Segmentation is a useful technique in our work to understand people’s viewing choices.B We divide and subdivide the general viewing audience into segments based on data describing people’s attention to television programming.B Then, we analyze how our segments tune in to different networks’ offerings and their responsiveness to different promotions.B B B

The analysis sensitizes us to trends in tune-in and promotional responsiveness.B It inspires ideas on how we can improve our segmentation and the classification of attention data that underlies the segments.B

Ultimately, we hope segmentation can help explain changes in tune-in.B Given how different segments tune in to a certain program, we want to make recommendations on the best promotional messaging strategy.B Or, when a program grows more popular, we want to look at how different segments tuned in and answer the questions of how and why did ratings improve.

In the meantime, through our sandbox, we want to share our early exploration into people’s viewing choices and get feedback on our approaches.B

As a foundation for taking in our exploratory work, we present our initial Genre Segmentation.B Genre Segments are unions of audiences who tuned in to similarly classified programs.B We’ve assigned a name to each Genre Segment corresponding, sometimes humorously, to the type of programming they’re likely to enjoy and written a brief persona description to help cement the relationship.B

The size of each Genre Segment population is a function of the definition we’ve issued to determine whether a viewer belongs to that segment or not.B In the table below, the size of the segment is indicated by the percentage of the viewing universe that belongs to the segment.B Segments with more restrictive rules are smaller; less restrictive, larger.B

You’ll find Genre Segments in our analyses of Audience Attentiveness and our Network Audience Heat maps.

B

To gain insight to those different motivations and discover which networks attract the attention of audiences with widely differing preferences for content, we’re experimenting with heat maps applied to tune-in data gathered through TNS Media Research‘s InfoSysTV.

In the example heat map below, we examine how ten audience segments, organized according to their attention allocations to different program genres, tune in to ten different networks.B The radius of the heat map circles indicate the relative size of the audience tuning in to the network.B The colors indicate the audience rating compared to the general population; the darker the hue, the greater the rating relative to the general viewing population.

For programmers interested in locating the audience likely to enjoy their fare, we think this is a good way to start.B We see that Crime Solvers have affinity for A&E and USA.B We see that the Family Oriented audience arrives at scale to ABC Family and Disney.B With a quick glance at this heat map, we’re able to focus subsequent analysis on the few networks presenting the best opportunities to learn more about the core audience.

A note on construction:B We’ve used the plug-ins available at Sparklines for Excel to build our heat maps.