Decision-support system optimization for advertising industry


Description

2018 Data Integration, Data Analysis

Improving performance of a system that is designed to compile best offerings for advertisers to broadcast their commercials. Tackling the problem of missing data through applying a data approximation technique.

Client

An American entertainment company that owns and operates several most popular and award-winning brands in cable television.

Challenge

The advertisers want their adverts seen ideally by specific audiences that possess certain characteristics.

For this, the client company gathers information on as many as ~60โ€™000 TV viewers that are described by ~30,000 different characteristics. By integrating, transforming and analyzing this data, the elaborated system is able to propose optimal broadcast plans to advertisers.

The optimal plans are best options found at the intersection of the following requirements:

  • characteristics of the people the advertiser is willing to target;
  • number of targeted audience views;
  • budget an advertiser wants to pay for the service;
  • duration of the commercials and more.

Importantly, the system also envisages and indicates the perfect TV time for an advertiser to place their advert, so that it is watched primarily by the people they are willing to target.

To make this happen, the system finds out most likely viewer characteristics intrinsic to the viewers of the particular program at a particular time in the future.

In order to make calculations of the optimal plan for any given point of time possible, it is necessary that the system contains the information on each characteristic at each point of time. However, the system contained only partial data, as the data was uploaded with some periodicity.

The major challenge was to restore a continuous dependence, using the known values โ€‹โ€‹of the characteristics at discrete points of time.

Solution

We analyzed the data for several periods of time. Then, with the use of linear regression with L2-regularization we were able to achieve approximate data with appropriate accuracy.

Therefore, the system became able to offer optimal plans for advertisers at any given piece of time.


Technologies:

  • Python
  • DB2
  • R
  • Netezza