Do you have to enchant your customers to gain loyalty?

What does it take to gain customer loyalty?
Beating their expectations is one way. But by how much?
Do you have to beat their expectations by a mile?
Do you have to forgo profits in the form of lower prices and higher service?
Can your business profitably beat customer expectations?For any marketer trying to gain customer loyalty in the form of repeat purchase, these are valid questions. After all there is no point in gaining loyalty of customers at the expense of profit.This article is about answering these questions using consumer behavior research.

Background and Hypotheses Development

Sometime back Tom Hulme sent me a tweet on his experience with Nespresso. Tom enjoyed using  his Nespresso machine but one day the water container broke. Tom said,

Did Nespresso price its part correctly?
Did it have to price it so low to gain loyalty?

I posited that Nespresso gave away too much, priced it incorrectly and should have given choices.

These discussions  led me to propose the  following two hypotheses

H1: Brands do not have to beat customer expectations by too much. They can get the sameeffect by beating it just enough.

H2: When customers are given choices at different price points, they will self-select themselves to the right version and will exhibit same loyalty as those receiving large price discount.

The loyalty here refers to attitudinal loyalty as there is no easy way to measure behavioral loyalty.

Experiment Design

I designed a between groups experiment to measure the difference between the stated attitudinal loyalty of different groups.  There are four groups in this experiment, all of them are filled in on Nespresso and were primed with a fixed willingness to pay of $30.

Since customers do not not what they are willing to pay and some of my experimental subjects may not know the cost of parts I used the price of $30 to normalize their willingness to pay.

Different groups were given different price rent by quoting them different price for the replacement part.

Group A:   WTP = $30, Quoted Price = $2.99
Group B:   WTP = $30, Quoted Price = $25.99
Group C:   WTP = $30, Quoted Price = $19.99
Group D:   WTP = $30,  Choices: Basic $9.99, Exact $19.99, Premium $28.99

Group B and Group C are similar but test different price points.

I designed the experiment using survey format (thanks to SurveyGizmo and its very powerful split testing functions) and ran it as a survey on people in my network and bunch of MBAs from Haas School of Business, Berkeley.

Respondents were asked to state their likelihood of repurchase  on a 6 point scale (a measure of loyalty). I also asked them to rate their likelihood to recommend the brand to others, more on this later.

Results

For testing the first hypotheses I compared the sample mean using 1 tailed t-test.  Between Group A ($2.99)  and Group B ($25.99) there was statistically significant difference (p=0.023) between the two samples. This could mean that beating customer expectation by a mile, in the form of very low price will have higher effect on loyalty than beating customer expectation just by a foot.

Between Group A ($2.99) and Group C ($19.99), the difference is not statistically significant (p =0.243). This is a critical finding. While $25.99 was no enough, $19.99 engendered the same level of loyalty as $2.99. That is a huge price difference. Brands do not have to give away the farm in  the name of loyalty. This also points to lost profit opportunity for Nespresso.

Next  let us take the second hypothesis that choices and self-selection (Group D) would perform at least as good as giving steepest price discount (the $2.99 option Group A).

Comparing sample means show there is statistically significant difference between mean likelihood ratings of Group A and Group D (p = 0.014). This is a big surprise for three reasons.

For one thing, when customers were given choices and self-select themselves to the version they prefer, they are more likely to feel ownership and increased utility.

Second, this Group was offered the same $19.99 price for the “Exact Match” version. This was the only option offered to Group C. While Group C showed no difference from Group A, this group did. Presence of choice negated any positive effect from $19.99 price.

Third,  if we looked at the sub-group  that chose the lowest priced Basic version ($9.99), there still is statistically significant difference between this sub-group and Group A.

One conclusion we can make is that presence of options for replacement parts causes customers to incur cognitive cost that is reflected in the form of low loyalty rating.  However, this requires further consideration before casting aside versioning.

One interesting corollary is the correlation between loyalty measured as intention to repurchase and likelihood to recommend. As I stated before, I asked respondents to rate both. There is very high correlation (0.99) between the two metric. Likelihood to recommend is not a better measure as contend.

Marketing Implications

Loyalty does not have to mean “delighting, enchanting, astonishing” customers. You can beat customer expectations by just enough. This is attitudinal loyalty and may not translate into behavioral loyalty. So in general using price discount to generate future sales is not recommended.

Statistical significance does not mean economic significance. The mean loyalty rating for lowest price group was 4.4 vs. 3.68 for $25.99 group. Will gaining loyalty at the cost of $22 per customer generate more profit in the form of future purchases?

For pricing replacement parts, brands need to do Willingness to Pay studies just as they do for the full product. There is no reason to sell the replacement part at cost due to fears of customer backlash. Same principles of value based pricing apply for parts.

While multi-version pricing is effective in most scenarios, offering choices for replacement parts comes at a cost to customer (See 4 costs of versioning). While versions enable profit maximization its effect on customer loyalty needs to be considered.

What does store design have to do with price increases?

In a WSJ interview  Wal-Mart’s chief of U.S operations says this about Wal-Mart’s attempt to re-design its stores,

WSJ: Is Wal-Mart as focused as it needs to be on offering the lowest possible prices?

Mr. Simon: A lot of things have distracted us from our pricing mission. We got enamored with presentation as an example. We walked people through our [remodeled] stores and they were gorgeous.

But they cost more. And if you spend more on your building, your prices can’t be as low as you want them to be.

“Every Day Low Price” can’t come from the supplier because they have to make money too. “Every Day Low Price” has to come from every day low cost, which means we have to operate for less.

Sustainability and some of these other initiatives can be distracting if they don’t add to every day low cost.

There are two claims made here that I believe the interviewer should have pushed Mr.Simon on but did not.

  1. If you spend more on your building, your prices can’t be as low as you want them to be.
  2. Sustainability and some of these other initiatives can be distracting if they don’t add to every day low cost.

I will discuss the first point in this article and defer the second for a future article.

Better design and presentation does not mean the sourcing costs (what Wal-Mart pays to suppliers) go up.  So why should the customers offset that cost in the form of higher prices?

If the design changes are already made and if there are no recurring costs to keep-up the design, the costs are sunk. So why do they matter?

Unless of course Mr.Simon is looking at an accountant’s definition of Cost Of Goods Sold (COGS) which includes in it a share of all fixed costs. To an accountant preparing the company’s financial statements, ever bar of soap and bottle of shampoo must be assigned its share of the building cost, employee cost, utility cost etc.

It is due to the quirky accounting rule of how costs are matched with inventories and how inventories are moved into expenses as Cost of Goods Sold.

But the accountants do not run businesses, set prices or make business decisions. They report on the business’ performance with just enough clarity and obfuscation at the same time.

A business cannot spend more on a building and expect to pass on the costs to customers in the form of price increases. Before spending money to improve the aisles, they should have estimated whether the improvements will nudge their customers’ willingness to pay higher and whether they can  generate enough profit to justify the costs.

Incremental profit need not come in the form of higher prices,  it can be in the form of increase in sales from new customers. Better design could bring in new customers who otherwise would not have stepped into the store.

To say, “If you spend more on your building, your prices can’t be as low as you want them to be” is neither true nor relevant here.

As an important side point, when a store spruces up and improves its design and shopping experience will the willingness to pay of its customers go up?

The answer will take us through the path set forth by Thaler on Mental Accounting and Consumer Choice.  It starts with the  story of you relaxing in beach and thirsty for an ice cold drink.

To be covered in a later article.

On Focus Groups: Anyone can convene a group, ask questions, and write up the answers

There is a great book titled,  “Prove It Before You Promote It“,  that I read  a while back.  It has some very sobering remarks on focus groups and how they are applied in product development.

I am reproducing in its entirety author Steve Cuno’s commentary on focus group:

Many companies hold focus groups. They fill a room with 10 to 20 carefully selected respondents and ask them questions. That much is fine. A problem occurs only when companies mistake the resultant feedback for data—and make decisions based on what they hear.

Focus groups, with an easy-to-imitate format, are a great place for incompetents to convince themselves and unsuspecting clients that they know what they’re doing. Anyone can convene a group, ask questions, and write up the answers.

I have seen focus group reports that say things like, “Seventy percent felt the packaging was too pink” or “Eighty percent said if you open a store on the West Side, they’ll shop there.” I have seen the people running the focus groups, whose role is to remain unbiased, ask leading questions like, “Would you be more or less likely to shop at a store that advertises on violent cartoons aimed at small children?”

Amazingly and sadly, businesses actually base big decisions on these groups. They make the package less pink. They open a store on the West Side. They pull their ads from Batman cartoons. And all too often they later find that consumers don’t behave the way they said they would in the focus group.

I completely agree. I have written previously about the relevance of focus groups, this book does a much better of job teaching us the pitfalls of misuse of focus groups.

What the book says about focus groups – asking a few (leading) questions and taking product decisions based on the feedback of handful of people-   is very relevant to basing product and startup strategy based on the interviews with handful of customers.

When you are talking to customers, you are still forming hypotheses not testing them. The hard part is not testing the hypotheses, but forming better hypotheses to test.  Focus groups and customer interviews help us make better and testable hypotheses.

If you are in marketing, run a startup, manage a product or do A/B tests you definitely should read this book.

Now I Know My p(A), p(B), p(C) …

This is not a primer or a refresher on probability but let us start with some probability calculations anyway,

  1. What is the probability of getting Heads when you toss a fair coin?
  2. How about getting  8 when you roll a pair of fair dice?
  3. I have a rod that is 1 meter long. I draw two random numbers between 0 and 1 and cut the rods into three parts using the random numbers as lengths of pieces. What is the probability the three pieces can be arranged as a triangle?

Some are easy, some are computationally intensive but eventually we can get to the answer. We arrive at the answer by counting – we count all possible outcomes (Sample Space N) and count the number of desired result ( n) and find the probability a n/N.  (I think the answer to 3 is 0.25)

This is the traditional (also known as frequentist approach (because it involves counting frequencies of events) to probability.

But consider these,

  1. You see the techcrunch headline about a new startup. Just from that headline, what is the probability that the startup will be a big success?
  2. You see a salon on Main St, what is the probability that the next customer who walks in will be named Jonathan?
  3. You run a freemium business. What is the probability that a user who signs for free version will upgrade to premium version in 6 months?

These are not easy to answer – definitely not by counting the sample space or the outcomes. Probability in these cases ceases to be a ratio of two countable events and becomes a representation of hunch, degree of belief, gut feel or a hypothesis.

I said it, hunch, belief, gut feel …

Probability now becomes a measure of our certainty (or uncertainty) about the outcome.  We are in the realm of  Bayesian probability. Despite the esoteric name, we all practice this in our every day reasoning.

A hunch, belief or hypothesis  has to start somewhere.

In some cases we start with  a probability value that is no different from the traditional approach. We remember reading somewhere that 90% of startups do not go big. So we answer the startup question as, 10%.   Similarly, the answer to the freemium question is 3%.

In some cases, it occurs from our domain knowledge and years of experience working with a specific area.

In some cases, it just occurs to us and we “just feel it in our gut”. So we state that the probability of next customer being Jonathan as 17%.

A hunch, gut feel or belief is not all bad when that is all we can make.

But problems arise when we trust our guts or pay grades,  even in the presence of prior knowledge or fail to take new knowledge into account to improve our hunch.

  1. We fail to recognize that our initial assessment of the probability is based on limited observations.  One cannot make claims about safety of nuclear reactors for the next 500 years based only on 60 years of observation.
  2. We straddle unknowingly from probability being a degree of our belief to the traditional definition of ratio of observed events. If the belief /hunch itself is based only on limited knowledge and information then we stand to commit serious errors of decision making, confusing our belief with real likelihood of occurrence of events.
  3. Since we tend to overstate our knowledge and suffer from optimism bias  we overestimate the probability of favorable events and underestimate the probability of less favorable events. We then confidently state our degree of belief as actual measure of likelihood of occurrence of an event.
  4. We stick to our initial estimate even when we uncover new information. In the traditional approach to probability, the values do not change.
    p(H) = p(T) = 0.5  for any fair coin
    But in the Bayesian world, probabilities change when we learn new information.
    For example, if you learn that the name of the salon is Rapunzel, you will restate the probability of next customer being Jonathan as a very low number.

Knowing p(A), p(B), p(C)  is not any more as simple as A, B, C.

Next time we state a probability value, let us stop and ask whether we are stating our belief or the real likelihood of an event.

Notes:

To the question of finding the probability of a free user upgrading to premium version, see here.

The answer to the triangle question is based on the sides rule for triangles.

For discussion of gut vs. mind see here.

For the need for evidence based marketing, see here.