Driven by the background of big data, more and more industries and enterprises pay attention to data mining analysis. In fact, the use of commercial data for marketing has been launched decades ago.
A well-known US department store teenage pregnancy case, using customer purchase record data, infers teenage pregnancy, and then recommends a series of maternity products. The parents of the girl were furious when they learned that, but the truth is that the girl concealed the pregnancy news.
You may smile when you see this, but if you take a deeper look, from teenage purchases to product recommendations to consumption, how department stores hit such a fact. The answer is data. All the behaviors of girls in this department store are digitized.
From the perspective of the enterprise, that is, turning information into observable and measurable data, making the marketing action goals clear, traceable, measurable, and optimizable, forming a consumption-data-marketing-effect- Data-centric marketing closed loop for consumption.
As a core product under the background of big data, business intelligence plays an increasingly obvious role in guiding enterprise marketing from various aspects. Through the practical application of business intelligence FineBI in various industries, we can divide how business intelligence achieves precision marketing into the following three points:
Business Intelligence Builds Digital Context
Business intelligence is to build an enterprise information decision analysis platform with information integration, resource sharing and functional integration, establish a data warehouse, collect and process data on business issues, establish a data analysis model, and use data mining technology for subsequent data interpretation. , to form a data network. For example, internally, we can conduct cross-contingency analysis on indicators such as sales and product prices from the perspectives of regions, customers, time, etc.; externally, through multimedia, text information, organizations, industries, etc. that provide decision support characteristics are analyzed. Use the results of data analysis to optimize the marketing model, and iterate through data optimization again.
Business intelligence geographing data
Enterprises have too much data with "regional" or "regional" characteristics, so the geographicization of marketing data is a very important "value factor". With the current level of business intelligence, these unstructured data can be handled very well. We can classify marketing data through business intelligence, conduct business circle analysis, location analysis, and serve marketing mix strategy and customer relationship management. Determine a business district by analyzing market share, penetration level, and surrounding construction infrastructure. If we need to further establish a branch in a certain area, we can further analyze the commercialization level, population density, consumption level, and traffic conditions of the area to determine the location and size of the store.
Business Intelligence allows you to better understand your customers
Any marketing is based on customer demand to profit, therefore, the analysis of customers is crucial. For example, if you are a real estate salesperson, when you are handing out flyers, how do you know that people in their 30s and 45s are more in demand, some say it's experience. But in fact, this only considers the factor of customer age. If it is at the marketing level, targeting thousands of customers, we must not only consider the age, but also consider the income level, education, consumption patterns, etc., including the analysis of the external environment. Business intelligence can help marketers identify opportunities and ways to influence consumers, achieve precise marketing to users at different levels, and further understand customers through data.
There is a saying that is very incisive, data analysis is intended for marketing, knowing and then hitting. The application of business intelligence by enterprises is no longer a trend, but inevitable. It helps enterprises to digitize information, make management scientific, and form an overall data network to make marketing more accurate.