Behavioral Analytics vs. Intelligence

Behavioral analytics reveals insights into the behavior of users on a mobile app. Games, Mobile-Commerce, social, enterprise and messaging apps want to optimize and automate engagement, retention and growth of users.  The volume of raw usage and event data generated by mobile apps goes way beyond the scale of typical demographic and segmentation-based analysis, and traditional analytics products that simply focus on past user actions.

Behavioral analysis focuses on understanding how users act and why, enabling accurate predictions about how they are likely to act in the future. Advanced capabilities allow marketers to make the right offers to the right user groups at the right time.

Behavioral analytics utilizes massive volumes of raw usage and event data captured during an user’s use of an app, including traffic data like navigation flows, clicks, social media interactions, purchasing decisions and marketing responsiveness. Usage and event data may include marketing metrics like click-to-conversion latency,  campaign effectiveness, user responsiveness, revenue generated, usage per screen etc.

Behavioral analysis allows future actions and trends to be predicted.

The goal of Behavioral Analytics is to uncover not only what is happening, but also how and why it is happening.  Behavioral Intelligence takes Behavioral Analytics further by influencing the user base to align them with desired goals.

Event Sequences and User Behavior

Let’s assume a sequence of events:

  1. A user walks into a Apple store
  2. The store app on the user’s phone is notified of the user entering the store as detected by the iBeacon located at the store entrance
  3. The user spends some time in the store and at some point, scans the Apple TV product barcode using the phone camera
  4. Details of Apple TV product appear on the users screen and the user views product reviews and ratings
  5. At some point, the user searches for “4K compatibility”
  6. Later, the iBeacon detects that the user left the store, without purchasing the Apple TV product.


Looking at the above events as individual data points does not represent what is really going on or why store visitors did not make a purchase. However, viewing these data points as a representation of overall user behavior enables marketers to possibly interpolate how and why users acted in this particular case.

Upon detailed analysis, maybe a large number of users left the store after after searching for “4K compatibility,” or similar keywords, indicative of  lack of information.

Behavioral analytics in this case considers all events, screen views and flows as a timeline of connected events that did not lead to a purchase.

Behavioral Analytics as basis of Intelligence

Behavioral analytics is widely used in political campaigns to determine how potential voters should be approached, detecting compromised credentials or insider threats, to recommend additional products or services.  Behavioral analytics is also used by banks and manufacturing firms to prioritize leads generated by their websites.

For mobile apps, behavioral analytics is used for

  • Product recommendations and predicting future sales trends in mobile commerce apps based on past user purchasing patterns both in-store and in-app
  • Predicting usage trends, load, and user preferences in future releases in gaming apps
  • Predicting engagement, future usage or attrition times of users in an app
  • Breaking users down into similar groups to gain a more focused understanding of their behavior by segmentation and cohort analysis
  • Presenting users with relevant personalized content based on user behavior

We wrote about Behavioral Intelligence, which uses behavioral analytics as a basis, to influence actions to achieve desired outcomes.

In the above example, the app publisher (developer of Apple Store App) could correct this behavior for the desired outcome of a purchase, by delivering a blog or targeted advertising about Apple TV’s 4K TV compatibility and an offer for free shipping of Apple TV product to user.


Behavioral analytics, marketing and intelligence

Pyze Growth Intelligence, in addition to basic analytics, includes behavioral analytics, categorization of installs, usage, engagement, app sessions, daily/weekly/monthly active users, activations and retention.  It also includes loyalty and attrition, retention and churn cohort analysis, automatic screen and scene flow analysis.  An industry-leading event analysis includes automatically generated events, custom events, times events, and a large library of curated events for 2o verticals.

Pyze Growth Intelligence is also a full featured engagement and mobile marketing product that utilizes contextual marketing, quota and campaign management.

The key offerings from Pyze includes Personalization Intelligence, Exploratory data intelligence (Intelligence Explorer) and Growth Automation to manage growth of users at every step.