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#BizTrends2018: Prescriptive data in the digital space, the next big thing
Paula Raubenheimer, head of programmatic at SouthernX, The SpaceStation.
2016 saw a shift from descriptive to predictive analytics, which was a big step for the media buying world; and now we’re taking an even bigger step into prescriptive – a clear indication of how quickly the environment is developing and improving.
What’s the difference between prescriptive and predictive data?
There are a few definitions but the most frequently used definition for this difference is that predictive analytics describes what might happen, while prescriptive analytics describes what should happen – a powerful difference when budgets are being cut and brands expect more granular and accurate reporting on media campaigns. In essence, prescriptive analytics can improve the outcome of campaigns by using cause and effect to determine the most likely outcome of a campaign.
What does prescriptive data mean for brands?
Prescriptive data and analytics allow us to take a macro look at the causes and effects of the data points used in both descriptive and predictive analytics, and use this to get a more a holistic view of the probable outcome of a campaign.
So, predictive data is used for forecasting, descriptive data mining creates business intelligence, and prescriptive provides information for simulation and optimisation – meaning that prescriptive data goes further than predicting the outcome of a campaign, but it also provides suggestions to the campaign manager in order to improve the probable outcomes. It can also show a variation of outcomes, so if you change X, then Y will happen, but if you change A and B, then C, D and E will happen.
This new way of using data means that so much of the guesswork that still exists in running online campaigns is mitigated. Brands and marketers will have more control over the actual outcome of campaigns, and proactively change elements of it in order to maximise the result, before the campaign is launched.
According to Digitaldefynd.com, “analytics would be used to understand customer patterns and programmatically suggest profitable customer paths to marketers to route customers in that direction. For instance, Amazon uses prescriptive analytics for product recommendations based on customer data around original purchase and product engagement patterns. This helps Amazon to provide better user experience and also increases customer spend.”
Where is prescriptive data currently being used?
An article on Mycustomer.com shows how retailers are taking advantage of this technology. “The use of prescriptive data analytics is of particular interest, as many experts believe that prescriptive tools give retailers more choice in terms of actioning insight from consumer data. Dealing with channels as individual silos will lead to inaccurate understanding of customers and ineffective decisions, but combining insights from assorted channels will provide a clearer picture of the overall business.”
The banking and credit industries are also already taking advantage of prescriptive data. By mining and analysing a customer’s past financial and credit data, they can automatically receive recommendations on how to proceed with applications, and be able to show a number of variant outcomes if the recommendations are applied.
Because this data is not easily gathered, those who have it are in the driver’s seat. Global platforms like Google and Facebook are looking to add this data to their arsenal to close the loop in the analytics and programmatic process.
2018 is going to change the way we market digitally in many ways. Personalisation and targeting through data are key to developing an effective customer journey, and a successful customer journey is what is going to make the difference between a campaign that delivers the right results and one that doesn’t. Prescriptive data will become an essential tool in this armoury.