Reference no: EM133358650

Case Study: Leading technology companies are using datagraphs to personalize customer recommendations, update products, optimize advertising, and more. The most successful examples- which include Facebook's social graph, Netflix's movie 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. 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 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

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

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).

Google

Google has been able to build something even more powerful. Its Knowledge Graph represents relationships between words and concepts in ways that help its algorithms understand context. This enables Google to respond to verbal queries such as: "Hey, Google, book two tickets to the Colosseum for next Wednesday and charge it to Google Pay." Because the underlying knowledge is represented as a graph, the algorithms understand what the user is asking; they know that the "Colosseum" is an attraction in Rome, that next Wednesday is May 25, that "book" means to buy tickets, and that "charge" involves using a stored credit card (as opposed to other meanings of these words). And with each query and customer interaction, the Knowledge Graph is refined to reflect new relationships as meanings change. Consider a search query by an avid mountaineer who has hiked Mount Adams and would like to hike Mount Fuji next. She may ask: "What should I do differently to prepare for Mount Fuji compared with Mount Adams?" Today, getting an answer requires multiple searches, but Google is working on a new model with more-complex knowledge linkages (with seamless translation across languages) to respond to such queries more effectively. To compete with digital giants, ask yourself: Does knowledge about our products exist mostly as separate data sets, or are we developing machine readable graphs to identify patterns of preference for our customers? They win customers' moments of truth. In 2001, only 2% of Netflix's recommendations were chosen by its 456,000 users. By 2020, the percentage had increased to 80%, and Netflix had more than 200 million subscribers. Netflix uses its movie graph to win the "moment of truth": the 90-secondto-two-minute window in which a viewer decides to watch something on Netflix or go elsewhere. Netflix algorithmically customizes and updates its home screen to continuously deliver targeted recommendations for every subscriber. By 2015, Netflix had prevented more than $1 billion a year in canceled subscriptions than to its personalized recommendation engine.

Moments of truth

To win its moments of truth, Facebook conducts A/B experiments across 3 billion users in near real time to personalize the social feeds of each user. Before Facebook displays a post, it sorts through an inventory of possibilities and narrows them down to about 500 that past behavior patterns suggest a user is likely to engage with. Then, Facebook's proprietary neural network scores the posts and ranks them before arranging them in a variety of media types, such as text, photos, sounds, and videos interspersed with ads. Unlike Facebook, whose library of digital content can be instantaneously delivered to its customers worldwide (subject to legal restrictions), Uber's ability to satisfy a customer's need for transportation is based on the availability of vehicles at a precise time and at an exact location. Uber's moment of truth is the five minutes customers are willing to wait for a driver. The ride-sharing company tracks drivers and passengers who have the app open on their smartphones (it previously tracked users even when they weren't using the app, a controversial policy it was forced to change in 2017 after customer backlash) and uses that data to analyze likely demand patterns. Then it provides incentives for drivers to be available at selected locations. The company continually optimizes its routing algorithms to win customers moments of truth.

Although many companies claim to be customer-centric, few use datagraphs and algorithms the way these leaders do. Ask yourself: Are we using AI-powered algorithms to deliver customers an ever-more-refined product offering to make sure they engage with our product rather than move on?

Questions:

Discuss TWO benefits that datagraphs provide in marketing of products of companies.

Discuss how datagraphs help Netflix and Grab "win their moments of truth".

You are a Competitive Intelligence Manager at a food company. Based on information in the article, you are to provide proposal on how to use Facebook data for your company. Your answer must include discussion of the market environment of Facebook users and your company business strategies to use the information of users.