Ecommerce ops · Production

Maisons du Monde boosts search conversion 5% and CTR 2.3% with Algolia AI Re-ranking

The problem

Maisons du Monde's previous search tool was purely technical with no e-merchandising console, preventing the team from optimizing result relevance or deploying brand strategies — an increasingly urgent gap as the marketplace launch expanded the product catalog.

First attempt

The previous search software was inflexible and had no merchandising controls, offering no business dimension for the e-merch team to tune result relevance.

Workflow diagram · grounded in source
1
AI Re-ranking surfaces top products
ai_action
“MdM notably used the AI Re-ranking feature, which suggests the most attractive products directly on results pages. This module contributed to a 2.3% increase in click-through rate and a 1.6% increase in add-to-cart rate”
2
E-merch console configuration
human_review
“the e-merch console that would allow the company to optimize its results”
3
A/B testing validates performance
validation
“A/B testing showed that product detail page views increased by 4% and conversion rates by 5%”
4
Insights drive roadmap iteration
feedback_loop
“They run numerous A/B tests and build their roadmap based on the insights collected”
Reported outcome

After deploying Algolia, A/B testing showed a 4% increase in product detail page views and a 5% increase in conversion rates; the AI Re-ranking module specifically contributed a 2.3% increase in click-through rate and a 1.6% increase in add-to-cart rate, significantly enhancing user experience.

Reported metrics
Search usage rate20%
Search share of online revenue35–40%
Product detail page views4%
Conversion rate5%
Show all 6 reported metrics
search usage rate20%
search share of online revenue35–40%
product detail page views4%
conversion rate5%
click-through rate2.3%
add-to-cart rate1.6%
Reported stack
AlgoliaAI Re-rankingDynamic Re-RankingA/B TestingPersonalization
Source
https://www.algolia.com/customers/maisons-du-monde
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

After deploying Algolia, A/B testing showed a 4% increase in product detail page views and a 5% increase in conversion rates; the AI Re-ranking module specifically contributed a 2.3% increase in click-through rate and…

What tools did this team use?

Algolia, AI Re-ranking, Dynamic Re-Ranking, A/B Testing, Personalization.

What results were reported?

Search usage rate: 20%; Search share of online revenue: 35–40%; Product detail page views: 4%; Conversion rate: 5% (source-reported, not independently verified).

What failed first in this deployment?

The previous search software was inflexible and had no merchandising controls, offering no business dimension for the e-merch team to tune result relevance.

How is this ecommerce ops AI workflow structured?

AI Re-ranking surfaces top products → E-merch console configuration → A/B testing validates performance → Insights drive roadmap iteration.