Input Data
Below is the Data we have taken as an input.
1. Data 6 TV plans executed in the specific market in various combination of NGRP/ACD/RF with PT-NPT CPRPs at overall market level.
2. Off -take data from Nielsen Retail Panel
Approach Taken to solve the problem
1. Evaluate the past plans and dissect the output for step 1 analysis
2. Deep dive into Genre / Channel /Program mix/ PT:NPT/ Sub TG shares used as well as “intensity of viewership” (Average Time Spent)
3. Identify “inefficiency” corridors using our proprietary Data POEM’s algorithm
4. Create multiple hypothesis for the AI engine to deliver target metrics, basis constraints
5. Use human intelligence to validate for logical checks
6. Repeat steps 1 – 4
7. 27 iterations resulted in the summary of observations & recommendations
Output
1. Platform was able to Reduce the media spends by 20% by delivering the same number of GRP’s.
2. 80% increase in the reach among the light TV viewers.
3. Reducing the frequency heavy TV viewers by 30% by maintaining the similar GRP’s.
4. Detailed media plan which can be executed directly.
5. Brand Guidelines to follow for future media plans.
The Brand received the Guidelines final with the executable plan comparisons of all the GRP levels which helped the planning teams to deliver better campaign results across the board for the specific market.
Guidelines address constraints to be taken into consideration for building the plan in the specific market.
- Optimization TG in terms of viewership intensity
- Delivered high GRPS from light viewers
- GRP dispersion among the top 4 GECs,
- Genre level dispersion
- HD channel weights
- Fiction Vs Non-Fiction GRPs
- spotting in the top programs
- we have also given the channel level dispersions for all the three GRP levels.
