I recently presented a deck on Audience Analytics for a multinational mass media company to a group of division heads from digital and traditional media, advertising, finance, broadcasting, production, privacy and beyond.
We did a deep dive into understanding the current industry trends in media & entertainment and the overwhelming technology barriers they face as they work to embrace big data analytics. We also looked at how leading organizations are moving towards a next generation consolidated audience insight platform to drive measurable business results.
However, before we dove into the Primary Use Cases for Advanced Audience Insight Analytics, we stopped to examine a slide which outlines how M&E organizations are typically in the beginning of a customer analytics journey aimed at discovering insights and driving relevance at an individual level. An open conversation followed in which we debated the order of the steps in this journey.
We started out with the maturity-to-value steps as follows: (click to enlarge)
The point that sparked the debate was based upon “predictive insights” being seen as placed too early in the maturity curve.
My take was that we need to better understand and predict what granular-level audiences truly want before we can target them precisely, personalize their interactions and maintain a relevant continuous dialogue. The other take on the placement of this step was that marketers and media need to get precise before they can become predictive. Honestly, I can see both side of this debate as making sense.
So, the question remains: where do YOU see predictive fitting into the journey and how do you define that particular step?
Let’s us know in the comments below.
