To make recommendations on:

• How many different shows to schedule in peak hours
• Which shows should be paired together as lead-ins and lead-outs
• Which timeslots in which to focus strong pairings
• Identify any strong potential pairs that are not currently being optimised

Using telecast schedules and Nielsen AMRLD data, we mapped the flow of viewing from slot-to-slot and programme-to-programme, correlating any changes over time with available promo time. By analysing % and volume retention, level of dropout, the number of new viewers added, and time spent per viewer, we calculated the impact of showing 1, 2, or 3 different titles within peak hours and the impact that this has on both reach and ratings.

We combined a variety of metrics to create a Retention Score, which allowed us to compare programme pairs like for like. We also analysed a broad range of demographic profiling elements and created an algorithm to give them a Profile Score where programmes with the lowest Profile Scores have the most similar audiences. Using these newly developed metrics we were able to make specific scheduling recommendations to optimise natural viewership on these networks.

Analysis of 18 months of programme data was condensed into an easy-to-use interactive tool, enabling the client to select a specific title and whether it was intended to be a lead-in or lead-out. The tool provides previous successful programme pairings and untried pairings with strong potential pairs based on fanbase overlap and Profile Scores. Programming teams use this information to make amendments to their schedule to better retain viewers during a period of reduced promo availability.
Click on image to zoom