Turning audience data into revenue: Part I
A few weeks ago Forrester released a case study on our efforts with client ESPN to maximize their audience data to drive marketing revenue. That prompted a post outlining what we believe to be the four fundamental drivers of value in the media industry, categorically (1) data volume, quality and value (2) targeted ad packages that are easy to sell (3) value models that prioritize your CRM efforts, and of course (4) the right supporting infrastructure.
The most critical advice we can give is, do not be overwhelmed by the amount or distributed nature of your data. You have to build on what's in place first, and in some instances you can have an actionable segmentation within 12-16 weeks. The consolidation and analysis of existing data alone can produce immediate insight while you lay the foundation for more scalable programs and packages. For instance, if you have minimal self-reported audience data to work with, there is still valuable information about behavior, affinity, and level of engagement in your anonymous data that can be used to deliver distinct segments of value. But how?
1. Start with a small analytic data environment. This does not have to be a production-ready database, just a stable sandbox housing 20% of the most valuable data attributes to develop the models and deliver proof of concept without major upfront technology investment.
2. Develop preliminary segmentation and analytics. Begin by analyzing your registered audience data to establish behavioral trends and benchmarks for the anonymous data. Then you can use available page view data to score UUs based on their content interests or affinities. Pilot these models to ensure you are reaching the intended target, and continue to refine. It may also be beneficial to append with some lifestyle data to round out your profiles.
3. You have to have a roadmap. This is an iterative process so coming out of the initial data assessment and analyses, you must have a viable, financially justified roadmap for building on and automating capabilities, eventually to the benefit of not only marketing, but ad sales, editorial, product, and even customer care.
In Part II of this series we'll talk about what happens next. What that roadmap might look like, including data strategy, analytics, program delivery, reporting, and enabling technology.
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