Move Beyond The Insight To Find The Prediction Question
Yes, insights are powerful but there is a downside. Focusing on “insights” as the endgame might be a barrier to what we must do…embrace big data.
So if “insights” aren’t the end game, then what is? In the age of data driven marketing, we need to find the prediction question in every study and address it.
When you are in the prediction business, you are trying to predict unknown values of importance to the enterprise. It might be a prediction about 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, or modeling what is the most relevant content possible to serve up a personalized experience. Or it might be how to most accurately predict who will control the senate, as Nate Silver just did using his data science-based approaches.
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. No NIH, no statistical snootiness about whether or not the data came from a random sample (as if that really exists anymore…). A focus on prediction leads you to integrate data from different sources 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!
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.
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 in the U.S. mid-term elections earlier this month.
Before Nate, political polling was centered on the single proprietary study. Each pollster ran their own poll, trumpeting its superiority, and implied 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. In other words, each poll, for all its sampling purity is INADEQUATE on its own at maximizing prediction accuracy. However, insights ARE important to framing his model. He would not use some data stream that made no sense, regardless of statistical correlation, like which league won the World Series this year. What he does is essentially use big data principles. He has Moneyballed political polling and is paid millions because he is the most accurate political forecaster on the planet. I think marketing research practice needs to follow Nate’s footprints in the snow and go beyond the survey.
In marketing research, we can find the prediction question by thinking about the future, differentiating one user from another in terms of how they would respond differently to a marketing stimulus, or sales response to a marketing activity.
For example, when we make a trial forecast from a concept test for a health-oriented new product we do so without reference to prior studies. Purchase intent results are just accepted without adjustment or enhancement. Is there really no Bayesian prior that we can extract from the hundreds of other concepts that were tested based on similar health claims? Also, we drop out at launch. Using prediction approaches we could provide guidance to algorithmic media approaches to predict and target the likely users. Couldn’t we harness other predictive factors like frequent shopper data patterns for that user or possibly that those visiting retailer websites are more likely to try new things?
Another example is brand tracking. Stop focusing on the report card and start thinking about brand health…predicting the FUTURE trajectory. To do this, certainly we need to include digital and social signals about the health and positioning of the brand. (Note: I am currently working with a leading supplier and have begun bringing this out to the marketplace.)
To become like Nate Silver, the Moneyballers, and the data scientists, Marketing Insight teams need to challenge themselves to find the prediction question in every study and commit to bringing together the data streams or conducting the experiments that are needed for prediction and then marketing action.