<iframe src="//www.googletagmanager.com/ns.html?id=GTM-TCDTRR" height="0" width="0" style="display:none;visibility:hidden">

Reinforce retention
with the power of predictive analytics.

Appuri Backstop uses machine learning to predict which users are likely to leave, so you can take action to stop churn before it starts.

Request a Demo

Identify retention drivers and diagnose churn

Let us do the heavy lifting. Appuri Backstop enables advanced segmentation based on behavioral events and profile properties, even by customer lifecycle stage. Retention cohorts make it easy to understand your retention patterns and investigate issues. Machine learning models score users on their likelihood to abandon, allowing you to drill into leading indicators with pinpoint precision.


Engage, retain, and grow your user base

Automate experiments on at-risk users to affect behavior and increase engagement. Intercept users likely to churn with retention campaigns, or send segments to testing platforms for targeted experiments. Appuri also integrates with popular marketing automation and media platforms, making lifecycle marketing a cinch.

Query all your user data in one place

Access your data, your way

Limited by the reports your analytics tool provides? With Appuri, you get raw SQL access to all your data, so you can answer any question any time. You can even schedule jobs in the language of your choice. Prefer to view your data in Tableau or another BI tool? Just connect to Redshift in your virtual private cloud; most tools have native connectors.

Spend less time managing data, and more time using it.

Accelerate time-to-value

Bypass IT backlogs and bogged-down BI - our data pipeline ingests your data into a dedicated Redshift data warehouse fast. Appuri automatically detects your data's schema and builds SQL tables, onboarding your data in hours - not days or months. So you can get smarter faster, and retain more users longer.

Hear What Our Customers Have to Say...

“If we didn’t use Appuri, we would have needed to hire 2 data engineers– people who are in high demand. And it’s not just the expense of the people, it’s also the time it would take to find those people and bring them up to speed and build out the pipeline.”

– Matt Augustine, PlayFab

Hear What Our Customers Have to Say...

“I’m really happy with the speed and reliability of Appuri. The integration with Appuri went very quickly. Before considering Appuri, we talked about a DIY solution that would refactor the way we store data, a project that would have taken up a lot of my time plus involve pulling other people off of their core work items.”

– Kael Hammond, Hardsuit Labs

Hear What Our Customers Have to Say...

Zombie Studios selected Appuri’s Customer Data Platform for an immediately deployable full-stack analytics solution. Integration was completed within a week and Zombie was able to investigate and resolve everyday issues right away. To date, Appuri has helped ingest over 1.4 billion events for Blacklight Retribution.

– Zombie Studios, Product Team

From our blog

Engage your Users Early and Often to Increase Retention – Part I

 Regardless of what business you are in, it’s not news that your users’ experience with your products starts from the very first day they interact with your company.  For some this happens during a free trial or the first time they open your mobile app or game, for others it may begin when they receive an email regarding their subscription.  Whatever form this first contact takes it is critical to ensure that every part of it is working in a way to optimally engage the user. 

Data Recipe: Comparing Groups of Users - Part 2

Executive Summary

Understanding the difference between two different groups of customers is very important for many aspects of a business. The information gained from understanding these differences could be used to understand retention, give you insights into campaign customization or even identify features to add to a product or service.

In Part 1 of this topic I covered example business questions we are answering, how to define groups of users, and how to get all of the variables that describe the users.

In this post -- Part 2 -- I will demonstrate a methodical, scientific process by which to understand the significant differences between two groups using a t-test.  Part 3 will wrap up the topic by demonstrating the significant difference between two groups using a Chi-Squared test.

Running Spark in Production: Choosing where to host Spark

Appuri helps customers diagnose and predict churn. This, by its very nature, is a Big Data problem because user behaviors are predictive of churn - and popular apps and games generate a lot of behavioral data. A popular game title, for example, can easily collect 5 to 10 billion events a day. Over the last few months, we decided to move our core ML pipeline over to Apache Spark to deal with the growing volume of data in Appuri. This series of blog posts covers real-world lessons in learning how to run Spark in production - first, where to host Spark.