Ecommerce ops · Production

Rugs.com returns to Algolia after nine-month OpenSearch experiment causes revenue decline

The problem

After replatforming to OpenSearch in March 2022, Rugs.com found business users nearly unable to make search changes without developer involvement, search speed was extremely slow, discoverability suffered, and revenue began to decline.

First attempt

OpenSearch lacked the basic features and ease-of-use that Algolia had provided, requiring developer involvement for nearly every change, producing slow search and poor discoverability, and ultimately causing revenue losses.

Workflow diagram · grounded in source
1
Replatform to OpenSearch
trigger
“In March 2022 Rugs.com replatformed their operations and left Algolia to adopt Shopify with OpenSearch”
2
Developer dependency blocks changes
validation
“business users found OpenSearch nearly impossible to make any search platform changes without developers, who began "spending a lot more time tweaking changes instead of working on other more important things"”
3
Search quality failures
validation
“search speed was extremely slow, and discoverability suffered”
4
Revenue decline signals failure
feedback_loop
“After its short months of working with OpenSearch, revenue started to decline”
5
Rapid re-onboarding to Algolia
integration
“Algolia had Rugs.com up and running in less than a day and was already sending click & conversion events to Algolia”
6
Ongoing iteration with Customer Success
feedback_loop
“Rugs.com also continues to have a standing call with the Customer Success team, which helps to iterate on their implementation and test other additional features”
7
Revenue gains and business recovery
output
“the company is posting revenue gains instead of losses”
Reported outcome

After returning to Algolia, Rugs.com was back up and running in less than a day, business users regained ease of search management, and the company posted revenue gains instead of losses.

Reported metrics
OpenSearch experiment durationnine months
Algolia re-onboarding timeless than a day
revenue during OpenSearchrevenue started to decline
revenue after returning to Algoliaposting revenue gains instead of losses
Show all 6 reported metrics
OpenSearch experiment durationnine months
Algolia re-onboarding timeless than a day
revenue during OpenSearchrevenue started to decline
revenue after returning to Algoliaposting revenue gains instead of losses
search speed on OpenSearchextremely slow
growth lost during OpenSearch periodlosing some of the growth it had nurtured earlier
Reported stack
AlgoliaOpenSearchShopifyDynamic Re-RankingVisual EditorDynamic SynonymsAI Distributed Search Network
Source
https://www.algolia.com/customers/rugs-com
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

After returning to Algolia, Rugs.com was back up and running in less than a day, business users regained ease of search management, and the company posted revenue gains instead of losses.

What tools did this team use?

Algolia, OpenSearch, Shopify, Dynamic Re-Ranking, Visual Editor, Dynamic Synonyms, AI Distributed Search Network.

What results were reported?

OpenSearch experiment duration: nine months; Algolia re-onboarding time: less than a day; revenue during OpenSearch: revenue started to decline; revenue after returning to Algolia: posting revenue gains instead of losses (source-reported, not independently verified).

What failed first in this deployment?

OpenSearch lacked the basic features and ease-of-use that Algolia had provided, requiring developer involvement for nearly every change, producing slow search and poor discoverability, and ultimately causing revenue l…

How is this ecommerce ops AI workflow structured?

Replatform to OpenSearch → Developer dependency blocks changes → Search quality failures → Revenue decline signals failure → Rapid re-onboarding to Algolia → Ongoing iteration with Customer Success → Revenue gains and business recovery.

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