The value of retaining the right customers
Find out why customer retention and loyalty are so valuable, anticipate challenges in improving retention and know how to calculate the value of your customer
According to The Conference Board, customer loyalty is one of the hottest topics in business today, and is standing on top of the list of concerns for CEOs of big corporations. In a previous article, we published a (simple) calculation to help companies if they should invest in retaining their customers. Let’s take a look at why customer retention and customer loyalty are so valuable to companies, and what are the underlying business concepts that marketers and sales professionals should master. We also explain what are the challenges in calculating the long term value of customers, and why machine learning can help .
The Value in Retaining Customers
Consider the cost of serving a long-standing customer versus the cost of courting one. If we recall one of the basic assumptions in microeconomic theory, a seller seeks to minimize their costs and maximize their revenues. Here customer retention affects both elements of the profitability equation, where Profit = Revenue – Expenses or Cost . Decreasing costs : acquiring a new customer is expensive (what is referred under CAC, i.e Customer Acquisition Cost). By increasing customer retention, you need to spend less for the same amount of customers, so your CAC decreases. Increasing revenues : The ability of the company to retain its customers over some specific time period, also helps to increase revenues and reduce the maintaining expenses or costs of generating those revenues.
How do loyal relationships translate into profits ?
Across a wide range of businesses, customers generate increasing profits each year they stay with a company. Frederick Reichheld of Bain&Company conducted a research showing that a 5% increase in customer retention produces more than a 25% increase in profit in the finance industry (Frederick Reichheld, Bain&Company). Here is why.
Why long-term, loyal relationships are so valuable
The importance of customer loyalty impacts almost every metric important for running a business. New customers tend to cost more to acquire, and don’t spend as much money as loyal, retained customers.
Return customers are valuable in several ways :
Return customers tend to buy more from a company over time.
Regular customers proceed to do business with you rather than switch to a competitor with whom they’re neither familiar nor comfortable, even if one of your competitors offers a more attractive short-term contract.
What is more, return customers refer others to your company,
Also, they will often pay a premium to expand their customer engagement or join a Loyalty Club , if one. Loyal customers have lesser sensitivity to price and high retention rates. ('The Effect of Service Price Increases on Customer Retention', John Dawes, Journal of Service Research). You’ll both save on switching costs, and if you are perceptive, you’ll maximize the efficiency of your transactions as your relationship matures.
Existing customers can not only provide feedback about products and services, but also work together with suppliers to add value to a particular product by improvising its functional features, or by modifying the manufacturing or work processes which use the product. It is not surprising that one of the reasons for Amazon’s stunning success is its focus on customer retention. Amazon.com Inc. Chief Executive Jeff Bezos said the e-commerce giant has exceeded 100 million paid Amazon Prime subscribers and will continue to invest to meet “ever-rising” customer expectations.
How to calculate the value of your Customers
What is Customer Lifetime Value ?
Customer lifetime value, or variously referred to as lifetime customer value or just lifetime value (CLV, LCV, or LTV) projects the value of a customer to the firm over the entire history of that customer’s relationship with a company. Often stakeholders use CVL as a tool which helps them maximize profit by analyzing customer behaviour and business cycles to identify and target customers with the greatest potential net value over time. Lifetime value is the value of the customer over the Life Cycle. Life Cycle is simply the behaviour of a customer with your company over time. The most difficult part of calculating lifetime value is deciding what a “lifetime” is, and it can be achieved only by using advanced marketing technologies.
Why is it so important?
If you can predict where your customers are in the Life Cycle, you can maximize your marketing ROI by targeting customers most likely to buy, trying to “save”, customers who have declining interest. Similarly, you will not “waste” money on customers unlikely to continue doing business with you.
Focusing on CLV allows you to design an efficient strategy where you will concentrate efforts on the most valuable customers. Your customers are not the same, in the sense that some bring more revenue than others, so it is crucial to know on which ones you should focus first and invest in.
CVL can also tell you how well are you resonating with your audience, how much your customers like your products or services, and what are you doing right-as well as how you can improve
Customer Lifetime Value is one of the major revenue metrics for companies because the cost of acquisition of new customers is much higher than the cost of retention of existing ones. Unfortunately, the benefits of determining the CLV are not well known, and the use of this marketing analytical tool is yet not well implemented.
Challenges for marketers
As explained above, not every customer has a potential to be profitable and long-standing. Cost-effectiveness dictates that you can target your investments in the segments that are the most valuable, or the most risky, etc.. .For this, you need to go through a few steps that can look challenging, and that are made easier thanks to Machine Learning and Artificial Intelligence.
Calculating Customer Lifetime Value
The calculation of CLV may be tricky. This depends on your business model, products, the company history, available data, etc. Machine Learning and Artificial Intelligence are increasingly used to calculate Customer Lifetime Value. As long as we have (or find) the data, a Machine Learning model can calculate CLV for you.
Calculating your churn rate
One of the key metrics in understanding how well your company is retaining customers is customer churn rate. In simple terms, churn rate shows the percent of customers who end their relationship with your company in a given period. You take the total number of customers who left your company during a given period, divided by total customers at the beginning of the period. As you can see, this is a post-hoc indicator, meaning you can only look at what’s happened, which is one of the metric’s downsides. By the time you see an increase in your churn rate it is six of eight months after the point in time when you actually failed the customer. Here again, Machine Learning can help by predicting which customers will leave.
Prioritizing and managing customer retention
Beyond metrics, companies must be able to know what we as a company doing to cause customer turnover? What are our customers doing that’s contributing to their leaving? Are we attracting the wrong kinds of customers? How can we better manage our customer relationships to make sure it doesn’t happen again? Which customers have potential to be profitable and long-standing? Answering to these questions will help them prioritize their retention plan. Once again, Machine Learning can find out what are the most costly drivers of churn and customer value so marketers can prioritize their retention plan.
Kantify specializes in calculating and predicting customer churn, using newest machine learning techniques. We have developed a series of Machine Learning models and techniques that can be tuned to provide your company answers to the above questions, predict when your customers are going to churn and provide you in-front analysis to help you maximize your marketing ROI and reduce costs. Read more about churn prediction on https://kantify.ai/use-cases/customer-churn .