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5 omnichannel examples of customers interactions

1. In-Bedroom Fashion Stylish. You will WOW your shoppers!

Read more here

2. In-Store Outfit Recommender. 2nd of the 4 Best Examples of Omnichannel eCommerce Personalization in Fashion Retail

Read more here.

3. Smart Fitting Room with Clothes and Outfit recommender

Read more here.

4. Digital closets

Read more here.

5. Adding your clothes to your digital closet

This approach works together with other approaches, not isolated. Read more here.

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resources

Our Manifesto

Our Manifesto for the Future of Fashion Retail

Technology is opening up a lots of options for fashion and we want to be very clear about what we consider the true building blocks are. 

Here’s our view, our Manifesto, for the Future of Fashion Retail:

1. A TASTE GRAPH FOR EACH RETAILER

Each fashion retailer will own its Taste Graph like Pinterest does.

This Taste Graph will contain the intelligence generated by shoppers and their interactions with products. A Taste Graph is your unique data asset and engine to build a competitive moat around your business.

Read here why it is important.

2. A TASTE PROFILE OF EACH SHOPPER

Retailers will build Taste Profiles of each shopper like Spotify or Netflix.

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.

Read about Taste Profiles of shoppers.

3. AN ONTOLOGY INCLUSIVE OF ALL CONCEPTS

Ontologies will include all fashion concepts, even non-physical clothes descriptors but very relevant when deciding an outfit.

Each retailer will manage its catalogue and its shoppers with their own ontology.

Read about the new ontologies and what they allow.

4. TOYS WILL WIN

Seemingly unimportant services that look like toys will win the heart of people, their data and their pockets.

These toys might look like In-Bedroom Fashion Stylists (watch video), In-Store Recommenders (watch video) or Digital Closets (watch video and read about the tech).

NOTE: We are not claiming that all fashion retailers are set for the above infrastructure. But it does apply to 100% of large fashion retailers.

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Our blog posts

Thoughts on the future of fashion interfaces, data systems, and fashion technology

July 17th, 2019

IN-STORE OUTFIT RECOMMENDER: MATCHING THE CLOTHES IN YOUR CLOSET WITH GARMENTS YOU ARE ABOUT TO BUY >

May 21st, 2019

TASTE GRAPHS: HOW TO UNDERSTAND FASHION TASTE LIKE SPOTIFY DOES WITH MUSIC >

April 25th, 2019

GOOGLE IS BEATING FACEBOOK IN THE SSO BATTLE, AFTER G+ DEBACLE >

April 4th, 2019

FASHION TECHNOLOGY: HOW TO UNDERSTAND FASHION TASTE >

December 29th, 2018

APPLE FEATURES CHICISIMO AS APP OF THE DAY IN 140 COUNTRIES >

January 30th, 2018

HOW WE GREW FROM 0 TO 4 MILLION WOMEN ON OUR FASHION APP, WITH A VERTICAL MACHINE LEARNING APPROACH >

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resources

Compliance at the Fashion Taste API

We at the Fashion Taste API respect the data concerns of our customers and have committed to making sure they can use our taste profile and personalization technologies, safely and compliantly. We are committed to the confidentiality, privacy and security of our customers data.

The Fashion Taste API works with respected teams

At the Fashion Taste API, our team believes that the ultimate customer experience relies strongly on transparency and integrity. The Fashion Taste API works with respected teams of IP and data privacy lawyers, financial experts and Venture Capitalists as venture partners.

We are dedicated to meeting global requirements and regulations when it comes to data protection, privacy, security, and operations. Having your trust as a partner in taste profiles and personalization is of the utmost importance to us, and we will continue to invest extensive resources toward maintaining the highest possible industry standards so you can use our technology safely, efficiently, and compliantly.

Fashion Taste API & GDPR

Fashion Taste API is committed to the confidentiality, data privacy and security of its customers and their end-users. We are and will continue to invest extensive resources towards maintaining the highest levels of data protection, privacy and security standards. We are compliant with applicable laws and regulations, and are committed to our ongoing compliance with the EU General Data Protection Regulation (GDPR) and related guidelines.

  • Data Storage. Fashion Taste API stores customer data in Ireland. Fashion Taste API has proxies deployed in Sao Paulo and Oregon to reduce latency and increase response time in those areas.

  • Satisfy the Definition of a Data Processor- “Processes Personal Data on behalf of the Controller”. Fashion Taste API provides a full fledged platform which allows marketers to segment and target users with personalized offerings across web, mobile, email and other channels. As such, Fashion Taste API processes its customers’ end-user data, thus, Fashion Taste API customers who are deemed as “Data Controllers” under GDPR and the end users would constitute the “Data Subjects” whose rights must be protected.

  • Act on Behalf of the Controller Based on Controller Authorization. Fashion Taste API only collects information based on a duly executed contract from the controller. Once an agreement to process data is terminated, Fashion Taste API ceases collecting personal data from the customer’s sources, and the records are deleted within a reasonable period of time from when a deletion request is made. The purposes of the data processing are determined by our customers, i.e. the Controllers.

  • Appointment of a Data Privacy Officer (DPO). Fashion Taste API has appointed a Data Privacy Officer who oversees our privacy compliance and development program.

  • Integrity and Confidentiality. Fashion Taste API has employs appropriate technical and organizational measures to safeguard Personal Data.

  • Demonstrate Compliance with GDPR. Fashion Taste API keeps a record of its processing activities carried out on behalf of the controller, its DPO is open for questions and data processing addendum is entered into with every customer.

  • Lawfulness, Fairness and Transparency. Fashion Taste API collects and processes Personal Data lawfully and is transparent with its customers about its processing activities. Fashion Taste API has entered with its customers in order to maintain the legal basis for the processing (usually “consent” or “legitimate interest”), which is warranted by the customer as the Controller.

  • Processing Personal Data- Purpose Limitation. Fashion Taste API only collects data which may be used to analyze user behavior and to provide personalized experiences. Fashion Taste API does not combine any customer collected data with data collected from other customers, does not determine the purpose of processing, and does not share data with third parties except where required to by law.

  • Processing Personal Data- Data Minimization and Proportionality. Fashion Taste API does not onboard any data which is unnecessary or disproportionate to its needs to best serve end users with personalized experiences. CRM and other data may be onboarded and Fashion Taste API expects its customers to only onboard data which satisfies the proportionality and lawfulness requirement. At the point when Fashion Taste API provides a mechanism for onboarding customer data, we request that sensitive or payment data not be onboarded without our consent.

  • Processing Personal Data- Accuracy. At the point when Fashion Taste API provides a mechanism for onboarding customer data, Fashion Taste API will allow its customers to rectify any errors or misapplications in onboarded data with new CRM data pushed by the controller.

  • Processing Personal Data- Storage limitation. Fashion Taste API does not store any data unnecessarily and (at the point when Fashion Taste API provides a mechanism for onboarding customer data) expects its controllers to refrain from onboarding data which is unnecessary for personalization purposes

  • Processing Personal Data- Accountability. Fashion Taste API’s DPO will work to continuously optimize and introduce improvements and customer feedback to the Fashion Taste API privacy program. The DPO will also cooperate with controllers in case of inquiries and data breaches.

  • Data Subjects Rights. Erase, Rectify and Export Personal Data. Fashion Taste API will cooperate in full with controllers for handling requests regarding their end users’ data.

  • Information Provisions- Transparency. Fashion Taste API will cooperate with controllers regarding data requests from customers.

  • Ownership of Data, Process and Tool. It is now gone the time to provide personalized recommendations to people, without understanding their taste. The Fashion Taste API has been designed to empower teams to own the process, the tool, and the data, while maintaining the highest levels of data protection, privacy and security standards.
    • DATA PRIVACY: The Fashion Taste API is committed to the confidentiality, data privacy and security of its customers and their end-users. We are and will continue to invest extensive resources towards maintaining the highest levels of data protection, privacy and security standards.
    • PROCESS & TOOL: The Fashion Taste API does not interfere with any of your existing processes. It starts registering data, as easy as installing Google Analytics. You speed up with whenever you want, with the advantage of having already learnt from the captured data. The fashion industry no longer believes in black-box approaches to personalization that provide zero ownership and control to teams. The Fashion Taste API is a tool for your teams to use on growth. You control the tool.
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technology

Vertical Machine Learning

A Vertical Machine Learning approach provides a competitive moat around your business

VERTICAL MACHINE LEARNING IN FASHION

We at the Fashion Taste API follow a Vertical Machine Learning approach to fashion. We believe in the unique advantage of having a profound understanding of the consumer problems, end to end.

With a focus on the consumers, we’ve applied machine learning to automate the two processes that we’ve considered game-changing:

  • Capturing shopper’s taste data (including what clothes they have in their closet);
  • Building the data infrastructure that allows us to understand input automatically, and to deliver personalized output also automatically.

Read our Manifesto on the Future of Fashion Retail.

THE ADVANTAGE OF FASHION RETAILERS

We believe that fashion retailers have a clear advantage over newcomers: they are the domain experts. The specific benefits, however, need to be captured:

  • Fashion retailers have a profound understanding of the fashion consumer;
  • Shoppers generate a unique body of data around the retailers properties. Retailers can choose to start capturing that intelligence and build their moat;
  • Physical stores are a unique layer of experimentation to test omnichannel solutions and receive fast feedback.

NEWCOMERS WILL SOON CATCH UP

At some point, newcomers will offer In-Bedroom Fashion Stylist or Smart Mirrors to end consumers.

Apart from the increase in market share that this new category will generate, In-Bedroom Fashion Stylists will have two very relevant consequences:

  • Data: The amount of incoming taste data will grow exponentially;
  • Learning: The speed of learning will accelerate thanks to better shopper feedback.

And the current competitive moat around traditional fashion retailers will be gone.

THE VIRTUOUS CYCLE OF DATA

The more digitally powered experiences you offer, the more taste data you will capture. The more data you capture, the better you can automatically serve your shoppers. The better you serve your shoppers, the more adrenaline you generate in your teams, the more ambitious they become.

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technology

Foundational patents in capturing and understanding fashion taste

Patents expected to provide a competitive advantage

PATENTS WITH RELEVANT MARKET ADOPTION

We’ve been analyzing fashion taste for more than a decade.

As a result, we own foundational patents in the online fashion market. An independent review of the Fashion Taste API portfolio uncovered relevant market adoption related to some of our patents.

This intellectual property powers our Digital Closet Technology, our In-Store Outfit Recommender, our In-Bedroom Fashion Stylist and other solutions.

A SYSTEM TO TAG FASHION IMAGES WITH SHOPPABLE PRODUCTS

We are all starting to see fashion images with dots on top of them, these dots enabling you to find and buy the products in the picture. This system is gaining popularity since 2016, and it was patented by us in 2013.

A SYSTEM TO CAPTURE AND UNDERSTAND HOW CLOTHES IN OUTFITS AND CLOSETS ARE MIXED, MATCHED AND RELATED

In our prior work with music, we extracted relations among songs by analyzing the sequence in which people would listen to music, and by analyzing our interpretation of items. Apple later bought the systems and patents for Apple Music. We then built similar systems for personal finance, and for content-agnostic input.

With this background, we thought that an outfit was like a playlist of songs: a way to communicate that it makes sense to consume several items together. This simple concept allowed us to conceptualize the first version of our Social Fashion Graph. In 2013, we protected a system to capture and understand how people mix and match clothes in outfits and closets.

Today, the technology has evolved significantly. It captures any input in our our Ontology, which includes any concept relevant to fashion, and assigns descriptors to products and to people. We can extract all type of relations among analyzed items.

SEARCH ENGINE FOR FASHION IMAGES AND OUTFIT IDEAS

Our initial interest was related to social tagging of images. Then, we invented more complex mechanisms to assign descriptors to clothes, outfits and people. Our IP protects a system to search outfit ideas and fashion images.

It could be a simple query such as “give me ideas to wear jeans” or a more complex one such as “give me ideas to wear my pair of red pants with my floral shirt, to go to the office in winter”, or “give me clothes that are “similar” grouped by your concepts of similarity”. This is strongly related to our Fashion Taste Graph Technology.

The Fashion Taste API patents are expected to provide a competitive advantage.

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technology

Our fashion ontology

Our tool to understand products AND people

THE ORIGINS OF OUR ONTOLOGY

As we wrote in our Taste Graph Personalization Technology, via our consumer apps, we have received millions of described outfits, the described clothes in millions of closets, and hundreds of millions of what-to-wear queries from people trying to decide what to wear. We originally built our Ontology to understand this input.

WHAT IS OUR FASHION ONTOLOGY?

Our fashion ontology is our tool to understand products and people: it goes well beyond assigning descriptors to clothing. It has a very strong emphasis on describing people.

Our ontology today is the classification of descriptors needed to define a fashion product and a shopper’s need, in terms that are relevant for shoppers, for retailers and for data scientists. It contains the vocabulary used by people and by fashion retailers, with an understanding of how these descriptors are used together. Our ontology covers all aspects related to fashion purchase drivers: product properties, occasions, brands or influencers, or trends: It is formed by +2500 unique concepts and a few hundreds of thousands of descriptors.

We care about::

  • The ontology contains clothing and non-clothing descriptors. Products and people;
  • The ontology contains descriptors used by your organization, and by your shoppers;
  • The ontology has several levels, descriptors are classified in categories, types, properties and more;
  • The ontology contains the correlations among your descriptors as defined by your products and your shoppers.

THE BACKBONE OF OUR OPERATIONS

The ontology is the first component required to enable the automated personalization carried out by our our Taste Graph Personalization Technology.

WHAT SERVICES DO WE OFFER?

We build your fashion ontology, which will help you automate lots of internal processes related to your catalogue, and then it will allow you to understand your shoppers.

Categories
technology

Our Fashion Taste Graph Technology for Omnichannel Personalization

HUNDREDS OF MILLIONS OF CORRELATED DATA POINTS

Through our consumer apps, we have received millions of described outfits, the described clothes in millions of closets, and hundreds of millions of what-to-wear queries from people trying to decide what to wear.

In 2013, with so much consumer data we built a Taste Profile for each person, and a Taste Graph to serve the right products to each individual based on her specific interests. It is important to note that a profile in fashion is not only driven by taste, but also by other triggers. You can read about it in the Taste Profiles section.

  • Described outfits: We capture this type of data from millions of described outfits and we also capture how other people interact with those outfit creating more relations among items and allowing the Taste Graph to assign further descriptors to the outfits and the Taste Profiles.
  • Described clothes in millions of closets: We also receive the described clothes in millions of closets from where we can extract a lot of meaning in terms of correlations and in terms of our own interpretation of the products.
  • Queries: We also receive hundreds of millions of what-to-wear queries from people trying to decide what to wear and what clothes to buy, for any occasion you can think of. Each of this queries informs the Ontology and the Taste Graph.

That’s the origin of our technology. The Taste Graph Technology receives data and correlations. It also assigns the correct descriptors to clothes, outfits and to people. It is the tool on top of which teams can effectively work in personalization. Delivered via a website, in a physical store, or delivered via Alexa in a bedroom or closet.

TASTE GRAPH TECHNOLOGY TO DESCRIBE SHOPPERS

Our Fashion Taste Graph Technology for Omnichannel Personalization is the engine that allows us to assign descriptors to shoppers. It allows us to understand the taste of each shopper, and provide them with personalized omnichannel recommendations.

It is the infrastructure and data that contains, manages and understands the taste of each individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases. The behaviour of each individual, the aggregated behaviour of everyone, and the behaviour of any subset we’d want to target.

The backbone of our Taste Graph is our fashion ontology, which you can read about here.

WHAT SERVICES DO WE OFFER?

We build your Taste Graph, your tool to understand the taste of each individual shopper.

Read about it here.

OUR PREVIOUS GRAPHS

During the last 20 years, we’ve solved specific problems in car manufacturing, music and finance by introducing technologies to understand specific behaviours and automating very concrete processes.

  • In music, Apple acquired the technology and intellectual property for Apple Music.
  • In finance, Barclays, Deutsche Bank, ING, BBVA and other 700 banks still use the technology that our team built before we moved to fashion.
  • Since 2010, we are exclusively focused on fashion.

OUR CONSUMER FASHION APPS

Our consumer fashion apps bring unique impact to our learning process.

They allow us to see the future with more clarity, by solving the problem end to end and receiving direct feedback from the end-consumer. Iterating a consumer app is a unique learning experience, and it contributes in a great way to the definition of the Taste Graph and of course of the Ontology. You can read about or approach to growth in our consumer product in this Medium essay.

Then, we from Using that data to build an automated personalization engine lead us to build our Taste Graph, and patent it in 2013. It has proven to be a unique personalization omnichannel technology, and a great internal tool for classification and categorization of clothes.

Read our Manifesto on the Future of Fashion Retail.

Categories
omnichannel-retail

Data audit for personalization

Understand how ready you are to implement personalization

HOW IT WORKS

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.

WHY IS THIS RELEVANT

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

HOW WE CAN HELP

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…).
Categories
omnichannel-retail

Capture the intelligence generated by your Editorial Stylists

Your Taste Graph will partly automate the job

FEED YOUR TASTE GRAPH WITH THE INTELLIGENCE GENERATED BY YOUR EDITORIAL STYLISTS

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.

WE HELP YOUR EDITORIAL STYLISTS WITH OUR TASTE GRAPH

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.