Deep Analytics for Planeta Kino: Marketing, Customers, Cross-Sales
Planeta Kino is a modern Ukrainian cinema chain that has transformed the movie-going experience into a high-tech service.
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We won the battle for every Planet Kino customer: reduced churn from 13% to 7%.
About the project
Data analytics turned emotions into profit – customer churn reduced by almost half
Planeta Kino is a brand that changes the way people think about watching movies, not just a chain of cinemas. They were the first in Ukraine to introduce IMAX and 4DX, and created their own formats – Cinetech+ and RE'LUX. Watching movies in their nine cinemas is not just a screening, but an emotional journey. Their own streaming platform, Planeta Online, offers viewers interesting content, exclusive materials, and premium quality.
The client's problem
Planeta Kino has high user loyalty and a large active user base, which has been built up over the years thanks to its high-quality service. However, over time, the company began to notice that some of its customers were gradually disappearing from view.
Therefore, the customer turned to us to resolve the following issues:
- Reduce audience churn: identify early signs of risk and take action before the user decides to leave.
- Retain the active base: not intrusively, but through personalized dialogue.
- Increase LTV: so that every customer wants to stay longer and enjoy Planet Cinema.
Our team had to focus on early risk detection and targeted intervention. We had to find a balance between analytics, hypotheses, and real-life user experience, and turn it into an effective churn prediction model.
Case information
From hypothesis to sustainable results: analyzing the behavior of Planeta Kino customers increased their loyalty
Technology brings customers back: analytics and communication have created a strong connection between the brand and viewers
Our approach:
We began our work on the case with hypotheses. We believe this is crucial, as it influences the user’s decision to return. In the first stage, we outlined several scenarios and determined how they could be verified with data. Then we developed pipelines and a special Silver Data Warehouse database. The team’s next step was to dive deep into transactions over more than a year. Thanks to ML algorithms, we broke down behavioral clusters. The results were verified together with the marketing team through in-depth interviews. We needed to understand what was behind each number. In the end, we built a model to predict the probability of churn. It helps us identify risks in a timely manner and retain customers in a smart, targeted, and effective way.
Work results:
We implemented personalized communication chains in Salesforce Marketing Cloud CRM to address the customer’s request. Currently, the system responds to and predicts customer behavior and intervenes in a timely manner when there is a risk of churn.
What our team has achieved:
Integration with Salesforce Marketing Cloud: users receive messages tailored to their personal risk level and behavior cluster.
Reduction in customer churn: the indicator decreased from 13% to 7%.
LTV growth: the average viewer lifecycle has lengthened. In addition, audience engagement has increased.
This case study is an example of how smart analytics, combined with automated communication, can deliver stable growth and user loyalty.
Project details:
Key facts about the project
8 months
Project duration
Large
Project size:
Project complexity
Completed
Project status:
Our Team: Project Manager Business Analyst Data Engineer Data Science Engineer Data Analyst Salesforce Consultant DevOps Engineer
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