Tell me about a time you had multiple solutions to a problem

We're interested in understanding your problem-solving approach and how you evaluate different options. Tell me about a time you faced a technical problem where you identified multiple potential solutions. Describe the problem, the various solutions you considered, how you evaluated them, and the reasoning behind your final choice. What were the trade-offs, and what did you learn from the experience?

Tell me about a time you had multiple solutions to a problem

Okay, I can definitely think of a situation. I was working at Google, on a team responsible for optimizing ad delivery. We had a problem where certain ad campaigns were consistently underperforming, meaning they weren't hitting their target impressions or conversion rates. This was impacting both Google's revenue and advertiser satisfaction.

STAR Method

• Situation: As I mentioned, ad campaigns underperforming relative to impression goals.

• Task: Our task was to identify the root cause of the underperformance and implement a solution that would improve ad delivery and campaign performance.

• Action: We initially brainstormed several potential causes:

  1. Naive Solution: Increase Bid Prices: Our first thought was the simplest: just increase the bid prices for those underperforming campaigns. The thinking was that higher bids would increase our chances of winning auctions and thus delivering more impressions.

     • Pros: Easy to implement, quick results (if it worked).
     • Cons: Could significantly increase costs for the advertiser without guaranteeing improved conversion rates. It's a blunt instrument.

  2. More Targeted Solution: Improve Ad Targeting: A second approach was to refine the ad targeting. Maybe the campaigns were targeting the wrong audience segments, or the keywords were too broad, leading to low-quality impressions.

     • Pros: More efficient use of ad spend, potentially higher conversion rates.
     • Cons: Required significant data analysis and experimentation, more time-consuming.

  3. Sophisticated Solution: Optimize Landing Page Experience: We considered that the landing pages associated with the ads might not be optimized for conversions. Perhaps the content was irrelevant, or the user experience was poor, causing users to bounce without converting.

     • Pros: Improved user experience, higher conversion rates, long-term value.
     • Cons: Required collaboration with the advertiser (which could be slow), significant development effort to optimize landing pages.

  4. Algorithmic Solution: Optimize Ad Scheduling: Our team decided to implement an algorithm that dynamically adjusts the ad scheduling based on real-time performance data. We could monitor conversion rates and user engagement and adjust the ad delivery schedule to focus on times when the audience was most responsive. This also took into account geographic differences, so that even within the same time zone, regions that saw higher conversion rates saw more of our impressions.

     • Pros: Automated, data-driven, adaptive to changing conditions. The data collected could also drive future decisions and models.
     • Cons: Complex to implement, required significant engineering effort, needed robust monitoring to ensure it was working correctly.

  We analyzed historical campaign data, conducted A/B tests with different targeting parameters, and monitored landing page performance using Google Analytics.

• Result: We ultimately implemented a multi-faceted approach. We combined improved ad targeting with algorithmic ad scheduling. By refining the audience segments and keywords and dynamically adjusting the ad schedule based on real-time performance data, we saw a significant improvement in ad delivery and campaign performance. Specifically, we saw a 15% increase in impressions delivered and a 10% increase in conversion rates for the underperforming campaigns. The first solution to increase bid prices was deemed to be inefficient and not worth the trade-off in increased costs for advertisers.

The biggest lesson learned was the importance of considering multiple solutions and choosing the one that best addresses the underlying problem while balancing cost, complexity, and potential impact.