Smart businesses are using datagraphs to reveal unique solutions to customer problems.
Of the 4,000 products Amazon sells every minute, approximately 50% are presented to customers by its personalized recommendation engine. When you visit the site, its algorithms select an assortment of products from about 353 million items and arrange them for you according to what they predict you will want at that precise moment. These recommendations are powered by Amazon’s ever-evolving purchase graph, which is a digital representation of real-world “entities”—anything about which it stores information, such as customers, products, purchases, events, and places—and the relationships and interrelationships among them. Amazon’s purchase graph connects purchase history with browsing data on the site, viewing data on Prime Video, listening data on Amazon Music, and data from Alexa-enabled devices. Its algorithms use collaborative filtering—incorporating factors such as diversity (how dissimilar the recommended items are); serendipity (how surprising they are); and novelty (how new they are)—to generate some of the most sophisticated recommendations on the planet. Thanks to its rich data and industry-leading personalization, Amazon now owns 40% of the U.S. e-commerce market; its closest rival, Walmart, has a market share of only 7%.
To compete with Amazon, in April 2021 Google announced its Shopping Graph, an AI-enhanced model that recommends products to users as they search. More than a billion people research products on Google each day, and Shopping Graph connects them with more than 24 billion listings from millions of merchants across the web. It builds on Google’s unparalleled Knowledge Graph, which captures information about the entities in its vast network and the relationships among them, including structured and unstructured data from Android, voice and image search, Chrome browser extensions, Google Assistant, Gmail, Photos, Maps, YouTube, Google Cloud, and Google Pay. With its Shopping Graph—which lets 1.7 million merchants feature relevant listings across Google using simple but interlinked tools—Google is ready to meet Amazon’s challenge.
Datagraphs like Amazon’s and Google’s rely on product-in-use data—that is, data on the behavior of customers as they use a platform or a product—to capture the connections, relationships, and interrelationships between a company and its customers. The datagraph concept is inspired by social network and graph theory, wherein a social graph is defined as a representation of the interconnections among individuals, depicted as nodes, and the relationships among them—with friends, colleagues, supervisors, and so on—represented as links. The concept derives from the work of the social psychologist Stanley Milgram, and over the past two decades, it has provided a useful lens for analyzing the structure and dynamics of organizations, industries, markets, and societies. Facebook popularized the digital social graph in 2007 when it introduced Facebook Platform, a tool that allowed developers to build applications that were integrated into the site’s information flow and connections of relationships.
Leading technology companies are using datagraphs to personalize customer recommendations, update products, optimize advertising, and more. The most successful examples—which include Amazon’s purchase graph, Google’s search graph, Facebook’s social graph, Netflix’s movie graph, Spotify’s music graph, Airbnb’s travel graph, Uber’s mobility graph, and LinkedIn’s professional graph—leverage the ongoing collection of customer engagement data, coupled with proprietary algorithms, to outcompete rivals in every way, from product creation to user experience.
This article discusses how companies can learn from the best practices of datagraph leaders to gain new competitive advantage.
Data Network Effects
To understand datagraphs, we first need to understand data network effects, which occur when data generated by users as they engage with a product or service makes it more valuable for other users. Unlike direct network effects, in which the value of a service grows as additional users join (as with Facebook or LinkedIn), data network effects do not require increasing numbers of users to enhance the value of the network. Instead, the continued engagement of current users generates broader and deeper product-in-use data, which allows algorithms to generate ever-improving results. For example, every one of Google’s 2 trillion annual searches helps the company enrich its Knowledge Graph and improve its search engine, which generates better and better search results for users. By contrast, if users stop engaging on the platform, it becomes stale and less useful.
Datagraphs are not static; they do not reflect information at a snapshot in time. They are dynamic, reflecting what data scientists refer to as data in motion. That’s partly why it is impossible to manually draw a datagraph. Technology is needed to gather and interpret in real time the data on the millions of units of a company’s products that consumers worldwide may be engaging with at any given moment.
Datagraph Success Factors
Datagraph leaders gather customer behavioral data and quickly incorporate what they learn to improve every aspect of their products and services. They constantly refine how they classify and label product data and uncover relationships among entities so that algorithms can better group offerings for personalized recommendations. And they continually update their algorithms so that the personalized recommendations are based on the most current and relevant data, which helps improve and prolong customer engagement. Let’s take a look at the key behaviors of companies that use datagraphs successfully.
They learn at scale and speed.
Datagraphs capture how individuals live, work, play, learn, listen, socialize, watch, transact, travel, spend, and do any other activity that can be associated with commerce. Digitalization has made it possible to observe and codify customer data in all these areas at scale, scope, and speed. Facebook’s social graph, for example, analyzes data on 2.8 billion individuals and their social activities from moment to moment: what they’re doing, whom they’re friending and unfriending, where they’re traveling to, what brands they’re talking about, what movies they’re watching, what music they’re listening to, and so on. LinkedIn’s professional graph captures in real time how 774 million professionals who work in more than 50 million companies and attended 90,000-plus educational institutions respond to job postings, status updates, and live videos. Moreover, it maps members to other entities, such as the skills they have, to serve users targeted ads, learning suggestions, news feeds, and more. LinkedIn is now a subsidiary of Microsoft and part of its data ecosystem, which allows it to create an even more vibrant datagraph.
At traditional companies, customer data is stored as independent records in various functional databases. To gain digital advantage, companies must organize data as a graph of interactions that are analyzable by algorithms that provide insight and deliver personalized value to every customer.
They use datagraphs to enrich product offerings.
Datagraph leaders organize their knowledge and expertise in machine-readable graph formats with a set of concepts—such as shopping, travel, or search—across categories. Take Airbnb’s travel graph. It depicts an inventory of more than 7 million homes, tagged in terms of entities (cities, landmarks, events, and so on), attributes (such as customer reviews and hours of operation), and the relationships among them to yield ever-improving recommendations about not just the type of house to rent but also the best places for dinner or the best times to visit attractions. This ability to expand the product scope allows Airbnb to serve its customers better than traditional hotels, whose data is housed in departmental silos (reservations for the room booking, concierge for restaurant recommendations, spa for massage appointments, and so on). Similarly, Netflix continually improves how it represents and classifies movies and television shows across 75,000 microgenres (just as Spotify does with music and podcasts).
ENDS
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This article first appeared https://hbr.org
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