Monday, January 9, 2012
Strategic Planning Analogy #431: More Efficient "Bribery"
THE STORY
Moneyball is the story of Billy Beane, the general manager of the Oakland Athletics baseball team in the early 2000s. Billy’s problem was that he managed a team in a small market. As a result, he did not have as much money to spend on baseball talent as teams from larger markets. For example, his total athlete salary budget was about a third the size of teams from large markets like New York.
Since Billy Beane could not outspend the other teams to attract talent, he needed to be smarter about how he spent his money. To become smarter, he turned to detailed statistics and analytics. Billy learned that certain athlete statistics were better correlated to baseball success than others. Then he went after signing up players who were great on those statistics but would otherwise be overlooked by the big-market teams (because the players appeared weak on subjective issues not well correlated to success).
As a result of becoming smarter on talent, Billy Beane was able to put a competitive team on the field while spending a lot less money than the big-market teams. The story is so remarkable that in 2011, it was made into a movie.
THE ANALOGY
Most athletes are not very loyal to their team. They will go play for whoever is willing to pay them the most money. The money is like a bribe. Whichever team bribes them with the most money gets the player.
This is a lot like the retail business marketplace. Consumers are not very loyal when it comes to where they shop. Instead, they shop at whichever store gives them the best deal. That is the bribe which gets them to the store.
This was pointed out in an article in the January 9, 2012 edition of Marketing Daily. The article talked about a study of 6,000 shoppers by Pricewaterhouse Coopers. The study found that consumers really don’t care much about retail loyalty programs. Loyalty programs were ranked last in a list of reasons for choosing a store. Only 1% of the shoppers cited loyalty programs as a reason for their store choice.
What was the #1 reason for store choice? It was price, mentioned by 55% of the shoppers. In other words, shoppers are like those athletes—not very loyal and can be bribed by being offered a better deal.
Yet about 92% of retailers have a loyalty program and many spend huge sums of money on their program. My keychain has three keys on it, but seven loyalty cards. I should probably call it a “loyalty chain” instead of a keychain. My wife is even worse. She carries two wallets—one is for credit cards and money and the other just holds loyalty cards.
Now, smart phones are making it even easier. They allow you to store all that information digitally, so you can, in essence, conveniently carry an infinite number of loyalty cards. If you are carrying a card for every store, then those cards are not making you very loyal to any particular store.
So what should retailers do? They should take a tip from Moneyball. Instead of offering ever larger “bribes” (deals) in the futile attempt to create loyalty, they need to get smarter. They need to use statistical analytics to make their spending more efficient.
THE PRINCIPLE
The principle here is that many marketing expenditures have more in common with bribery than they do with loyalty. This is particularly true in retailing. Therefore, when creating marketing strategies, we should be more focused on increasing the “efficiency of the bribe” than the “effectiveness of the loyalty.”
Principle #1: Just Because it Looks Like Loyalty Does Not Mean it is Loyalty
At first, great bribery can look like great loyalty. Great bribery allows you to lure people back to the store, time after time after time (each time caused by a great bribe). Great loyalty means that customers voluntarily come back to the store, time after time after time. Since the behaviors appear similar (repeat purchasing), one may look at the behavior and mistakenly think that they are witnessing great loyalty when in fact they are witnessing great, sequential bribery.
Why is this distinction important? If the motivation is bribery, then the favorable behavior will stop when you stop the bribe, or a competitor offers a better bribe. If the motivation is loyalty, then you are better insulated from competitive attacks and less reliant on bribes for success.
If you get these two motivations confused, then you may create the wrong marketing strategy. For example, if you think you are building loyalty, then you may create a strategy with unaffordable discounts in the beginning, which you justify by saying that those discounts create long-term loyalty. Once loyal, you can then cut way back on the deals later and still keep the customer.
Terms like “lifetime value” are used to justify this approach. They say you can lose a lot of money up front if it creates a lifetime of loyalty. Then you make up for the losses in the beginning during the later years of the lifetime by cutting back on the size of the deals.
Of course, if your tactics are only creating a series of bribes, then you can never stop the bribery. If lifetime loyalty is a fallacy, then you will not only lose a lot of money up front under such a program, but you will lose it forever if you want return visits.
Therefore, if behavior is more bribe-induced than loyalty-induced, you need a way to bribe over the long term which is profitable. That requires a focus on bribe efficiency rather than lifetime loyalty value.
Principle #2: Loyalty Programs Aren’t Bad, They Are Just Misnamed
So, if loyalty is virtually non-existent, does that mean that loyalty programs should be abandoned? In general, I’d say no. They can still have great value as a bribery tool. It isn’t that the tool is bad; it is just misnamed. They should be thought of as “bribery programs” rather than loyalty programs.
Think back to Moneyball. Billy Beane could afford to pay less for quality baseball players because he was smarter about how he pursued players. He studied the statistics related to success and used that knowledge to find valuable players who could be lured with a lower bribe.
You can do the same. Those loyalty cards can provide a lot of data. They can help you get smarter about your customers. You can learn from their behavior. Analytics around this data can provide knowledge about which bribes are most effective with that customer. Using this knowledge, you can offer bribes more appropriate and more luring to that individual. And, in most cases, because the bribe is more specifically targeted to that individual, it will be more effective at a lower cost.
For example, you may find that a particular customer is easily lured by a small discount on cat food. Therefore, instead of offering huge bribes on lots of things, you can narrow your expense to a small bribe on cat food to get pretty much the same end result.
In other words, by using the data from loyalty programs, you may not make the customer more loyal, but you can make your bribery more cost efficient and more profitably effective. And that makes the program worthwhile.
Now, if you are NOT using the data from a loyalty program to get smarter, then you may be wasting a lot of money on that program. You’d probably be better off shutting down the program and using all that money to pay a bigger bribe to people at the cash register when they check out.
Principle #3: Metrics Are Valuable Only if You Use the Right Ones
In Moneyball, Billy Beane was successful because he focused on the right metrics. He looked at the statistics which really lead to wins and ignored the rest. By contrast, the big market teams were often evaluating the wrong metrics, things like how a player looked or their demeanor. By looking at the wrong metrics, the big market teams were paying too much for the wrong players.
The same problem applies to marketing. If you are looking at loyalty metrics instead of bribery metrics, you may end up rewarding the wrong behavior. Set up metrics to measure and reward efficient bribery rather than nearly non-existent loyalty.
SUMMARY
Although businesses want loyalty from their customers, usually the primary customer motivation is not loyalty, but bribery. As a result, we should convert our loyalty programs into bribery programs. That requires using data and analytics to discover the most efficient ways to bribe, and then keep using the bribes forever in order have superiority over the competition.
FINAL THOUGHTS
Just because bribes may be the most effective motivator, that does not mean that you can ignore all the other operational variables. Bribes are more efficient when the other operating factors are working well, because then you have less negativity to have to overcome with a bribe (so the bribe can be smaller).
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Gerald Nanninga,
ReplyDeleteI see a hidden "crowding effect". One store offers loyalty card and competitors follow. This crowding effect, which I may rename to Crowding illusion builds up momentum and brings along the false assumption that such programs are functional.
You remind me of a similar experience that I went through. Banks bribed customers by offering a monthly drawn prize for savings account. Soon, many banks followed. I was later proven right when I advised a new bank not to follow steps. Later, many banks withdrew this program for they realized that important accounts such as businesses never considered such a program. It was for the majority who had very small accounts hoping that one day they would be the lucky ones to win the fat monthly prize.
I agree with you, Gerald all the way
Gerald Nanninga,
ReplyDeleteI am commenting on this post after I had finished reading your lost post on bribery (# 431)
I feel the two posts are supporting each other. Bribery tailored for specific customers is what is needed today.Marrying the two posts together gives the complete story
Ali Anani;
ReplyDeleteI agree with your comments. Your banking example reminds me of the fact that tactics are often not inherently good or bad. It depends on who you are or whether you are early with the tactic or late to the market with the tactic. I talked about this in an earlier blog: http://planninga-from-nanninga.blogspot.com/2007/06/early-bird.html