As data is the new oil, data analytics is the new oil field pump. The oil pump is used to draw oil from underground reservoirs, so is data analytics used to uncover something you knew you had, but had little understanding of its quantity, quality or value.
Brands are on a marathon of collecting and gathering data. And because of this, the analytics landscape is constantly changing and surfacing more and more accurate metrics and analytics models. As a result, marketers are becoming obsessively focused on testing their hypothesis and showing ROI for their spending. Marketers are not happy anymore with only measuring likes and shares. The existence of sophisticated analytics solutions empowers them to capture the right data and find practical ways of capitalizing on it. However, sometimes the paradox is that the more data is available, the more questions still need to be answered.
2015 has been a generous year for digital marketing, with so many trends that aimed at revolutionising the industry and so many technologies created to help marketers make a difference. Prescriptive analytics is the topic that I’ve enjoyed the most reading about, however, I must note that I couldn’t see as many research papers or use cases as I wished.
Prescriptive analytics help companies find the most likely outcome for a situation and find the best course of action based on existing goals, requirements, and limitations. We know how great Amazon are doing in this area with their “anticipatory shipping” (Amazon is able to start shipping products to you before you even order them). Also, some airline companies and supermarkets are using prescriptive modeling to anticipate our buying needs or determine the optimal pricing structures. I think it’s super cool that some companies have the resources and processes in place to even be able to perform outcome simulation for potential marketing or sales campaigns.
But, here is my first question:
Is prescriptive modeling available and relevant only for companies such as Google, Amazon, IBM or can it work in the environment of SMB or even non-profits? And if so, what would be the challenges?
Big data may look like a challenge by default for many companies. However, the real challenge with applying prescriptive modeling in the context of big data is ensuring that the right problems are tackled. Worldwide there are dozens of startups with noble causes that could benefit from such solutions without having the need to hire an army of excessively expensive data scientists and data analysts. However, it’s necessary to consider that prescriptive analytics is not just about technology, it’s also about having the right people asking the right questions in an environment that could implement the change in the right ways.