It costs 6-7 times more to acquire new customers over retaining existing ones …

No my account was not hacked (not yet, at least). I deliberately let this commonly repeated statement be the title without qualifying it.  Of course statements like these, (this particular one made famous by Loyalty Effect) cannot stand by themselves regardless of how popular the Guru who said this is.

Let us look at this closely

  1. Let us assume this statement is true. So shall we fire our sales team, shut down all marketing spend, stop product innovations and get rid of business development?  After all this statement indicates new customers are far more expensive. Then why bother?
  2. Let us take it to the extreme. Shall we stop after the first customer?
  3. Extending this even further, say you acquired the first customer at a cost of $1 and second at the cost of $7. Then by this logic does it cost $49, $343 etc to acquire third and fourth customer?
  4. What if you are essentially in a transactional business where you really need new customers every day because the current ones won’t be there tomorrow?
  5. How do you know it is 6-7 times or only 6-7 times? What are the data and metrics used? Was it based on experimental study?
  6. How generally applicable is this to your businesses – small and large, early stage vs. mature? Is the cost the same to all businesses?
  7. What about profits from new customers, is that 6-7 times as well?

You can see how ridiculous the statement sounds now. Here is a further breakdown of problem with this retention vs. acquisition costs statement.

First it is framed around cost and does not base it on marginal benefit and opportunity cost. I also doubt that the proponents know how cost accounting is done and most likely are allocating all kinds of fixed cost share to new customers. You need to have a costing system that can correctly capture only truly incremental costs for both acquiring and retaining. Simply distributing all costs to all customers won’t cut it.

Second it suffers from sunk cost bias. The fact that you spent some money to acquire a customer in the past does not matter in the decision to do everything to retain them. If you cannot recover the acquisition cost it is sunk. You should only look at future unearned marginal profit from each customer – existing or new. At decision time of spending capital on retention vs. acquisition you need to compute the opportunity cost and truly incremental profit from each path, not encumbered by the money you have already spent on existing customers.

Third, if the cost of acquisition is indeed high don’t you think you have a marketing problem? Is it likely that you are targeting wrong customers in wrong places with wrong product, versions, messaging and prices and hence wrong low value customers are self-selecting themselves to your service? Don’t you want to spend your resources fixing this strategic problem vs. worrying about retention?

Lastly the Innovator’s Dilemma.  What if the current customers are NOT the representation of future?  By choosing to focus your resources on them instead of new customers do you lose sight of new market opportunities, how the customer needs are evolving and how their choices for the job to be done are impacted by market trends and innovations?

Does the retention vs. acquisition pronouncement sound as profound as it did before?  I hope not.

How do you make business decisions?

Familiarity Breeds Price Sensitivity

One of the commonly cited business value from loyalty is that, loyal customers are less price sensitive. How true is that claim? Previously I raised questions about one such study. Now I have more data to state that it is exactly the opposite – loyal customers are actually price sensitive. The key to this claim comes from Reference price – the price set in the minds of customers based on their past purchases.  Reference price is reinforced by frequency of purchases.

Customers who remember past prices because of frequent exposure are the most price and promotion sensitive because they continuously monitor the pricing environment -  Thomas and Menon,  Journal of Marketing Research 2006

For example, suppose you are a regular loyal customer at Chipotle, go there for lunch 2 times a week and always order their vegetarian burrito (the only one that includes guacamole for no extra cost):

  1. You may not know the exact price you pay but you will know when the price goes up
  2. If you had a coupon, you won’t hesitate to use it even though you love it
  3. If the product integrity changes, e.g., they stopped adding guacamole, you will notice it

Customers clearly see deviations from reference price and react to it:

  • Any price higher than the reference price may be seen as a rip-off
  • Any price lower than may be seen as a a deal. As a side effect frequent exposure to lower price due to discounting or promotions  lead to training the customers to this new low reference price
  • Reference price is the reason you see customer backlash when you charge for extras

So a loyal customer who purchases repeatedly becomes very familiar with price, value and alternatives and become product experts which makes them sensitive to price increases. Any price insensitivity is lost due to loyalty program discounts.

A customer level longitudinal study of mail order company and a French grocery chain, published in Harvard Business Review 2002[pdf], states

Regulars consistently paid less due to discounting many received from loyalty cards

This flies in the face of the claim that loyalty allows a marketer to charge price premium. Even in the absence of the studies I quoted you can see the contradiction – if a customer is spending consistently and repeatedly on your product, will they demand lower price or accept higher price than less frequent buyers?

What are you charging your most loyal customers?

Is Customer Loyalty A Predictor Of Profitability?

Much has been said and written about the need for customer loyalty. The need to focus and attain customer loyalty is intuitively clear to all marketers. Some of the key arguments for customer loyalty include

  1. Reduced Customer Acquisition costs – Since it costs $X to acquire new customers, any customer you hold on to saved you $X. For example, it takes mobile providers $350 to acquire new customers and there are similar metrics for most products.
  2. The Loyalty Effect: Longer a customer stays longer they keep paying you. There was a book by the same name that claimed up to 75% increase in lifetime value of a customer if they stayed longer.
  3. Cross-Sell & Up-Sell: Since you keep your customers and come to know more about them it creates additional revenue opportunities through cross-sell and up-sell opportunities.
  4. Price Tolerance: Loyal customers keep buying from you because they are delighted by your product and are less sensitive to prices.  Some even claim that loyal customers do not even bother to use coupons and promotions, thereby saving you money.
  5. Decreasing Cost to Serve: The more you understand your customer’s usage behavior and needs fewer the mistakes in servicing them and hence lower the cost to serve them.
  6. Bump From Word of Mouth: Loyal customers are also your best marketers, they are happy to write online reviews and promote your products to all their friends and web communities. This means they generate additional incremental revenue.

All these factors seem plausible and the “gut feel” says these must be true.  If even a subset of these six factors are a work, customer loyalty must be a very good predictor of sales growth and profitability.

We should be able to validate the following models

Sales Growth =   Constant  +   ß1 * (Customer Loyalty)

Profitability =  Constant  + ß2 * (Customer Loyalty)

(ß1 and ß2 are the weights of  customer loyalty )

In a study published in circa 2000 in the Total Quality Management journal, researchers studied precisely these two models for a large set of products and services. The result?

Loyalty is a poor predictor of both sales growth and profitability. Their R-square values are 6% for profitability and 2% for sales growth. (For services the number goes to 14.7% and 7.8% respectively). That means only a tiny fraction of the changes in sales growth and profitability are explained by changes in customer loyalty.

Loyalty has positive impact on sales growth but more strikingly, for products, the impact on profitability is negative, which means higher the loyalty lower the profitability. This means any attempt to “buy loyalty” with price cuts does bring you loyalty but at lower profitability.

The net is, what seems too obvious isn’t so. This is not to categorically dismiss need for loyalty but the positive effects of loyalty are clearly overrated. If their effects are so low then there is a high opportunity cost to improving them. You cannot put all the  wood behind the loyalty arrow!


Correlation means two variables are associated and the extent of association si expressed as correlation coefficient. It ranges from -1 (low,high)  to +1 (high,high). A value of 0 means no correlation.

Predictability, R-square, means one variable is a predictor of other. It is measured as a square of correlation coefficient. So two variables that have a correlation coefficient of 0.8 have a predictability of only 0.64. R-square is usually expressed in %, so 64% means 64% of changes in dependent variable are explained by changes in predictor variable. That said, correlation does not mean causation. There are other factors to consider including but not limited to statistical significance of weights of variables, omitted variable bias, etc

Are Loyal Customers Less Price Sensitive?

Let us start with seed questions on loyalty:

  1. Are loyal customers more likely to be less price sensitive and hence more profitable?
  2. Can a marketer maintain price premiums with loyal customers?
  3. Does customer loyalty drive long term profitability?

To some the questions will come as a surprise because the answers to them are self evident. But leaving out intuitions, is the causation relationship that loyalty means price tolerance and consequently higher profitability supported in the data? A few studies suggest this causation relationship based on correlation ( see here).

There is one other popular book, The Loyalty Effect, that states, “Companies can boost profitability by 75% by increasing loyalty by 5%”. But that is not based on research. The author makes that statement based on his example that reducing customer churn from 10% to 5% for a business with 90% customer loyalty doubles customer lifetime  (that is correct) and hence the profitability increases by 100% (tautology). However, his model and numbers are not correct  because he confuses two different percentages (see here for explanation).

Let me raise questions on what seems like a self-evident truth:

  1. Yes there is correlation but does that imply causation?
  2. Is there a hidden variable that drives loyalty and profitability?
  3. Is there Omitted Variable bias – that is there exists another variable that drives loyalty?
  4. What if price tolerance (and profitability) instead of being caused by loyalty is actually the driver of loyalty? In other words, is it possible that sensing price tolerance and high profitability of certain parts of their customer base, businesses may be doing everything they can to keep them (i.e., increase their loyalty).

I do not have data to prove these three possibilities but I raise here questions you as a decision maker must ask before accepting any claims of the need for driving customer loyalty.

Loyalty Reality

“0.5% reduction in mobile subscriber churn rate can increase revenue by 74%” – No really, but don’t stop reading, let us talk realistically about customer loyalty, customer lifetime value and  effect of loyalty on your profit.

Let us take AT&T as an example. It has a current monthly churn rate of 1.17%, the lowest among top 3. Churn rate is the % of current subscribers leaving the service provider. This translates to 14% yearly churn rate. Conversely, AT&T’s customer loyalty is 86%. So lifetime of an average customer with AT&T is  1/14% = 7.14 years.

Let us assume all customers bring in same revenue for simplicity (even though this is wrong)  of $x/year. So customer lifetime value is 7.14x. Let us say AT&T spends $200 to acquire a customer and the monthly bill is $50. The Lifetime Value of the customer (ignoring PV calculation) is$4084.

If AT&T can reduce their monthly churn by 0.5% -  0.5% reduction in churn makes the churn rate 0.67%/month, 8.04%/year, 12.4 years, that is 5.26 years more than previous state and hence 74% increase in revenue. If only AT&T can decrease its monthly churn by 0.5%, it can increase its revenue by 74%.


Let us pick apart this spurious reasoning and sleight of hand:

  1. Anytime you increase average lifetime of customers you increase revenues proportionately. There is nothing magic here. So stating, ” increasing loyalty, when everything else like prices is held constant, increases revenue ” is self evident truth and not an insight.
  2. I said reduction of 0.5% monthly churn rate. In reality this is reduction from 1.17% to  0.67% – this is confusing two different percentage scales. In reality this is 43% reduction in monthly churn.
  3. What is it going to cost me to reduce churn by 43%? What is the new infrastructure investment needed? What is the opportunity cost of this investment? What is the incremental profit from this investment? Is this going to require “buying loyalty” with price cuts and promotions? Even if you gain or buy loyalty, is that stable and sustainable? If AT&T is going to spend money on this, will Verizon and Sprint stay still? Can the money be better spent in acquiring new customers?

Without answering these questions, it is pointless for me or any management guru to say to you to focus on customer loyalty because, “5% increase in customer retention (from 90% to 95%) will increase profit by 75%“.  It is irresponsible for a decision maker to accept these pseudo-facts without challenging them.