Reduce time to resolution of User Acquisition Fraud in your Marketing Efforts

 

About the Customer

Chief Marketing Officer

eCommerce ($20m+ revenue)

Second largest mobile loyalty app provider

in North America with over 23 million customers

 

The Pain point

  • Runs a number of marketing campaigns around the year and requires dedicated data scientists/analysts to build checks to monitor and alert fraud as the campaign gets underway and prevent
  • Manual intervention to go through dashboards, detect and block fraudsters  
  • 10 mins spent on each fraudster for validation (180+ users flagged on some days)
  • Type of fraud keeps changing based on data and nature of acquisition campaigns – ads, seo, gift cards, affiliate, invitation perks

 

What is their starting point / ecosystem / tech stack

  • Java, Python, Docker, Kubernetes
  • Streamsets
  • Google Cloud Platform
  • MySql, BigQuery
  • Third party machine learning tools – R, Python

 

Compelling Event (Trigger point)

  • Internal initiative to drive towards high-value user acquisition
  • Lack of understanding in behavioral data from user leaves with less confidence on existing fraud rule engine
  • Data collection and analysis largely solved by BigQuery + Tableau
  • Initial fraud rule engine largely static and manually patched for every new discovery of fraud
  • Customer support Workflows for inspecting and flagging fraudsters is done in another interface ; with communication lag between the fraud rule engine and experts

Predera Benefit

  • Mature end-to-end AI driven platform for fraud
  • Machine Learning continuous improvement  using self-learning and updation techniques
  • Business stakeholders can define their own definitions of Fraud and contribute to the improving AI engine
  • Customer service gets direct alerts in their work environment (Slack) from the AI engine and the feedback updates the model

Evaluation

  • Easy to install and setup
  • Business stakeholders can teach AI for Fraud Engines in 8 hours
  • Able to migrate all existing ML models and rules in less than a week
  • Integrated with data stores on AWS/ GCP – no extra support needed from IT/ data teams
  • Didn’t need to re-write workflow integration as it directly integrates with Slack and Hipchat for feedback from Customer support
  • Dedicated expert level support for onboarding

Business Impact

  • Business stakeholder -> fraud analysts -> customer support representatives  loop was tightened, reducing the feedback time by 80%
  • Saved 280 hours of stakeholder productivity per year
  • Reduced time to resolution of Fraud by 75% from ~20 mins to ~5 mins
  • Handle more fraud queues and volume with fewer people

ROI

“We would need two full-time data scientists with knowledge of marketing and acquisition campaigns for 6 months just to build out our Fraud Engine capability let alone the rest of monitoring and continuous automatic improvement provided by Predera. We could spend $300,000 to do that, or just buy Predera and have a mature Fraud and AI / ROI management platform today”

 

Are you ready to optimize your Fraud efforts?

 

Talk to us Today – hello@predera.com