Why is the data map the next big advantage of digitalization?

Amazon sells 4000 products every minute, about 50% of which are presented to users through personalized recommendation engines. When browsing the Amazon website, the algorithm will predict what you want at the moment and choose a group from about 353 million items to push to you.

What drives personalized recommendation is Amazon's constantly developing purchasing map, that is, the real "entity elements"-all store information such as customers, products, purchases, activities and store locations-and the digital presentation of the relationship between these elements. Amazon's purchase atlas links the purchase history with website browsing, Prime video viewing, Amazon music listening and data from Alexa devices. The algorithm uses collaborative filtering, combining diversity (degree of dissimilarity of recommended products), surprise (degree of surprise of recommended products) and novelty (degree of freshness) to generate the most complex recommendation in the world. With rich data and industry-leading personalized recommendations, Amazon now accounts for 40% of the US e-commerce market, while its nearest competitor Wal-Mart's market share is only 7%.

In order to compete with Amazon, Google announced the launch of Shopping Graph in April 20021year, which is an AI model for recommending products when users search. Every day, more than 654.38 billion people use Google to search for goods, and shopping pictures link them with more than 24 billion commodity lists of millions of merchants all over the network. This model is based on Google's unique knowledge map, which captures information about entities and their relationships in the vast network, including structured and unstructured data from Android system, sound and image search, Chrome extension of Google browser, Google Assistant, Google Email, Google Photos, Google Maps, YouTube, Google Cloud Services and Google Pay. Google Shopping Atlas allows 654.38+0.7 million merchants to display related products on Google with simple but interconnected tools, and Google can meet the challenge of Amazon.

Data maps like Amazon and Google rely on product usage data (that is, behavior data generated when users use platforms or products) to grasp the connection and relationship between enterprises and their customers. The concept of data map comes from social network and graph theory, and defines social map as the presentation of contacts and relationships between people, such as friends, colleagues, bosses, etc. Everyone is presented as a node, and the relationship is the connection between points. This concept comes from the work of social psychologist Stanley milgram. In the past twenty years, this concept has provided a practical perspective for analyzing the structure and dynamics of organizations, industries, markets and society. In 2007, Facebook launched a social platform with the same name, allowing developers to create applications and integrate them into the information flow and interpersonal relationships of the website, making digital social maps popular.

Leading technology companies use data maps to provide personalized recommendations, upgrade products and optimize advertisements. The most successful examples, such as Amazon's purchasing map, Google's search map, Facebook's social map, Netflix's movie map, Spotify's music map, Airbnb's travel map, Uber's travel map and LinkedIn's professional map, all use the constantly collected user usage data and unique algorithms to get rid of competitors from product development to user experience.

This paper discusses how enterprises learn from the methods of leading enterprises in data mapping to create new competitive advantages.

Data network effect

To understand the data map, we must first understand the data network effect, that is, the effect that the data generated when users use a product or service makes this product or service more valuable to other users. Different from the direct network effects (such as Facebook and LinkedIn), the data network effect does not need to increase the number of users to enhance the network value, but the existing users continue to use and generate more extensive and in-depth usage data, thus making the algorithm produce continuous improvement results. For example, Google's 2 trillion searches each year help Google enrich its knowledge map, improve its search engine, and provide users with better search results. If users no longer use the platform, the improvement of platform service quality will stagnate and become less helpful.

The data map is not static, it reflects not the data at a certain point in time, but what data scientists call dynamic data. This is also part of the reason why it is impossible to draw the data map manually. It is necessary to use technology to collect and interpret millions of pieces of data generated by a company's products being used by consumers around the world in real time.

Success factors of data map

Data map leading enterprises collect user behavior data and quickly use these data to improve all aspects of products and services. These companies constantly modify the methods of classifying and labeling product data, looking for the relationship between entities, so that the algorithm can better classify and provide personalized recommendations. The company also constantly updates its algorithm to generate personalized suggestions based on the latest and most relevant data to help attract customers. Let's look at the key behaviors of companies that successfully use data maps.

Learn quickly and widely. Data map captures personal life, work, entertainment, study, listening, socializing, watching, trading, traveling, consumption and other activities that can be linked to business. Digitalization enables companies to observe and sort out these customer data extensively, thoroughly and quickly. For example, Facebook's social graph analyzes the data of 2.8 billion people and their social activities all the time: what they are doing, who are their friends and non-friends, where they have been, what brands they are discussing, what movies they are watching, what music they are listening to and so on. LinkedIn's career map captures in real time how 774 million professionals who work for 50 million companies and participate in more than 90,000 educational institutions respond to recruitment information, update their status and use live videos. In addition, the professional map also provides users with more information such as targeted advertisements, study suggestions and news push according to other factors such as user skills. Now LinkedIn is a subsidiary of Microsoft, and it has been incorporated into Microsoft's data ecosystem to create a more dynamic data map.

User data of traditional enterprises are independently stored in databases of different functional departments. In order to gain digital advantage, enterprises must organize data into interactive maps, which can be analyzed by algorithms to generate insight and provide personalized value for each customer.

Enriching product lines with data maps. Leading enterprises in data mapping use a series of cross-domain concepts such as shopping, travel or search to organize professional knowledge into a map format that can be recognized by machines. For example, Airbnb's travel map gives a list of more than 7 million houses with attributes (cities, landmarks, activities, etc.). ), characteristics (customer evaluation and business hours, etc. ) and their relationship to generate more advanced recommendations, not only recommend rental housing, but also recommend the best place for dinner and the best time to visit scenic spots. This ability to expand the product range enables Airbnb to provide customers with better services than traditional hotels. The data of traditional hotels are stored in isolated departments (reservation department is responsible for booking rooms, concierge department is responsible for recommending tours, convalescent department is responsible for booking massages, and so on). Similarly, Netflix is constantly improving the display and classification of 75,000 sub-categories of movies and TV works, as are Spotify's music and radio programs.

In order to win at a critical moment, Facebook conducted a near-real-time personalized social network content comparison test for 3 billion users. Before pushing the content, Facebook will filter the list to be pushed, and according to the user's past behavior, it will narrow the scope to about 500 articles that the user may care about. Then, Facebook will use a proprietary neural network to rate and sort these contents, and then sort them according to media types, such as text, photos, audio and videos with advertisements.

Although many companies claim to be customer-centric, few can make good use of data maps and algorithms like leading enterprises. Think about it: does your company use AI algorithm to provide customers with continuous improvement products so that they will not turn to other companies?

start off

If you want to compete with leading enterprises in data mapping, you must understand one thing: the success of the strategy depends not only on whether you have a lot of information, but also on collecting relevant product usage data in real time, realizing data network effect and creating advantages. If we can observe the interaction between more users and products, enterprises can get richer data; By selling more products to more diverse user groups, we can accumulate more diverse data and help achieve product differentiation. Companies that are not good at using data maps can refer to the following suggestions for improvement:

1. Develop data mapping strategy. First of all, we should let executives who know the industry cooperate with data scientists to build data maps conceptually, examine future trends and think about possible business impacts. Many companies with less resources than Amazon or Netflix have already done so. For example, 20 10 Stitch Fix, a personalized fashion service company founded by a business school student, now has a market value of more than1600 million US dollars, largely because of its fashion map.

Think about whether the data our company has can provide unique advantages. You may have a proprietary data collection method, and you can get detailed information that other enterprises can't. Maybe you have an advantage in the depth and breadth of data, and you can get complementary data from your partners. Your mobile data (relative to the scattered data used by competitors for batch processing) may be faster. Think about whether we can improve the data range, depth and speed of our company through acquisitions (such as Microsoft's acquisition of LinkedIn and Activision) and alliances (such as the cooperation between Google and Shopify).

2. Establish a proprietary algorithm. It is no longer enough to conduct different types of analysis independently. Leading enterprises in data mapping use proprietary algorithms to conduct descriptive analysis under the overall framework ("What happened?" ), diagnostic analysis ("Why?" ), predictive analysis ("What will happen?" ) and specification analysis ("What should happen?" )。 Your data mapping infrastructure can change from the traditional structure of analyzing static data (batch and independent analysis) to analyzing changing real-time data. Reference should be made to other enterprises in the industry and other similar algorithms. For example, if your success indicator is the degree to which customers accept recommendations, how does your recommendation engine compare with leading companies such as Netflix, Spotify and Amazon?

3. Build trust. Managing customer data is a major responsibility. Most customers regard computers, algorithms and machine learning as complex black boxes, and many people think that digital companies use or even abuse their personal data to make a fortune. Enterprises must use algorithms in a trustworthy way and must obtain permission to collect and analyze data and provide value. Explain what your company wants to do with data in a language that consumers can understand.

If consumers feel that personal data is abused, they will lose trust in the company. Enterprises should not only invest resources in technology, but also explain it in a way that consumers can understand and accept. Customers are increasingly looking forward to improving their understanding of digital products and how to realize the services supported by artificial intelligence. Countries require enterprises to use data within the limits of local laws.

4. Organizational upgrading. Business leaders must deploy necessary resources, upgrade technical infrastructure and meet the requirements of data mapping. Talents with extensive and in-depth knowledge in data science and business must be hired. Data organization must be regarded as a connected organization connecting all parts of the enterprise, and it is recognized that modern organizations must properly deal with two powerful factions that conflict with each other: one thinks that data and algorithms have strong problem-solving ability, and the other thinks that they do not. The contradiction between the two sides is a major feature of modern organizational operation culture: for example, reed hastings, CEO of Netflix, balances the emphasis on analysis in Silicon Valley and creativity in Hollywood.

5. Earn profits through data graphs. The construction of data map is used to support and formulate strategies, indicating that the value lies not only in product design and manufacturing, but also in how to solve specific problems for customers. The insight provided by the data map will help you choose the most suitable profit mechanism and plan a clear path from data to business results. Personalized recommendation based on data network effect can be used to maintain current income and profit. For example, Netflix uses real-time data to improve user retention; You can also use data maps to develop more perfect ways, strive for new sources of value, and broaden income and profit streams, such as Apple's entry into credit card, television and medical industries; It can also counter the competitors who have mastered the data map in the market, such as Disney+ successfully entering the streaming media industry.

Rebuilding advantages

Data maps will reshape competition in every field faster than most people expected. Every enterprise should surpass the demand of using data to improve operational efficiency and realize the competitive advantage of data map. Senior leaders must invest in upgrading data infrastructure to fully understand the interaction between consumers and their products and services in real time. With this structure, we can work out unique solutions to solve customers' problems.

For leading digital enterprises, continuous exploration in data mapping and other fields is creating new competitive advantages, and they are getting rid of competitors in product development and user experience. Therefore, their experience is worth learning widely.

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Vijay govindarajan (Vijai Govinda Rajan and Venkat Venkatraman) |

Vijay govindarajan is Cox distinguished professor of Tucker School of Business at Dartmouth University and an executive researcher at Harvard Business School. Kevin Kartman is David mcgrath Professor of Management at Boston University Business School.