Data audit for personalization

Understand how ready you are to implement personalization


Data auditing helps your teams understand how ready you are to implement a personalization or omnichannel project, before committing a large budget.

“Do we have enough user data to build Taste Profiles or a Taste Graph?” All our conversations with fashion retailers start with this question.

Whether you are capturing taste data or not, each shopper is telling you what motivates her purchases. Your KPIs tell you specifically where you stand.

We help you start registering the data without interfering with any of your existing processes. As easy as installing Google Analytics.


Many teams feel the urge to implement personalization or omnichannel, and we understand why. However, we encourage a data audit before committing a budget.


At the Fashion Taste API, we help you:

  1. Understand where you stand in terms of taste data, and understand the likelihood of success of your omni-channel strategy.
  2. Define Strategy and Roadmap. We help you with your intentional omni-channel strategy, and with your personalization strategy.
  3. Taste Profiles for each shopper. We build clean, structured and correlated Taste Profiles of each individual shopper
  4. Your Taste Graph. We build your taste graph with the data generated in around your shoppers, your products and the experience you offer.
  5. Our Taste Graph. The personalization algorithms will be powered by your Taste Graph, or by ours. Initially, your Taste Graph not have a large enough dataset nor the correlations to be effective.
  6. Break organization silos. Turning a personalization & omnichannel vision into a reality requires your organization to break down its silos. Silos keep business/product teams from fully understanding tech teams, and keep online channels from fully integrating with physical ones. The Fashion Taste API is a like central repo with different accesses for different types of teams (ie: API vs dashboard…).

Capture the intelligence generated by your Editorial Stylists

Your Taste Graph will partly automate the job


Your Editorial Stylists are carefully curating outfit suggestions for your shoppers. When they handpick and match two or more items to go together, they are establishing a very well thought correlation among those items. Over time, your Editorial Stylists are generating a unique body of data for your future machine learning efforts.

Your Taste Graph captures this intelligence. Eventually, your Taste Graph will partly automate the job of your Editorial Stylists, or it will help them do a better and faster job.


Until your Taste Graph can provide suggestions to your Stylists, our Taste Graph can do that job. It offers you a pre-selection of clothes recommendations that your Stylist to validate.


Taste Profiles of each shopper

A summary of the taste of an individual shopper


A Taste Profile summarizes the taste of an individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases.


We build a Taste Profile for each of your shoppers, as part of your Taste Graph. Taste Profiles include:

  • 1. We summarize taste. We register taste-related data of each shopper, and assign clean, structured and correlated descriptors to her profile. These descriptors are generated by our interpretation of their activity in your site. We use our ontology, graph and Smart Virtual Closet Technology.
  • 2. We connect the Taste Profiles to our own interpretation of your products and to all the elements of your Taste Graph.
  • 3. We offer an API and Dashboard. Our Dashboard helps business teams to fully understand Taste Profiles and have a sense of control. With the Dashboard, business teams end up working hand-by-hand with tech, with the same understanding.
  • 4. Taste KPIs. Whether you are capturing taste data or not, each shopper is telling you what motivates her purchases. KPIs tell you the % of shoppers for which you capture clean data, broken down into types of data.


Spotify and Netflix also build Taste Profiles for each of their users.

  • Spotify registers the music you listen to, and builds a Taste Profile with their interpretation of what each song means. As you listen to more music, your Taste Profile reflects pretty accurately what are the true motivations of you listening to a song. On top of this data, they automate several internal processes, detect early adopters of future trends, and offer personalized content.
  • Netflix follows a similar approach. Netflix ontology plays a crucial role in their interpretation of what a movie means, and how that defines the person watching it. Our fashion ontology, although very different in structure, plays a similar role.
  • In fashion, building the infrastructure to create Taste Profiles is way more complex that in music or movies. But once it is done, the richness and cleanness of the data is extremely eye-opening.


Taste Profiles mostly contain data related to Products, Occasions, Influencers, Brands and Trends:

  • Products. Products contain properties. One shopper might find relevant one or more properties of a product, or even none of the product properties.
  • Occasions. A shopper will most likely have different purchase drivers for each occasion they dress for, special moment or everyday occasions.
  • Influencers. Sometimes purchases are driven by an influencer. At the Fashion Taste API, we care about this data and register it.
  • Brands. The brands someone has in her closet are not always easy to capture, but there are approaches to obtain this data depending on your needs. Brands are definitely an element that might predict what someone is going to buy in the future.
  • Trends. The Fashion Taste API pays attention to the likelihood of someone buying a trend. We want to know if a given shopper is a heavy shopper of trends. The information provided by this cohort of shoppers is interesting in terms to revenue generation, but also in terms of data collection.

Product and Outfit Recommendations

Provide ideas on how to combine your collection, and the clothes in the shopper’s closet, or similar products


We provide product and outfit recommendations for different contexts.

Some of them are:


  • Shopper is looking at a product:
    • A product she just bought, added to favorites, or a Product Detail Page;
    • We display other products in your catalogue that match well with that first product, personalized to the shopper;
    • We display clothes in the shopper closet that match well with what she’s about to buy.
  • Shopper is looking at a garment in her personal closet:
    • We tell her how that garment matches with her other garments;
    • We tell the shopper what clothes in your catalogue match with that garment.


  • Shopper accesses your homepage or any of your categories:
    • We provide a list of products she’ll find most interesting.
    • Suggestions are grouped by similarity, “complete the look” or other criteria to be defined.


Our product and outfit recommendations are delivered taking into account three factors:


Physical Store Outfit Recommender

Matching your clothes, with clothes you are about to buy


The In-Store Outfit Recommender helps shoppers understand how a product they are about to buy, matches the clothes in their closet at home.


In the video above, you can see our colleague Maria looking at clothes at a physical store, and how the In-Store Outfit Recommender recommends what outfits she can create with the clothes in her closet and the garment she is about to buy.

The device reads the QR, extracts the images, and sends the selected image to our system where a Deep Learning algorithm extracts the descriptors of the garment. These descriptors are then sent to our Taste Graph, in charge of identifying how to best combine the new garment with the clothes in the shopper’s closet. (Note: We do not develop our own image recognition algorithms, it is not our focus. We use 3rd party algorithms). Read about our Smart Virtual Closet Technology.

Smart Fitting Rooms

Our Smart Fitting Room helps shoppers make purchase decisions. When they are about to purchase a product, the Smart Fitting Room tells them how they can combine that product, with the clothes they have in their closet at home. You might want to offer this service together with the In-Store Outfit Recommender.


In-Bedroom Fashion Stylist

A New Product Category in Fashion e-Commerce


The In-Bedroom Fashion Stylist helps your shoppers decide how to wear their clothes. Right in their bedrooms!


In the video above, you can see our colleague Maria asking the In-Bedroom Fashion Stylist what to wear with her black jacket, and the In-Bedroom Fashion Stylist returning that same black jacket, together with other garments that she has in her closet. 

The In-Bedroom Fashion Stylist is powered by our Taste Graph and the Alexa Platform, understands the user taste, and knows what clothes the user has in her closet. It can be programmed to be connected to your app and your catalogue, and offer the shopper unique services with her closet and your catalogue.


We provide fashion retailers with the complete infrastructure to fully offer In-Bedroom Fashion Stylists to your shoppers.

  • 1. Our Smart Virtual Closet Technology.
  • 2. Our fashion ontology connected to Amazon Alexa.
  • 3. Product and outfit recommendations combining the clothes in a closet and your collection.
  • 4. We offer an API and a Dashboard. Our Dashboard helps business teams to fully understand Digital Closets. In the Dashboard, they view the specific clothes that are being captured, what outfits are being suggested, and participate in the decision making process with a complete understanding and sense of control.
  • 5. Shopping your collection. The In-Bedroom Fashion Stylist can obviously display your collection as one of its key features, allowing shoppers to buy directly from the screen.


Omnichannel in fashion retail is like the iPhone in 2006: we are about to see a game-changing consumer experience.

Omnichannel goes way further than physical stores and apps. It is going to reach bedrooms, and this will change everything.

Without any doubt, this experience will be built on top of a Taste Graph, and on top of shoppers Taste Profiles. Read the brief summary of the 5 omnichannel examples of customers interactions.


Smart Virtual Closet Technology

Our Smart Virtual Closet Technology allows your shoppers to digitally store all their physical clothes, without any friction, in their own smart closet.

Our Smart Virtual Closet Technology increases your shopper engagement. With zero friction, it allows you to:

  • 1. Offer outfit advice on how to wear her existing clothes;
  • 2. Help shop for new clothes that match her existing needs;
  • 3. Help her plan outfits.

Smart Virtual Closet Technology in action

Enjoying your virtual closet
Virtual closets offer several mechanisms to introduce shopper’s clothes. All of those mechanisms are built on top of the same infrastructure, connected by the ontology.

These services can be provided via your traditional website or your mobile apps, or via new omnichannel retail experiences such as an In-Bedroom Fashion Stylist or an In-Store Outfit Recommender.

Smart Digital Closet Technology
Connecting a shopper’s virtual closet with the catalogue of a fashion retailer.


We provide you with the complete infrastructure to fully offer Smart Virtual Closets to your shoppers, with a strong focus on a great, frictionless experience.

  • 1. Our Smart Virtual Closet Technology composed of (i) our interfaces to capture clothes; (ii) a Taste Profile for each shopper; and (iii) your Taste Graph;
  • 2. Product and outfit recommendations combining their clothes and/or your collection;
  • 3. We offer an API and a Dashboard. Our Dashboard helps business teams to fully understand Virtual Closets. In the Dashboard, they view the specific clothes that are being captured, what outfits are being suggested, and participate in the decision making process with a complete understanding and sense of control.


Continue reading if you’d like to have some details on how the Smart Virtual Closet Technology works. Our Smart Virtual Closet Technology focuses on 3 components:

  • Interfaces to capture input;
  • Fashion ontology to interpret input;
  • Taste Graph to capture and store the intelligence generated by shoppers’ closets, and to deliver personalized content.


We are fascinated by the efforts required to build the Smart Virtual Closet Technology. Of all the interconnected efforts involved, there are two aspects that we particularly love:

  • Interfaces + Data infrastructure. How the interfaces to capture input are so strongly related to the data infrastructure interpreting that input. One cannot evolve without the other;
  • Incentives to provide data + Requirements to provide value. How the incentives offered to shoppers to provide their personal data are strongly related to the consequences of having lots of closets created. People offer you their closet data because you give them something in return (normally, outfit ideas). But you can only provide ideas if you have a large dataset of closets.


A relevant area of our Smart Virtual Closet Technology has been conceptualized and delivered to the market because of the need to onboard new users to a virtual closet, without friction.

When a new user opens an app (or any other interface) for the first time, she needs to be onboarded in a way that she sees value in seconds. Under no circumstances someone is going to upload her closet if the system is complicated. With that objective, we looked very carefully at the query data of our own consumer apps, and shipped lots of incremental iterations of our capturing interfaces and our data infrastructure.

The result is a very effective Smart Virtual Closet Technology interface and a very sophisticated data infrastructure to onboard users to a Digital Closet.

We define onboarding as the process by which new users find the value of the Virtual Closet as soon as possible, and before they abandon.


This Digital Closet Technology, together with our In-Bedroom Fashion Stylist and the In-Store Outfit Recommender, are the current consumer-facing expressions of our data solutions, our omnichannel retail solutions. All of them are built on top of our Taste Graph. Read our Manifesto on the Future of Fashion Retail.


Depending on your business needs and how you deploy your smart virtual closet, using the Taste Graph might help you to isolate early adopters of third party trends.

We are looking for partners to deploy our technology to a small subset of a few thousand shoppers in Seoul, Tokyo and Rio. Then, do a close follow up of the 3rd party brands they buy, and analyze how early-stage collections evolve to mainstream or not. This would become an automated system to find people who detect trends, early.

We built a similar system for songs, back in 2006, which was later acquired by Apple for Apple Music. It was an anomaly detection system that identified popular songs and traced them back to their early beginnings. There, it identified who were its first listeners. The behaviour of “first listeners” was used to inform of potentially future trends.

This is a brief summary of the 5 omnichannel examples of customers interactions, and you can read here about what is a virtual closet.