Customer data
Calculating the CLV requires that you have detailed customer data and this presents a problem for many companies – they don’t have much information beyond the absolute basics.
Peter Drucker, the management guru said “If you don’t measure it you can’t manage it” and this certainly applies to customer activities.
The extent and type of customer data that are collected will vary from company to company and from B2B to B2C business models. For some customer retention is all important while for others, for example, a custom builder, referrals are the key to success. Whatever the differences the key point is that the more customer data that is collected the more successful will be the company.
Collecting customer data doesn’t come free – some will judge the expense as reducing profits and so will collect as little as possible, others will judge it as an investment and collect as much as is appropriate. Companies with the latter belief will prosper; those with the former opinion will just have to get lucky!
What’s appropriate? It’s a judgment call but basically the goal is to collect data that can be of practical value to help measure and increase;
· Retention
· Referrals
· Purchases
· Margins
If you sell an expensive high margin product then you will benefit from collecting as much customer data as possible, if however you sell an inexpensive low margin product then your customer data can be less detailed.
All companies need to identify their most profitable customers (80/20 analysis) plus those thought to have potential to join that group. It is important to collect more data on these “A” customers, even for low price, low margin product companies. These customers must be retained and the more you know about them the more likely it is that this will occur.
Most companies will have basic customer data: sales history showing purchases by date, product(s) purchased and margins generated on the purchases.
Thereafter the information about customers that can be collected is almost unlimited, some of it valuable to develop customer profiles to assist in new customer acquisition and some directly related to calculating and then increasing the CLV, including the following.
Retention
The CLV is dependent on the length of time (usually years) that the average customer continues to buy from the company. The current figure, to act as a starting point, can be determined by tracking all, or a representative number of, customers for each of the market segments for which the CLV is to be calculated, by going into historic customer records. New or start-up companies will not have historic customer records so judgment must be used to estimate a figure (to be refined as data becomes available) for most B2B companies a 3-5 year time scale can be used.
Interestingly many companies do not know when, or if, a customer has defected – much less why. It’s necessary to know when a customer has defected so that retention rates can be monitored. If, for example, a company’s usual customer purchase frequency is every 30 days then it would be reasonable to assume that a customer has defected if there has been no purchase for 90 days – different companies will have different purchase frequencies and so defector dates, but the method used to calculate it is the same.
Clearly the CLV will increase if the retention rate improves (less defectors) so trying to spot a potential defector is a worthwhile endeavor. Generally there will be changes in a customer’s purchase habits – lower purchase frequency, smaller orders, some products not purchased and so on, that hint at the prospect of defection. Knowing this allows the company to take action to try to prevent the customer leaving. Even though a customer has defected it doesn’t necessarily mean that all is lost. Some defectors can be reclaimed; studies have shown that it is less expensive to reclaim a lost customer than it is to acquire a new one.
Retaining customers is best achieved by converting simply satisfied customers into loyal ones (see August 18 post) and companies that buy into this strategy will have dedicated programs in force to achieve this. These programs will be discussed in a future posting in this series.
Referrals
In most cases the best customer is one referred by an existing customer, they come largely pre-sold and their acquisition cost is low, or none.
The average CLV is increased if existing customers can be encouraged to refer more potential customers – to become advocates for the company. Again one way to do this is to convert simply satisfied customers into loyal ones.
It is vital that referral statistics are collected so that the changes in referral rates and the affect on the CLV can be monitored.
The source of each enquiry must be noted and the progress of each tracked. This will show the increase/decrease in referrals and their conversion rate to actual customers. Ideally details of their purchases would be collected.
Purchases
Loyal customers are more likely to be accepting of new products, they will tend to commit more of their purchases to the company (“share of wallet”) and react more positively to cross-selling. The average customer purchases by customer segment is easy to calculate and trends in average customer purchase is an important control metric.
Cost of acquiring a new customer and programs to retain existing ones
Determining the CLV requires that the cost of acquiring a new customer is calculated. This is a hidden figure in most companies. The calculation is simple enough, take your entire new client acquisition budget and divide it by the number of new clients acquired, not counting those that came from referrals. Then take the annual cost of all internal customer satisfaction programs and divide it by the total number of customers to get a rough cost per customer.
Frank Friend Any questions? Contact me at Email. friend@ffauk.com