Since entering the Internet era, more and more new models have been continuously derived to provide more strategic support for cabinet companies. Big data has set off a new trend in this context. Relying on the accumulation of huge network user behaviors, more and more industries have realized the importance of data for marketing. The industry revolution in the IT era represented by the Internet has been gradually replaced by the 'DT' era represented by big data.
The importance of big data is gradually recognized by all walks of life. Many cabinet companies have accumulated huge database resources through years of effective accumulation. How to effectively use these data has become a realistic problem for these companies.
One is to improve the quality of information and realize multi-layer data collection.
As a preliminary project to support market research and demand forecasting, the current market data collection system of cabinet companies has basically taken shape. The customer information management system, inventory management system, and terminal sales subsystem have generated massive amounts of operating data. Goods and information collection samples can provide enterprises with raw data such as social inventory and market prices. However, there are also problems such as independent system data, poor integration, and insufficient representativeness, accuracy, and timeliness of terminal data.
In view of this, cabinet companies should establish data inspection, classification and conversion standards, and implement hierarchical management in the back-end database according to the original data type and purpose of use to enhance the efficiency and applicability of data extraction.
The second is to explore model methods and carry out big data analysis.
The meaning of data is not to list, but to discover the hidden value behind it. At this stage, cabinet companies collect a lot of data, but they are independent of each other and lack correlation, and they cannot provide multi-dimensional information support for marketing decisions.
Big data analysis objectively requires cabinet companies to use statistics and mathematical models to extract data from multi-level databases according to different marketing goals, and to correlate and cluster consumer data, terminal sales data, and wholesale and retail volume and price data. In the analysis, find valuable information, and provide support for later data applications in easy-to-understand, scientific and reasonable forms such as trend charts.
The third is to accurately grasp the market and promote the application of big data.
The cabinet marketing business process is a complete closed loop that covers forecasting market demand, organizing marketable sources, launching sources, and balancing market supply and demand. Making full use of big data can effectively improve the efficiency of all aspects of marketing and enhance the ability to control the market. Therefore, the application of big data in the cabinet industry is result-oriented, starting from the actual needs of marketing work, extracting relevant data for analysis, and designing algorithms for data analysis and mathematical models.
Serving demand forecasting, cabinet companies can start with consumer databases and massive retail terminal transaction data, apply data mining and intelligent computing technology to accurately grasp consumer trends and changes in market conditions.
