which helps you retain customers and save marketing budget.
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In a competitive market the cost of customer’s acquisition (CAC) can be high therefore
To increase business profitability it is essential to increase customer’s lifetime in order to prevent unchurn
- customers’ behavior,
- their consumer patterns,
- social-demographic data,
- and other parameters
- loyal, who will continue to stay with the company,
- those who left and are unlikely to return,
- and those who are inclined to leave.
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Friendly interface
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Using your data
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Easily integrates with inner systems
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Newest AI methods
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System was made up by marketologists and data scientists
A non-disclosure agreement between our companies
What kind of data you’ve got,period during which it was collected, the way its connected and how it’s renewed
Carry out data analysis demonstrating product capabilities
What kind of result you will get,Discuss technical issues and final cost
For Celado Unchurn Implementation
Implement the product for 1-2 months.
You get 2 months technical support and consultations
Your Client Base Is Kept Safe
We do not need personal data to build models.
We work with impersonal client IDs and their behavioral parameters. Therefore, your customer base will remain with you.
At the same time, we are a personal data operator registered with the Ministry of Telecom and Mass Communications, which allows us, if necessary, to process the data of your customers, if this requires the development of your individual solution. These cases are always discussed separately and accompanied by separate documents
FAQ
How many Clients Should I have?
The more customers the better, but you need at least 2,000 customers, on average, making at least 3 purchases every quarter.
How Much Data Is Required?
Ideally, we need statistics of at least a year to take into account all aspects related to seasonality. At least three years is best.
We work in b2b, will your system work?
We have successful examples of using our system in b2b segment.
Cases
Recommender system for steel plant
Implementation of machine learning expert system reduced defect production by 34%.
It allowed to save energy and time, increase productivity and quality of final product.
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