3 Reasons to Incorporate Data Mining with CRM

Data Mining is a step towards Knowledge Discovery (KD), which is critical in a market flooded with competitors. Losing deals by making the wrong suggestions or overpricing goods can continue to be a downward spiral, unless the intelligence is used fruitfully. The unprecedented growth rate of databases, larger product offerings and the need for rapid decisions are some factors for increasing the popularity of data mining.

The whole concept of devising and improvising customer relationships and inter-department communication, so as to make them more accordant, may be termed as customer relationship management (CRM). The reason why data mining is considered important for CRM is because of its role in the utilization of various modeling and data analysis methods used for detecting customer patterns and relationships in the data.

The key to success in this century is to know what your customers need. The use of data mining with CRM solves this problem exactly and enables a higher return in revenues. This is in most cases is only possible when you are able to utilize minimum resources and generate maximum value to achieve the following:

  • Understand and respond to customers' needs. 
  • Send them the right products and offers.
  • Reduce the resources required for managing consumers. 
In commercial sectors, Data Mining and CRM software have become a strategic tool. In the following pointers, a few key reasons to integrate CRM in Data Mining.
  • Creation of strong marketing databases:  This consists of making the right choices for marketing niches, the adept methods to apply in your marketing and the resources you plan to use in the marketing procedure. Creation of database is a critical step - the data one may have stored may not consist of what is relevant to you. There is a need to sort this out and develop a new one by employing data mining tools that are acclaimed in precise data collection.
  • Data analysis: The process consists of probing for information related to consumers on various rostrums such as forums, review sites and many other social media networks. Relevant data is collected and analyzed for the application and construction of business models, thus, leading to the development of a good predictive model. Consultants are appointed so that the data can be amalgamated and arranged in appropriate formats. An effective use of this unique information is through CRM solutions - users can view an analysis of each record along with cross sell effectively. Visualization tools and graphing techniques may be used to display the information in a more palpable format.
  • Identify correct model: After analysis of customers’ data, one can visualize a model conceding to behavior and trends like demand, purchasing patterns, investments and incomes. The model created should be monitored right away for assistance in decision-making regarding the firm’s relationship management.
The employment of data mining techniques and CRM software enables competent customer relationship management in companies of all sizes. Understanding customer mind sets and their propensity to buy particular products will help sales and services teams to identify the right suggestions when up-selling or cross-selling with a financial CRM . Marketing teams can also effectively pinpoint target audiences for specific products through data mining and accordingly devise bespoke strategies thereby increasing cost effective lead generation and overall ROI.