Is predictive analytics due for a growth spurt as customers look for more consistent targeting in their hunt for ROI?
Predictive analytics is gaining momentum and is rapidly becoming a part of every marketer's lexicon. The allure is obvious: the term itself portends the ability to foretell the future; in our case, the ability to know what the response will be to your content based on comparative analysis of data. But "predictive" today is often not connected directly to action, except for a certain few vendors in the market.
Let's look into our crystal ball and see what materializes about predictive analytics.
The features of predictive analytics assume the following capabilities (or "stack"):
As you can see, neither of these actually guarantees anything. So perhaps we should call it "pundit analytics" - but that might be giving pundits too much credit.
That said, the difference between the above two models is substantial.
In the first example: There is combination of data and then it remains up to an analyst, working with a marketer (these days the hybrid skill is rather famously called "data science") to determine what the content should be, given the comparison metrics. For instance, if your analytics tool can combine data from geographic plus behavioral plus calendar information, you might be able to understand that your customer most likely comes from South Bend on a certain Saturday in September; and you would target her with offers that coincide with her behavioral and geographical patterns. And assuming your effort created an uplift in conversions, you'd then be able to take credit for predicting the outcome.
In the second example: The vendor offering itself contains a "decision engine" or "predictive layer" that automatically takes the same data your marketer would have reviewed and then automatically serves up that content to the South Bend user on a certain Saturday in September. And these companies will live and die by the measure of the uplift they achieve over a non-predictive alternative. That's because it still isn't inexpensive to engage a fully automated predictive engine - so it had better work!
Predictive analytics is one of the key features of a capability stack (and industry transformation) I've called convergence analytics: where customers are demanding the ability to track and take action upon multiple data streams; and where vendors are rapidly taking up the challenge to track multiple silos of data and perhaps even take action on them.
Convergence analytics assumes the following capabilities:
The tea leaves in the bottom of my cup suggest that predictive analytics, a subset of overall convergence analytics, is due for a growth spurt as customers look for more consistent targeting in their hunt for ROI. What predictive analytics really does is act as a better targeting device. It weight-balances the bow so that the arrow flies more true. Sometimes the marketer will be holding the bow. In more advanced systems, the arrow is aimed and sent flying automatically.
The same technologies that power convergence analytics - cloud computing, big data, connectors, algorithms, display layers, and sometimes decision engines - also power predictive tools.
It's not hard to imagine a growing number of vendors working to differentiate themselves by making claims to predictive capabilities. The challenge for vendors will be to make sure they're not trying to claim a dashboard is a Ouija board; and if they do manage to get some magic into their algorithms, to allow for easy testing of the results.
Either way, it's customers driving the pack. Nearly every marketer today is feeling the pressure to see more data at once and do more with it. Predictive is part of their future.
Prediction image on home page via Shutterstock.
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Andrew is a digital marketing executive with 20 years' experience servicing the enterprise customer. Currently he is Managing Partner at Efectyv Digital, a digital marketing consulting company, and Managing Partner at Technology Leaders, a web analytics consulting firm he founded in 2002. He combines extensive technical knowledge with a broad strategic understanding of digital marketing and especially digital measurement, plus hands-on creative in the form of the written word, user-experience and traditional design.
His practice is dedicated to building customers' digital marketing success and helping them save money during the process.
He is a writer, a public speaker and a visual artist as well.
He writes a regular column about analytics for ClickZ, the 2013 Online Publisher of the Year. He wrote the groundbreaking "Dawn of Convergence Analytics" report which was featured at the SES show in New York, and the second report in the series will be featured at the same show in San Francisco.
In addition to speaking at SES, he has presented at eMetrics; and his session was voted one of the top ten presentations at the DMA show in Las Vegas. He is speaking again at the DMA in Chicago in the fall of 2013.
In 2004 Andrew co-founded the Digital Analytics Association and is currently a Director Emeritus. He has designed analytics training curricula for business teams and has led seminars on digital marketing subjects.
He was also an adjunct professor at The Pratt Institute where he taught Advanced Computer Graphics for three years. Andrew is also an award-winning, nationally exhibited painter.
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September 23, 2014