InTune | PREDICT
The Challenge
Accurate predictions of TV ratings are crucial for broadcasters, advertisers and agencies alike, forming the basis for commercial airtime planning, rate cards and optimisation of yield management. However, generating accurate predictions is a complex and time consuming task. The expanding number of broadcasters and of target groups means that an enormous number of ratings must be predicted per month.
The Solution
Pointlogic has more than 10 years of international experience with ratings prediction. This experience has led to the development of InTune|PREDICT, a proven ratings prediction technology which produces fast, accurate results.
InTune|PREDICT can provide predictions for all target groups and has been proved, via scientific
research, to offer the maximum possible accuracy (Admap, February 2003).
InTune|PREDICT predicts ratings for both programmes and commercial breaks, taking into account all relevant variables such as historic viewing behaviour, counter programming, ratings for comparable programmes, seasonality, days of the week and the weather. Depending on available variables, InTune|PREDICT will report the level of reliability of the specific prediction on a scale of 1 to 5. Pointlogic also recognises the value of human insight and InTune|PREDICT is designed to allow user interaction: Once a ratings prediction has been made, users can change this number based on their knowledge of program budget and program content, for example.
In the Workplace
InTune|PREDICT is currently in use in 15 European countries, underpinning the buying and selling of commercial TV airtime, the sealing of TV sponsorship deals and the optimisation of alternative programming contracts. Our clients use InTune|PREDICT on a daily basis, updating ratings predictions in a matter of minutes according to the latest historic and future programming
information.
The Results
InTune | PREDICT offers professionals:
- The best possible ratings predictions
- Better output through better planning
- More flexibility in the prediction process
- Direct knowledge of prediction reliability
- Highly efficient predictions processing
