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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.

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Join us May 4th at 11am PST for a Webinar:

Amazon Redshift data warehousing from the trenches - lessons learned over 3 years


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

Data Recipe: Comparing Groups of Users - Part 1

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.

This is a deep topic so I will cover everything in a series of three posts.  This post -- Part 1 -- will cover example business questions we are answering, how to define groups of users, and how to get all of the variables that describe the users.  Part 2 and Part 3 will demonstrate a methodical, scientific process by which to understand the significant differences between two groups.  Part 2 uses a t-test, while Part 3 uses a Chi-Squared test.

The Four Horsemen of Churn and What Retention Marketers Can Do About Them

Churn happens all over your product - that's the bad news. The good news is that as a retention marketer you have access to a treasure trove of user data in Appuri - literally every click and tap the user made is available for analysis. Appuri's churn drivers feature uses all this data to diagnose and predict churn. If this magic 8 ball told you why users are churning, what would you do about it? In this article, I make an argument that not all types of churn is equal,  and provide a framework for prescriptive actions and decisions for different types of churn drivers based on examples from our customers.

How to load billions of JSON events into Amazon Redshift every day using Go and Kafka

Amazon Redshift forms a crucial part of our ETL pipeline. For our larger customers, we need to load 4 billions+ JSON events into hundreds of tables in Amazon Redshift every day. In this blog post, I dive into how we identified a bottleneck in our ETL pipeline, removed it and launched a new Go service with horizontal scalability and zero downtime.