Lifetime Value or Return on Investment Which is the Better Measure of Success?

More and more companies are starting to use Lifetime Value as a means of predicting the success or failure of new marketing strategies before serious money is committed.

In the past, return on investment was what everyone used. ROI is neat and easy to understand. You invest so much in a promotion, and you measure the response. You calculate the net profit from the sales to the respondents, and divide by the amount invested. The result: return on investment. For example, you might invest $40,000 in mailing a 100,000 piece promotion. If you have a 2% response rate, and sell 2,000 items at $100 each, with a net profit of $50 each, you will have a gross profit of $100,000. Subtracting the $40,000 invested in the mailing from the $100,000 gross profit, your return is $60,000. Your return on investment is 1.5 which is very respectable, and understandable.

ROI is clearly the way to measure the immediate result of any direct marketing effort. But with database marketing, the marketer is able to consider more factors and project his profits over a series of years, rather than just looking at the return from a single campaign. The reason why Lifetime Value has proved to be a more sophisticated measurement is that the LTV calculations include such factors as the retention rate, the referral rate and the (long term) spending rate, rather than just the response to an immediate promotion. We are judging our marketing efforts by our ability to retain and increase the loyalty and spending rate of our customers, rather than just considering the result of a single investment. Lets see how this is done.

Here are a set of lifetime value charts adapted from the work of Andy Kardos, President of BCP Direct in Toronto. Andy has worked with a number of different clients. This chart is based on experience he had with several retailers. In this simulation, the retailer was able to keep track of her customers through a proprietary card, so she had a pretty good database built up. She had more than a million customers. She knew the spending habits of the customers who used the card (about 40% of all sales – or about 400,000 customers). She decided to experiment with a long term loyalty program. To do this, she set up a test group of 5,000 customers. The program involved personalized mailings, special offers, an incentive to refer new customers and other special benefits. The program cost about $12 per person per year. To determine whether her program really paid off, she set aside a control group of 5,000 customers who did not receive any of the loyalty program communications or benefits. She compared the results over a three year period.

Chart A shows the lifetime value of the customers in the control group (those not in the loyalty program). Their lifetime value, in the third year, was $162.91. Their initial retention rate was 73% in the first year and 79% in the second year, Their spending rate was $138 and $149 and $161 in the first three years.

In Chart B we look at the lifetime value of the 5,000 customers in the loyalty program. The loyalty program has had a measurable effect on the customers enrolled in it. In the first place, their retention rate increased to 79% and 84%. This added 434 customers who would otherwise have disappeared in the third year. These retained people spent $78,988 alone in the third year. In addition, the referral program really paid off. For a net referral incentive of $15 per customer, in the first year, 6% of existing customers encouraged their friends or relatives to become customers of the retailer. This added 300 new customers in Year 2. Furthermore, the loyal 4,250 customers who were still shopping in Year 3 increased their referral rate to 8%, bringing in a net 340 new customers. These 640 new customers (the referrals over two years) made all the difference in the loyalty program. They brought in $116,480 in sales in Year 3.

The loyalty program also affected the spending rate. It increased to $162, $175 and $182 – a big jump over the spending of the control group. Why did these people spend more? Because they were being treated a special people. They were recognized, communicated with. They felt a new special relationship with the retailer. The results could be seen in the numbers on the chart.

To accomplish this gain in spending and retention, the retailer had to spend $12 per customer per year. This was spent on communications, a special 800 number for this favored group with special treatment when they called, member nights and a host of other benefits which amounted to 6.6% of total sales revenue from this group in the third year. Was it worth it? Can the loyalty program be justified? This is where Chart C comes in. In this chart we compare the lifetime value of the control and the test group. In the first year, the LTV of the test group is actually lower than the control group. Why? Because the extra $12 per customer per year spent on loyalty maintenance exceeded the gains from increased retention and spending.

Post Footer automatically generated by Add Post Footer Plugin for wordpress.