Digital Marketing is the subject and focus of so many things nowadays that many have come to accept the axiom that “all marketing is digital marketing”. Techniques once limited to specific channels, segments, and departments have become increasingly ubiquitous in both function and form. The problem now is not what do we do to market, but what do we NOT do. With so much data becoming available, and the potential reach of marketing efforts growing with each passing day, digital marketing must adapt and grow to the ever increasing insights available in big data.
Marketing Analytics are not new, they have been around since the dawn of ecommerce. What has changed in recent years in the depth and breadth of data available to the marketing analyst. Sure we have your basic things like page views, click-through rates, AOVs, conversion rates, bounce rates etc. However new sources of semantic data such as social media and real time behavior data coming from devices is changing the landscape.
Traditionally big data was a space reserved for the sciences. However, it is becoming an area where smart companies trying to increase their marketing reach are delving into, with the help of some slick new technologies. They are also employing some good old fashioned statistical reasoning techniques, leveraging both deterministic and probabilistic methods to really analyze and predict consumer behavior.
Big Data Impact on Marketing
So what does this mean for digital marketing professionals? The outcome of sophisticated data modeling and clustering is going to be sharper more streamlined market segments. Digital marketing is going to have to start to really consider the “segment of one” idea and personalize their efforts and messaging as much as possible. As digital marketing professionals, we need to offer more customized vocabularies and solutions that possess the ability to change on the fly, responding to shifting consumer variables and dynamic models. The adaptive systems ingesting and analyzing big data are going to need sharper and more streamlined controlled vocabularies tailored to each individual segment. They are going to need deeper semantic relationships between terms to be able to more quickly formulate several possible answers to any given question. Additionally, all of this will need to be supervised and governed as much as possible.
In order to accurately pinpoint a customer’s needs, wants, and preferences, we need to ensure that a customer-centric approach is always employed. Regardless of the technique or analysis, it can be tempting to take a firm-centric approach, which was often the case with more traditional forms of marketing. In today’s increasingly personalized world, it would be a fatal error not to leverage the large amount of personalized data available to establish a streamlined customer-centric marketing paradigm. We also need to keep in mind that despite all of this data, marketing is fundamentally a strategic exercise, not an analytic one.
Predicting the Future – to a Point
Perhaps the most significant takeaway from this explosion of data for marketing professionals is moving from a deterministic model to a probabilistic model. In the past we were limited by computing power to only provide deterministic output, that is analyzing the data in merely a descriptive way. Now we can leverage cognitive computing and learning algorithms to provide probabilistic analysis, that is really predict what will happen next to a relatively good degree of accuracy, and act accordingly.
Lastly, we must keep in mind that no analysis or model will ever be perfect, and we mustn’t expect them to be so, or dismiss them simply because their predictions are not 100% accurate. If we can avoid some common errors like using the wrong sample frame, not taking into consideration outliers, and not making the models too complicated, we can glean some very good marketing insights from big data. With both traditional and semantic data growing, and consumer expectations growing along with them, it is critical that modern marketing efforts be savvy to the methods and means of handling both.