Buying the vintage lamp that will perfectly suit your newly-decorated living room is hard. You know that you want a 3-legged lamp, but you can't find the ideal item. For ManoMano customers, we built a ‘same style’ recommendation engine. Based on your search, the engine presents your tailored selection of ‘same style’ vintage-looking lamps.
ManoMano is the online reference for DIY and gardening in 5 European countries. It is among the 50 most visited e-commerce websites in France. With a revenue increase of 70% in 2018, ManoMano aims to become the French Amazon of DIY and the next French unicorn by pushing continuous web innovations for their customers.
Revenues in €
Sicara’s support stands out for 3 reasons. First, the Sicara teams have seamlessly integrated into the ManoMano teams. Second, the Sicara teams adapt to all business matters and are proactive in offering innovative solutions. Third, the ManoMano teams took advantage of the agile dynamism brought forward by Sicara.
We launched this ROI-driven recommendation engine to answer customers’ needs identified in pre-production extensive user research. ManoMano visitors browse similar products based on their style. Offering a tailored selection of products based on customers’ style preferences increases the conversion rate and enhances customers’ experience.
(Re)Qualifying existing products based on their style is time-consuming and very subjective. Thanks to our library of image recognition, we knew that an algorithm could recognize visual similarity (or same style) and push forward adapted product recommendations based on the visitor’s browsed article. To put in production the recommendation engine for 10 categories of product in minimal time, we put forward a pragmatic solution: an open-source and pre-trained convolutional neural network. Meet VGG16 deep convolutional neural network!
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