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- Our powerful, data-driven churn prediction provides the insights needed to re-engage customers based on their risk of churn, and keep them engaged with exact targeting, perfect timing, and redefined messaging.
- Leverage our multi-modeling propensity prediction technology to account for multiple factors such as profile details, satisfaction scores, engagement level, business volume, and industry trends.
- Develop targeted customer retention strategies by predicting the individual risk of churn.
Client:
Canada’s Leading Protection Provider
Time taken for prediction and validation was
< 7 Min
for 60,000 customers record
Actual churn is
86%
accounted by first two declines
Service quality accuracy were predicted has
>80%
for three customer feedback metrics
Problem
Canada’s leading provider of protection and wealth products/services wanted to improve its customer engagement strategy.
The company wanted to identify the customers at higher risk of attrition and predict overall customer satisfaction rating for key service parameters across three business units.
Solution
A proprietary self-learning, multi-modeling automatic predictive technology from Findability.AI was used to:
- Divide customer records into deciles on likelihood to churn.
- Predicts customer satisfaction rating for three measures of customer service quality such as ease of doing business, proactive contact, and problem resolution.
Client:
Telecom industry
Fine-tune promotional offers
were they divided in sub-groups.
For churn pre-emptive strategies
formulated by client because of external events
Highest risk of churn were predicted
Problem
In telecom industry, customer behavior is influenced by several external factors like new phone launch, festivals, time of the year etc. making it difficult to predict likelihood of retention.
Solution
Findability.AI's, Proprietary self-learning, multi-modeling AI technology predicted customer churn based on CRM information, plan details, usage, and billing.
Findability.AI also analyzed unstructured content for chat bots and emails and accounted for reported malfunctions/defects while predicting churn.
Client:
Global multimedia and creativity software company
Additional retention of
40%
over the base retention rate of 15% was observed in the highest rank customer segment.
Proactive outreach plan
formulated by client for customers who were mostly likely to be retained
Customers can target remedial measures at others
Problem
A global multimedia and creativity software company wanted to determine retention propensity for retail consumers who had contacted their call center with the intention to cancel subscription.
Solution
Customer tenure and product subscription history was analyzed using a self-learning, multi-modeling prediction technology from findability.
AI The proprietary algorithm ingested historical data to develop multiple prediction models to account for all possible scenarios in the dataset.