Who makes the best quality products? The Japanese. Whose customers are most satisfied? The Japanese. And most loyal? The Japanese. Ever heard of a Japanese loyalty programme? For that matter, how many Japanese marketing books or gurus have we heard about? They may not like to boast, but shouldn’t we translate (I assume there is much worth translating)?
Friday 15 June, 2007
Japanese loyalty
Why 24 hours?
Companies promise response within 24 hours of complaining. Why not 24 minutes? What comes in the way? Perhaps little would be gained by super-fast response in most categories. Yet an innovator can do it and set a new standard, which others will struggle to meet. He will get plenty of publicity as well.
Monday 4 June, 2007
As the chowkidar said
Said the old judge to the youthful civil servant, “When you are a bit older, you will not quote Indian statistics with that assurance. The government are very keen on amassing statistics – they collect them, take the cube root and prepare wonderful diagrams. But what they must never forget is that every one of those figures comes in the first instance from the chowkidar, who just puts down what he damn pleases.”
Substitute ‘buttonholed shopper’ for chowkidar, and that becomes good advice for every survey spouting MBA.
Substitute ‘buttonholed shopper’ for chowkidar, and that becomes good advice for every survey spouting MBA.
All active!
Credit card companies claim that their lists are the best because all the contacts belong to active cardholders. Strange, but for the two years I was in my previous residence I got mails to the previous tenant from his credit card company.
Maybe that was an exception. So I have a simple suggestion: Would the credit card company wanting to rent its list agree to address verification of about 100 names before the list is rented? Even if each verification cost Rs 50, and we found that 1 in 4 addresses were wrong, you’d save 4 to 8 times of the verification cost on a mailing to 10,000.
Not bad.
Maybe that was an exception. So I have a simple suggestion: Would the credit card company wanting to rent its list agree to address verification of about 100 names before the list is rented? Even if each verification cost Rs 50, and we found that 1 in 4 addresses were wrong, you’d save 4 to 8 times of the verification cost on a mailing to 10,000.
Not bad.
Direct marketing needn’t make sense…
…it only needs to make money. Didn’t Denny Hatch say something like that? Anyway, here’s something similar from a data mining book (Data Mining Techniques, Berry & Linoff): “If a direct mail firm obtains a model that can accurately predict which numbers of a prospect pool are most likely to respond to a certain solicitation, the firm may not care how or why the model works (italics mine).”
I’ve heard similar statements elsewhere.
And all database marketing books teach you basic statistics, e.g., confidence intervals, comparing percentages, and so on, so that you can take dispassionate decisions based on response data.
The first thing we are taught is that we must ‘test, test, and test.’ And we enviously read Western case studies of the wonders wrought by testing.
But something is missing.
When in college, we were taught that physics is, in a way, applied mathematics. It has to make sense. The calculations have to model the real world.
From what little I’ve read about business statistics, its purpose doesn’t seem to be very different.
Right now, I have two (naive?) questions:
1. Given that there is bound to be a time gap between the test and scale-up, and that much can change during that gap, how prudent is it to base the scale-up on test results? I suppose there cannot be any easy (rule of the thumb) answer to this. And some gurus have advised that tests should be repeated before one scales up. Nonetheless, I am yet to come across a case study on ignoring test results. (There are plenty on chimpanzees and ink dots choosing investment portfolios that outperform analysts’ selections [though they do sound too good to be true].)
2. Which of these will be most surprising?
a. Software meant for the express purpose of making segments makes some (peculiar) segments
b. It would have made segments even if the data wasn’t about neighbourhood demographic profiles and frequency of previous orders but about first alphabet of middle name and direction of office desk
c. These segments, no matter how they are created, can be projected into the future, e.g., if Segment A shows significantly higher response for products of Type X than Segment B today they will do so (in most cases) in the future
I’d be unsurprised by if statements a and b came out true, but somewhat surprised if c did, unless the differences between A and B are readily explicable (e.g., A are male, B are female, and X are feminine cosmetic products).
I am also very curious to know what loyalty marketers do with their double-digit segments.
There are quite a few enlightening and entertaining books against the senseless use of statistics to sensationalise or sell or both, particularly in the stock markets. Shouldn’t there be one examining its application to marketing, particualrly direct and loyalty marketing, too?
I’ve heard similar statements elsewhere.
And all database marketing books teach you basic statistics, e.g., confidence intervals, comparing percentages, and so on, so that you can take dispassionate decisions based on response data.
The first thing we are taught is that we must ‘test, test, and test.’ And we enviously read Western case studies of the wonders wrought by testing.
But something is missing.
When in college, we were taught that physics is, in a way, applied mathematics. It has to make sense. The calculations have to model the real world.
From what little I’ve read about business statistics, its purpose doesn’t seem to be very different.
Right now, I have two (naive?) questions:
1. Given that there is bound to be a time gap between the test and scale-up, and that much can change during that gap, how prudent is it to base the scale-up on test results? I suppose there cannot be any easy (rule of the thumb) answer to this. And some gurus have advised that tests should be repeated before one scales up. Nonetheless, I am yet to come across a case study on ignoring test results. (There are plenty on chimpanzees and ink dots choosing investment portfolios that outperform analysts’ selections [though they do sound too good to be true].)
2. Which of these will be most surprising?
a. Software meant for the express purpose of making segments makes some (peculiar) segments
b. It would have made segments even if the data wasn’t about neighbourhood demographic profiles and frequency of previous orders but about first alphabet of middle name and direction of office desk
c. These segments, no matter how they are created, can be projected into the future, e.g., if Segment A shows significantly higher response for products of Type X than Segment B today they will do so (in most cases) in the future
I’d be unsurprised by if statements a and b came out true, but somewhat surprised if c did, unless the differences between A and B are readily explicable (e.g., A are male, B are female, and X are feminine cosmetic products).
I am also very curious to know what loyalty marketers do with their double-digit segments.
There are quite a few enlightening and entertaining books against the senseless use of statistics to sensationalise or sell or both, particularly in the stock markets. Shouldn’t there be one examining its application to marketing, particualrly direct and loyalty marketing, too?
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