How Data Mining Helps Customer Relationship Management in 2024


What is data mining?

The process through which raw data is turned to useful information is known as data mining. Businesses use software to obtain customer information to develop marketing strategies, increase sales, and decrease costs by searching for patterns in large batches of data.

Data mining is dependent on factors like the active collection of data, computer processing, and warehousing. Machine learning models are built using data mining processes that power applications like website recommendation programs and search engine technology.

How it works

In data mining, large blocks of information are analyzed and explored to find useful trends and patterns. Several methods are there for finding it, like credit risk management, database marketing, fraud detection, or spam email filtering. It can even be done by discerning the opinion or sentiment of users.

Five steps are involved in the data mining process.

  1. Data is collected by organizations and then loaded into their data warehouses.
  2. The data is stored and managed in servers or the cloud.
  3. The organization of the data is determined by information technology professionals, Business analysts, and management teams.
  4. Application software is used to sort the data according to the user’s results.
  5. The data is presented in an easy-to-share format like a table or graph.

What is CRM?

CRM (Customer Relationship Management) is a form of technology used for managing a company’s relationships and interactions with existing or potential customers. In short, CRM improves your business relationships. It helps you to stay connected with your customers, improve profitability and streamline processes.

CRM is a system, unlike a single entity that assists with productivity, management of sales and contact, and much more.

A CRM solution will aid your organization’s relationships with individuals (service users, customers, suppliers, or colleagues) throughout your lifecycle with them. It will also help find new customers, along with providing support and additional services throughout the relationship.

Purpose of data mining in CRM

Img source:

No matter what industrial field you are in, data mining can help you take advantage of historical data. Data mining techniques in CRM can help you find and select relevant information that can be used to obtain a holistic view of a customer’s life cycle. Four stages are involved in this process.

  1. Customer identification
  2. Attraction
  3. Retention
  4. Development

The higher the amount of data present in the database, the better and more accurate the models will be. More accurate models will mean more business value.

The key elements involved in data mining are:

  1. predictive modeling
  2. forecasting
  3. descriptive modeling techniques

Exploiting CRM can enable your organization to:

Img source:
  1. manage customer retention
  2. select the right prospects & customer segments
  3. set optimal pricing policies
  4. objectively measure and rank the suppliers

Applications of Data Mining in CRM

  • Basket Analysis: Find out what items customers typically purchase together. This can help to improve stocking, promotions and store layout strategies
  • Sales Forecasting: Re-stocking decisions can be made easy by examining time-based patterns. It also gives control over internal operations and helps in supply chain and financial management.
  • Database Marketing: Factors like demographics, buying behavior, tastes, and preferences can help retailers design customer profiles. It can also help the marketing team design promotional offers and personalized marketing campaigns. This can help your organization’s productivity level to increase, optimally allocate the company’s resources, and have a desirable ROI.
  • Predictive Lifecycle Management: You can predict a customer’s lifetime value and service each segment appropriately through data mining.
Img source:
  • Market Segmentation: Design and market your campaigns and promotions by keeping the tastes and preferences of customers interested in purchasing your products in mind. Your organization will experience increased efficiency and achieve the desired ROI as you won’t be busy targeted customers who show minimal interest in your products.
  • Product Customization: Products can be customized by manufacturers according to the exact needs of customers. They must predict which features should be bundled to meet the demands of customers.
  • Fraud Detection: Analyzing and studying past cases of transactions that later turned out to be fraudulent can help your organization take corrective measures and pre-emptively stop such events from occurring again. Banks and other financial institutions will find this feature extremely helpful by reducing cases of bad debts.
  • Warranties: Manufacturers must be able to predict how many customers will make warranty claims and the average cost of the claims. Effective and efficient management of company funds can then be ensured.

Data Mining Techniques in CRM

Img source:
  • Anomaly Detection: Looking for information that does not match a projected pattern or expected behavior is known as anomaly detection. Anomalies deviate from the average in a data set, which can help to provide actionable information.
  • Association Rule Learning: In massive databases, relations can be found between data items. Hidden patterns are discovered with Association Rule Learning that can help your organization to understand customers better, predict their decisions, and learn their habits.
  • Clustering: Similar data sets are identified, and the similarities and differences within the data are interpreted. Data sets with similar traits can help increase conversion rates. For example, If two groups of customers have the same buying behavior, then both groups can be targeted with similar products or services.
  • Classification: This technique helps to place data sets into proper categories by gathering information about the data. An example can be classifying email as regular, acceptable email or spam.
  • Regression: It’s one of the advanced data mining techniques in CRM. In this technique, the dependency between different data items is identified, which helps to map out the variables which are affected by other variables. Customer satisfaction levels and their impact on customer loyalty are determined through this technique.
Img source:

According to Ergo-Ventures, If you’re looking for an effective CRM solution, then look no further than Odoo. Odoo is an easy to use business management software that helps you to run your business efficiently. Odoo CRM module tracks leads, closes opportunities, and gives you accurate forecasts in real-time. Odoo is very easy to use, and all modules in Odoo are integrated, making your business needs seamless.