Uncover actionable factors behind DAU

Make Daily Active Users more than a vanity metric with faster analysis of every available factor.

Analytics Challenge

Business Challenge

KPI Impact

Analytics Challenge

Moving beyond simple Daily Active Users (DAU) reports to concrete, actionable growth insights is an ongoing, frustrating, and time-consuming challenge. But done right, it’s the path to understanding what truly engages and excites your customers.

Tracking DAU starts by defining the specific actions that indicate real activity. It’s frequently more than just a login or pageview, which can be passive. For example, active could be:

  • Publication: Visit multiple pages, read a story, share a link
  • Mobile Gaming App: Create an account, play a game, complete an in-app purchase
  • SaaS app: Add a user, run an analysis, view a report, set up a connection.

The challenge in analyzing DAU is in identifying the activities that lead to meaningful engagement. With dozens of possible factors, if you’re not analyzing why DAU is changing you’ll easily overlook meaningful factors and opportunities to retain and engage users.

DAU

Daily Active Users measures how many unique visitors or users interact with your app or site in a given day.

DAU = Count of active users in a period 

Analytics Challenge

Moving beyond simple Daily Active Users (DAU) reports to concrete, actionable growth insights is an ongoing, frustrating, and time-consuming challenge. But done right, it’s the path to understanding what truly engages and excites your customers.

Tracking DAU starts by defining the specific actions that indicate real activity. It’s frequently more than just a login or pageview, which can be passive. For example, active could be:

  • Publication: Visit multiple pages, read a story, share a link
  • Mobile Gaming App: Create an account, play a game, complete an in-app purchase
  • SaaS app: Add a user, run an analysis, view a report, set up a connection.

The challenge in analyzing DAU is in identifying the activities that lead to meaningful engagement. With dozens of possible factors, if you’re not analyzing why DAU is changing you’ll easily overlook meaningful factors and opportunities to retain and engage users.

Business Challenge

User activity is the first indicator a customer is realizing value from your product — whether that’s a website, app, service, or game.

For a business, measuring this — whether it’s in terms of DAU, Weekly Active Users (WAU), or Monthly Active Users (MAU) — this metric answers the question, “Are customers actually using my product?” Keeping a pulse on this metric enables your business to understand just how “sticky” a product is, identify any trends in churn or growth, and intervene immediately.

However, most businesses calculate DAU as the first engagement metric but see it as unactionable. It’s a number analysts spend time assessing but then it is only used for a board report or shareholder meeting. Because DAU is calculated in aggregate, it’s difficult to understand and act on the root cause of changes.

KPI Impact

A critical part of driving user growth is having consistent DAU growth.

Understanding this will give you a look into the life of a user and also your MRR

By quickly diagnosing the factors behind the growth or decline of DAU, you’ll be able to identify and replicate ideal active users. For most businesses, small changes in DAU can have a compounding effect as customers join (and leave).

Key factors for analyzing changes in DAU

To understand the key drivers behind a changing DAU and to diagnose how it relates to other engagement metrics, start by tracking as many factors as possible. Each row should represent a unique user, with one record per time period.

For diagnosing DAU, the wider your data, the better. Start with the recommended fields below and add as many descriptive variables as you can to augment your analysis.

Schema Blueprint:

  • One row per user
  • Metric column =
    Product Usage

Recommended

User ID

Subscription Value

Transaction Amount

Product SKU(s)

Calendar Month

Customer Acquisition Date

Customer Tenure (Days)

New Customer (T/F)

Reactivated (T/F)

Last Activity

Days Since Last Log In

Augmented (Optional)

Product Category

Product Sub-Category

Customer Tier

Customer Age

Customer Gender

Customer Income

Customer City, State, Zip

Email Engagement

Number of Users on Account

Account Source

Marketing Channel

Device Type

Schema Blueprint:
  • One row per user
  • Metric column =
    Product Usage
Recommended

User ID

Subscription Value

Transaction Amount

Product SKU(s)

Calendar Month

Customer Acquisition Date

Customer Tenure (Days)

New Customer (T/F)

Reactivated (T/F)

Last Activity

Days Since Last Log In

Augmented (Optional)

Product Category

Product Sub-Category

Customer Tier

Customer Age

Customer Gender

Customer Income

Customer City, State, Zip

Email Engagement

Number of Users on Account

Account Source

Marketing Channel

Device Type

Housecall Pro Logo

See how Housecall Pro uses Sisu to get insights on their users quickly.

Read the case study

More resources for augmenting your analysis

Diagnosing Session-Level Data for a Streaming Service in Seconds

Learn how to go from the “what” to the “why” faster and more comprehensively. In this post, we’ll walk through how a data analyst at a large streaming services company can use Sisu to find the facts in session-level data.

Read more

Two new ways to answer why, faster: Text and segment analysis

Two new ways to answer why, faster: Text and segment analysis

Read more