Damn, Got The Easy One Wrong!

In my last post, I responded to ph hanky on his/her comments on P&G’s marketing mix modeling efforts, reported last year.  Ph asked a question about P&G’s MMM partner, which I said was an easy one to answer.  So I did.  Incorrectly – as my friend Mark Weiner, President of Delahaye, kindly pointed out to me in an email yesterday.  The correct answer should have been Analytic Partners and the modeling group.  I apologize to the parties involved for the error.

Mark went on in his email to ‘set the record straight’ with respect to a couple of points in ph hanky’s post.  Mark gave me permission to share the email with you, so here it is.  -DB

Don,

 

Thanks for your perspectives on market mix modeling:  I agree with your position on the unfortunate absence of any evidence to support the diminishing returns of PR spending.  While our marketing mix experience has given us examples of the diminishing returns of advertising and price promotions, there isn’t enough spending in PR to have yet reached that threshold.  I am certain that such a phenomenon exists within PR for the same reasons that make PR so uniquely powerful within the marketing and communication mix, and that is the power of the journalist’s third-party objectivity…at some point, the journalist can’t digest another story about mattresses.

 

That being said, I must say that ph hanky’s slip is showing:  the sweeping generalizations which are directed at P&G and Delahaye actually belong to hanky.  Here’s what I would have said to ph hanky had I been given an opportunity:

 

• Delahaye DOES conduct marketing mix modeling.  However, in the P&G case, Delahaye provided only the proprietary PR data which enabled their pre-existing model to work.  Many PR data sources were tested but only Delahaye’s data succeeded in representing the unique role of PR within the marketing mix .  So it would be fair to say that while Delahaye has done marketing mix analyses for companies whose names you’d recognize, P&G’s MMM partners and in-house team took care of it in this case.  We’re sufficiently proud of our role in helping P&G specifically and in elevating the role of PR universally.
• I can assure you that a great deal of vigor (sic) was applied to the analysis, and that “vigor” was magnified with a great deal of “rigor.”   I suppose that one can argue that if P&G is allocating it’s significant marketing resources based on their marketing mix analysis, it has to be sound research.  As for Delahaye’s data, it has successfully passed testing for Six Sigma compliance (3.4 defects per million opportunities) not once but twice…that’s a result of our rigor and vigor.

With the emergence of blogs and consumer-generated media, we run the risk of creating an entire media category based on assertion rather than validation.  With so many resources available via the web, it is easy to avoid sweeping generalizations.  It’s in the spirit of accuracy — and the fact that I believe that this blog is an important one — that I feel compelled to set the record straight. 

 

Mark Weiner

President

Delahaye

Ph:  203-663-2446

Fx:  203-899-1612

Marketing Mix Modeling (MMM) Follow-up

Last June I wrote on P&G’s work with Delahaye in marketing mix modeling (Original Post).  There have been a lot of interesting comments (see comments), and I wanted to take a moment to answer a few questions posed by ph hanky in his/her January 10 comment.  Here is the comment:

ph hanky – January 10, 2007[Edit]

who does P&G’s MMM work anyway? Delahaye isn’t a modeling agency.

The problem with companies like P&G and Delahaye making sweeping statements like this is that there is no one to audit or verify the findings. Anytime you have someone doing a regression analysis like MMM, you have to question if the regression analysis was done with the right vigor and make sure enough factors were factored in the analysis.

P&G has been making lots and lots of money and AG Laffley has been talking about their MMM and how they are cutting back on TV in favor of other vehicles. I don’t doubt P&G pulls off some world-class MMM, but I don’t know if they promote stuff like this to put the screws on their ad agencies and TV advertising rates.

If they spent half of what they spend on TV on PR, then they would saturate the airwaves, the web, and print with PR. They don’t address diminishing returns on PR. I would assume you reach saturation point with PR long before you reach saturation on Tide commercials.

First, the easy question.  The company that worked with Delahaye to develop the model is a firm called Communications Consulting Worldwide (www.ccworldwide.com). 

Regarding the audit or verification of findings, my personal view is this is not the right way to think about modeling.  A properly built model should yield a highly correlated approximation of the relationship between public relations results and downstream outcomes, usually sales.  There are certainly some issues that the modeling company may find uncomfortable – co-dependence of variables and reverse correlation to name two.  So while there may be a little leap-of-faith with the model, the rigor is generally much greater than is normally applied with simple media content analysis – and the leap of faith to ROI is much shorter. 

The other issue ph hanky raises is diminishing returns on high levels of PR investment.  In my experience we should be so lucky.  I have never seen a completed model that demonstrated PR investments were on the diminishing returns side of the curve.  On the contrary, every model that I have seen suggests PR investments are well below diminishing returns.  If a company is spending $60 million in advertising and say $1 million in PR (actual numbers from a former client), then the issue of diminishing returns on PR is not applicable.  Moving just $1 million from advertising to PR would show nice leverage in ROI.  I would love to be in a situation where I had to go to a client and recommend we reduce PR spend because we have reached diminishing returns, although I not holding my breath on this one. 

Thanks for reading, DB