Retailers Tap Metric-Based Boosting to Enhance eCommerce Search Results

Consumers expect more relevant and personalized search results when shopping online. Leveraging  artificial intelligence (AI)-powered search with traditional strategies helps companies use data like product reviews, inventory levels, and revenue to optimize search results to assist consumers and meet business goals.

Meanwhile, eCommerce companies want to improve search results and conversion rates, often using AI-powered search. When combined with traditional strategies like metrics-based boosting, businesses can leverage existing data to better align search results with customer needs and business goals.

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Metrics-Based Boosting

Metrics-based boosting, a technique rooted in search merchandising, is effective when paired with AI tools. It uses key business metrics to prioritize certain products in search results, helping businesses maximize sales and achieve their objectives.

For example, if a retailer wants to highlight highly rated products or move excess inventory quickly, it can adjust search rankings accordingly. As sales, product ratings, and inventory levels change, metrics-based boosting adapts in real time, continuously driving business goals while enhancing the customer shopping experience.

According to Eric Brackmann, VP of Commerce Media at Koddi, metrics-based boosting works in tandem with AI-powered search to enhance both product relevance and digital engagement.

“Metrics-based boosting leverages business data such as stock levels, profit margins or seasonal trends to align search results with key business objectives like maximizing revenue or reducing excess inventory,” Brackmann said in an interview with PYMNTS. “AI-powered search enhances relevance by understanding user intent and personalizing results based on factors like browsing history, past purchases or demographics.”

For example, Brackmann noted if a retailer has an excess inventory of running shoes, metrics-based boosting can prioritize those products in search results. At the same time, AI-powered search ensures the promoted shoes are still relevant to the user. This synergy drives product relevance and digital engagement, improving conversion rates, and driving profitability.

As business goals and market conditions evolve, metrics-based boosting can adapt to meet shifting priorities.

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Boosting Naturally Adapts

“Business goals and market conditions are never static, and metrics-based boosting is flexible enough to evolve in step,” Brackmann said. “The beauty of tying search ranking to live business metrics is that the system naturally adapts as those metrics change. As new trends emerge or seasonal demand shifts, the underlying data, like conversion rates or inventory levels, will reflect those changes and boosted search results adjust automatically.”

For example, he said, during the holiday season a retailer might prioritize “in-stock and fast-shipping” items, and once the season passes, those boost rules can be dialed down or retargeted to the next priority.

“Modern merchandising tools even allow scheduling of boost rules or campaigns, ensuring that during a spring promotion, seasonal products get elevated, then automatically revert after the sale,” Brackmann added.

Many retailers leverage continuous feedback loops, he explained, where the system monitors results and suggestions are made to fine-tune the weighting of metrics. Over time, this leads to stronger long-term performance as search results consistently hit the mark for what customers want and what the business needs.

“The impact is a virtuous cycle,” Brackmann noted. “Better search relevance drives more purchases, which provides more data to further improve relevance, steadily lifting revenue and customer satisfaction in tandem.”

Businesses want to enhance and personalize their search results through AI.

According to the PYMNTS Intelligence report, “What Generative AI Has in Store for the Retail Industry,” 92% of companies use AI-driven personalization to drive growth. Additionally, 77% of business leaders rank generative AI as the most impactful emerging technology.

What challenges could businesses encounter when integrating metrics-based boosting into their existing search engines?

“Key integration challenges include data silos, data quality issues, scalability constraints, and gaps in expertise within an organization,” Brackmann explained. “Overcoming these challenges requires a careful strategy of preparing quality data, leveraging the right tools, phasing the rollout and continuously optimizing. Once in place, metrics-driven AI-powered search becomes a self-improving asset, constantly aligning your product discovery experience with both shopper preferences and your evolving business objectives.”