GATHER AND LEVERAGE EXHAUSTIVE SALES INTELLIGENCE TO DECREASE CUSTOMER CHURNS, TRIGGER MORE SALES RENEWALS AND IMPROVE CLIENT LOYALTY.
REDUCE CUSTOMER CHURN BY ACCURATELY IDENTIFYING WHICH THEIR CUSTOMERS ARE AT RISK.
Churn Prediction Modeling for Increased Loyalty and Renewals. Discover Machine Learning strategies to proactively reduce customer churn, improve customer engagement and grow revenue. Although each customer is unique, many of their behavior follow similar patterns throughout the different moments of their lifecycle. AI-Surge helps companies analyze customer behavioral data to identify risk factors and predict customer attrition. Reach out to customers with more personalized offers to increase loyalty and preempt churn.
Churn prediction directly impacts growth
Churn prediction is key for any company, from healthcare, to telecom, e-commerce and insurance companies. Tracking churn, i.e. users that stopped using your product(s) or service(s) is key as the cost of retaining existing customers is often lower than acquiring new ones. Reducing the revenue slackening due to churn, businesses with strong levels of customer success eventually grow even faster.
A McKinsey report from 2016 showed that lower net-revenue churn, i.e. the percentage of revenue lost from existing customers on a given period, is correlated with higher growth.
Hence the importance of churn prediction and controlling it effectively.
Common pitfalls of churn prediction
Churn prediction, because of the business impact it generates and its relative ease to execute, is often seen as a good first project to address using machine learning and AI.
However, such projects can be slowed down due to several pitfalls:
Data availability and capture: coming from multiple, cross-functional tools
Predictions timeframe and scope: predicting people that will obviously churn?
Output integrations into your existing sales pipelines
Machine Learning strategies that proactively predict churn
The AI-Surge Platform is a Machine Learning platform aimed at helping companies build and own their path to ML at scale. Creating and maintaining in production a churn prediction pipeline becomes simple thanks to AI-Surge’ killer features:
Data collection and preparation: pick preconfigured sources in AI-Surge marketplace (including your favorite CRM) before analyzing, cleaning and organizing your data in the most relevant data model
Predictive modeling: build, train, score and run your predictive models, manage your project versions to deploy the best model and monitor performance efficiently
Deployment: leverage ForePaaS managed deployment to turn your POC into a production-grade business application ready to deliver value to your sales and customer success teams
Detect your most vulnerable customers from your history of customer interactions and help your teams renew them efficiently and increase their loyalty to better customized offers.
You might want to read
Big Data, Low Code, Preidtive Analysis
Low-Code Data Fabric: Unlocking Productivity and Cost Savings
Big data, Low Code, Data Analytics
Data Fabric Architecture: The Future of Data Integration and Management
What is Centralized or Decentralize Decision Making
ML And The Problem Of Bias
Data Strategy for Startups