Big data advocates point to the benefits of harnessing big data for insights purposes: Learning what your customers actually care about by forensically analyzing their transaction, clickstream, social media, etc. behaviors.
I want to suggest a way of strengthening the argument for achieving buy-in. Instead of stopping at “insights”, express the benefits of big data in terms of “prediction”. That is really where the rubber meets the road in a data-driven marketing world.
The prediction business is critical for marketers because it drives up marketing ROI in a repeatable way. Consider the world of programmatic digital advertising. For every million page view requests, algorithms are PREDICTING which one thousand should be targeted with your ad because they are most likely to respond. Such targeting can be based on models that use surveys, clickstream patterns, social media profiles, demos, time of day, weather, etc. and has been proven to drive up marketing ROI.
To be good at prediction, you will need to integrate as many data sources as possible to determine empirically which ones demonstrate predictive value. That is why prediction questions encourage big data approaches and score the usefulness of information based on its incremental prediction value.
Data science is an equal opportunity employer. If the data make sense to use AND they have predictive value, they’re hired!
Other prediction questions that have huge value to the enterprise include:
- Predicting the future share of a brand.
- Identifying which users are most likely to be in play from their cookies to deliver advertising selectively to the right user, at the right time on the right screen.
- Modeling what is the most relevant content possible to serve up a personalized experience to a given user.
A great example of moving to prediction-based thinking comes from Nate Silver, creator of the fivethirtyeight blog and author of the book, “The signal and the noise". Also, acknowledged to be the most accurate source of election results predictions and he nailed it again this election cycle.
The prediction business is critical for marketers because it drives up marketing ROI in a repeatable way.
Before Nate, political polling was centered on the single proprietary study. Each pollster declared who will win an election as if no other pollsters or predictive factors existed. Nate takes an unprejudiced view. ALL polls have value and need to be weighted together but the weights are not equal…they depend on prior track record, sample size, “house effects” leaning towards one party vs. the other, etc. Also, he doesn’t only use polls. He finds that other factors add predictive value such as fundraising, candidate ideology vs. voter views, economic index, job approval ratings, etc. But it all starts with managing the data. Without that, Nate would not be prepared to conducted any of this analysis.
To add value the way that Nate Silver, the Moneyballers, and the data scientists do it, the marketing enterprise needs to do the following:
- Identify the most important business needs that can be expressed as prediction questions.
- Identify all data you can have access to that might be relevant, and commit to bringing it together via an extractable and analyzable platform.
- Commit to creating your data infrastructure.
- Begin modeling and keeping track of your predictions to establish and continually improve your prediction track record.
Finally, create an implementation platform so you can act on predictions and demonstrate the improvement in marketing productivity.
