Big Data-based Precision Marketing and Application Scenarios

The Era of Big Data Marketing is Coming The development of the marketing field in the past half a century or so has allowed us to witness the transformation from "product-centered" to "customer-centered". With the development of the Internet, mobile Internet, new social media in recent years, information overload, data explosion, consumer personalization needs, consumers have become the master of business behavior; on the other hand, the development of distributed storage of big data, big data analysis and mining technology makes it possible to collect, analyze, integrate and analyze massive data. Based on big data precision marketing this process presents a great opportunity and challenge to the marketing strategy of the enterprise.

The basic process of data-based marketing:

The process of big data-based precision marketing is divided into three major levels: collecting and processing data, modeling and analyzing data, and interpreting data. Through the collection and processing of data on customer characteristics, product characteristics, and consumption behavior characteristics, it is possible to conduct multi-dimensional analysis of customer consumption characteristics, product strategy analysis, and sales strategy guidance analysis. By accurately grasping customer demand and increasing customer interaction to promote the planning and execution of marketing strategies.

1, the data layer: the collection and processing of data

Big data processing data types include: including images, text, web pages, social networks, and traditional transaction data.

Not limited to the traditional process of collecting data is generally limited, conscious, structured data collection you can collect

2, business layer: modeling and analysis of data

The use of data analysis models, such as basic statistics, machine learning, such as data mining algorithms such as classification, clustering, association, prediction.

3. Application layer: interpreting data

The most important thing about data-guided marketing is interpretation. The traditional general is to define the marketing problem after the collection of corresponding data, and then according to the identified modeling or analysis framework, the data is analyzed to verify the assumptions and interpretation. The space for interpretation is limited.

And big data provides a possibility, both according to the marketing problem, closed to mine the corresponding data for verification, but also open to explore, and come up with some conclusions that may be completely different from common sense or empirical judgment out. The points of interpretation become very rich.

Types of big data marketing data:

Demographic data: including the user's age, gender, nationality, and information provided at registration;

User behavioral data: visits, length of time spent on the page, touchpoints, and so on.

User content preference data: topics of interest, commented content, brand preference, location preference, time preference, etc.

User content preference data: topics of interest, commented content, brand preference, location preference, time preference, etc.

Transaction data: actual orders, customer orders, order conversion rate, promotional response rate, etc. Big Data Marketing Application Scenarios: From the enterprise marketing application level, it is mainly around the three elements of customer, product, and consumer behavior for the development and implementation of marketing strategies. These three elements are independent of each other and interconnected, each independent element can be developed marketing strategy, while the three elements of the association between the combination is the key to the development of effective marketing strategy.

Application 1: Customer Value Identification (User Characteristics)

Through the collection of user transaction history data;

RFM analysis to locate the most valuable user groups and potential user groups. The most valuable customers to improve loyalty; potential users: active marketing to promote the actual purchase behavior. Low-value customer user groups are not considered for marketing promotion if the marketing budget is small.

Through factor analysis, discover the main factors that influence users to repeat purchases, identify the main factors and influence weights from information such as: price factors, word-of-mouth reasons, review information, etc., and adjust the product or market positioning. Identify the reasons that motivate customers to buy guidance, adjust the focus of publicity or a combination of marketing methods.

Application 2: User Behavioral Indicators:

Through the collection of user behavioral data;

Through the automatic tracking of the source of user behavioral channels: the system can automatically track and discriminate between the source of visitors to classify, according to the three major marketing process of the paid search, natural search, cooperative channels, banner ads, email marketing, and other marketing channels for marketing tracking and The system can automatically track and classify the source of visitors according to the three major marketing processes.

Marketing effectiveness: Knowing which media marketing is affecting specific users, how they get to a particular site, and what they do when they cross-screen or browse a particular site.

Separate targeting based on geographic location, for example, most upper-middle class people are more centrally located. Not generalized customer segments.

Application 3: personalized correlation analysis

Through the collection of website behavioral data such as what products users have purchased, what products they have browsed, and how they have browsed the website; by analyzing the degree of similarity of the needs of the customer base, and the degree of similarity of the products, the personalized recommendation engine will recommend to the user which products or services are of interest to which users. To what extent they are influenced by promotions, reviews of products by other buyers.

Big data precision marketing challenges:

1. Multi-channel convergence for precision marketing: the global data explosion, mobile Internet, social media, the increase in optional channels and devices, changing consumer characteristics, marketing automation: marketing and sales behavior, supply chain, customer relationships are integrated. How to better realize the integration of data across channels poses a challenge to improving the accuracy of precision marketing.

2. In recent years, Internet products have shown a round of explosive development. In particular, the popularity of mobile terminals, so that many traditional Internet products have begun to mobile. Geographic location into the social media marketing is the precise marketing to consider.

3, instant marketing based on data mining: companies are now gradually away from batch processing, turning to real-time analysis to gain a competitive advantage. Precision marketing also requires that we get data at the same time as the activity to optimize the marketing effect immediately.

4, precision marketing system: self-service marketing, scalable scenarios and marketing rules management features.

The above is what I shared with you about the precision marketing based on big data and application scenarios, more information can be concerned about the Global Green Ivy to share more dry goods