After receiving the results from the 2016-2017 Big Data Analytics and Digital Marketing Latam report we understood why companies are today more confused than they was in the past with scarce information.
Today more than 80% say they have enough information but just 12% are making decisions based on it.
What are they using the information for? The main topics are “Planing marketing or advertising information”, “Customer Segmentation” and “Predicting consumer Behaviour”. All those activities require to process a huge amount of data (even for an SMB), ergo, require the use of Data Science techniques.
However, when we asked what kind of platforms was they using to capture, process, merge and blend all that data, the answer was revealing.
Google Analytics and Excel was, by far, the chosen couple. Those two platforms are great and very useful but not for the above mentioned proposes. Google Analytics is platform for tracking user behaviour on Web Sites and Web/Mobile Apps. But big part of the information required for the above mentioned proposes are not hosted by GA. You can always merge and blend data but do it INTO GA is definitely not the most efficient way to do it. That’s why Google Analytics launched Big Query, one of the most useful GA features. Excel is also a great platform for any Data Analyst, you can definitely do Machine Learning on it, but is not meant to be used for Big Data.
On the other hand the use of Big Data platforms like Hadoop are used by a very small portion of the companies (less than 3%). So it’s fair to expect that companies invest more in Big Data to improve their poor current situation. But even when most companies will increase their Big Data Budgets in 2017, almost 20% won’t invest in Big Data and 53% will invest less than 10% of their marketing budget in Big Data Analytics.
So while Digital Budgets are getting bigger and the volume of data is suffocating digital marketers, this is not being followed by a proper investment in Platforms and Analyst to generate more “traction” in their decisions.
So it’s like the brakes of our car are not working properly and we try to solve the problem pushing the pedal to the metal. Makes no sense right?
In our experience all the companies that are taking advantage of the information are investing at least 10% of their marketing budget in Big Data Analytics. From that more than 30% goes to Analytics activities while the rest goes to technology, mainly to guarantee the information reliability. At the end of the day, why are you going to invest part of your budget in take value from data that is not right?