The 5 most common analytics mistakes

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These days it seems that every marketer is focused on getting more data- and analytics-focused. The promise of digital has always been getting real insight into the true impact of our marketing investments on sales. But for most of us, that promise has been just that -- a promise. The notion is appealing, perhaps, but it's often been more of a future hope than a current objective.

The problem is that most of us are going about it wrong. Here are the top five mistakes people make as they try to take an analytics-based approach to marketing.

The 5 most common analytics mistakes

Insufficient investment

Most companies are underinvested in marketing analytics infrastructure. OK, that's not necessarily what a frugal marketer wants to hear. But think about it. You have tens of thousands -- perhaps millions -- of customers. That's potentially hundreds of data points per customer -- across time. And customer information is just the tip of the iceberg. You also need to know about the people you touched but didn't convert so you can know if what you did actually had an impact.

You can't analyze tens of millions of events in Excel. And, by the same token, you can't derive the true value of all this data from a software-as-a-service (SaaS) tool that took two hours to implement. It's millions -- possibly billions -- of marketing events! When someone contends that you can unlock the value of all this information quickly with an off-the-shelf tool, does that sound credible?

The sooner that we all accept that investing appropriately in analytics infrastructure is critical, the sooner we can actually know what works. Only then will we have real guidance on what to do instead of basing our budgets -- and careers -- on hunches, confirmation bias, and the like.



Matthew Anthony
Matthew Anthony January 2, 2013 at 5:16 PM

Sione -

Yes, a great example. Though I would settle for a basic understanding of scientific principles like experimental design, hypothesis testing, principles of causality, etc. Right now, our industry is still in the grips of "lies, damned lies, and statistics" where most of what's trumpeted out there as knowledge is simply data dredging or misuse of terminology that's aimed 100% at promoting their own business (whether or not that's helping the advertiser). For those in our industry using terms like "causation", "incremental value", etc., I would surely hope they've read the extensive literature from Rubin et al stretching back to the 1970s, and the principles therein .. if not, then they really have no business using those terms.

It will take a while for more rigorous science & math to really penetrate the industry - simply bc there is too much ego and profit that depends on the current less-than-scientific way that lots of things are done. But we believe that advertisers' need for truly optimized business strategies to better deploy their budgets is going to force the industry to adopt better principles in analysis in the long run - and we're developing the technology, staff, and knowledgebase to service this need today for those who are ready for it.

Sione Palu
Sione Palu November 18, 2012 at 1:18 PM

Mathew, yes advanced algorithms & mathematics had been pouring out of academic publications over the last decade even today, new ones keep appearing in the literature. The oldest mathematical techniques that I'm aware of that has caught the attention of data-analysts & researchers of today is more than 100 years old. This technique is called "Tensor Calculus" also known as "Tensor Vector" which first surfaced in early 1890s. The meaning of tensor is multi-dimensional (dimension of more than 2). The flat surface of a dining table is 2D (x & y), while a rectangular box is 3D (x, y & z). Tensor of 2 modes (or 2D) is what statisticians/mathematicians call a matrix (data is represented by a row-by-column). Tensor of 3 modes (or 3D) data is row-by-column-by-depth. There's tesnor of 4 modes, 5 modes, 6 modes or more, which depends on the data that the analyst has collected and want to analysed. Einstein popularized tensor mathematics when he proposed & published his GTR (general theory of relativity) in early 1910 decade (1913). This is the reason that science fiction writers are fascinated with phrases as "hyper-space" or "multi-dimensional-space" because Einstein's GTR is derived using tensor calculus in which tensor itself is a multi-mode (multi-dimensional) concept.

Einstein made space-time as a 4D tensor (x, y, z, t) which is the 3 spatial coordinates plus the 1 time coordinate. We can't visualize what a 4D tensor is like, since human senses can only sense 3D. After Einstein brought forward the idea of tensor in his GTR, then tensor mathematics went to sleep in the next 50/60 years or so. It started re-appearing in the late 1960s with new algorithms but sparingly. Tensor researches & publications had resurfaced in the last 10 years or so with vengeance mostly from statistics, engineering, computing, neuro-science, data-mining & machine learning, communities. Tensor can play a huge role in marketing of today because marketing data can be multi-mode (multi-dimensional).

The data structure for analytics of today is still pre-dominantly 2D (ie, rows-by-columns). However, there are times that the data to be analyzed is more than just a matrix (rows-by-columns). It can be 3D or more, such as the data shown in the following paper (see link below), which is 3D of the spending (households by weeks by stores), with each tensor entry indicating the total number of dollars spent by a household in the corresponding store visit on a specific week. ["Simultaneous Co-segmentation and Predictive Modeling for Large, Temporal Marketing Data"] and download here : []. I'm not aware of any commercial analytic tool that has made tensor analysis capability available, but I'm sure that it won't be too long before it does.