Chapter Thirty

Customer Retention

Your customer retention is measured by a very common metric known by several different names. Some companies call it a retention rate, turn rate or attrition rate. The concept implies that companies look at all the customers they had at the beginning of the period and ask how many of them stayed or what percentage of that original group of customers stayed with the company. That's known as the retention rate.
Similarly, a churn or attrition rate looks at what percentage of customers left. “Customer Churn Rate = Number of preexisting customers who left during a given period / Total customers at the start of that period. For example, assume your company has 50 customers at the beginning of the month. During that month, 12 customers left. That would mean you had a monthly customer churn rate of 24% (12/50 = 0.24). Mathematically, this means churn is the inverse of customer retention.“ reference: Lighter Capital.
This is the typical metric that firms use to evaluate how well they are doing at retaining their existing customers. Unlike customer acquisition cost (CAC) or subscription acquisition cost (SAC), retention or churn rate can serve as a good metric that contributes to better decision making. When studying the information public companies share regarding their retention or attrition rates, it is also important to acknowledge how many of their customers stick with the company. A rule of thumb: it costs much less to convert an existing satisfied customer into a repeat sale than to ‘conquest’ a new sale from another brand.
The difficultly lies with determining who “stuck around”. The key difference for non-subscription-based ecommerce companies is that they need to clearly define what constitutes a churn event. For example, if a company knows that most of their customers who will make a repeat purchase do so within 90 days, they may choose to mark any customer who has not made a purchase in that time period as being “churned.” Further, it is necessary to think about what implications these metrics have. For instance, putting the information to use to develop a financial value of the entire customer base.

 

Quote Icon In research and consulting on customer journeys, they’ve found that organizations able to skillfully manage the entire experience reap enormous rewards: enhanced customer satisfaction, reduced churn, increased revenue, and greater employee satisfaction. They also discover more-effective ways to collaborate across functions and levels, a process that delivers gains throughout the company. Quote Icon
HBR – The truth about customer experience

 

Let’s start with an example from AT&T. They distribute statements to their investors that includes their attrition rate and how it varies over time. After reviewing their data for the year 2014, we see that AT&T's attrition rate is 1.04% for their wireless business. (information from the AT&T web site). For 2014 AT&T added 3,290,000 customers. If the churn rate remains at 1.04% they can expect to lose 34,216 customers a year. At this rate the typical customer can be expected to stay with AT&T 3,290,000 divided by 34,216 or 96 years. A very good metric for AT&T, but remember that AT&T was one of the the largest if not the largest providers for the Apple iPhone when it was launched. With the release of the new iPhone 6s the Apple 6s unlike the earlier models was no longer locked to a provider. Making it easier for owners to shop for best deals from providers. This may have an effect on retention in the future.
To illustrate the importance of a low churn rate consider that an attrition rate that is around 20%, results in a typical customer lifetime that is around 5 years. Slightly less than 20% will equal to a little greater than 5 years. A simple and quick formula for the entire customer base suggests when the attrition rate is a little under 20%, the typical customer base is expected to remain for a little over 5 years. When we multiply that by the amount of revenue per customer and multiply that by the size of the customer base that's the customer equity and the value of the firm. This is the case, at least, as a first pass approximation and we don't want to understate that. Many companies are doing exactly that kind of calculation to determine what their customers are worth.
As important as it is to recognize the firms attrition rate for their customer base, one concept is clearly missing. In the customer-centric world we celebrate heterogeneity.

Differences among people, either in terms of observable characteristics, such as demographics or behavior, or in terms of unobservable characteristics, such as preferences or purchase intent.

In the heterogeneity concept, an "average" customer does not exist, and as a result a single number is not conclusive of the overall picture. A more important question to keep in mind is “How does the attrition propensity vary across the customers?” Just imagine if we could read the mind of each and every customer and determine just how churn prone, or not churn prone, they are. Does our inquiry lead to customers who will stay for a while, or are they apt to be churn-prone customers? Will it be a nice bell-shaped distribution? These are significant questions to consider.
Here is the celebration of heterogeneity for a fictitious company we will call ABC Ink. ABC Ink divided their customer base into three groups and discovered that these groups vary in terms of their return or attrition rates. The smallest group with the highest attrition rate is composed of the people who are very likely to leave at the next possible opportunity. Then there is the middle size group or middle attrition group. Finally the largest group has a fairly low attrition rate. First, let’s ask ourselves, is this good news or bad news for ABC Ink and for most companies? Generally, this is pretty good news as it suggests most of their customers tend to stick and don't tend to have a propensity to take their business elsewhere. Most companies and managers would describe customers with fairly low attrition rates as loyal customers and that they love them. Loyal describes some of their customers, but what about the others? What words would describe them? Indifferent? Perhaps they just don't care very much or they're not very invested with this particular product or service. At the present time, they are working with the service or product provider and they are indifferent as to whether to stay or seek services elsewhere. For these customers the firm might simply not care much one way or another if they come or go.
Some of these customers are loyal and some of them may be unconcerned. We don't know and for the purpose of this example it doesn’t really matter. We will concentrate on customers who tend to have a low attrition rate and this may not be a result of great marketing or strong branding on ABC Ink’s part, although it might be. Now that we are celebrating heterogeneity, what difference does it make? We must keep in mind that a lot of customers are unconcerned. We will study the information on the chart to help us make a more informed assessment of what the customer base is worth. When we observe the graph carefully, and pick off the various numbers, we see the table it shows us both the size of each of the three groups. What percent of the customer base is associated with each of the three groups? Let’s do the math. In fact, take a moment to think about how we would take this information and combine it together in order to come up with an overall value of the customer base.

 

ATT attrition chart ATT Heterogenity
A logical approach may be to take a weighted average based on the breakdown of the customer base, 70%, 20%, 10%, into the low, medium, and high risk groups, then determine their associated attrition rates. We multiply 0.70 by 0.06, and so on. The calculation resulted in the overall average attrition rate of 17.7%. What does that average attrition rate say about the overall length of the customer’s life and therefore, the overall financial value of the customer base? 1 over 17.7% is 5.6 years. This sounds familiar as the number is similar to the number that we obtained when we did not celebrate heterogeneity. The calculation was incorrect as we did not calculate an overall average, but as mentioned previously, there is no average customer. According to the ABC Ink analysis, a 17.7% attrition rate is not applicable as a customer who has that kind of attrition rate does not exist. Therefore, we've calculated an expected lifetime, and an expected financial value for a customer who does not exist.
The question remains, what's wrong with this calculation and how can we correct it? How do we truly celebrate heterogeneity? Let's turn to the words of Frederick Reichheld who wrote the book entitled, 'The Loyalty Effect'. His book contained excellent conclusions regarding loyalty including what it is, how it is measured and how it is captured. Reichheld was quite familiar with loyalty and as a result helped a lot of companies create and monetize it. “Most of today's on-line customers exhibit a clear proclivity toward loyalty, and Web technologies, used correctly, reinforce that inherent loyalty. If executives don't quickly gain the loyalty of their most profitable existing customers and acquire the right new customers, they will face a dismal future catering to the whims of only the most price-sensitive buyers.” Grow by Fixing the Leak in your Bucket… “Say you steadily add new customers to the top of your inventory, but old customers are steadily vanishing from the bottom. If you could slow the defection rate, the new customers you gained would increase the total at a much faster rate. It’s like a leaky bucket. The bigger the leak in your bucket of customers, the harder you have to work to fill it up and keep it full.” reference website Loyalty Rules. The logic is very apparent however, the average does not make sense. It mandates that the calculations be done separately, group by group.
This is where the celebration of heterogeneity is going to come in. Let's understand the separate value for the high, medium, and low, and then combine them together, instead of combining them together first. Perhaps this sounds similar. Let's take an average, and then, calculate the lifetime. If we revisit this example and do the lifetime calculation first we will see the difference. For our low risk customers, the average attrition rate is 0.06. What's 1 over .06? It equals to about 16.7 years. If we repeat that calculation for the other two groups, we can see their expected life time, and it is here we see dramatic differences.
CLA Chart
This is heterogeneity as we see a magnitude of difference between the best and the worst customers. We don't want to ignore that, we don't want to eliminate that, and we don't want to average over that. Instead, we want to celebrate that. Once we know the expected life times for each of these respective customer groups, then calculate the weighted average, multiply that by 70%, 20%, and 10%, then we determine our overall expected lifetime for this customer base to be approximately 12.4 years. Once we acknowledge and explicitly take into account heterogeneity, we have more than doubled the value of our customer base just by doing the correct calculation. This is the celebration of heterogeneity.
You might be wondering how this works in general. The answer is it always works the same way. Heterogeneity must be taken into account in terms of customer attrition rates or there will always be money left on the table if is disregarded. Further, the value of the customer base will be understated by ignoring heterogeneity. The big question is by how much? In the ABC Ink example, it's more than a twofold increase and the significance of that increase depends entirely on the magnitude and nature of the heterogeneity. In addition, if the customers are more spread out, then failing to take heterogeneity into account will cause an even steeper understatement of the customer value. This is not a question of whether it will occur or whether it will be an overstatement or an understatement, it always works this way. Instead, it's a question of how much. This demonstrates that by explicitly accounting for heterogeneity and by comprehending the differences among good customers and not so good customers, the value in the company can double. For those companies that believe customer retention is the leading area to focus on, what are the implications of using that formula to manage the customer base?

    The importance of customer retention



  • A 5% reduction in the customer defection rate can increase profits by 25-95%. - Bain & Co/HBR
  • A 2% increase in customer retention has the same effect as decreasing costs by 10%. - Emmet and Mark Murphy
  • The probability of selling to an existing customer is 60-70%. The probability of selling to a new prospect is 5-20%. - Marketing Metrics
  • Customer profitability tends to increase over the life of a retained customer. - Emmet and Mark Murphy
  • 55% of current marketing budget is spent on new customer acquisition and only 12% on customer retention. - McKinsey
  • It is 6 to 7 times more expensive to acquire new customers than it is to keep a current one. - White House Office of Consumer Affairs
  • A 10% increase in customer retention levels result in a 30% increase in the value of the company. - Bain & Co
  • Most important marketing objectives? 29.9% think it should be customer acquisition, and 26.6% think it is customer retention; however, 62.2% admit that they concentrate on customer acquisition, with only 20.6% focusing on customer retention. – Emarketer
  • 80% of your future profits will come from just 20% of your existing customers. - Gartner
  • A 10% increase in customer retention yields a 30% increase in the value of the company. - Bain & Co
  • Repeat customers spend 33% more compared to new customers. - Laura Lake
From: "The-secret-to-customer-retention" posted by Annette Franz
Summarizing our thinking regarding customer retention includes two major points. First, there is no "average" customer and calculations cannot be done based on an average customer. The value of the customer base will be understated if heterogeneity is ignored. The second point is much more subtle. If we ever want to calculate elasticity, [elasticity - a measure of a variable's sensitivity to a change in another variable] and determine what is the incremental gain that we get for a 1% reduction in the attrition rate, then that calculation can be done in two ways. Calculations can be completed by ignoring heterogeneity, as many firms do. Alternatively, calculations can be done by taking heterogeneity into account, as we just did, which will result in a very different conclusion.
The retention elasticity, the gain that we get by lowering the attrition rate by one percent, is much less when we account for heterogeneity. This means that efforts to boost retention, or decrease attrition or churn, are much more modest than we think they are when heterogeneity is considered. This should not mean stop spending money on retention as retention is one of the three major pillars of customer centricity. When heterogeneity is taken into account, it implies that companies should determine who the good customers are and do whatever it takes to keep them around. Companies do this at the margin where they want to spend more on retention as that seems to be the constraint for them. As a result, some companies may over spend on retention. A better use of the money may include taking some of the retention dollars and spending them on customer acquisition. These are some of the trade-offs that arise between acquisition and retention when there is an explicit focus on heterogeneity.
Review this computation example of customer life time value at Starbucks. LTV How is LTV used in practice. Companies use free samples and trial periods on subscription services. Many companies (such as Graze, Blue Apron, and Amazon Prime) offer generous free trials. How can they afford to do this? It comes down to expected value. The companies know how much the free trials cost them. They also know the probability of someone's paying afterwards and the lifetime value of a customer. Basic math reveals why free trials are profitable. Say that a free trial costs the company $10 per person, and one in ten people then sign up for the paid service, going on to generate $150 in profits. The expected value is positive. If only one in twenty people sign up, the company needs to find a cheaper free trial or scrap it.
Similarly, expected value applies to services that offer a free “lite” version (such as Buffer and Spotify). Doing so costs them a small amount or even nothing. Yet it increases the chance of someone's deciding to pay for the premium version. For the expected value to be positive, the combined cost of the people who never upgrade needs to be lower than the profit from the people who do pay.

smartcycledesigns are the best and we love feedback – click Here