ANA School of Marketing: Marketing Accountability

Three Steps to Get Web Data to Speak English

By Jim Sterne

When your web analyst walks up to you with a spring in his step, a gleam in his eye and a pie chart in his hand do you inwardly wince at the conversation to come? Unique visitors, bounce rates, Twitter uptake ratios - it's all perfectly logical - if that's your full time job. If not, here are the three things you can do to make that experience less cringe-worthy:

Step One: Forgive Them Their Passion

These people are truly excited about the fact that people who clicked on this banner ad on that website between  noon and four pm looked at 26% more pages than in the morning. They are the ones who can read the tealeaves and really know where the tea was grown, how it was harvested and how the healing properties interact with your physiology depending on your circadian rhythm phase. They are excited and they like to share.

You should encourage their enthusiasm but you need to train them. I recommend an avuncular approach.

Step Two: Explain Where They Fit In

"Your reports are very interesting, but what do they mean to our business?"

If you want ROI out of these cool new tools and the people who work with them, you have to take responsibility for the reports they hand you. Intricate detail on every click is important to website designers and campaign managers - but not you. Do not get caught up the in minutiae of data gathering techniques. Do not ask them why your web analytics system numbers and your agency reports don't match. They will tell you and you don't need to know.

Instead, turn the conversation toward your goals and your company's goals. "Can you think of a way to measure the quality of leads that come through our website? Can you then devise an experiment to determine how we can get better leads by spending less money for them?"

Step Three: Do Something About It

You want to see better, more usable web intelligence? Use it. Take action on the information. Make changes in your promotional mix, your market segment messaging or your reach and frequency practices - and then encourage your web data people to report on the business impact of those changes. Follow that up with asking for their interpretation of the data in business terms and you have made a giant step forward.

You will have made them feel like an important part of the continuous improvement process because they will be an important part of your process. And the best part? The next time you see that web analyst with a spring in his step, the conversation will be about better leads and better marketing ROI.

Jim Sterne is the founder of the eMetrics Marketing Optimization Summit and
the Chairman of the Web Analytics Association

Mix Models Mislead Marketers?

By Pat LaPointe

Being a huge proponent of structured measurement and decision modeling (and alliteration too), it’s with mixed emotions that I raise the topic of the enormous dangers facing marketers tied to their mix models. If you are using a mix model to help you refine your marketing mix resource allocation, listen up…

The vast majority of mix models out there are regression models which seek to find correlations in historical data, and then apply those correlations to forecasting what would happen next “if…”. The problem is the historical data part. In a discontinuous environment like our current economy, the old “norms” of consumer/customer behavior no longer hold true. Consequently, all those model correlations painstakingly developed over 2 to 5 years of data are now largely invalid as predictors of the future.  Using a mix model developed over the past 5 years or so, even if it’s been regularly updated, would be like using last year’s Wall St. Journal to pick stocks. Bad idea.

So what’s a marketer to do?

First of all, don’t abandon modeling as a practice. On the whole, modeling is a terrific exercise that gets us to clearly state and test our assumptions and capture our experience in structured ways. However, your mix model needs supplemental assessment right now.  Continue to feed it new data and refine the inner correlations, but be more diligent in challenging the outputs. Form a team of your best internal and external experts to A) predict how you think the model will perform in the new reality of today; B) test it by flexing the most sensitive driver variables to see how it responds; C) challenge and interpret the possible error ranges in the outputs; and D) explore alternatives to the current model structure in anticipation of managing in an era of greater uncertainty and discontinuity.

This is no time to stick your head in the analytical sand. Your model may have gone bad, but your efforts to tune/evolve/replace it will surely pay hansom dividends moving forward.
________________________   
Pat LaPointe is Managing Partner at MarketingNPV – specialists in measuring the payback on marketing investments, and publishers of MarketingNPV Journal.

It’s All Relative

By Pat LaPointe:

Quick… what’s the most common mistake in marketing measurement?
Give up?

Unilateral analysis. Looking at the world only through your own company’s eyes as if there was no competition.

It sounds stupid, I know, but yet most of us perform our analysis of the expected payback on marketing investments without even imagining how competitors might respond and what that response would likely do to our forecast results. Obviously, if we do something that gets traction in the market, they will try to respond to prevent a loss of share in volume or margin. But how do you factor that into a business case?
Scenario planning helps. Always “flex” your business case under at least three possible scenarios – A) competitors don’t react; B) competitors react, but not immediately; C) competitors react immediately. Then work with a group of informed people from your sales, marketing, and finance groups to assess the probability of each of the three possibilities and weight your business case outcomes accordingly.

If you want to be even more thorough, try adding other dimensions of “magnitude” of competitive response (low/proportionate/high) and “effectiveness” of the response (low/parity/high) relative to your own efforts. You then evaluate 8 to 12 possible scenarios and see more clearly the exact circumstances under which your proposed program or initiative has the best and worst probable paybacks. Then if you decide to proceed you can set in place listening posts to get early warnings of your competitor’s reactions and hopefully stay one step ahead.

In the meantime, your CFO will be highly impressed with your comprehensive business case acumen.
________________________   
Pat LaPointe is Managing Partner at MarketingNPV – specialists in measuring the payback on marketing investments, and publishers of MarketingNPV Journal.