29. NET PROMOTER SCORE® (NPS®)

OBJECTIVE

Identify customers’ likelihood of making recommendations.

DESCRIPTION

Usually the value of customers is calculated using only the variables amount spent and frequency of purchases. However, customers can create value in several other ways, one of which is by recommending the service to other potential buyers. The so-called “word-of-mouth” phenomenon is nowadays empowered by social networks, metasearch websites, or portals that provide customer feedback on products.

Positive recommendations can not only increase sales but also allow companies to save money on advertising. The Net Promoter Score® is an indicator that estimates the probability of recommendation of a group of customers based on the recommendation intention of single customers. It was developed and registered as a trademark by Fred Reichheld, Bain & Company, and Satmetrix.

The data are collected through surveys, and the respondents are asked to score from 0 to 10 the likelihood of recommending the product or service. Those who respond 9 or 10 are the real promoters, while those who respond 6 or lower are the detractors. The respondents whose score is from 7 to 8 are considered passive, since, even if they say that they would recommend the product or service, in reality they do not recommend it. The NPS® is the percentage of promoters minus the percentage of detractors.

TEMPLATE

Download the Net Promoter Score template

28. CUSTOMER LIFETIME VALUE 2

OBJECTIVE

Estimate the retention and future spending amount of customers.

DESCRIPTION

In the previous chapter I explained the principles of CLV and its calculation. However, one of the problems was the estimation of the retention rate (for which the simplification was to apply the average retention rate of similar customers) and future spending amounts (we assumed that the average spending amount of each customer will not change in the future). Despite the facility of the implementation of this approach, it can be much too simplistic and fail to estimate CLV reliably.

There are several methods for estimating retention and spending amounts, but some of them can be far too complex. The method that I will propose has a good balance between accuracy and implementation simplicity and is based on customer segmentation and probability.

The first step is to take the customer data of year -2 and segment them based on their value and their activeness. For the value we can use the amount spent in a specific year (which is a mix of the average amount spent per purchase and the frequency of purchases), and for activeness we can use the recency of the last purchase (for example the number of days between the last purchase and the end of the analyzed year). For activeness we can also use a mix of recency and frequency. In the second step, we have to define a certain number of customer segments. The segmentation technique can be either a simple double-entry matrix or a statistical clustering technique. We can for example end up with six clusters:

• -          Active high value
• -          Active low value
• -          Warm
• -          Cold
• -          Inactive
• -          New customers

The idea behind this technique is to estimate the retention and spending amount using the probability of a customer remaining in the same segment or changing segment and by applying to this customer the average spending amount of the new segment. To calculate the probability of moving from one segment to another, it is necessary to segment the customers into year -1 and create a transition matrix (transition among different segments from year -2 to year -1) in which probabilities are calculated for each combination of segment groups.

Transition Matrix of Customers’ Segments

With the probability transition matrix we can simulate how the segments will change in the future and maybe realize that we are dangerously reducing active customers in favor of inactive ones and that we need to acquire a slightly bigger number of each kind of customer to avoid a decrease in profits. In any case with this matrix we can simulate several years ahead and estimate how many customers will still be active. We can also estimate their value by multiplying the average value of each segment by the number of customers of the same segment in a specific year (year 0, year +1, year +2, etc.).

In the proposed template, I have added an estimation of new customers acquired each year to simulate the total number of customers and their value a few years ahead. However, to calculate the CLV of the current customers, this value should be set to 0 and then the total value of each year discounted by the discount rate.

TEMPLATE

Download the CLV2 Template

27. CUSTOMER LIFETIME VALUE 1 - Principles and Calculation

OBJECTIVE

Estimate the lifetime value of a customer or group of customers.

DESCRIPTION

Customer lifetime value is an indicator that represents the net present value of a customer based on the estimated future revenues and costs. The main components of this calculation are:

• -          Average purchase margin (revenue – costs);
• -          Frequency of purchase;
• -          Marketing costs;
• -          Discount rate or cost of capital.

There are several ways to calculate it, and different formulas have been proposed. The most difficult part is to estimate customers’ retention (in contractual settings) or repetition and to estimate the monetary amount that a customer will spend in the future. It is important to remember that CLV is about the future and not the past, which is why using past data of a customer is not the best method for calculating CLV. A good practice is to segment customers and estimate the retention and spending patterns based on similar customers. Then, the following formula can be applied:

• CLV = customer lifetime value
• MC = yearly marginal contribution, that is to say the total purchase revenue in a year minus the unit costs of production and marketing
• R = retention rate (yearly)
• D = discount rate
• CA = cost of acquisition (one-time cost spent by the company to reach a new customer)

Customer Lifetime Value of Different Customers

The discount rate can be the average cost of capital for the company or the related industry, and it is used to depreciate the value of future benefits to estimate what they are worth today. With this formula we can estimate the CLV of a single customer or a segment of customers. In the case of estimating it at the individual level, we should use the retention rate (r) of similar customers, for example customers who buy similar products, or more sophisticated techniques, for example cluster analysis.

When we define the value of a customer or a group of customers, we can make decisions concerning the level of attention, the investment in marketing and retention costs, or the amount that we can spend (cost of acquisition) to attract customers with a similar CLV.

Even though it is quite difficult to estimate, we have to consider that the CLV formula does not take into account the value generated by referrals. Although some formulas have been proposed,[1] this calculation is seldom used due to the lack of information. In fact, to calculate the customer referral value, we need information about the advocates and the referred customers, and for the latter we should be able to distinguish those who would have made the purchase anyway (without the referral). As a proxy we can use the NPS (see 29. NET PROMOTER SCORE^® (NPS^®)) combined with other information from surveys, such as asking whether a customer has been referred and how much the referral has affected the purchase.

The market’s historical data is the main source of information (at the individual level, usually from CRM systems), but it can be enriched with survey data, for example concerning the likelihood of repeating the purchase or recommending the product.

TEMPLATE

Download the CLV Template

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[1] https://hbr.org/2007/10/how-valuable-is-word-of-mouth

26. RFM MODEL (Recency, Frequency, Monetary Value)

OBJECTIVE

Estimate the lifetime value of a customer or group of customers.

DESCRIPTION

This is probably the simplest model for the estimation of customers’ value. In spite of its simplicity, it is also famous for its reliability, which is based on three variables:

• -          Recency: the more recent the purchase or interaction, the more inclined the client is to accept another interaction;
• -          Frequency: the more times a customer purchases, the more valuable he or she is to the company;
• -          Monetary value: the total value of a customer also depends on the amount spent in a given period.

Customers’ Value Calculated by an RFM Model

Usually, these three variables are transformed into comparable indicators (for example into a “0 to 1” indicator) and summed up to obtain a total value indicator. We can also define different weights for each indicator.

TEMPLATE

Download the RMF Model Template