Ever wondered how marketing teams can leverage the vast silos of data they use? In this article by Uli Bethke, Sonra Co-founder and Dataiku partner, discover what it takes for marketing teams to go beyond a fragmented vision of analytics.
In a recent study, CEB interviewed nearly 800 marketers at Fortune 1000 companies with some interesting results. Only 11% of their decisions are based on data. In a similar survey performed by eMarketer magazine, more than 54% of marketers in retail admitted that they are either not aware of the concept of Big Data or struggle to apply it in practical terms. These results are astonishing as marketing is at the forefront of the digital revolution. Some of the very first Big Data use cases such as clickstream analytics have been in marketing. Why is it that even the most advanced users of data analytics are struggling? Like users in other fields, marketers put the cart before the horse when it comes to Big Data.
Why Big Data Marketing Projects Are Bound to Fail
Contrary to common belief amongst marketing execs, insights are not created by simply hiring a bunch of data scientists and throwing a heap of data at them. Data science is actually hard work. It requires a precise definition of the business problem at hand, the type of analysis to be performed, and the actions that will be taken based on the analysis. Paraphrasing Picasso: big data is useless; it can only give you answers. So asking the right questions is key. From our experience, this is the number one reason why marketers fail to benefit from the data revolution.
Marketers have created lots and lots of data silos for themselves. They have Google Analytics or Omniture for web analytics, MailChimp or Bronto for email campaigns, all sorts of tools and channels for running ad campaigns, typically multiple CRM systems, tools for marketing automation. The list just goes on and on. This makes it difficult and costly to get a 360 degree view on what is going on with the customer. The free version of Google Analytics does not even allow you to extract the data at the lowest level of granularity, which makes it very difficult to build meaningful predictive models (tip: stay away from Google Analytics, at least the free version). At Sonra, we have recently helped HostelWorld, one of our customers, to overcome this obstacle by building a data lake that integrates data from web analytics, ad campaigns, and various other channels. HostelWorld is now able to build predictive models using Dataiku DSS on top of the integrated data.
Going Beyond Traditional Analytics
While marketers are often familiar with standard business intelligence and reporting use cases, they struggle to understand advanced analytics. Traditional aggregate analytics helps us to measure and compare performance of segments in our data, e.g. the PPC channel outperformed SEO conversions. This is useful in identifying that a problem exists. However, it does not tell us why a problem exists or how it can be resolved. Having said this, some of this simple type of analysis is still actionable. You can take action without knowing why a problem exists.
In the following example, which course of action would you take? As a decision maker, I let (1) all of my under-performing employees go or (2) try and understand why certain segments of my workforce under-perform and then address the underlying reasons. The first addresses the symptom, the second gets to the root cause of the problem.