The next big advantage of digitalization, why is data mapping?

Amazon sells 4,000 products every minute, about 50% of which are presented to users by personalized recommendation engines. When browsing the Amazon website, algorithms predict what you want at this moment and select a group of approximately 353 million products to push to you.

What drives personalized recommendations is Amazon’s ever-evolving procurement map, which is the “entity elements” in reality—all store information such as customers, products, purchases, events, and store locations—and the relationships between these elements. The digital representation of sex. Amazon's purchase graph links purchase history with website browsing, Prime Video viewing, Amazon music listening and data from Alexa devices. The algorithm uses collaborative filtering to combine diversity (how different the recommended products are), unexpectedness ( Factors such as the surprising degree of recommended products) and novelty (freshness) generate the most complex recommendations in the world. With its rich data and industry-leading personalized recommendations, Amazon now accounts for 40% of the U.S. e-commerce market, while its closest rival Walmart has a market share of only 7%.

In order to compete with Amazon, Google announced in April 2021 the launch of Shopping Graph, an AI model that recommends products when users search. More than 1 billion people search for products on Google every day, and shopping images connect them to more than 24 billion product listings from millions of merchants across the web. The foundation of this model is Google's unique Knowledge Graph, which captures information about entities and their relationships across the vast web, including from Android, voice and image search, Google Chrome extensions, Google Assistant, Google Structured and unstructured data from Email, Google Photos, Google Maps, YouTube, Google Cloud and Google Pay. Google Shopping Graph allows 1.7 million merchants to use simple but similar tools to display relevant products on Google, and Google can meet the challenge of Amazon.

Data graphs like Amazon and Google rely on product usage data (that is, behavioral data generated when users use the platform or product) to grasp the connections and relationships between enterprises and their customers. The concept of data graph originates from social network and graph theory. This theory defines social graph as the presentation of connections and relationships between people, such as friends, colleagues, bosses, etc. Each person is presented as a node, and the relationship is between points and Connections between points. This concept emerged from the work of social psychologist Stanley Milgram and over the past two decades has provided a practical lens for analyzing the structure and dynamics of organizations, industries, markets, and societies. In 2007, Facebook launched the social platform of the same name, allowing developers to create applications integrated into website information flows and interpersonal connections, making digital social graphs popular.

Leading technology companies use data graphs to provide personalized recommendations, upgrade products, optimize advertising, and more. The most successful examples, such as Amazon’s purchasing graph, Google’s search graph, Facebook’s social graph, Netflix’s movie graph, Spotify’s music graph, Airbnb’s travel graph, Uber’s travel graph and LinkedIn’s career graph, use The continuous collection of user usage data, coupled with unique algorithms, has left competitors behind in all aspects from product development to user experience.

This article discusses how companies can learn from data mapping leaders to create new competitive advantages.

Data Network Effect

To understand the data graph, you must first understand the data network effect, that is, the data generated when users use a product or service make this product or service more useful to other users. value effect. Unlike direct network effects where the value increases as more users join (such as Facebook and LinkedIn), data network effects do not require an increase in the number of users to increase the value of the network. Instead, existing users continue to use it, resulting in more extensive and in-depth use. Data enables algorithms to produce continuously improving results. For example, Google's two trillion searches every year help Google enrich its knowledge graph, improve its search engine, and provide users with better search results. And if users stop using the platform, improvements in platform service quality will stall and become less helpful.

Data graphs are not static and do not reflect data at a certain point in time, but what data scientists call dynamic data. This is part of the reason why manually graphing your data is impossible. Technology must be leveraged to collect and interpret in real time the millions of pieces of data generated by the use of a company's products by consumers around the world.

Data Graph Success Factors

Data Graph leaders collect user behavior data and quickly use it to improve all aspects of their products and services. These companies are constantly modifying the methods they use to classify and label product data, looking for relationships between entities so that algorithms can better categorize and provide personalized recommendations. The company also continuously updates its algorithms to generate personalized recommendations based on the latest and most relevant data to help attract customers. Let’s take a look at the key behaviors of companies that successfully use data mapping.

Learn quickly and broadly.

The data map captures personal life, work, entertainment, learning, listening, socializing, watching, trading, traveling, consumption and other activities that can be linked to business. Digitization allows companies to observe and organize these aspects of customer data extensively, thoroughly, and quickly. For example, Facebook's social graph analyzes data on 2.8 billion people and their social activities every moment: what they are doing, who they are friends with and unfriended, where they go, what brands they are discussing, what movies they are watching, and what they are listening to. What music and so on. LinkedIn's Career Graph captures in real time how 774 million professionals working for 50 million companies and participating in courses at more than 90,000 educational institutions respond to job postings, update status, and use live videos. In addition, Career Map also provides users with targeted advertising, study suggestions, news feeds and more information based on other factors such as user skills. Now that LinkedIn is a subsidiary of Microsoft, it has been included in Microsoft's data ecosystem, allowing it to create a more dynamic data map.

In traditional enterprises, user data is stored independently in databases of different functional departments. To gain digital advantage, companies must organize data into interactive graphs that can be analyzed using algorithms to generate insights and deliver personalized value to each customer.

Enrich product lines with data maps. Companies leading the way in data mapping use a range of cross-cutting concepts such as shopping, travel or search to organize professional knowledge into machine-readable graph formats. For example, Airbnb's travel map provides a list of more than 7 million residences, tagged with attributes (city, landmarks, activities, etc.), characteristics (customer reviews, business hours, etc.) and relationships between each other to generate more advanced recommendations. Not only does it recommend rentals, but it also recommends the best places to have dinner and the best times to visit attractions. This ability to expand its product range allows Airbnb to offer customers a better service than traditional hotels, where data is stored in siled departments (reservations for booking rooms, concierge for recommending tours, spa for booking massages, etc.) etc). Similarly, Netflix continues to improve the way its film and television works are presented and classified under 75,000 subcategories, as does Spotify's music and radio programs.

In order to win at the critical moment, Facebook conducted a near-real-time control test of personalized social network content on 3 billion users. Before pushing content, Facebook will screen the list to be pushed and narrow the scope to about 500 pieces of content that the user may care about based on the user's past behavior patterns. Facebook then uses a proprietary neural network to score and rank the content, sorting it by media type such as text, photos, audio and video with ads.

While many companies claim to be customer-centric, few make the same use of data graphs and algorithms as leading companies. Think about it: Does your company use AI algorithms to provide customers with ever-improving products so they don’t switch to other companies?

Get started

If you want to compete with the data mapping leaders, you must understand one thing: strategic success depends not only on having a lot of information, but also on collecting relevant product usage data in real time. , realize data network effects and create advantages. If more users can be observed interacting with products, companies can obtain richer data; by selling more products to more diverse user groups, companies can accumulate more diverse data and help achieve product differentiation. Companies that do not make good use of data mapping can refer to the following improvement suggestions:

1. Develop a data mapping strategy. First, executives who understand the industry must work with data scientists to conceptually build a data map, examine future trends, and think about possible business impacts. Many companies with less resources than Amazon or Netflix have done this. For example, Stitch Fix, a personalized fashion service company founded by a business school student in 2010, is now worth more than $1.6 billion, in large part because of its fashion graph.

Consider whether the data your company owns provides a unique advantage. You may have proprietary data collection methods that allow you to obtain detailed information that no other business can obtain. Maybe you have an advantage in data depth and breadth and can get complementary data from a partner. Your streaming data (as opposed to the fragmented data used by competitors for batch processing) is likely to be faster. Consider whether acquisitions (such as Microsoft's acquisition of LinkedIn and Activision) and alliances (such as Google's partnership with Shopify) can increase the scope, depth and speed of the company's data.

2. Create proprietary algorithms. It is no longer enough to perform different types of analysis independently. Data mapping leaders use proprietary algorithms to perform descriptive analysis (“What happened?”), diagnostic analysis (“Why did it happen?”), predictive analysis (“What will happen?”) and Prescriptive analysis (“What should happen?”). Your data graph infrastructure can move from a traditional structure for analyzing static data (batch processing, independent analysis) to analyzing changing, real-time data. It is necessary to refer to other companies in the industry and other algorithms of the same type.

For example, if your success metric is the extent to which customers accept recommendations, how does your recommendation engine perform compared to leaders like Netflix, Spotify, and Amazon?

3. Build trust. Managing customer data comes with a lot of responsibility. Most customers see computers, algorithms, and machine learning as complex black boxes, and many feel that digital companies are making a fortune out of, or even abusing, their personal data. Businesses must use algorithms in a way that earns trust, and they must gain permission to collect and analyze data and provide value. Explain what your company will do with the data in language consumers can understand.

Consumers will lose trust in companies if they feel their personal data is being misused. Companies must not only invest resources in technology, but also explain it in a way that consumers can understand and accept. Customers are increasingly looking to improve their understanding of digital products and how AI-powered services can be implemented, and countries require companies to use data within local legal restrictions.

4. Organizational upgrade. Business leaders must deploy the necessary resources and upgrade technology infrastructure to meet the requirements of the data map. Hiring people with broad and deep knowledge in both data science and business is a must. Data organizations must be viewed as the connective tissue that connects all parts of the enterprise, recognizing that modern organizations must grapple with two powerful, conflicting factions: those who believe in the power of data and algorithms to solve problems and those who do not. The contradiction between the two sides is a major feature of modern organizational operating culture: for example, Netflix CEO Reed Hastings balances Silicon Valley's emphasis on analysis and Hollywood's emphasis on creativity.

5. Make profits through data graphs. Build data maps to support and develop strategy, showing that value lies not just in product design and manufacturing, but also in how specific problems are solved for customers. The insights provided by the data map will help you choose the most appropriate profit mechanism and plan a clear path from data to business results. You can use personalized recommendations based on data network effects to maintain current revenue and profits. For example, Netflix uses real-time data to improve user retention rates; you can also use data maps to develop more complete methods to strive for new sources of value and expand revenue and profits. Streaming, such as Apple's entry into the credit card, TV and medical industries; it can also counterattack competitors in the market that have already mastered the data map, such as Disney's successful entry into the streaming media industry with Disney+.

Reshaping Advantage

Data graphs will reshape competition in every field faster than most people expect. Every company should move beyond leveraging data to improve operational efficiency and recognize the competitive advantage of data graphs. Senior leaders must invest in upgrading data infrastructure to gain a real-time, comprehensive understanding of how consumers interact with their company’s products and services. With this structure in place, unique solutions to customer problems can be developed.

For leading digital companies, continuous exploration in fields such as data maps is creating new competitive advantages for them, leaving behind their competitors in aspects such as product development and user experience. Therefore, their experience deserves to be widely used.

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Vijay Govindarajan N. Venkat Venkatraman | Text< /p>

Vijay Govindarajan is Cox Distinguished Professor at the Tuck School of Business at Dartmouth University and an Executive Fellow at Harvard Business School. Venkat Katraman is the David H. McGrath Jr. Professor of Management at the Questrom School of Business at Boston University.