Maxmise Your Recommendation Stream.


The Scenario: Two people meeting for a coffee around three o'clock, both having had lunch nearby. They opt for an old haunt, the cafe at Bibendum where they have enjoyed coffees, snacks, dinners and lunches at various times in the past.

The Promise: The website says the cafe "is a wonderful spot to watch the world go by while enjoying a coffee and croissant" and the adjacent oyster bar allows for "A quick glass of wine with or without a nibble".

The Reality: The courteous manager regrets that head-office has instituted a new policy - lunch now continues till 5 p.m. and you can only have coffee in the open-air section. The "protection from the elements means that it’s a perfect all-year-round venue" no longer applies.

The Logic: Head-office presumably saw a fall in revenue per table between 3 and 5 p.m. on their spreadsheet and instituted a policy designed to increase those average revenues.

The Flaw: Revenue figures on a spreadsheet aggregate individual transactions but don't aggregate the overall expenditure of those individuals. Numbers are not human. Customers are not numbers.

The Outcome: Available tables remain empty, revenue not increased one penny and customers disgruntled. Worse still, revenue may actually fall because my companion's lunch partner (on hearing of her plan to go to the cafe) had remarked "oh yes, now the weather's improved we must start having lunch there again".

The Moral: Deciding who you want as customers is crucial, but you need to do it right. If you only see business in terms of your revenue streams and forget about your recommendation stream, you may well run aground.


(photo courtesy of kexi's flickr)

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