A major services company had many customers churning to a competitor. Insight was needed into which customers were at risk of leaving, why they were leaving, and what customers to focus the retention effort on.
A trend was noticed that customers who contacted a call centre were more likely to leave the company within the next 6 months.
The company wanted to confirm whether this trend was genuine and if so, was anything that a customer said or their pattern of contacting call centre indicative of their propensity to leave?
We used the company’s own data to deliver the solution.
- Applied text mining to the inbound call centre records of the conversations between customers and staff.
- Uncovered early warning signs of customer churn, enabling successful customer retention before they had committed to leaving the company.
- Built profiles of the different segments of at-risk customers, showing their needs, motivations and their value to the company.
- Adding insights from text data improved accuracy of churn prediction by 12%
The company was able to identify customers with a high likelihood of churn and prioritise its retention resources on profitable, risky customers first, and therefore deliver maximum return on investment.
The approach used the power of contemporary Data Science methods and was transparent, repeatable, scientifically valid and accurate.
Data Science, also referred to as Advanced Analytics or Predictive Analytics, is an analysis approach that provides businesses with accurate What-If scenarios and evidence-based proactive decision-making tools.
- It is based on predictive analysis of domain-specific organisational data. If an outcome of interest to the business can be measured, then Data Science methods can determine which factors influence it and to what extent – and based on the delivered insights, suggest the call to action.
- It has been proven and pressure-tested globally across many industries. It has been a key to the success of Google and Amazon. It is used by leading banks, insurers, telcos, retailers, manufacturers, utilities and governments to gain insight into how to efficiently improve business outcomes including:
- identification of high value customers
- improve customer retention
- develop data-driven customer segments and align products/service to maximise customer value and satisfaction.