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Huckberry improves conversions and revenue with Algolia AI Personalization

We opted for Algolia primarily for its performance, alleviating concerns about scalability. Moreover, the streamlined optimization, storage upgrades, and product indexing handled by Algolia eliminated any worries about speed.

Josh Hepworth

Head of Technology @ Huckberry

Summary

In its mission to help people encourage new product discovery and inspire everyday adventure, popular men’s wear, and outdoor gear retailer Huckberry focuses on better understanding the customer through data. Recognizing the need for a custom search experience, the company implemented Algolia to improve search performance, provide data analytics, and build personalization that would impress its customers. Learn how adopting a host of rich Algolia features has helped Huckberry grow revenue and improve conversions.

Huckberry
Use case

B2C Ecommerce

Headquarters

Austin, Texas

Customer since

since 2016

Feature usage

Algolia Search API, Dynamic Re-ranking, A/B Testing, Filters & Facets, Revenue Analytics, AI Personalization

Key Numbers

9.4% increase in revenue related to AI Personalization

Key results
  • Increased insights into revenue analytics 

  • Customized experience for users

  • Greatly improved reliability

  • Consistent search functionality across site

The challenge

  • 1.

    DIY search platform without customization

  • 2.

    Need to improve search performance

  • 3.

    Lack of search analytics

The solution

  • 1.

    Algolia Search and a wide range of features

  • 2.

    Able to handle scale and customization needs

  • 3.

    Fully custom implementation in 4 to 6 weeks

  • 4.

    Able to handle multiple data mixes

The result

  • 1.

    Able to serve individual customer-specific results

  • 2.

    9.4% increased website revenue related to Personalization

  • 3.

    Analytics to understand customer behavior

  • 4.

    Reduction in merchandising time

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.

“The data team is responsible for helping us as a company, not just within our technical team, to understand our customers better and have a better idea of what they’re purchasing and why, and for helping us better understand site usage, enabling us to better meet our customer needs,” says Josh Hepworth, Head of Technology at Huckberry.

Hepworth says the company combines traditional data aggregation methods with surveys and other qualitative research to create a holistic understanding of its customers.

Huckberry image 1

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. 

Huckberry image 2

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.

Highly personalized and high-performing search

Through AI, Algolia has empowered Huckberry to create the highly personalized experience it strives to deliver to its customers, resulting in greater conversions and revenue.

With more than 1800 products, and sometimes as many as 4000, on the site for customers to discover, highlighting the right products for the right user is essential. But at the same time, surfaced products need to reflect stock levels, and current relevance and be tailored to showcase features the customers feel strongly about. AI Personalization allows customers to explore more easily by automatically curating the results for them.

“AI personalization gives us the ability to say, ‘Here are the things that we know our customer cares about,’” Hepworth says.

“We’re starting to explore things like fit, color, styles, and collections as ways to also get a hint at what might interest specific customers. That’s the sort of data we’re starting to feed into Algolia to even better tailor things to a particular person.”

The company has seen significantly improved revenue and conversion rates for customers who were matched with products based on AI Personalization, Hepworth says. Since implementing Algolia, Huckberry has seen a 9.4% increase in revenue for customers with a Personalization profile.

The company is an early beta adopter of Algolia Revenue Analytics, providing it with a better overview of site performance. It provides the company with a truly deep dive into how Algolia is performing and improving its business metrics.

Huckberry image 3

“It’s been pretty reliable and the insights in the tool have been very helpful for us,” Hepworth says. “The different data points it gives us access to have been helpful for context, confidence and providing the granularity needed to look at different ways a particular change can affect users.”

Having developed a completely customized solution, Algolia support as a strategic partner has been a vital component to success.

“The Algolia team was extremely helpful in helping us reach our custom search and discovery goals and helping us understand the Algolia capabilities to achieve them, and it provided great documentation to work from,” Hepworth says. “The Algolia team is truly interested in helping the customer, and it has shown through in everybody we’ve been able to work with at every level.” 

“My team is capable as well,” he adds. “And it just really felt like a partnership, because we're able to help each other in so many different ways and be able to find creative and novel solutions to achieve what we wanted to achieve.”

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