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:
Understand where you stand in terms of taste data, and understand the likelihood of success of your omni-channel strategy.
Define Strategy and Roadmap. We help you with your intentional omni-channel strategy, and with your personalization strategy.
Taste Profiles for each shopper. We build clean, structured and correlated Taste Profiles of each individual shopper
Your Taste Graph. We build your taste graph with the data generated in around your shoppers, your products and the experience you offer.
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.
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…).
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.
An Ontology inclusive of all fashion concepts, to understand and manage your products and your shoppers
We build your Fashion Ontology.
OBJECTIVE OF YOUR ONTOLOGY
Your ontology will help you describe products and shoppers in a way that allows you to:
Understand products and shoppers;
Manage products and shoppers.
Your ontology will help you manage your catalogue with new browsing methods, humanized categories adapted to different types of shoppers or situations. It will reflect how your organization wants to talk to shoppers, always retaining total control over your voice while understanding the voice of your shoppers.
It will guide your data-attaching mechanisms to attach the right descriptors to products and shoppers. Data-attaching mechanisms include your taste graph, your editors, your deep learning algorithms, and other sources of product and shopper descriptors.
CHARACTERISTICS OF YOUR ONTOLOGY
Your Fashion Ontology is a tool that contains the organized and structured descriptors that define your products and your shoppers, in the terms that are relevant for your organization and your shoppers.
Your ontology includes clothing and non-clothing descriptors;
The ontology includes descriptors used by your organization, and by your shoppers;
Descriptors are classified in categories, types, properties and more;
We capture the correlations among your descriptors as defined by your products and your shoppers.
HOW YOU CAN USE IT
The Fashion Taste API main focus has been about the search for structure, in the unstructured world of clothing classification.
Once we learnt about clothing classification, we learnt that purchase drivers go well beyond clothing taste, and we gave a structure to those as well. So, believe us, we love clean and structured data that our algorithms can work with.
SHOPPERS’ UNSTRUCTURED INPUT
Despite of the above (or because of the above), we have found amazing value in shoppers’ unstructured input. This input are descriptors that you would not expect your shoppers to be using when interacting with you.
We’ve learnt that people refer to their fashion needs in ways that are very different to how retailers do. Also, the type of taxonomies that fashion retailers tend to have cover mostly product descriptions. But drivers of purchases are related to other factors: products, occasions, influencers, brands and trends. You can read about it here.
WHAT WE OFFER TO YOU
The Fashion Taste API gives you easy access to a categorized list of all descriptors that people use in your web/app… that you don’t have in your taxonomy. This input might come from the search box, or voice input in you have it, or any other field where shoppers write or say something unstructured.
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:
register taste-related data of each individual, and convert it into clean, structured and correlated data.
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.
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 AS EXAMPLES
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 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.
5 DRIVERS OF PURCHASES
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.
Your tool to retain the intelligence generated by your shoppers. Your engine to provide true omnichannel personalization.
WHAT IS YOUR TASTE GRAPH?
Your Taste Graph contains the intelligence generated by your shoppers and their interactions with your products. It’s formed by your shoppers Taste Profiles, your classification system, and your evolving catalogue of products.
Your Taste Graph is trained to assign descriptors to your products and your shoppers. It is your unique Omnichannel Personalization Engine.
By building your Taste Graph, you are retaining that intelligence and building the competitive moat around your business that will help you delight your shoppers, with true omnichannel personalization.
Our Taste Graph Technology executes a number of processes to register the taste of each individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases.
Imagine that someone buys a “red and black oversized sweater“. In our opinion, this is not necessarily a predictor of her future purchases, not a descriptor of her taste. We focus on looking for the true motivations of a purchase. That’s the role of the Taste Graph and it’s underlying backbone: the ontology.
We patented in 2013, and we’ve perfected it since then. Your Taste Graph captures all the taste-related activity and data generated in your properties. It captures all the relations among your shoppers, your products and your descriptors.
A STRATEGIC ASSET THAT IMPROVES EXPONENTIALLY
The Taste Graph forms a strategic and tangible body of data that provides you with the intelligence generated in your properties.
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, and the more data you will capture. It’s the the virtuous cycle of data. Read our Manifesto on the Future of Fashion Retail.
OFFER TRUE PERSONALIZATION
In terms of personalization, there is nothing more powerful that the data asset that understands the taste of each individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases.
No traditional personalization vendor with their legacy personalization approach can beat the intelligence generated in your properties, if you keep it captured, cleaned, structured and correlated.
Any personalization attempt gets exponentially better, as you scale your Taste Graph. Any new sale, any new interaction, any new query or event, and your Taste Graph gets better, your personalization potential gets exponentially better.
This is strategic. If you are here to stay in the next years, you need this dataset. Also, any sustainable personalization strategy will require the intelligence behind data and correlations that you are most likely letting go today.
Matching your clothes, with clothes you are about to buy
WHAT IS THE IN-STORE OUTFIT RECOMMENDER?
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).
We provide the complete infrastructure to fully offer In-Store Outfit Recommenders to your shoppers.
3. We offer an API and a Dashboard. Our Dashboard helps business teams to fully understand Smart Fitting Rooms. 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.
In-Store solutions to help shoppers make purchase decisions
WHAT IS THE SMART FITTING ROOM?
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.
We provide the complete infrastructure to fully offer Smart Fitting Rooms to your shoppers.
4. We offer an API and a Dashboard. Our Dashboard helps business teams to fully understand Smart Fitting Rooms. 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.
The In-Bedroom Fashion Stylist helps your shoppers decide how to wear their clothes. Right in their bedrooms!
HOW IT WORKS
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.
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.
THE 2006 IPHONE
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.
3. 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.
CURIOUS ABOUT OUR TECHNOLOGY?
Continue reading if you’d like to have some details on how the Digital Technology works
Our Digital Closet Technology focuses on 3 components:
Interfaces to capture input;
Ontology to interpret input;
Taste Graph to capture and store the intelligence generated by shoppers’ closets, and to deliver personalized content.
ALL STEPS IN THE PROCESS INFORM EACH OTHER
We are fascinated by the efforts required to build the Digital 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.
ONBOARDING USERS TO A DIGITAL CLOSET
A relevant area of our Digital Closet Technology has been conceptualized and delivered to the market because of the need to onboard new users to a digital 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 Digital Closet 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 Digital Closet as soon as possible, and before they abandon. Our Digital Closet Technology has been built thanks to our Vertical Machine Learning approach. We wrote about it, and about our approach to learning, in this essay here.
Depending on your business needs and how you deploy your digital 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.