How can RTB House improve its Deep Learning algorithm to better predict user intent in real-time bidding?

Introduction

To improve RTB House's Deep Learning algorithm for better predicting user intent in real-time bidding, we need to analyze the current system, identify key pain points, and develop innovative solutions. I'll outline a strategic approach to enhance the algorithm's performance and ultimately drive better results for RTB House and its clients.

Step 1

Clarifying Questions (5 mins)

• Looking at the product context, I'm thinking about the specific types of data RTB House currently uses. Could you elaborate on the data sources and features currently fed into the Deep Learning algorithm?

Why it matters: Determines the baseline for improvement and potential areas for data enrichment. Expected answer: Current data includes user browsing history, demographic information, and contextual data from ad placements. Impact on approach: Would focus on identifying new data sources or improving data quality if current sources are limited.

• Considering user behavior, I'm curious about the real-time aspect of the bidding process. What's the current latency for making a bid decision, and how does this compare to industry standards?

Why it matters: Helps understand if speed is a limiting factor in prediction accuracy. Expected answer: Current latency is around 100ms, which is average for the industry. Impact on approach: Would prioritize algorithmic efficiency if latency is higher than competitors.

• Regarding product lifecycle, where does RTB House's Deep Learning algorithm stand in terms of maturity and market adoption?

Why it matters: Influences whether we should focus on incremental improvements or more radical innovations. Expected answer: The algorithm is well-established but facing increased competition from newer entrants. Impact on approach: Would lean towards more innovative solutions if the product is mature and facing market pressure.

• Considering company alignment, what are the primary KPIs that RTB House uses to measure the success of its Deep Learning algorithm in predicting user intent?

Why it matters: Ensures our improvements align with the company's core metrics and business goals. Expected answer: Key metrics include click-through rate (CTR), conversion rate, and return on ad spend (ROAS). Impact on approach: Would tailor solutions to directly impact these KPIs, potentially prioritizing different aspects of the algorithm.