Measuring search functionality changes is tough. Even experienced teams often use feedback labels to gauge results. While A/B Testing is common, few use it to improve search systems at the query level. A/B Testing for search is complex, with challenges like choosing metrics, randomization units, and dealing with sparse data.
We've refined our "Query Testing" capability over time. Here's what we've learned.
The drive for economic efficiency in retail and eCommerce often leads to echo chambers and biased data, hindering optimal outcomes. Balancing exploration and exploitation is key for long-term success. Focusing on customer experience over transactions can shift priorities and boost success.
He’s showing how the pure focus on tracking data limits your abilities to learn and explore what customers want. You have to use exploration to challenge the current state and discover new opportunities.
Andreas Wagner speaks at MICES 2019 (#mices2019) about Measuring and Optimizing Findability and GMV in e-commerce Search.
He’s shedding light on how explicit relevance judgements and several types of biases could decrease your Search Quality and why “relevance” alone is not a reliable estimator for User Engagement and even less for GMV contribution.
Pavel Penchev from SearchHub discusses the need for a transparent, and flexible site-search analytics tool, to allow for true intent based optimization of customer search journeys. SearchHub reveals the solution... it's called "search collector".
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