Yesterday, it was the BAM’s kick off… Belgian Association of Marketing.
Let’s make it Meaningful.
Yesterday, it was the BAM’s kick off… Belgian Association of Marketing.
Let’s make it Meaningful.
Hubert de Vauplane est avocat dans une cabinet américain à Paris. Il est aussi professeur à Science Po Paris.
Dans quelle mesure ne sommes-nous pas en train d’assister à l’émergence d’un nouveau monde? Certes, il ne s’agit pas de la découverte d’un continent ou d’une planète, mais de la transformation en profondeur de la société par la digitalisation des activités humaines. Et le monde de la finance n’est pas exempt de ce mouvement. Ainsi, dans quelle mesure ne voit-on pas apparaître une nouvelle classe d’actifs qui pourrait bouleverser le fonctionnement traditionnel de la finance, tant dans le financement des entreprises que dans la gestion d’actifs? Les cryptomonnaies, protocoles blockchain et ICO sont de moins en moins considérés comme un phénomène de mode mais comme une nouvelle forme de la finance 3.0, marquant une évolution profonde du fonctionnement de la finance moderne en ce qu’ils répondent aux mêmes besoins que la finance traditionnelle (financement des entreprises) mais de manière totalement différente.
Si les levées de fonds sous forme d’ICO ne peuvent plus être ignorées compte tenu des montants levés (annexe 1), celles-ci posent tout un tas de questions, à commencer par leur définition. Car tout comme il n’existe pas une blockchain mais plusieurs protocoles de blockchain, (bitcoin, Ethereum, mais aussi Litcoin, Dash, blockstack…), il existe plusieurs crypto-monnaies liées à chacun de ces protocoles. Quant aux «tokens» (jetons), il y en a autant que de sociétés qui en émettent.
Fondamentalement (ou sociologiquement), une cryptommonnaie n’est pas différente d’un jeton. Une fois que les jetons ont été créés par une société, achetés et acceptés au sein d’une communauté, ils deviennent des cryptomonnaies. Mais la réalité est plus complexe (annexe 2). Une cryptommonnaie est, comme son nom l’indique, un mode de paiement (au sens économique du terme) quand un token répond à des fonctionnalités beaucoup plus larges, pouvant aller jusqu’à conférer des droits sur des revenus, voire sur la gouvernance d’un projet (annexe 3). Une crytomonnaie est un logiciel p2p ou un programme construit à partir d’une source de code unique ou cloné à partir d’une autre source de code, comme litecoin, peercoin, ou bien sûr bitcoin ou ethereum, alors que le jeton n’est pas lié à un seul protocole spécifique. En pratique, la ligne de partage entre une cryptommonnaie et un jeton n’est pas claire et nette. Les deux peuvent être utilisés dans une fonctionnalité de paiement. En fait la différence majeure entre une cryptommnnaie et un jeton tient à leur structure: les premières sont des modes (ou monnaies) d’échange liées à leur propre protocole blockchain, alors que les jetons ne sont pas liés à un protocole blockchain spécifique mais à chaque entreprise émettrice. Dit autrement, une cryptomonnaie est «émise» dans le cadre d’une blockchain, généralement en rémunération d’un travail (le minage) alors que le jeton représente un actif sous-jacent, qu’il s’agisse d’un droit sur des biens sous jacents, des revenus futurs ou d’échange contre services.
Un jeton est avant tout un concept technologique. Il confère à son détenteur certains droits en fonction du contenu de la chaîne de blocs ou du contenu d’un contrat intelligent. S’il s’agit d’un actif ou d’un droit sur une chaîne de blocs, un jeton peut fonctionner sans cadre juridique ad hoc sous-jacent. Les jetons peuvent également représenter des actifs hors chaîne de blocs mais nécessitent alors un cadre juridique spécifique. On peut aujourd’hui schématiquement distinguer quatre types de jetons. Tout d’abord, les «jetons intrinsèques». Ce type de jeton représente des droits ou des actifs sur la chaîne de blocs, comme le bitcoin ou l’éther. Ensuite, les «jetons à support d’actifs». Ce type de jetons est adossé à un ou plusieurs actifs hors de la blockchain, comme de l’or, des devises, des biens immobiliers, des droits de propriétés intellectuels. L’utilisation de ces jetons nécessite un cadre juridique particulier, notamment pour en reconnaitre la propriété. Il y a aussi, les «jetons liés à des droits» qui, tout comme les jetons d’actifs, sont adossés à des éléments hors de la chaine de blocs, mais alors que les jetons d’actifs sont adossés à des biens, ceux-ci sont adossés à des droits, comme des droits aux revenus, aux dividendes, à la gouvernance. Là encore, pour être pleinement utilisés, ces jetons nécessitent un cadre juridique ad hoc. Enfin, tous les jetons peuvent être utilisés dans une fonction d’échange monétaire.
Comme son nom l’indique il s’agit d’un actif digital dont la technologie repose sur la cryptographie. Leur nombre ne cesse d’augmenter (annexe 4) et leur capitalisation aussi, représentant à l’été 2017 environ 160 milliards de dollars (annexe 5). En fait, les tokens comme les cryptomonnaies sont des actifs cryptographiques dès lors que leur usage ne consiste plus uniquement en une fonction d’échange monétaire, mais en une valeur d’investissement. Cela tient au fait que les cryptommonnaies ont en fait le plus souvent deux fonctions: paiement et actif. La valorisation de celles-ci leur confère de plus en plus une fonction d’investissement aux lieu et place de celle traditionnelle d’échange. Ce qui explique que de plus en plus de personnes achètent des cryptomonnaies dans une optique d’investissement. Au point de voir fleurir des fonds (le plus souvent non régulés) investis dans ces monnaies cryptographiques, des indices ou paniers de cryptommonnaies (annexe 6), voire même des produits financiers.
De manière très simple, il s’agit d’un processus de levées de fonds utilisant des tokens comme actifs émis par une société, et des cryptomonnaies comme valeur d’échange pour acheter ces tokens. Le principe emprunte au fonctionnement d’une introduction en bourse (IPO). Originellement, une ICO est une opération réalisée par une société travaillant dans l’orbite de la blockchain permettant de financer, le plus généralement, la recherche et développement dans les protocoles de blockchain ou dans les usages de ces protocoles, mais aussi parfois pour lancer un fonds d’investissement non régulé. Le concept doit être reconnu pertinent par la communauté blockchain pour que l’ICO soit un succès. C’est là le rôle des experts, notamment pendant la phase de pré-vente où la communauté va discuter autour de la pertinence, en particulier technologique, du projet (ce qui nécessite des connaissances de codage). L’entreprise va alors émettre des jetons, représentant le plus souvent un droit sur des revenus futurs liés au développement de son projet. La valeur initiale de ce jeton est calculée en fonction de l’espérance de revenus attendus. Si le projet se révèle être un succès, le jeton prend alors de la valeur sur le marché secondaire, où il peut s’échanger sur des places de marchés.
Le fonctionnement d’une ICO emprunte largement au monde des IPO: il y a des advisors (l’équivalent de banque conseil, comme Argon Group), des investisseurs et fonds spécialisés (Polychain, BKCM, Metastable…), des prestataires de services comme ceux fournissant des informations sur les opérations passées et leur capitalisation (cryptocurrency market capitalization, Brave New Coin…), ou des analyses financières (Coincenter, Smih + Crown), des agences de rating (ICO Rating, Token Market, ICO COuntdown…), des places de marchés (Kraken, Bitfinex, Poloniex…), des market markers (Cumberland Minding, Genesis Trading…), des cabinets d’avocats spécialisés (Cooley ou MME), des dépositaires des tokens (Ledger, Trezor…). C’est donc tout un écosystème qui est en train de se créer autour des ICO (annexe 7).
En définitive, c’est bien à l’émergence d’une nouvelle classe d’actifs que nous assistons (annexe 8).
Hubert de Vauplane est avocat dans une cabinet américain à Paris. Il est aussi professeur à Science Po Paris.
Over the past few years, voice activated search has come a long way.
When Apple first integrated its voice assistant, Siri, into the iPhone 4S in 2011, it was considered more of a gimmick than anything else. Six years on, and a report by ClickZ and Marin Software reveals that 7% of marketers now mark voice search and digital assistants as top priorities in their marketing plans.
Interestingly, 4% of marketers reviewed in the same report also stated that they would be prioritising ‘smart hubs’ in 2017.
ComScore said that by 2020, 50% of all searches will be voice searches
Since the launch of Amazon’s Alexa, so called ‘smart hubs’ have grown in popularity with consumers. Even more so, there is now a demand from consumers to have these as part of their ‘connected’ homes.
As AI technology gets smarter and smarter, it’s evident that we are shifting into a voice led revolution. ComScore said that by 2020, 50% of all searches will be voice searches and Google’s recent statistics show that 83% of people surveyed agreed that voice search will make it easier to search for things anytime they want.
Speaking to a machine may have felt unnatural and futuristic only a few years ago, but consumers are now embracing the revolution. Smart hubs have championed the growing possibilities of search, and they have now become genuine channels for daily activities, as consumers are excited and impressed by the speed and efficiency with which these devices can help them complete day-to-day tasks.
With this in mind, it’s clear that there is potential for advertisers and brand marketers to make use of voice assistants.
In terms of search functionality, marketers need to be aware of the varying capabilities of each smart hub on the market, as each one works slightly differently and is powered by a different search engine. With each brand’s product portfolio continuously growing, this becomes even more of a challenge.
Amazon’s Echo, which has been on the market the longest, operates with Bing, whereas Google Home relies on Google to answer questions. Apple’s highly anticipated HomePod, due out in December, will have Siri integrated into the device.
consumers may be slightly reticent when it comes to inviting advertisers and brands into this personal space
The efficiencies of each search engine vary, and for marketers, these characteristics are crucial in deciding how their brands can attract the right attention.
Understandably, we need to remember that marketers are still testing the waters on how smart hubs can be implemented in marketing plans in the most seamless way. After all, as these voice assistants become part of a consumer’s connected home – and at the centre of the family – it’s natural that consumers may be slightly reticent when it comes to inviting advertisers and brands into this personal space.
This was certainly the case for Google, who was immediately hit with criticism after playing what sounded like an advert for Disney’s Beauty and the Beast film, during Google Home’s ‘What’s My Day Like?’ feature.
Similarly, there was disdain after Amazon introduced sponsored audio messages before and after conversations with Alexa. It’s inevitable that there will eventually be paid opportunities on voice assistants, but they need to be able to integrate these messages in a way that doesn’t interfere with the user experience.
Voice assistants are now part of the omnichannel consumer experience. If used correctly, they are an effective – and natural – conduit between consumer and brand.
Although Burger King’s ‘Whopper’ TV advert caused a stir by hijacking Google Home devices by prompting the speaker to search for the definition of the Whopper burger, it won a Grand Prix at this year’s Cannes Lions, and also helped the brand win overall Creative Marketer of the Year.
This nifty hack was hailed ‘the best abuse of technology’ for generating a direct response between consumer and company, and sparked conversation and awareness around the brand and campaign.
soon, we could see brands bidding for the top spots on voice-activated results
This was clearly a stunt ad, and not a long-term use of the voice activated technology. However, its success highlights the opportunities available to advertisers – and interest from consumers – in engaging with this technology.
Could this be a sign that the future of advertising and marketing is heading in the direction of voice search?
So, what could the future look like? At mporium, we know that many marketers have mastered search-based advertising, and are reaping the rewards. Soon, we could see brands bidding for the top spots on voice-activated results.
We may even see brands collaborating with the technology companies to integrate special offers that would be delivered via voice assistants, or suggest alternative solutions to specific queries.
What the future holds remains to be seen. However, it’s clear that as the technology behind voice activated search undeniably progresses, marketers will find a way to adapt to this new search reality that presents itself in the form of voice assistants.
Imagine a world where it is possible to implant a magnet that detects electrical current or utilize an exoskeleton to enhance strength. Now consider the potential abuses of requiring employees to have chip implants before they can work. Human augmentation has the potential to use technology to enhance bodies and minds, but also raises ethical and legal questions.
Nevertheless, the technology would offer higher levels of performance from employees and offer businesses an edge. This technology is upwards of 10 years from mainstream adoption, but has the potential to create a multi-billion dollar human augmentation market.
While human augmentation is just at the beginning of the innovation trigger phase of the Hype Cycle, complementary emerging technologies such as machine learning, blockchain, drones (commercial UAVs), software-defined security and brain-computer interfaces have moved significantly along the Hype Cycle since 2016.
The Gartner Hype Cycle for Emerging Technologies, 2017 focuses on three emerging technology mega-trends: Artificial intelligence (AI) everywhere, transparently immersive experiences and digital platforms. Enterprise architects and technology innovation leaders should explore and ideate these three mega-trends to understand the future impacts to their business.
“Organizations will continue to be faced with rapidly accelerating technology innovation that will profoundly impact the way they deal with their workforces, customers and partners,” says Mike J. Walker, research director. “Our 2017 Hype Cycle reveals three distinct technology trends that profoundly create new experiences with unrivaled intelligence, and offer platforms that propel organizations to connect with new business ecosystems in order to become competitive over the next five to 10 years.”
This Hype Cycle looks at technologies that show promise in delivering a high degree of competitive advantage.
Consider the potential impact of AI-enabled autonomous vehicles. They could reduce accidents, improve traffic, and even slow urbanization as people can use travel time and won’t need to live near city centers. “When autonomous vehicles, AI, IoT and other emerging technologies are combined with economic trends like the sharing economy, we truly see different business designs that profoundly disrupt the market,” Walker says. Uber is a prime example of how a business is fundamentally shifting an industry dominated by private vehicles to potentially upending the industry with transportation as a service.
The media has been consumed with hype stories about autonomous vehicles, and it has led to inflated expectations for the technology. However, given that AI is critical for the technology, this has led to an increase in the development of machine learning algorithms. While continued advancements in sensing, imaging and mapping — as well as AI and computing — are helping to evolve the technology, the reality is that the complexity and cost requirements are presenting challenges.
“AI technologies will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data and unprecedented advances in deep neural networks,” Walker says. “These will enable organizations with AI technologies to harness data in order to adapt to new situations and solve problems that no one has ever encountered previously.”
Also in the realm of AI, machine learning, one of the hottest concepts in technology, has the potential to benefit industries from supply chain to drug research. It will soon become impossible for conventional engineering solutions to handle the increasing amounts of available data. Machine learning offers the ability to extract certain knowledge and patterns from a series of observations.
At Facebook’s F8 Conference this year, the company introduced the Camera Effects Platform, launching a connection between augmented reality (AR) and social media. It ignited conversations about the potential for AR in the consumer world. However, the technology, which integrates virtual enhancements with real-world objects, could have big potential for industry. For example, instead of a relying on printed paper or separate screens, AR could overlay a maintenance schematic on a broken lamp post. Enterprises should create an AR strategy in the business with specific goals and tasks for trials and benchmarks.
When it comes to transparently immersive experiences, technology is introducing transparency between people, businesses and things. As technology evolves to be more adaptive, contextual and fluid, it will become more human-centric. Besides AR, companies should look to digital workspaces, connected homes, virtual reality and 4D printing in this realm.
With bitcoin and Ethereum constantly in the news, blockchain might seem like it’s just around the corner. However, most initiatives are still in alpha or beta stage. Enterprises are still deciding how to navigate this technology, but the lack of proven use cases and the volatility of bitcoin have created concerns about the viability of the technology. Long-term, Gartner believes this technology will lead to a reformation of whole industries.
Of the two types of blockchain — permissionless-public ledgers and permissioned-public ledgers — enterprises should look toward the latter option. Permissioned-public ledgers have access controls owned/managed by rules, but still allow for a community. For commercial transactions, companies might look to permissionless-public ledgers such as bitcoin, which allows unknown or untrusted users to access the ledger.
Read More: Are You Ready for Blockchain? [Infographic]
As digital business moves away from siloed business ventures and toward interconnected ecosystems, technology is evolving from compartmentalized technical infrastructure to ecosystem enabling platforms. Businesses must think about how to create platform-based business models and what technology is needed to support that move. Other technologies in this area include 5G, digital twins, IoT platforms and quantum computing.
Gartner clients can read more in the report “Hype Cycle for Emerging Technologies, 2017.” This research is part of the Gartner Trend Insight Report “2017 Hype Cycles Highlight Enterprise and Ecosystem Digital Disruptions.” With over 1,800 profiles of technologies, services and disciplines spanning over 100 Hype Cycles focused on a diversity of regions, industries and roles, this Trend Insight Report is designed to help CIOs and IT leaders respond to the opportunities and threats affecting their businesses, take the lead in technology-enabled business innovations and help their organizations define an effective digital business strategy.
IoT Leadership eBook
Learn more about IoT strategy in the complimentary Gartner e-book, Leading the IoT.
Gartner Symposium/ITxpo 2017
Learn more about CIO leadership and how to drive digital innovation to the core of your business at Gartner Symposium/ITxpo 2017. Follow news and updates from the events on Twitter using #GartnerSYM.
Few industries are impervious to the ongoing artificial intelligence revolution. Driven by a host of open source technologies, brands of all stripes are tapping into the potential of both artificial intelligence and machine learning to make sense of big data.
However, some industries are set to benefit more than others. Fashion and artificial intelligence have always seemed likely bedfellows, for example.
First of all, there is no trendier topic in tech than AI. Chanel hinted at this link between fashion and tech (very overtly) with their robot-themed fashion show in late 2016.
If fashion brands want to be de rigueur nowadays, they need to be investing in artificial intelligence to personalize consumer experiences. AI can also make quick work of the huge amounts of data generated by retail activity, as well as tackling more prosaic pain points like supply chain optimization.
Every aspect of the fashion industry is ripe for disruption by AI, but fashion is also a uniquely subjective pursuit. Deciding on new trends is typically the reserve of an exalted elite; however, IBM’s Watson has also taken a crack at predicting next season’s trends by analyzing the recent offerings by renowned designers.
As such, the scene is already set for AI to have a positive impact on the interaction between fashion brands and consumers.
While some continue to ponder this technology’s potential, other fashion brands are reaping the rewards today. Below are five examples from brands who have been getting AI right.
Many beauty brands have experimented with AI through chatbot interfaces, but American brand Sephora has taken the technology a significant step further with its Visual Artist product.
Sephora Visual Artist allows potential customers to “try on” cosmetic products including lipsticks, eye shadows, and highlighting palettes via the Sephora app or website.
Powered by Modiface AI technology, the Visual Artist can map and identify facial features, then use augmented reality to “apply” the user’s selected product and shade. Moreover, it can automatically apply suggested shades based on the consumer’s skin tone.
In the reader’s best interests, I have selected to trial the feature with an image of one of Sephora’s models in the screenshot below, rather than a picture of me.
The Sephora app also features video tutorials and provides a platform for customers to upload their own videos, if they wish. The overall impression is of a very effective and useful application of AI to personalize the shopping experience.
It works because it goes beyond some of the PR stunts we have seen AI used for, instead using the technology to solve a perennial problem. Trying on make-up can be laborious in person; pretty much impossible online.
The tie-in to Sephora’s inventory of products is seamless and, driven by the same AI engine, personalized recommendations can be provided instantly. All of this makes it a grown-up, sophisticated use of technology that feels natural.
The Visual Artist is also hosted on Messenger, so shoppers can send a picture to the Sephora chatbot and it will come back to them with a range of suggested products, along with an augmented reality image of how they look once applied. Of course, purchasing these items is made as painless as possible.
This universality of the AI Visual Artist throughout the Sephora ecosystem will only serve to entrench this technology further in their customers’ digital experience with the brand.
Thread is a UK-based fashion retailer, launched in 2012. Its core premise is that it will pair customers with a stylist and create tailored recommendations on a weekly basis, based on the customer’s stylistic preferences.
During the sign-up process, customers submit photos of themselves and select a range of images of outfits that best reflect the styles they would like to emulate. They are then introduced to their stylist and asked a few more questions about particular items they are seeking.
It is worth noting that these human stylists play a vital role in this process, selecting the inventory for the Thread website and fine-tuning suggested outfits.
Recommendations are sent to customers once per week and they can use a Tinder-style interface to feed back whether they liked the outfit or not.
This is central to the optimal functioning of Thread’s technology; the more feedback they receive on suggested outfits, the better their recommendations become over time.
Of course, this would be a very difficult process to emulate at scale manually, which is precisely the reason it hasn’t been done until recently. By using artificial intelligence, Thread can spot patterns in images that reflect a customer’s preferred style and comb through thousands of products to find the right item, in the right size. If it gets it wrong, its AI system (known internally as ‘Thimble’), will probably get it right next time.
Thread’s approach to shopping removes some of the layers of frustration that go hand in hand with scouring through clothes rails to find the style you want, only to realize they don’t have your size in stock.
Perhaps owing to this seamless experience and the (somewhat earned) cliché that men hate spending time shopping for clothes, Thread has primarily focused on menswear since its inception.
It serves as a worthy example of the best way to maximize human skills, but also scale these capabilities through the use of artificial intelligence.
There are plans afoot to integrate external data sources into the core AI technology at Thread, including social media accounts and weather forecasts, so those recommendations may start to get spookily accurate in the near future.
The digital revolution has taken its toll on traditional retailers like Macy’s. The convergence of many changed shopper preferences has left them at a disadvantage when it comes to competing for online shopping dollars.
That is not to say that Macy’s have resisted change altogether. They have experimented with some interesting technology to try and unite the online and offline worlds, most notably with Macy’s On Call.
Macy’s On Call is an in-store smartphone-based helper, powered by IBM’s Watson AI technology. When a customer enters a store, they can go to macys.com/storehelp and start chatting with the digital assistant. Using natural language processing, Macy’s can understand a wide variety of requests and direct shoppers towards their desired items within the store.
It can even detect when users are growing frustrated with the information provided and alert the closest member of in-store staff.
Once more, we see the application of AI to help a fashion retailer solve an age-old challenge. Within a vast department store, it can be challenging for consumers to find specific items and sizes. In-store staff can also spend a lot of their time shepherding consumers in the right direction.
Although still ultimately dependent on people visiting the physical store to begin this digital interaction, it is a sign that Macy’s is embracing the latest technology.
While the technology is only currently available in a small subset of stores, the list is expanding, and the longer-term ambition is for Macy’s On Call to function as a personal AI stylist as well as a store guide.
The aim of acting as a personal AI stylist is much closer to being a reality with the Amazon Echo Look. This hands-free camera, available for $200 in the US (by invitation only, for now), is powered by Amazon‘s AI assistant Alexa.
Put simply, this new Amazon product is intended to help users select the right outfit. Based on voice commands, it can take a picture and review the stylistic merits of the outfit the subject is wearing. This being Amazon, it can also make personalized recommendations for more suitable items – all available to purchase directly from the world’s biggest ecommerce website.
It has not gone unnoticed that the Echo Look is the closest thing we have seen to a real version of Cher’s Closet in the 1995 film Clueless.
At the heart of the Echo Look’s repertoire is the Style Check feature, which is capable of processing images and understanding the relationships between its component parts. Combined with input from professional stylists, it can then decide which outfit looks better on the user.
This is a highly contentious, subjective field, of course, so it is unlikely that users will take Amazon’s word as the gospel truth every time.
Early reviews suggest that the product still has some way to go before it even reaches Cher-level accuracy. In particular, it has been noted that it does not have enough contextual detail at its disposal to make judgments on individual outfits.
Nonetheless, this is exactly the kind of data Amazon is aiming to gain by placing an AI assistant in the center of our homes. Whether enough people want an online retailer to have a camera in their bedroom is another question altogether.
Nonetheless, even if the Echo Look fails to take hold, this quite brazen launch from Amazon serves as a bellwether of the direction the online fashion industry may be heading in over the next few years.
The final entry on our list is also the most recent development. Earlier this month, online fashion retailer ASOS launched their new visual search technology, available via their app.
Visual search is a fascinating area of technological innovation, with companies like Google and Pinterest investing heavily in image recognition AI systems.
What these all have in common is the desire to monetize the ‘discovery’ phase of the shopping journey by making personalized recommendations to consumers before they even know exactly which kind of product they want.
ASOS visual search turns the user’s smartphone camera into a discovery tool, allowing them to take a picture of a product
By identifying the shape, color, and pattern of the object, ASOS’ AI technology can then cross-reference its own inventory of products and serve up the most relevant results.
This is an entirely organic development for ASOS. As an online-only platform, the business has always been at the forefront of digital innovation in fashion. In fact, the ASOS name originally stood for ‘As Seen on Screen’, and much of its early success was due to its recreation of celebrity outfits, available for consumers to buy at a low price. As such, we can view this as ASOS using technology to deliver on its original business proposition in a more effective way.
There is still room for this product to grow, too. At present, it captures those transient moments where we see something and want to recreate the look. By expanding this to recommend complementary items and complete a new look, ASOS will open another new revenue stream. With the vast quantities of user data it has captured over the years, the possibilities for personalization will be endless.