How would you measure the success of RTB House's Deep Learning-based retargeting solution?

Introduction

Measuring the success of RTB House's Deep Learning-based retargeting solution requires a comprehensive approach that considers multiple stakeholders and metrics. To effectively evaluate this product, I'll follow a structured framework covering core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

RTB House's Deep Learning-based retargeting solution is an advanced advertising technology that uses artificial intelligence to deliver personalized ads to users who have previously interacted with a brand's website or app. This solution aims to re-engage potential customers and drive conversions by showing them highly relevant ads based on their browsing history and behavior.

Key stakeholders include:

1. Advertisers: Seeking higher ROI on ad spend and increased conversions
2. Publishers: Looking for increased ad revenue and improved user experience
3. End-users: Expecting relevant, non-intrusive ads
4. RTB House: Aiming for market share growth and technology leadership

User flow:

1. User visits an advertiser's website and browses products
2. User leaves without making a purchase
3. RTB House's algorithm analyzes user behavior and creates a personalized ad
4. User sees the retargeted ad on a different website or app
5. User potentially clicks on the ad and returns to the advertiser's site

This product fits into RTB House's broader strategy of leveraging cutting-edge AI technology to revolutionize the digital advertising landscape. Compared to competitors like Criteo or AdRoll, RTB House's deep learning approach promises more accurate predictions and better performance.

In terms of product lifecycle, this solution is in the growth stage, with increasing adoption but still room for significant market expansion.

Software-specific context:

• Platform: Cloud-based, scalable infrastructure
• Integration points: Ad exchanges, advertiser websites, publisher networks
• Deployment model: Software-as-a-Service (SaaS)