The Loyalty Effect: A New Look at Lifetime Value

One of the best books on customer retention I have ever read is The Loyalty Effect by Frederick Reichheld (Harvard Business School Press 1996). In this insightful book, he opens up several new ways to look at customer lifetime value. I have been studying this field and writing about it for the past eight years. I must say, that I learned a great deal from this book.

Successful companies, Reichheld points out, have three things in common: loyal customers, loyal employees and loyal owners. The customers are often loyal, not so much to a bank, for example, as to employees that they know and trust who work for the bank. If you have loyal customers, several things happen:

• Retention rates go up
• Referrals go up
• Spending rates go up
• The customers are less price sensitive
• The costs of servicing them go down
• Initial processing costs go down
• Returns and losses are lower
• Profits go up

Reichheld maintains that a five percent increase in the retention rate can increase lifetime value by as much as 75% in such industries as insurance, banking, and auto service. When I saw this number, I had doubts. I decided to apply Reichheld’s principles to a set of standard lifetime value charts. The results are shown as Chart A and Chart B. In most LTV charts, the variables are customer retention, referrals and spending rates. During the first year with any supplier, the customer defection rate tends to be high. But after that crucial first year, those loyal customers still remaining have much higher retention rates. These loyalists tend to refer their friends, relatives and business associates more and more as they continue with the same bank, brokerage firm, automobile service, or department store. Finally, these loyal customers tend to spend more every year with their chosen supplier. This fact is well known to any company that maintains a customer marketing database which contains purchase history.

Reichheld adds two more important variables which affect lifetime value: price sensitivity and cost of service. Both of them make a lot of sense.

Concerning price sensitivity, he makes the point that loyal customers tend, not only to buy more over their years with a supplier, but also to buy more expensive products. As one example, he points out that personal insurance premiums tend to go up by about 8% per year for a number of reasons:

• Typical families earn more each year
• They buy more expensive cars
• They add to their homes requiring higher coverage
• They buy vacation homes and boats
• As their family grows, they add more life insurance coverage
• As they age, they think about long term care.

Cost of service is one area not commonly covered in LTV tables. It should be. Reichheld points out that the costs of servicing a new customer are typically much higher in the first year. Software customers call for help more in the first sixty days than they do in the next sixty months. In the first weeks, HMO customers come in to see what is available, to get checkups, to talk to service providers. The cost of servicing a bank loan or insurance usually occurs heavily in the application phase. The cost of service after the first year is usually very low. Since these costs do drop with length of service, the cost reductions should be reflected in an accurate lifetime value table.

I took Reichheld’s ideas and applied them to a normal modern LTV table. Essentially, I was trying to replicate his concept that lifetime value could increase as much as 75% if the retention rate increased by 5%. Here are the variables that I used:

	Year 1	Year2	Year3	Year4	Year5
Retention	60%	80%	83%	86%	90%
Referrals	4%	8%	9%	9%	9%
Purchases	5	10	12	13	14
Price	$48	$56	$60	$64	$68
Cost	70%	60%	58%	56%	56%

Imagine that we are looking at customers of a department store, bank or insurance company. In the first year, the supplier loses 40% of their customers. Thereafter, however, the retention rate jumps to 80% and rising. In the first year, 4% of the customers refer others who become customers to the business. In subsequent years, the referral rate more than doubles. In the first year, the customers make only five purchases, averaging $48 per time. For those loyalists who last to the second year, the purchases double, and the price they pay for products increases as well. Finally, the first year costs (which include the cost of the products delivered, plus the cost of delivering those products) amount to 70% of revenue. Thereafter, the costs drop to 60% and below.

These are the assumptions that we made to validate the Reichheld thesis. We now plug them into two lifetime value tables: a base table (Chart A) and a 5% increase in retention rate table (Chart B).

These tables are familiar to most readers. We will explain a few terms. We are assuming that we acquire 100,000 customers. These may be acquired over several years. Year1 is not a calendar year, but the year of acquisition. Year2 is the year after that. In Year5 there are 62,245 customers still buying, which include about 20,000 referrals. The retention rates in the first chart average 80%. Those in the second chart average 85%, so they represent a 5% increase in the retention rate. It costs $80 on the average to acquire a new customer.

The discount rate is based on an interest rate of 16%, which includes inflation and risk as well as the cost of capital. We are looking at the lifetime value in the fifth year as being representative of the net result of the Loyalty Effect that Reichheld is describing. Some companies, of course have much shorter customer lifetimes than three years, and many have longer lifetimes. Five years is a good average number to use to test the Reichheld concept. What is the final result of increasing the retention rate by 5%?

	Year 1	Year 2	Year 3	Year 4	Year 5
80%	-$8.00	$98.19	$207.37	$312.16	$410.35
85%	-$8.00	$106.49	$230.88	$356.77	$480.93
Difference	$0.00	$8.30	$23.51	$44.61	$70.58
Percent	0.00%	8.45%	11.34%	14.29%	17.20%

As I see it, the lifetime value is increased by 17%. That is not 75%, but it is a very healthy and profitable increase. I have worked with the figures for some time, and I cannot get them to go much higher than 23% with any set of reasonable assumptions.

There is an additional problem with the Reichheld thesis which has to be recognized. How do you get the retention rate to increase from 80% to 85%? Everyone that I know in our business assumes that the increase has to come from treating the customers better in some way. You build relationships with them. You send them letters, birthday cards and newsletters. You put them on advisory boards and survey them. You give them gold cards and gold card treatment. All of these things cost money, and the extra costs are not factored into these tables. We cannot get the retention rate to increase by just willing it up. We have to do something to make it go up, and most things like this cost money – which cuts into the bottom line. So, if I were to include these extra costs, the increase in lifetime value would not be 17%, it would be less than 17%. This is where one of Reichheld’s key insights comes to our rescue. Reichheld says that the increase in retention comes not so much from the way you treat customers – although treating customers well is important – but from recruiting the right customers in the first place.

He develops three principles:

• Some customers are predictable and loyal. They prefer long term relationships
• Some customers are more profitable than others. They spend more and need less service.
• Some customers like your products and services more than those of your competitors.

We should look for people who have one, two or three of these factors. Customers have what he calls a loyalty coefficient. He describes an insurance company that studied the demographics which explained the loyalty of its customers. It discovered that:

• Customers in the Mid West and rural areas tended to be more loyal
• Customers in the North East, and city dwellers tended to be less loyal.
• Married people were more loyal than singles
• Renters were less loyal than homeowners.
• Young people were less loyal than older people
• Income levels also affected loyalty.

The differing segments of the population had average retention rates varying from 72% to 94%. He went on to show how the success of many companies was due to their method of attracting the right customers – those who had a high loyalty coefficient. These acquisition methods might be more expensive, but if they resulted in more loyal customers, they would be worth it. For example:

• USAA targets a special niche: active duty military officers who are unusually loyal, reliable, and honest customers. As a result, less than 2% of USA auto insurance policy holders defect each year.
• Lexus targets former Mercedes and Cadillac owners, whereas Infiniti seeks younger people who drove BMWs and Jaguars. Infiniti focusses on fashion and high performance. Lexus owners are older, and more attracted to service, reliability, and long-term value. The result, the Lexus repurchase rate was 63% for two years in a row, while Infiniti rates were stuck at 42%. Lexus got loyal customers by seeking loyalists in the first place.
• An auto insurer found that its worst risks and least loyal customers came from respondents to yellow page advertisements. They discouraged their agents from placing such ads. Another company found that recent arrivals in town had higher defection rates. They now ask new applicants how long they have been in town. Progressive Insurance found that the worst customers tend to walk into agency offices off the street. To avoid these customers, the company encourages agents to locate their offices in out-of-the-way office buildings.
• MBNA acquires a very large and intensely loyal credit card customer base by cultivating 3,800 organizations and developing an affinity card for each. The resulting loyal customer has a $59,000 income, has been employed for 14 years, is a homeowner who has paid his bills regularly for 14 years. Their members include half of all dentists in the US, 43% of all doctors, 827,000 teachers and 200,000 attorneys. Their balances are 67% above the national average. They use their cards 12% more than the national average and spend 4% more per transaction.
• Customers who are acquired by coupons and price discounts are less loyal than those who come from other sources. Companies that expect to achieve sustainable high performance are studying lifecycle profit and tenure patterns, and using this insight to find the right customers.

If you look for the right kind of customers in the first place, then you can increase your retention rate without spending additional money. You have to treat people right, of course, but the job is much easier if you begin with loyal people.

So what can we conclude from this effort? Reichheld is right that loyalty (the retention rate) is vital to success today. As he points out, with the maturing of the markets for most companies today, “the smartest of the smart will shift their growth strategies away from new-customer acquisition and toward building and broadening their relationships with the good customers they’ve already won.”

He goes on to point out that getting the right customers is only the first step. The next step is to use the superior cash flow from superior customers to hire and retain superior employees. Loyal employees are a key factor, in many companies, in having loyal customers.

In summary, Reichheld’s book is must reading for everyone in the direct marketing industry. It has some new insights that many companies can put to immediate profitable use. For my part, I will be rethinking my lifetime value calculations to include increasing selling price levels, and decreasing service costs as normal complements of loyal customers. I will take to heart Reichheld’s key point that the most important way to build customer loyalty is to recruit loyal customers to begin with.

Chart A

		Year 1	Year 2	Year 3	Year 4	Year 5
Referral Rate	4%		8%	9%	9%	9%
Referred Customers	0		4,000	5,120	5,069	4,663
Retention Rate	80%	60%	80%	83%	86%	90%
Retained Customers	100,000		60,000	51,200	46,746	44,560
Total Customers	100,000		64,000	56,320	51,814	49,224
Items Purchased	5.0		10.0	12.0	13.0	14.0
Average Price	$48.00		$56.00	$60.00	$64.00	$68.00
Spending Rate	$240.00		$560.00	$720.00	$832.00	$952.00
Total Revenue	$24,000,000		$35,840,000	$40,550,400	$43,109,581	$46,860,943
Direct Percent		70%	60%	58%	56%	56%
Direct Cost	$16,800,000		$21,504,000	$23,519,232	$24,141,365	$26,242,128
Acquisition Cost	$80	$8,000,000	$0	$0	$0	$0
Total Cost	$24,800,000		$21,504,000	$23,519,232	$24,141,365	$26,242,128
Profit	-$800,000		$14,336,000	$17,031,168	$18,968,216	$20,618,815
Discount Rate	1.00		1.35	1.56	1.81	2.10
Net Present Value	($800,000)		$10,619,259	$10,917,415	$10,479,677	$9,818,483
Cumulative NPV	($800,000)		$9,819,259	$20,736,675	$31,216,352	$41,034,835
Lifetime Value	80%	($8.00)	$98.19	$207.37	$312.16	$410.35

Chart B

		Year 1	Year 2	Year 3	Year 4	Year 5
Referral Rate		4%	8%	9%	9%	9%
Referred Customers		0	4,000	5,520	5,775	5,602
Retention Rate	85%	65%	85%	88%	91%	95%
Retained Customers		100,000	65,000	58,650	56,470	56,643
Total Customers		100,000	69,000	64,170	62,245	62,245
Annual Purchases		5.0	10.0	12.0	13.0	14.0
Average Price Paid		$48.00	$56.00	$60.00	$64.00	$68.00
Spending Rate		$240.00	$560.00	$720.00	$832.00	$952.00
Total Revenue		$24,000,000	$38,640,000	$46,202,400	$51,787,757	$59,257,145
Costs
Direct Percent	70%	60%	58%	56%	56%
Direct Cost		$16,800,000	$23,184,000	$26,797,392	$29,001,144	$33,184,001
Acquisition Cost	$80	$8,000,000	$0	$0	$0	$0
Total Cost		$24,800,000	$23,184,000	$26,797,392	$29,001,144	$33,184,001
Profit		-$800,000	$15,456,000	$19,405,008	$22,786,613	$26,073,144
Discount Rate		1.00	1.35	1.56	1.81	2.10
Net Present Value		($800,000)	$11,448,889	$12,439,108	$12,589,289	$12,415,783
Cumulative NPV		($800,000)	$10,648,889	$23,087,997	$35,677,286	$48,093,068
Lifetime Value	85%	($8.00)	$106.49	$230.88	$356.77	$480.93

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