A beauty brand spent significant monetary funds to acquire a certain number of app installations. Their strategy to gain customers was aggressive and on point, which enabled them to build a strong customer base. But there was one issue – A Large Number of App Uninstalls and this was catastrophic. They were rapidly losing customers and could not comprehend the cause of the drop in the numbers.

The business team tried everything from getting customer insights to hiring data companies to understand the major cause for this drop but couldn’t understand the core issue. This was a huge cause of concern as the budget for customer acquisition was high, and the company needed to understand the app install leak. Seeing the installs flat-lining, the company decided to hire Sciative.

Team Sciative Got on Board….

The problem of uninstallation was a direct loss to the beauty brand. Being an AI-powered data company, Sciative began to dig deep into the company’s customer insights. We strategized a way to study the consumers’ browsing behavior and purchase behavior.

Our data scientists began scrutinizing each aspect of the problem and created a roadmap for the analysis. 

STEP 1: Building The Roadmap for Problem-Solving 

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The first step was to build a solid 4-step plan that would enable us to understand the reasons for uninstalls and how we would tackle it.

  1. Last Purchase Experience

This would help us understand if there was an issue linked to the last purchase that caused the customer to uninstall the app. Here we found 2 Types of Customers. 

Type 1: Issue linked to last purchase

Type 2: No issue in the last purchase

  1. Overall Purchase Behavior 

The next step was to understand the overall purchase behavior of each customer and which would tell us what was causing the app to uninstall.

Our goal was to Identify early warning signs in purchase behavior that indicate a high tendency to un-install and find correlations between purchase behavior and purchase to Uninstall period.

  1. Overall Browsing Behavior 

The third step was studying the overall browsing behavior. Fluctuations in the browsing behavior might indicate customers’ interest in using the app. This would help us understand whether the browsing experience was causing customers to uninstall the app.

  1. F2U Period (The time between first purchase and uninstall) Vs First Notification

And then came the final step. Here we calculated the period between their first app purchase and uninstall and compared it to when they received their first notification. By measuring this, we wanted to understand the time between the app install and the first notification, the content of the notification and its effectiveness.

Once the roadmap was concretized, we began scrutinizing the population and created the following category for the next step of our analysis.

STEP 2: Logical Segmentation of the Customer Base

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To understand the core reason for app uninstallations, we divided the customers into two major categories and dissected the second category further into 3 types of uninstallers.

The first category was customers who uninstalled without a single purchase. These customers had reported issues with their last purchase, which was the major cause of uninstallation. The second category of customers were those who uninstalled the app after one or more purchases. To further understand the frequency and pattern of the installs, we identified customers who frequently installed and uninstalled the app.

We also noticed customers who were unsure about the install & uninstall; finally, there were those who never returned to the app after uninstalling it the first time.

Our next step included an analysis of the purchasing and browsing behavior of the uninstallers.

STEP 3: Studying the Purchasing & Browsing Behavior

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Since shopping journey of a customer gives us a deep insight into their shopping behavior. Our goal was to find out the reason for uninstallation; we started by analyzing the customers’ ‘Last Purchase Experience’.

  • Study of the Last Purchase Experience of the Customer

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The study of the last purchase experience gave us profoundly interesting insights and major clarity on what caused the customers to leave the app after their last purchase.

We discovered that the customers who raised an issue did not receive the desired customer support. There were a number of product returns, and we saw cancellations in the last transaction. 

The sum of these issues caused 17% of customers to uninstall the app. While 8% of the customers hit the uninstall button because their delivery took over 5 days. But the dilemma was that 84% of the customers uninstalled the app even when the delivery time was less than 5 days. This led us to conclude that the delivery time was not the reason for app uninstalls. 

Now that there was clarity on the last purchase behavior, we studied the discount shopping behavior.

  • Understanding the Discount Shopping Behavior of Customers

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We discovered that 74% of regularly installing and uninstalling customers had shopped for products with >15% discounts, while only 9% had shopped for full price or <5% discounts.

In terms of their engagement rate – 47% of engaged customers retained the app for 1-45 days. Customers who were browsing were interested, but since they were not engaged, they ended up uninstalling the app. 

Thus concretizing the fact that there is a huge opportunity to engage customers before they become uninstallers.

  • Scrutinizing the Browsing BehaviorSciative, pricing, dynamic pricing, artificial intelligence, pricing algorithms, pricing company, AI-powered pricing, dynamic pricing India 

A customer’s browsing behavior enables us to understand how long they are staying on the app, how many times they are logging into it and what’s making them uninstall. We saw that in terms of time spent on the app, 48% of customers had an increase in the number of special days of visit from 135.1 seconds to 347.2 seconds.

At the same time, the frequency of app visits saw a 74% increase in the number of distinct days of the visit from 3.1 days to 11.5 days. But the leak was due to a lack of engagement. Customers who had added a product to their wish list or card were not engaged for 10-12 days. This is where a large number of uninstalls were taking place.


The in-depth study of the purchase and browsing behavior showcased that the major reason for app installations was a lack of engagement. On seeing the gap, we engineered segment-wise strategies. These included loyalty programs, the types of notifications, and when these notifications needed to be sent out. And upon implementation of these strategies, the beauty brand was able to reduce its app uninstalls by 15%.

If you would like us to conduct an in-depth analysis of your pricing needs, please book a free demo with us or email our pricing consultants at info@sciative.com.

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