The supply and demand dynamics of Television is shifting. Increased choice has driven higher demand from consumers for quality content, which has led to something akin to an arms race amongst content producers to offer newer and better programming to capture our attention.
Covid-19 has unceremoniously upended one half of this dynamic as show production around the world is halted for an indeterminate amount of time. But the other half is not. Consumers are not going to stop demanding quality content – and given that we're all spending more time at home now, there is more time to consume it.
Mining the archives
There is a unique opportunity for networks that have hundreds of hours of excellent archive content and, based on recent ratings at least, growing audiences. At a time when "new" is on hold, they can offer the next best thing – call it "new to you."
We have been looking at data science techniques we can apply to granular datasets to identify the shows that are likely to perform well with higher volumes of repeats. There are shows that individuals are watching multiple repeats of that don't air during popular timeslots, and we can use data science to look at the similarity of the viewing behaviors of these people to primetime audiences.
Part of this is learning what the audience for archive shows and repeats is. We know that some people are likely to spend the same amount of time watching Television regardless of what is airing, and whose consumption will increase during a stay-at-home order. There are then those whose use is more likely to stay constant or decrease, particularly those wedded to a particular genre (sport is an excellent example of this).
Modeling will help us to understand who your likeliest cohort of new repeat viewers. The audience you aim your repeats at is possible neither your existing daytime audience, nor your casual viewer who watches a little at the peak, but somewhere in between.
Where are my undiscovered gems?
Everyone knows House and The Office repeat well. These were much-loved shows that did huge audiences at the time of broadcast. What other shows have high repeat tolerance?
We can answer this by mining existing data and figuring out what shows are going to best suit the repeats audience. We need to look at what they watch - specifically, what repeats they view and understand what the decay curve is - how many times a show can be repeated and maintain the audience – both on your network and competitors.
When we have done this, we can start looking at schedules. Where are shows that repeat well under-represented in the data vs. over-represented? Where are there instances where a competitor network is picking up minutes that your station could be picking? What is the content in your archive that isn't achieving its full potential?
Answering these questions will fill the schedules once new productions dry up and enable to best programming to flourish during these difficult times.