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The industrial absorbent industry often doesn’t use the same keywords in product descriptions that customers use to describe the products they’re looking for. This left customers with zero search results and empty carts, and New Pig missing out on potential revenue.
With Fusion’s machine learning algorithms, New Pig can identify search terms that aren’t resulting in add-to-carts. Query pipelines, set up to curate search results for these problem queries, will improve search results and, ultimately, the customer experience.
By capturing search requests, clicks on products, add-to-carts, and conversions, New Pig can view a user’s session from end to end, allowing it to determine what clicks are selling products and how to adjust search results to continually improve conversions.
Industrial absorbent manufacturer New Pig had a problem. They make “the world’s best stuff for leaks, drips, and spills,” but the industrial absorbent industry doesn’t always use the same words to describe their products that customers use to search for them.
“They’re not looking for products. They’re looking for, ‘Give me something that cleans up water, but not oil,’” says John McQuade, New Pig Director of Software Development.
This meant returning customers weren’t finding freshly available products they didn’t know by name, and newcomers were getting null search results and abandoning before they became customers.
What New Pig needed was a way to understand the intent behind search terms and to match those intentions with a list of products that provided solutions. And they needed to do this without changing product descriptions. “In our world, the product development folks don’t like their product descriptions changing,” says McQuade. “We needed a good way to get the right user experience without having to dismantle the descriptions we were given.”
Their search platform at the time was built with DIY Apache Solr, which required developers to maintain and made curating the customer experience difficult for non-technical New Pig team members. They attempted to ease the burden by outsourcing their search, but that didn’t work. Looking for another solution, they came across Lucidworks Fusion.
Fusion’s application development platform, built on top of Solr, removed the heavy lifting Solr required and provided the friendly user interface New Pig needed to augment their website’s CX.
With Fusion deployed on their website, Fusion AI went to work analyzing user behavior. By detecting patterns in the data, search terms that weren’t performing well could be identified. Using Fusion’s query pipelines, New Pig curated search results for these problem queries that more accurately responded to customer requests.
“Currently we’re capturing search requests, clicks on products, add-to-carts, and conversions so we can look at a user’s session from end to end, and really determine what clicks are selling products.”
New Pig has been a Lucidworks customer for over five years. Fusion’s evolving capabilities and UI have kept New Pig armed with up-to-date tools to meet increasing customer expectations.
“Search has changed quite a bit,” says McQuade. “A lot more AI and machine learning coming along. A lot more personalization. Users are seeing that on B2C sites and they expect the same shopping experience in B2B. If you’re collecting enough signal data from your users, you can get rid of a lot of that manual, tedious, look-at-the- analytics work and let the users define those behaviors for you. Then let the AI apply those behaviors to your search automatically.”
With Fusion’s AI and machine learning in their pocket, New Pig makes light work of a potentially messy search experience.
