Worth no more than…

We were talking to an HR executive recently, and came across something quite horrifying. The conversation went like this:

“How did you find Mr X, who I had recommended for the loyalty manager’s post?”

“Oh, he wasn’t any good. Incidentally, another chap from your office turned up on his own, Mr Y. He was ok, but he wanted Rs 12 lakh (1.2 hundred thousand). We reckoned that he was worth Rs 10 lakh, at most.”

“Who did you take ultimately?”

“Mr Z. He has 15 years’ experience, and came for 18 lakhs.”

We couldn’t believe our ears. How can you have salaries as different as Rs 10 lakh and Rs 18 lakh for the same position! One expects a range of ± 10%, but +80%! The profiles and responsibilities have to be very different for that to make sense. Were they?

Because if they weren’t, it tells you a good deal about how companies value CRM and evaluate CRM managers.

Who do we send the cake to?

Meet any B2B marketing manager and about the second thing he tells you is that he wants to do CRM with his customers.

“What exactly?”

“Send cakes and cards on birthdays. Take the relationship to the next level. There’s so much you can do…”

(To yourself) “Can’t think of one thing I want any of my vendors and suppliers to do for me except do my work, and not overcharge me – but maybe I’m ultra-professional.”

(To him) “Of course, of course, there’s so much one can do.”

“Yes, but you see the problem is that we don’t know who to send the cake to. The person who books the call may be just a flunkey, with no say in the decision. We want to reach out to the decision makers and influencers (with cakes). We have to do the ‘mapping’.”

And so it goes on.

While you wonder what stops them for doing the far easier job of sending B2B offers and deals, which, if they are meaningful, should land up on decision-makers’ desks.

Then you realise you are seeing a replay of one of our favourite excuses: Because the impossible cannot be achieved, the obvious shouldn’t be attempted.

We Indians are great at ‘change at the grassroots’ and ‘rethinking the entire structure’. No doubt these are necessary. But what are to do till we do these? Twiddle our thumbs?

What can you say about this man?

“He has bought Brand A. What does that tell you about him?” How many times have you heard this question? How many times have you had to made up a wonderful pen picture of this customer, based on this one single purchase and agreeing with brand manager’s reading of who constitute his market?

And how many times have you told yourself that this Sherlock Holmes act is totally absurd?

Holmes would at least have a well-used object, with plenty of tell-tale marks on it, to base his deductions on. All you have is a single purchase. And some completely unsubstantiated assumptions.

Yet brands’ creative and media plans are based on these mental gymnastics.

Is it so difficult to say, “The only thing we can tell about the customer is that he can, most probably, afford this brand. If we have additional data on customers, we can probably hazard a few more guesses. For instance, if we know that 67% of customers are Sindhi grandfathers with four-and-a-half gold-filled teeth, we can say, ‘There’s a 67% chance that our new friend is a Sindhi grandfather with four-and-a-half gold teeth.’ Beyond that we can’t say anything.”?

Why must we know our customers profiles? Why can’t we just restrict our interest to the whys and wherefores of their liking our brand?

We use many brands ourselves: In how many cases do we fit those brands’ (apparent) target markets? Or are we ‘beyond marketing’, non-slot-able, unique, different?

Lend your card

Would it make sense to ask loyalty programme members to lend their cards to friends and relatives during festive seasons? If there are exclusive offers to cardholders, and the primary purpose of these offers are to hike sales among heavy users, not necessarily to curry favour among them (‘Here’s your bulk discount’ vs. ‘Something special for someone special’), loyalty cards can become, in effect, reusable discount coupons. Members won’t, of course, mind some extra points on their friends’ purchases.

In short, card lending can do, more or less, what a referral programme does.

There are three immediately discernable drawbacks though:

(a) We may not get the card borrowers’ data, as we’d do in a referral programme;

(b) The card borrowers may be lost as prospects to the loyalty programme (Why should I become a member when I can enjoy membership benefits by flashing my friend’s card? Well, because you won’t get points. But I don’t buy often enough to collect very many of them. In that case, both sides are perhaps better off with you not having a card.); and

(c) The members’ data can get somewhat contaminated (Data says grandmother is buying itty-bitty bikinis; in reality, her next-door neighbour bought them, while swiping her card. This may not be so horrible though, as long as grandma doesn’t mind passing on the bikini catalogues, sent as a result of this purchase, to her neighbour.)

I ‘eurekad’ when I read Caples the first time, but when I started reading again! –

John Caples’s Tested Advertising Methods is a book I will unhesitatingly recommend to any copywriter. As I will recommend his How to Make Your Advertising Make Money and Making Ads Pay.

So what I am about to write is, for me, sacrilege. Yet it must be committed.

The tests that Caples talks about leave a lot to be desired. I was just going through Tested Adverting Methods (5^th edition, revised by Fred E. Hahn [a revision that does it only harm]) again, and found the inadequacy of the data particularly puzzling.

Take the famous example where Caples says changing an ad headline from “Repair Cars – quickly, easily, right” to “Fix Cars – quickly, easily, right” increased response by 20%. He gives no information about what the actual numbers or percentages were, or where the ad came out.

So let’s suspend belief for a while and pick some numbers out of the Web.

The average weekday circulation of a newspaper in the US (whatever that is supposed to mean!) in 1940 was 21,902 and the Sunday circulation was 61,659 (Please see this section of The State of the News Media 2004 report at journalism.org).

Tested Advertising Methods came out in 1932, so here are our assumptions: (a) The circulation was not too different in 1932 (we have no reason to do that, but the data at the site is only till 1940) and (b) Readership was equal to circulation (again, a rather silly assumption, but the purpose of this to explore a possibility and not to prove a point).

The situation we can imagine goes like this:

The ‘repair’ ad could have pulled up to 201 responses

And the ‘fix’ ad 20% more, that is, 241 responses

Without the response rates being significantly different (at a 5% level of significance).

Had the ad come out on an average Sunday newspaper, the responses could have gone up to 209 and 250, respectively, without the response rates being significantly different.

The same complaint can be made against the comparison between “Save one gallon in every ten” and “Car owners! Save one gallon of gas in every ten” where, on testing in a daily newspaper, the latter pulled 20% better than the former.

Another famous example is the one where “Hay Fever” pulls 297 sample requests while “Dry Up Hay Fever” pulls 380, a ‘27% increase’. The increase in response rate (assuming the ads came out in average newspapers on weekdays) could have been between 0.15% and 0.61%.

In quite a few cases, neither response rates nor responses have been quoted; we’re simply told A did better than B.

Now, if the differences in response rates were not always significant, from either a statistical or business perspective, the businesses involved in those testing decisions would not have gained or lost much.

The trouble lies elsewhere, with direct marketing copywriters who believed the ‘tested’ fact that ‘straight and simple always out-pulls the creative’ and put their own careers into jeopardy, because that belief is almost always seen as an excuse for lack of talent.

To all such writers, and to writers who have not yet formed their beliefs, I would recommend this site: Statistics Every Writer Should Know. A little knowledge may be a dangerous thing, but none at all can be disastrous.

PS: I used an Excel template from Aczel & Sounderpandian for my calculations. My calculations are at http://docs.google.com/Doc?id=dd3bjnd7_28rt37t and the templates are available here.