A typical Life time value calculation:
LTV traces the buying behaviour of group of existing and new customers under different cluster groups in present and future period. To make it simple, we can presume that LTV is the profit that we will receive from a given group of customers within next 3 to 4 years. This may be taken as a trend for future and it may be revised once in three to four years. For working out a typical LTV calculation, we can take customers who are acquired in 2006 and measure their behaviour in that year, in 2007 and in 2008. This is almost similar to NAV value of investments for the purpose of balance sheet.
Sale of books through catalogue customers:
For calculating the LTV, the following details are required.
- The number of customers acquired in the referred year no.1
- The retention rate of the customers in year no.2 and 3 and so on.
- The number of orders placed by customers for all the years under ref.
- The average order size for all the years under ref.
- The number of catalogues used to attract these customers for all the years
- The additional catalogue sent to customers during 1st and subsequent years.
- The cost of catalogue per piece
- Discounted rate for finding the net present value.
Table 1:Example showing calculation of LTV for one new sales campaign for sale of books
From the above table we can observe that for getting 5 lakh new customers, an amount of Rs.35 lakhs was spent by way of catalogue in the first year. The retention rate was presumed to be 35% in the first year and hence the 2nd year started with 1.75 lakh customers. The retention rate for second year increased from 35% to 60% and in third year it is 70%. From this retention rate, we can understand that many of the disloyal customers disappear after the first year. But in the second and third year, customers stabilize with the book store as loyal existing customers as compared to new customers. Not only they are loyal to book store but their average number of orders and average order size are also increased. For introduction to new customers, initially 2 catalogues per customer were circulated in the first year followed by 5 supplement catalogues. In the second and third year, in place of introductory catalogues, 10 supplementary catalogues per customer were circulated to keep them in regular contacts for increasing the business. The total cost of catalogues was calculated taking cost of one catalogue as Rs.1/-. In the same way, the total cost of sales was presumed to be 60% of sale value. Thus total costs and revenues are calculated and gross profit is arrived at.
The discount rate (based on prevailing interest rates) is also included because future profits are not worth as much in today’s money as present profits. The formula for the discount rate is D= (1+( i x rf))n
Where D= Discount rate, i= interest rate in %, rf= Risk factor and n= number of years after which we receive money. For e.g. , if a risk factor is 1 and an interest rate of 12%, the discount rate for the second year (one year from now) is
D= ( 1+(0.12 x 1))1 or D= (1.12) 1 =1.12
In the similar way, the discount rate for the third year (two years from now) is
D= ( 1+(0.12 x 1))2 or D= (1.12) 2 =1.25
The LTV is calculated by dividing the cumulative LTV by the originally acquired 5 lakh customers. The LTV of three years are Rs.45, Rs.66.04 and Rs.81.52 respectively.
Profit per customer is arrived at by dividing the gross profit by number of customers of respective year. In the above example, the profit per customer increased from Rs.45 to 82.59. The profit is less in first year as it is the year of capturing new customers. The second and third year, it is only retaining customer costs and it has given improved profits. From this we can understand that acquiring new customers is not a profitable action in the initial stage but subsequently when new customers become existing customers, they are more profitable. That is why money spent on increased retention has a higher pay off than money spent on acquisition.
We have calculated an average LTV for a group of 5 lakh customers. This may consist of senior citizens, college students, professionals, high spending group, low spending group, no spending group, group preference to superior, medium and economy quality etc. If we want to create customer data profile by creating customer segments by different age group or by spending habits or by geographical locations etc, it can be possible with the above data with segregation under different groups.
C R Venkata Ramani