Identify churn and proactively improve customer retention

Understand what your customers want, make relevant product recommendations in real-time and increase customer satisfaction and revenue.

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Churn Prediction on Lentiq EdgeLake

Why predicting churn is important

Preventing churn helps increase customer satisfaction while having a significant impact on your company’s bottom line. That’s because it costs more to acquire a new client than to retain an existing one. An increase in customer retention of just 5% can create at least a 25% increase in profit.*

That’s because returning customers will likely spend much more on your company's products and services than newly acquired ones.

*Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data - Omer Artun, Dominique Levin


Predicting churn using Lentiq

Aggregating all data, creating localized ML models, productifying them are difficult in centralized data lakes.

With Lentiq, each branch, department, division can develop localized ML models, can use best practices developed organization-wide, tweak models by localizing feature selection and deliver real, scalable, reliable results for customer churn prediction. 

Customer churn prediction can improve your business in multiple ways:

  • personalize communication channel and offerings for churning customers, preventing churn
  • identify key problem areas in product and associated services that determine churn
  • identify trends, and optimizing revenue predictions
  • improve the value proposition for product

Simplify implementation with Lentiq

Our platform provides as a service infrastructure, resources, application, data and metadata, notebooks management and helps data teams be independent and focus on data analysis and insight extraction. Lentiq empowers users to:

  • abstract the computing and storage layer and run in multiple clouds simultaneously
  • deploy use case specific application stacks
  • easily develop and scale machine learning models
  • productify machine learning models through the "reusable code block" technology
  • share data across multiple data pools
  • localize machine learning models and improve prediction results
  • build an internal knowledge repository out of models deployed in multiple regions

How to start?

Aggregate data and perform data pre-processing
Aggregate data from customer plans, payment transactions, product usage, customer support requests.
Perform exploratory data analysis
Analyze statistics, support intuition, discover some trends and patterns that can produce refined results through an explainable model.
Model training and feature selection
Benchmark different machine learning models and identify the most relevant features and the model with the best accuracy.
Productify data science
Move your models to production easily and start predicting churn and apply strategies to improve retention.

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