Inspired by Steve Gray's article published on INMA website: "Predictive analytics: the next media disruption tool" I want to share my thoughts on this.
I believe that many media companies struggle in their digital business because of giving out to 3rd parties the most valuable asset - their audience data. Tens or sometime almost a hundred advertising scripts publishers use to monetize their content pull out this data. Also, publishers tend to use many analytic tools, but they don't allow to collect and use first party data to build new business models, often based on that predictive analytics.
All that went wrong.
Reversing this situation by implementing the right data platforms as the biggest players like FT, Guardian, NYT, Washington Post did is the first step. Then, we are able leverage the asset we have and build value around it. The automotive and real-estate ideas mentioned in the article look very promising.
I would add more to it:
- use raw data to build custom metrics and performance indicators for your teams (content, product, sales, etc.) and use predictive analytics models to predict if goal will be accomplished
- predict how content will perform based on early signals, especially what would go viral
- build scoring models for subscribers to predict churn
- score leads in selling subscriptions
- build precisely crafted segments for advertisers and use lookalike predictions in performance-based campaigns (e.g. cost per lead or cost per sale models)
- and the really important thing: content recommendation systems that predict what's interesting for users and are able to deliver it on main page, newsletter or chatbots.
All of this brings me to the conclusion, that there’s a huge potential to re-innovate business models for publishers based on technology and data.
Maybe we should be inspired by other industries like telecommunications, where fixed-line players leveraged their user bases and became 3P then 4P and now are still innovating by building their financial or energy offerings?