The ecommerce world is abuzz with talk about exciting ways to improve search results, increase conversion rates and maximize revenue with help from AI-powered solutions like vector search.
To be sure, AI technology is one key resource that online retailers should be capitalizing on to help guide customers toward relevant products and grow margins. But that doesn’t mean businesses should abandon traditional ecommerce practices. On the contrary, they should pair them with practices like AI search. In many cases, it’s by using AI search in tandem with more traditional techniques that retailers unlock the greatest value.
To prove the point, this article explains the benefits of a method called metrics-based boosting, which allows online retailers to leverage the data they already have on hand to align ecommerce search results with business goals. Although metrics-based boosting is not new, it remains as relevant as ever in the AI era. In fact, it’s even more important in the sense that it complements and helps to supercharge AI search.
What is Metrics-Based Boosting?
Metrics-based boosting is the practice of using business metrics – such as product review and rating scores, inventory data and revenue earned – to shape search results on ecommerce sites. The goal is to ensure not just that the products customers see are relevant for a given search, but also that search results are optimized to align with the retailer’s goals to maximize sales or promote certain products.
For example, a retailer could factor in customer reviews of TVs when determining which TVs to highlight in search results. By drawing shoppers’ attention to highly rated TVs, the business is likely to increase sales rates because it’s promoting products that customers prefer.
Or a retailer might know from its warehouse data that it has a large number of a certain type of TV in stock. Using metrics-based boosting, the business could place that item near the top of search results for TVs. Doing so would help increase sales of that particular TV and move excess stock out of the warehouse more quickly.
Finally, revenue earned and conversion rates can be used to influence the position of important products. Used in concert with AI techniques and other metrics, this often provides a vital boost to products that will perform well and be highly satisfactory choices for the shopper.
Metrics-based boosting is one form of search merchandising, which means the practice of influencing search results with the goal of optimizing business outcomes.
How Metrics-Based Boosting Works
For retailers seeking to take advantage of metrics-based boosting, the good news is that they likely already have in place the two key resources that this practice requires, which are:
- Data that reflects business status, goals or priorities.
- An ecommerce search engine capable of tailoring search results based on data points supplied by the business.
Implementing metrics-based boosting is simply a matter of connecting business data to search engines. Products can be directly influenced by these metrics as shoppers navigate and search on the site. The data, continuously supplied from backend sources, is consistently providing a helping hand toward meeting a shopper’s needs and driving business goals.
Search merchandising tools can do this by allowing business users (including those without specialized technical skills) to select business metrics that they want to use to shape search results. For example, they can apply a configuration that would either gently influence matching products with a high conversion rate and good customer reviews at the top of the results page, or can choose to amplify products that match special conditions such as high-inventory items on clearance.
How Metrics-Based Boosting can Benefit Businesses
The beauty of metrics-based boosting is that it’s relatively simple, fast and inexpensive to implement. Again, the data that drives this practice is already readily available to most businesses, and it’s easy enough to connect that data to your search engine with help from search merchandising tools.
Plus, another key benefit of metrics-based boosting is that it constantly evolves and adapts along with your business. As sales increase, products with higher revenue gain more traction, providing you with data that you can use to influence search results. Likewise, product ratings that are constantly changing will shift in position naturally within search results, if you use ratings to help influence listings.
All of this happens without needing to make direct modifications or reconfigure your search rules – as long as you have a search merchandising tool in place that determines what to prioritize, the results will naturally evolve based on changing business data.
When you do this, you enhance the effectiveness of ecommerce search as a way of driving sales and generating revenue. You transform search engines from being merely a tool that shoppers can use to navigate your products into one that also guides shoppers toward making decisions that deliver the greatest benefit for your business.
Metrics-Based Boosting and AI: Better Together
If you’re familiar with AI ecommerce technology, you might know that retailers can also use AI to create powerful search results. But that doesn’t mean ecommerce businesses should think of metrics-based boosting and AI search as competing solutions.
Instead, they’re complementary techniques. For instance, a retailer could use AI-powered search to personalize search results based on a shopper’s purchasing history or demographic profile. At the same time, the retailer can influence search results using business metrics to highlight certain products over others. The result would be search results that are highly relevant to the shopper because they are personalized, while also being highly relevant to the business because they reflect the company’s inventory, sales or other priorities.
So rather than thinking of metrics-based boosting as something retailers don’t need if they’re investing in AI search technology, smart businesses should leverage this technique as a simple and high-ROI solution for improving ecommerce results starting now. There’s no need to wait for shiny AI tools to help boost revenue or profit. You can do so today using search merchandising tools that are already at your disposal.
Jason Hellman is Senior Solutions Architect at Innovent Solutions and Product Manager for FindTuner, an ecommerce search merchandising and machine learning solution for Solr.
