Avoidable customer churn is estimated to cost UK companies at least £25b ($A45b) per year. 84 percent of UK customer across the banking & finance, utilities and communications sectors are estimated to have switched 1.91 times in a 12 month period.
Callminer is a UK company whose stated goal is “to help companies automate the overwhelming process of extracting insight from phone calls, chats, emails and social media to dramatically improve customer service and sales, reduce the cost of service delivery, mitigate risk, and identify areas for process and product improvement.”
Noble goals indeed. And to emphasise the need for its services, the company recently conducted a survey of 1,000 UK consumers that, they say, “reveals that British businesses are driving customers away for completely avoidable reasons. And it’s costing them billions.”
The total cost of churn
An estimated £25b ($A45b) per year, in fact, according to Callminer’s estimates. Here’s how it came to that figure.
A conservative estimate of the cost of acquiring a new customer across all the main sectors in the survey (banking, insurance, electric utility, gas utility, water utility, private medical company, telephone company, mobile telephone company, broadband supplier, online supermarket, online travel vendor) was £300 ($A537) per person, based on the level of incentives applied to attract new customers and a conservative estimate of associated sales and marketing costs.
The survey identified that, in the preceding 12 months, 84 percent of adults had switched 1.91 times. “This represents 43.745 million people,” Callminer said. “The total cost of churn is therefore at least £25.05b,”
You can read the full report of the survey here, along with reports covering each of the banking and finance, insurance and utilities sectors. But what is to be done about this?
Curb the churn
According to Bill McMurray, Managing Director Asia Pacific and Japan of US-based Qualtrics, the answer is driver analysis and predictive modelling.
“Addressing customer churn has traditionally been time-consuming and manually-driven, but organisations are now using driver analysis and predictive analytics to combat customer churn more effectively and efficiently,” he says.
Driver analysis is defined as “a top-down approach that identifies the leading indicators of customer churn and assesses their relative strength.”
Qualtrics says that, used for high-level strategic planning, driver analysis can help businesses easily identify the highest priority, underperforming areas to focus on and address.
Predictive modelling, on the other hand, is “a bottom-up algorithmic approach that assesses each customer based on their likelihood of leaving … [enabling organisations to] proactively identify the customers most at risk of churn and target specific remedial activities to retain that customer.”
There are, according to Qualtrics, three types of data required for predictive modelling:
- Operational data: the customer transactional data that sits within an organisations’ business intelligence or finance team, including customer ID, order size, tenure, average order value and history of products ordered.
- Experience data: the structured and unstructured customer feedback data collected from a variety of sources including: net promoter scores (NPS), customer satisfaction scores, customer effort scores (CES), websites and likelihood of next purchase.
- Churn data: all the information that has been collected from customers that have previously churned.
That’s a lot of data, from a lot of disparate sources – so predictive modelling presents some considerable challenges, and costs. But as McMurray reminds us:
It is considerably cheaper to maintain a customer relationship than to win a new customer.
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