One of the key applications of analytics has been in the area of customer retention. Successful customer retention starts with the first contact an organization has a customer and continues throughout the entire lifetime of a relationship. A company’s ability to attract and retain new clients, is not only related to its product or services, but strongly related to the way it services its existing customers and the reputation it creates within and across the marketplace. Without customers there is no business, so central tenet for any business is “Thou shall keep your customer”. And if you lose a customer it is still ok, as long as your acquisition rate is greater than your attrition rate, which means that business is still growing. Problem with this strategy is that getting a new customer is far more costly than keeping an existing customer, and in some industries – up to 7 times. So, it just makes so much sense to work on successful retention strategies with the aim not only to give the customer what they expect but to exceed their expectations so that they become loyal advocates for your brand.
Creating customer loyalty puts customer value at the center of business strategy’ rather than maximizing profits and shareholder value. Logic is that if the customer is happy with our product and services he will award us with a larger share of his wallet for a longer period of time. And if that is not happening we need to know why. Therefore, before an organization can stop and possibly reverse customer attrition it is important to understand it and know its causes, whether is product dissatisfaction, poor customer service or something else. Whatever it is – it is within company control most of the time.
Knowing how many customers do you have, how many customers you have acquired or lost last year, and how many customers will you attract and lost coming year is important to quantify your retention and attrition rates. Retention rate is simple to calculate! It is the ratio of number of customers at the end of period less number of customers acquired during that period divided by the number of customers at the start of period and multiplied by 100. And the goal is to keep retention rates as high as possible not only because expensive to land new customers but because it has a direct effect on overall profitability. However, not all customers are worth keeping! There are those whose cost of keeping far outweigh any value to the company. So, when we talk about retaining the customer, or awarding him for his loyalty we refer to the customer whose current or future value justify efforts in prolonging business relationship with such customer.
And how do we use analytics and data mining to help us to retain the clients? Over the years, this approach evolved greatly. Initially it was all about figuring out which customers will leave based on mostly behavioral characteristics. This is still in a heart of retention modeling, but the change occurred when organizations realized that not all customers are worth retaining so they started bringing variables that would indicate value and profitability groupings. Such variables were used either during the modeling with all the other “input” variables, or they were used in post-modeling phase as filter variables. And together with variable indicating other variables could also be used for the same purpose, credit scores, risk indicators, response indicators and affinity groupings. These powerful customer dimensions lead to far greater levels of customer intelligence which in turn enable the companies to devise more effective customer programs.
Latest developments in churn models involves bringing in free-text call center and social media information and integrating it with other types of structured data for the purpose of increasing robustness and predictive power of their current churn/attrition models. However, to be really effective in using analytics in combating attrition – good quality data-mining model may not be enough. Companies need to have comprehensive retention strategies that are geared toward not only understanding drivers of attrition for different business segments – but determination to work on addressing the issues that can be addressed, and using analytics as one of the tools in overall retention strategies. Right incentive to the right customer using the right communication channel can stop him leaving, but if he continues to get real (or perceived) poor quality of product or service – this will only have limited effect.