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Success stories

La Maison Simons

In a two-month AB test against a standard Fit Finder setup, Fit Analytics’ Product Suggestions feature delivered a 2% increase in conversion rate and an additional 10% increase in net revenue per visitor for one of Canada’s most iconic retailers.

vertical:Department store
market:North America

About La Maison Simons

Proud Canadian fashion destination Simons is a family-owned retailer providing the most sought-after global assortments to Canadian consumers since 1840. In addition to a robust online shop, operates fifteen stores across Canada including the nation’s first net-zero ecological footprint store, a ground-breaking flagship store in Galeries de la Capitale in Quebec. The company offers an exciting mix of designer and in-house brands for men, women, and home.

Challenges Faced

Simons has enjoyed a positive sales impact from Fit Finder on its e-commerce store since 2016 and was keen to see if the new Product Suggestions feature could help drive discovery online and unlock additional revenue.

“We were extremely impressed with the additional uplift delivered by Product Suggestions in such a short timeframe and look forward to exploring the innovative methods Fit Analytics offers to drive discovery and optimize the customer journey.”


Product Suggestions is easily enabled in any existing Fit Finder integration so there was zero additional work for Simons in activating the feature.


By enabling Product Suggestions, shoppers were automatically shown compelling alternatives in scenarios where they had reached a sizing dead end and would normally have been forced to either restart their shopping journey or leave empty handed.

Increase in conversion rate


Increase in revenue per visitor


The results were impressive: shoppers using Fit Analytics’ Product Suggestions feature showed a 2% increase in conversion rate. Following recommendations for items with a more accurate fit also led to a 10% increase in net revenue per visitor and a 5% increase in average order value.

How Product Suggestions Works

When a customer finishes the Fit Finder journey and a product is not available in the recommended size, the Fit Finder Product Suggestions feature automatically recommends other in-stock items that are sure to fit.
Product Suggestions can also kick in when a customer receives a recommendation for an item that is not likely to fit well (and therefore more likely to be returned). In this case, Product Suggestions surfaces similar available items that are more likely to fit.

Solve Sizing Today

With just a couple of lines of easily integrated code, brands and retailers can unlock enormous value from their existing online apparel shops.
Contact us today to discuss setting up a hassle-free, no-obligation A/B test on a subset of e-commerce data to immediately experience benefits in conversions, returns, and bottom-line results!

About Fit Analytics

Our Fit finder size advisor helps leading apparel brands and retailers instantly access the power of machine learning to deliver a perfect fit for customers, boost revenue, and optimize e-commerce performance. We work with major global retailers and brands such as ASOS, The North Face, Tommy Hilfiger, and Aeropostale.
Time period
Oct. 19 – Nov. 31, 2017 and Dec. 26, 2017 – Jan. 25, 2018
Number of shoppers
Type of test
A/B test with Fit Finder versus Fit Finder with Product Suggestions.

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