enough; location data companies need to be collecting data on a daily basis. Frequent data is more telling than sporadic data, and it leads to better insights.
Looking Ahead: Future-Proof Privacy & Data Quality
We recognize that changing the industry metric for scale is a tall order, but it’s an important one. And our industry is no stranger to change. Let’s consider, for example, that bidstream comparison we made earlier.
Collecting location data via bidstream used to be the norm. When we at Cuebiq introduced our first-party data collection methodology via app-direct relationships, it allowed us to be the first to accurately measure dwell time and collect high-quality location data at the same time — challenging the status quo. Now, it is widely accepted that bidstream data is deeply problematic for both brand safety and privacy compliance. While bidstream data sources enable massive scale, they sacrifice quality and trust in doing so. Bidstream data collection does not allow the end user to provide consent, have control over how the data is used, or even know who it is sold to. In all of these respects, bidstream data lacks transparency and is a major threat to brand safety.
Just as bidstream data went from au courant to passé, MAUs are failing to provide the information we need them to in order to keep up with the times. As follows, we at Cuebiq are now only measuring scale in terms of our DAUs. We embrace collecting location data on a daily basis as the future of measurement, as it provides data that is truly meaningful.
Check out our blog “6 Truths About Location Data” for more insights about location data quality.
