Heuristic modeling? Not to my knowledge, but I didn’t build the models. My job was to interpret the results and translate them into Marketing action.

I don’t know how to answer about Simplicity; this is definitely the most complex “Band” due to the need for modeling - if you take it to this level.

However, what I think people often miss is you can get about 80% of the way to what is represented here with MUCH simpler models. After all, the FIPS model was the culmination of about 6 years worth of test activity, most of it done with very simple behavioral models.

In fact, it was the extensive data set created by using the simple models (Recency - Frequency) that facilitated the creation of the FIPS model. We already knew from prior testing that Recency was hugely predictive for Interactive. So FIPS was really about “just how could can we get at this?”

On the last regarding “world size”, I think the question is above my pay grade, if I understand it. My understanding has always been that the advanced predictive models (FIPS) work much better with larger data sets. If you’re talking about simple models like Recency, they work well with small data sets. So my impression was the more advanced the model, the more data it needed, with machine intelligence / data mining requiring the largest data sets of all to produce actionable insight.

But like I said, I don’t actually create any models above the RF level, which requires only simple counting. I just translate the model output into money!

That’s an awesome question and I can answer it a couple of ways.

If we’re talking strictly Marketing (including Mail Order) budget of about $15 million, prior to the Optimization, 50% of that was spent on Band 1, 5% in Band 2, none in Band 3, 25% in Band 4, none in Band 5, 20% in Bands 6 - 8. This analysis ignores outlier years; one early year we spent $50 million on testing mass media alone that didn’t work, excluded this spend for a fair comparison.

After the Optimization, 3% in Band 1, 7% in Band 2, none in Band 3, 75% in Band 4, 10% in Band 5, 5% in Bands 6 - 8. It’s worth reminding here that the ROMI in Band 5 was much higher than in Band 4, but the volume lower. We were constantly looking to reallocate funds out of Band 4 into Band 5, but you have to find the opportunities first - test, test, test.

The shift in spend above, from the “Front End” (Bands 1 - 2) to the “Back End” (Bands 4 - 5) is why this topic is so important.

Interactivity, the Pull, really changes where you should focus spend. Dollar for dollar, Interactivity is great for acquiring customers, tends to suck at retaining them, hence the shift in the allocations above after Optimization. It’s worth saying that this is somewhat opposite of the traditional Mail Order (Push) model, where dollar for dollar acquiring customers is very costly but keeping them not as challenging, all else equal. For example, in a pure web customer database you will often see 70% - 80% 1x buyers; in a pure catalog database it’s more like 40% - 50%, both stats depending of course on how good the Marketers are :O.

Now, if you are talking “company” budget, then you get a different picture because of all the Infrastructure costs related to Band 3. I have no idea how many hours were spent on the development of the systems to support Optimizing the Interface, but a lot of IT work went into that. A lot of Research & Analysis work went into optimizing Bands 4 and 5. Both efforts primarily labor cost.

Sometimes, as in the case of Nice to New Customers, Marketing felt so strongly about the idea we paid for the costs incurred by other departments to execute (and I was ultimately proven wrong on this one - another example of why Interactivity is different). We also engaged in various kinds of internal resource Barter, especially with IT, to get things done.

Not included in Band 2 above are Affiliate fees, because we had contractual obligations to pay commissions on sales for cable distribution. This was non-controllable variable cost so I didn’t include these costs above because they could not be Optimized. However, many Marketing programs done with these Affiliates could be Optimized and those costs are included above. This particular Affiliate model doesn’t have a great correlation with a web scenario, unless the web model relies solely on affiliates or platforms like Amazon or eBay to make sales, in which case you have a pretty similar situation.

Did that answer your question?

For now though, I’m kind of curious about what the beginning and end state looked like in terms of the proportion of the total marketing budget that was allocated to push and pull. How much of a re-allocation did you go through?
Thanks again,
Moe

The first step to doing any of this is to redefine what Marketing is and what Marketing people are responsible for. It’s rarely the customer’s fault, in my view. People just don’t know how or where to look for Root Cause.

I keep saying that the web site team is a prototype for this kind of business success, and I really think it is - testing oriented, looking for Root Cause, Six-Sigma / cross-functional SWAT, Failure is a Learning Experience, all of that. You see the same type of work I provided in this example with a lot of the great web teams.

The question is: when will they cross over and do the Optimize thing for the Enterprise? That’s a Business SWAT team I want to have working on the whole damn thing.

If they have to end up working for the Chief Customer Officer because Marketing “doesn’t get it”, so be it…but that would be a shame, in my opinion.

What I find most… fascinating is the honest and hence *hard*, appraisal that “you” were somehow failing the customer. Bad products, misleading copy etc.
And presumably act on fixing that accordingly.

Another pearler of an article Jim!

Cheers!
- Steve