Ecommerce ops · Production

Algolia becomes a key ingredient in Breville Group's global ecommerce stack

The problem

Breville was consuming most of its engineering bandwidth managing a self-hosted Solr search infrastructure, leaving insufficient focus on consumer-facing experiences. As the company invested heavily in inspirational content such as recipes mapped to appliances, making it accessible with the existing setup would have required a significant additional engineering commitment.

First attempt

Breville's self-managed open source Solr search infrastructure was too demanding on engineering resources and could not scale to support the company's growing content and ecommerce ambitions.

Workflow diagram · grounded in source
1
Consumer searches Breville site
trigger
“helping consumers navigate the many choices and decisions that lead them to not just the right machines but the outcomes they want”
2
Dynamic boosting and personalization
ai_action
“Using dynamic boosting and triggering capabilities, he says, helps to guide the consumer along their journey easier than a simple basic search would, by showing only the content they need when they need it”
3
Faceted search across products and content
ai_action
“Faceted search combining products and content”
4
Customer quiz for product guidance
ai_action
“Breville uses Algolia to power several customer quizzes to aid them in making better, more informed choices. A prime example is the quiz the company has created to help guide its customers to coffee beans with a taste profile they'll enj…”
5
Coffee Essentials bundle output
output
“Coffee Essentials bundles, which combine a coffee machine, coffee subscription, accessories, and training — all based on customer decisions, all powered by Algolia. "That's led to a pretty high attachment rate," Ball says, as significant…”
Reported outcome

With Algolia, 1 in 5 search users make an online purchase, conversions and attachment rates improved, and the engineering team was refocused on high-value consumer experience development instead of search maintenance.

Reported metrics
Search users making online purchase1 in 5
ConversionsImproved conversions
Attachment ratepretty high attachment rate
Search panel conversion jumpdramatic jump in conversions
Show all 5 reported metrics
search users making online purchase1 in 5
conversionsImproved conversions
attachment ratepretty high attachment rate
search panel conversion jumpdramatic jump in conversions
engineering team focusTech team refocused on higher value activities
Reported stack
AlgoliaSolrAdobe Experience Managercommercetools
Source
https://www.algolia.com/customers/Breville-Group
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

With Algolia, 1 in 5 search users make an online purchase, conversions and attachment rates improved, and the engineering team was refocused on high-value consumer experience development instead of search maintenance.

What tools did this team use?

Algolia, Solr, Adobe Experience Manager, commercetools.

What results were reported?

Search users making online purchase: 1 in 5; Conversions: Improved conversions; Attachment rate: pretty high attachment rate; Search panel conversion jump: dramatic jump in conversions (source-reported, not independently verified).

What failed first in this deployment?

Breville's self-managed open source Solr search infrastructure was too demanding on engineering resources and could not scale to support the company's growing content and ecommerce ambitions.

How is this ecommerce ops AI workflow structured?

Consumer searches Breville site → Dynamic boosting and personalization → Faceted search across products and content → Customer quiz for product guidance → Coffee Essentials bundle output.

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