Last week Ogilvy announced a partnership with Marketshare Partners, a marketing science company based in LA with offices in NY, San Francisco, Chicago and London.  I had been looking for a while for a partner who could help us with econometric modeling, the type of analysis that can help you tie overall marketing spend to business outcomes and can help you determine how much to spend on marketing and how to allocate it by product, segment, geography and medium. 

I won’t go into the details of why Ogilvy chose Marketshare Partners or what the joint offering exactly entails.  This would not be the right place for it.  However I did speak to a lot of different companies in this space over the last couple of months and I thought I’d share my thought on how they differentiate from each other.  Hopefully this could be useful when you are looking for a partner - if you need one just give me a call

I started with laying out the various tasks involved with running an econometric modeling project that would result in a marketing investment strategy.  These steps are listed below.

Data Gathering : you need to collect data on marketing spend and performance from a wide range of sources.

Data Aggregation : you need to put all that data in one place where it can be accessed easily by analysts.

Statistical Modeling : you need to build the statistical models that link marketing spend to business outcome.

Optimization : you need to use those models to determine what the optimal marketing spend and allocation is.

Scenario Planning : you need to build several investment scenarios that demonstrate the impact of different investment strategies on business performance.

Dashboards & Management Systems : you need to give non technical users access to the data and scenarios through decision support tools.

Strategic Consulting and Planning : you need to help decision makers use these tools for decision making.

These are generally the tasks at hand when you run an investment optimization project that uses some science to get to the answer.  These tasks map perfectly to the market differentiators that started to emerge throughout my conversations with the various vendors.  This is illustrated in the diagram below.

Data Access : Some vendors had access to special data sources through alliances they had with 3^rd part data vendors or search engines.

Automated Data Preparation : Data preparation can use up a lot of your resources on projects like these.  Some vendors had automated a lot of these steps so that they could do the data gathering and aggregation described above faster, better and more cost effectively.

Unique Algorithms :  The algorithms have been around for decades so you would think all econometric modeling skills are pretty level.  However, you would be surprised how much variance there is in the quality of the statistical modeling between different vendors.  Make sure you look underneath the hood and talk to who is actually doing the modeling.

Decision Support Tools : Some vendors have packaged their tools in dashboards and scenario planning and decision support tools.  These tools can be extremely useful for helping the end users adopt some of the scientific evidence into their decision making.

Value Added Services : This is probably the most important area.  I have seen so many modeling projects collect dust on the shelf of the marketing intelligence managers.  Building models is easy.  Using them for decision making always seems to be the hardest.  You should always look for a partner who can help you with both.

Hopefully some of these learning can be useful for you at some point.  The reality is that far too few marketers are using econometric modeling to really understand the impact of their marketing efforts on their business.  As a result, they end up making multi-million dollar decisions on pure gut feel.  If there is anything this recession has taught it’s that that will ultimately get you into trouble!

On Monday, British scientist Stephen Wolfram is probably going to go live with his Wolfram Alpha (or “Alphram”, as it’s called affectionately) – a giant electronic brain that has been touted as the next Google Killer.  If you want to get a good understanding of how it works have a look at this webcast.  Though was pretty impressed when I first saw this, it’s clear that Alphram is quite different from Google or any other search engine.

It doesn’t search the web –Rather than searching the entire web, Alphram sits on top of a huge database that has been combed over by scientists on Wolfram’s team.  This database currently holds mainly academic data and facts.  Wolfram claims that “probably 90% of the shelves in a reference library we can say we’ve got a reasonable start on”.  According to some test users it is light on popular culture.

It’s computational knowledge engine, not a search engine – The system computes answer to questions such as “What is the average temperature in Chicago?” or “What is the average wage of a high school teacher”.  It does this by mapping the questions to data components in the underlying database which are then combined to compute the answer.  This is fundamentally different from how search engines work.  If you ask them a question, they will retrieve answers that have already been formulated somewhere on the web by someone else.  That, of course, already covers a lot of ground so it remains to be seen whether computing answers to new questions is actually going to be perceived as a real advantage by the users.

It returns more than just a list of answers – The display and organization fo the information is where the Alphram adds a lot more value than search engines.  Search engines have put all their energy in becoming really good at finding information but in doing so, one could argue they have neglected the actual organization and display of the information they return. 

It is in this area that Google has really been trying to spoil Alphram’s coming out party.  As mentioned in a previous post, Google bought Gapminder but they haven’t really done anything with it other than create the Public Data feature they came out with recently.  More promising is the beta version of Google Squared they will be launching later this month.  It will return search data as a matrix.  The video below shows a demo.  The quality is pretty bad but you do get a good view on how it works.

One potential downside of Alphram is that it only likes “sensible” questions.  In the official screencast mentioned above, every question gets answered properly.  However, more objective reviews, like the one from CNET below, show that about 2/3^rd of the searches did not return the appropriate results.  This is because Alphram only seems to work well with structured questions, which suggests users will need to learn how to use the tool properly.

So Alphram is no replacement for Google.  But it feels like a new and very exciting cross between Google and Wikipedia.  And if people have the patience to learn how to use it, it may challenge search engines to become more innovative in the way they display search results.  As for the launch on Monday, it’ll be hard to have one that is worst than Cuil’s!

It’s probably the last thing we need in marketing today – it being criticized for embracing the very basics of math.  But that’s exactly what Al Ries did in Adage yesterday.  He made some sweeping statements about the state of marketing today, arguing that the marketing community seems to be drifting from “a right-brain approach to a left-brain approach” and that left brain marketers will run companies into the ground by focusing on the numbers.  Calculating ROI, according to Ries, is a waste of time. 

Judging by the responses to the article, the Art vs. Science debate is very much alive in marketing today.  It is also as old as Ries’ thinking.  In 1959 CP Snow did his legendary “Two Cultures” speech at the yearly Rede lecture in Cambridge.  Snow was a novelist who had also studied physics at the famous Cavendish Laboratory alongside Ernest Rutherford.  He had spent his life moving between the literary intelligentsia and the science community, two groups he found to be opposite poles and which he described as two vastly different cultures who speak different languages.  He claims that the lack of mutual respect between the 2 groups creates a polarization which “is a sheer loss to us all.  To us as a people and to our society”.

Although Ries acknowledges that mathematics can play a very small role in marketing, the overall tone of his article is polarizing : it’s either math or marketing.  He fails to see that one of the most interesting areas in marketing today is where the worlds of math and creativity come together in a way that was not possible at the time Snow gave his speech :

“There seems then to be no place where the cultures meet. I am not going to waste time saying that this is a pity. It is much worse than that. Soon I shall come to some practical consequences. But at the heart of thought and creation we are letting some of our best chances go by default. The clashing point of two subjects, two disciplines, two cultures of two galaxies, so far as that goes ought to produce creative chances. In the history of mental activity that has been where some of the break-throughs came.”

Next week Tuesday, Dan Burrier, Chief Creative Officer Ogilvy LA and I will be speaking about this topic at the nGenera conference in Toronto.  I’ll probably post some of the materials here after that. 

You can download CP Snow’s speech on The Two Cultures here.