Empower Your Business With The Magic Of Machine Learning!
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Empower Your Business With The Magic Of Machine Learning!
Fashion e-shops have specific needs that they need to address in order to offer personalized experiences to customers and help them discover the products they want.
A particular need is that products are seasonal and are purchased during the summer or at certain times of the year.
Some products live only in winter , some only in summer , some only for 2 years.
Some products have a short shelf life and are not available after a few weeks.
Consumers usually buy clothes to accompany other clothes in their wardrobe and need help with the matching process.
People buy clothes but wear sets and this is a difficult problem to solve.
Identifying the style in each consumer is very important and we should always try to infer it.
People inevitably develop a sense of relationship with clothes or objects, some of which are based on their appearance.
Some pairs of products can be seen as alternatives to each other (such as two pairs of jeans), while others can be seen as complementary (such as a pair of jeans and a matching shirt).
This information guides many of the choices people make.
However, style changes at some point in our lives and we should always help customers discover the new.
People’s tastes evolve , something you didn’t like 2 years ago you like now.
Some years the trend is jacket; the next year it’s something else.
Recommendation System is an important system with many functions , which decides which products at which price level are presented to each customer of the e-shop and in a short time to the store in order to complete the purchase.
It is the system that every e-shop should have if you need significant performance.
It is a Visual, Relational and Textual System that uses the information from product images as an important parameter of an object based on the user’s interest.
To learn how the top companies in the world increase revenue up to 30% from existing customers using Recommendation Systems and Personalization read the book below.
https://mobiplus.co/ebooks/customer-rediction
mobiplus fashion Recommender provides e-shops experience to their customers, helping their customers discover products they want with a 30% increase in revenue.
mobiplus fashion recommender is an end to end text and image based fashion recommender , uses Collaborative filtering and Convolution Neural Nets and recommends products to consumers.
It takes into account the seasonality , the short life cycle of the product, the identification of style and trends.
It uses the shopping Data in e-shops , product images and text.
It is a simple model that is easy to understand and develop.
Use the visual aspect of products to recommend products to your customers.
Collaborative Filtering tells us which customers are similar.
Then an Annoy model is used on top of Collaborative Filtering to predict customers with similar buying behavior in 30 Dimensional Latent vector space.
We use product images and put them through an engine ResNet50 (Convolution Neural Net) to extract product features.
And then we use them to enrich the Product Vector.
Then we find similarities between users and products.
Then we use recent purchases to find out which products to recommend.
Then we produce promoted products popular in the cluster (cluster) where the customer belongs.
The types of proposals are:
Then it finds the optimal distance between these clients automatically.
When a new customer enters your e-shop for the first time it is very important to understand which cluster they belong to.
You have to show about 10 different products set from different cluster and then by thumbing up or down the mobiplus shopping recommendation engine finds out which cluster it belongs to.
This functionality can be implemented at another time, e.g. after a few minutes in the e-shop or by sending a mail after a day.
When Customers buy products then it is the best time to get more data about their wishes and significantly improve your Recommendation engine.
You can send emails with questions such as:
mobiplus member Elevate Greece
Confirmed Innovation .
Test out the Recommendation Engine for up to 20.000 euros in revenue from recommendations. No credit card required. Just enter your company information and we’ll contact you with all the details.
