Over the years, enterprises have been adopting various approaches to preserving their customers / clients and winning over their competitors. Thanks to the development of information systems applications, companies were able to capture a huge amount of customer and product data by scanning bar codes, online purchases, surveys, etc. This data, however, can help in making informed business decisions, but this for many years kept intact in huge databases. But in order to understand customer behavior, companies require the integration of innovative tools that can discover hidden valuable information in a huge data warehouse.
In addition, emerging competition and affordable alternatives for customers have changed the need to maintain effective customer relationship management. For this reason, owners use a knowledge management approach to transform this customer knowledge into sound business decisions. Here is the role data mining services and methods come into play to identify new opportunities by converting this hidden client data into useful information. Knowledge Management (KM) is central to this.
Data collection
Data mining is basically a process that uses intelligent methods to identify useful knowledge models in large databases. Using different algorithms, it can predict useful information from stored data, which also helps to interact between data subsets. The challenges of data mining include two aspects: prediction and description. If the prediction predicts unknown values of variables using some known variables in the data sets, the description retrieves interesting patterns and trends in the data.
Knowledge management
With knowledge management (KM), we mean converting data into relevant knowledge. However, the protection is precisely what the CM can challenge because of the intangible nature of knowledge; where knowledge is defined as the ability of an organization to effectively share knowledge in order to gain a competitive advantage. In addition, KM is seen as one of the most important business aspects, and therefore companies need to know how to acquire, capture and share this knowledge in order to increase productivity in the long term.
Data mining and its applications for the process of knowledge recovery
The role that data mining plays in managing business knowledge for obtaining and extracting useful information is discussed below:
Decide
Applying data mining helps an organization make informed decisions. Consequently, interactions are created by Business Intelligence (BI), which helps companies use and convert available information and knowledge in real time for business development. In addition, data mining hides hidden information about customers and products for businesses that can provide valuable knowledge and establish BI. This makes it easier to analyze product sales information, which, in turn, helps the marketing department shape a product promotion strategy.
Next, we will discuss some DM and KM applications in business domains that use data mining techniques to find interesting data patterns in the form of knowledge:
Retail: This industry collects huge sales data, customer purchase history, etc. Due to the growing popularity of e-commerce these days. Here, data mining can help create extensive knowledge of customer buying behavior and trends. Knowing this, retailers can achieve greater customer satisfaction, lower operating costs and expand their brands.
Banking and financial sector : The banking and financial sector has huge databases filled with critical financial and economic data. Here, DM techniques can provide the benefits of identifying patterns and deviations in business information and market prices necessary to recognize global risk and ROI. Helping banks in the areas of risk management, fraud detection, customer relations, etc. This facilitates decision-making and knowledge sharing.
Health institutions: Extraction techniques, such as clustering, can help reach the demographics of patients with serious diseases, such as cancer, a tumor, etc. This knowledge can help physicians investigate symptoms of the disease and relationships, which in turn can improve treatment and treatment .
Aviation industry: For this sector, an association rule or clustering method can be beneficial to gain knowledge about customers, which can later be used to provide discounts on airline tickets, determining the frequency of flight of passengers.
Internet business: E-commerce stores can take advantage of the integration of DM tools and methods to extract information stored in a customer profile. Once the information has been collected, the owner can offer customers reliable product recommendations based on their interest in increasing sales.
Insurance companies: Insurers can sell more policies and increase conversion rates by conducting effective campaigns, running processes, and reducing operating costs, knowing how many customers are interested in purchasing policies, their requirements and interests.
production A: Manufacturers will be able to produce products, people are more interested in finding out their choice through the DM and KM process.
