Huckberry is the one-stop men’s shop for a wide range of gear and clothing staples. 

It was founded in 2010 by Richard Greiner and Andy Forch, two twenty-something outdoor lovers with a dream of creating “a brand that was equal parts store, magazine, and inspiration to help guys suck the marrow out of life.” The two bootstrapped the company with $10,000 of their own money and hustled.

Fourteen years later, Huckberry has become a go-to source for millions of men (and women) for high-quality apparel, footwear, and gear — encouraging new active lifestyle experiences and inspiring adventure in keeping with the founders’ goals.

The company sells a new lifestyle category dubbed Everyday Adventure, which sits between traditional outdoor and fashion industries. It does this by combining strong retail and media practices and through its community.

From a technical perspective, that demands that huckberry.com must have a solid user interface and user experience, both for internal employees and external customers. It also requires a deep understanding of the customer, which is gained through data collection.

Customizing the search experience

Initially, Huckberry had a DIY search experience built using Elasticsearch with basic querying capabilities. It wasn’t giving the company the customization ability it needed, nor providing any analytics into performance.

They recognized delivering the impressive user experience they wanted required a more reliable and robust search solution that could handle custom personalization. The solution was Algolia Search.

“We moved to Algolia at the time largely for performance reasons,” Hepworth says. “It meant we didn’t need to worry about scaling. We didn’t need to think about speed, because of how Algolia handles optimization, storage upgrades, and product indexing for the site.”

He adds, “We’ve always used Algolia for more than just Search. We use it for our browse pages, and not just categories. We use it for brand-based pages and category-based pages, and we use it for point-of-view pages, which would be any facet of a particular product that the merchandising team might be interested in building a page around.” 

“Algolia’s range of tools has let us lean into automating merchandising around our point of view, which previously took our site merchandising teams hours and hours of manual effort weekly. This has been critical to our success across several of our campaigns.”

The company performed a completely custom implementation of Algolia, taking approximately four to six weeks in early 2022. 

Since first implementing Algolia to handle its product record data, Huckberry has expanded its use to index three different types of data in Algolia: product data, call-out data, and page data. “That information is something that we wanted to sometimes display in our mix for our customer and storing it that way has been very useful for us,” Hepworth says.

This approach also allows Huckberry to gain greater insights, using Algolia’s analytics engine and A/B Testing for further insight into customer behavior, click rates, and search terms. The company has adopted Algolia end-to-end, from the moment a customer enters the site until they leave. Using clicks and conversion data, Dynamic Re-ranking delivers a highly relevant AI-driven search experience, after which AI Personalization powers the exceptional, individualized 1:1 experience that customers crave.

“We spend a lot of time thinking about how discovery and our customers’ values interact, and considering how we can create a nice balance by using personalization. It dynamically creates a balance between new customers who are probably more interested in best-selling products, and returning customers who are visiting for discovery. Algolia AI Personalization, Dynamic Re-ranking, A/B Testing and Analytics are key to that.”

Huckberry uses Algolia AI Personalization to offer bespoke content for each site user — both new and existing — to create an impressive, customized experience for all customers.