Property advertising powered by data science

REA Group is a multinational digital advertising company specializing in property and the provider of realestate.com.au.

This talk will explore how REA is using data science and machine learning to personalize property experience for consumers and provide value for property developers.

Specifically, we will share a case study involving building a personalized email marketing campaign tool powered by data science.

Attendees will learn how we used consumer engagement scores and consumer profiles derived by machine learning for driving personalized marketing campaign.

Problem:

REA helps property developers advertise properties to potential buyers through variety of channels including email marketing campaigns. The ROI for property developers is measured by potential buyer leads generated by the campaigns. Traditional mass emailing approaches return low value ROI due to high un-subscribe rate, low click through and open rates and emails marked as spam. We needed the campaigns to be more data driven e.g.: consumer behaviour.

Solution:

We developed an automated email marketing campaign tool that uses location and property-type targeting. The solution involved:

  • Using machine learning derived consumer engagement scores and consumer profiles to test hypothesis “Location based email marketing campaign improves consumer engagement and ROI for property developers”
  • Building campaign-booking tool for account managers to visualize engaged consumers by location
  • Scaling proven hypothesis to improve targeting by location and property-type
  • Monitoring results
 
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Outline/structure of the Session

We will structure the session as below:

  • Introduction: Property Advertising
  • Challenges: Traditional email marketing
  • Case Study: Data science to personalize email marketing
  • Impact: Consumer Engagement and ROI
  • Q&A

Learning Outcome

Attendees will learn:

  • REA's data science journey
  • How to turn data and analytics into revenue
  • How to balance consumer needs vs business needs

Target Audience

Data Engineers, Data Scientists, Developers, Product Managers, Data Analysts, Marketing Managers

Prerequisite

This talk is aimed at people interested in building products to deliver value for consumers using insights generated by data science and machine learning.

schedule Submitted 4 months ago

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  • Josh Graham
    By Josh Graham  ~  4 months ago
    reply Reply

    For a 30 minute talk, I think I'd prefer a single speaker. Who would it be if that were a restriction?

    • Mahesh Shivamallappa
      By Mahesh Shivamallappa  ~  4 months ago
      reply Reply

      Hello Josh,

      For this presentation, we need both of us - Gregg to cover the business aspect and myself to cover the engineering/delivery aspect.

      Regards,

      Mahesh