Decision Support Systems, Data Mining, Supply Chain Solutions from Decisioncraft Analytics


HOME WHY DECISIONCRAFTCASE STUDIES
CASE STUDY: Demographic Ad Targeting
 


Project Objective
To develop a statistical model to estimate the demographic profiles of websites for enabling accurate audience targeting.

Client
Pioneer of online ad targeting specializing in providing innovative performance-based online media solutions for response-driven marketers, advertisers and publishers.

Approach
The modeling process was executed in three phases:

  1. Identifying a stable sample of users who exhibit sufficient and regular activity. These users and their web traversal data will be used for estimating the demographic profiles of websites.
  2. Demographic profiling of the users belonging to the stable sample using a set of skewed websites (both age and gender) using a secondary data source.
  3. Demographic profiling of websites visited by a statistically sufficient number of profiled users at day of the week and day-part granularity.
Approach for Developing Demography Identification Model

Solution
The model calculated the skewness of traffic visiting a particular website for a particular day and day-part combination.

Select Months
Feb - 05 Mar - 05 Apr - 05 May - 05 June - 05
July - 05
Aug - 05
Sep - 05
Oct - 05
Select Days
Weekdays Weekends Mon Tue Wed Thu Fri Sat Sun
  MALE Age Groups FEMALE Age Groups
Day Parts <15 15-25 26-40 40+ <15 15-25 26-40 40+
1:00- 2:00                
2:00- 3:00                
3:00- 4:00                
                 
22:00-23:00                
23:00-24:00                                  
Sample Demographic Model Output for a Website

The model output was used to answer questions like:

  1. What is the age and gender profile of a particular website on a particular time of the day.
  2. A client wants to buy one million impressions to be targeted at 'Males between 26 to 40 years of age' over next 60 days. What websites will be the most appropriate?
  3. Given the following requirement of audiences for next three months, what are the best sites to buy?
  4. Given our existing inventory of media bought, what kind of surplus audience we have on hand?

The model output was accessed by the client's ad-serving engine for targeting audiences. Web history data for approximately 1 million users was processed on a daily basis using a scalable architecture.

Benefits
The client was able to improve the effectiveness of online ad campaigns by ensuring that online ads were placed on appropriate websites based on target audience.

Back to Top

Related Links
Case Studies
Download pdf version (217 kb)

Other Case Studies
Profitability Analysis for Mortgage Lenders
Behavioral Online Ad Targeting
Churn Prediction

thoughtpost - certainty