Improving Data Quality in CRM Using Data Mining Techniques

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Improving Data Quality in CRM Using Data Mining Techniques

Improving Data Quality in CRM Using Data Mining Techniques

Due to the constantly growing quantity of data necessary to keep track of, many businesses struggle to effectively process it and derive some truly meaningful insights. One of the modern ways to deal with the problem is data mining. The ancestors of this process have started appearing back in 1960s and were merely retrospective data delivery methods.

Nowadays, data mining reaches deep into databases and is capable of providing proactive information and insights. It aims to answer the questions that have not even been asked yet. Moreover, with the rapid development of technology and growing popularity of customer relationship management solutions, data mining has been added into CRM platforms.

An effective usage of various data mining techniques in CRM holds the promise of optimizing the resource distribution, increasing profits and improving the data quality. For example, the insights derived with the help of data mining can help to determine the channels and offers that particular groups of clients are most likely to respond to, thus increasing the potential returns.

All in all, data mining activities can be divided into three general categories:

  • Discovery - inspecting the database to find possible hidden patterns without a predetermined hypothesis about what those may be.
  • Predictive Modeling - using the discovered patterns to predict the future results.
  • Forensic Analysis - comparing the data against established patterns to detect unusual elements.

Each of the aforementioned categories encompases certain particular data mining techniques. Depending on the tasks necessary to perform, companies may choose to implement one or a few of them.

Association Rule Learning

This technique is one of the most important and widely used ones in the CRM. It helps to uncover hidden patterns by determining the relations between different data elements in large databases. The information gained with the help of this technique may be useful for understanding customer behavior and habits, as well as predicting their future decisions by pointing out the correlation between their actions.


This technique allows to create classes by categorizing various objects or customers by type. It gathers the information about each particular item and places it into a proper category. This is very broad technique that includes a whole range of approaches and models. Here are a few of the most prominent ones:

  • Decision Tree is a classification model in the form of a tree structure. It divides a dataset into smaller subsets, at the same time developing an associated decision tree. The result of such  a division is a tree with decision nodes and leaf nodes. The crowning decision node in a tree is called a root node and corresponds to the best predictor. Decision trees can be used for both categorical and numerical data.
  • Rule Induction is an area of data mining which deals with extracting the formal rules from a particular set of observations. The rules extracted may represent either a more global data model, or a simple local pattern.
  • Neural Networks are nonlinear statistical data modeling tools that are used for modeling more complex relationships between various pieces of data. Neural networks comprise three pieces: the architecture, the learning algorithm, and the activation functions.
  • Nearest Neighbour Classification is a simple algorithm that was used in statistical estimation back in 1970s and now consists in storing all available elements and their categories and classifies new elements based on a similarity measure.


Clustering is a more complex form of classification that determines common attributes in different classifications and identifies clusters. Such an approach allows to understand both the similarities and differences within the data. Using the information derived via this method can help to target campaigns and sales better.

Anomaly Detection

Anomaly detection is used when it is necessary locate the items that don’t match expected behavior or a projected pattern. Such items are called anomalies, outliers or exceptions and are usually good indicators that additional analysis needs to be performed. Detecting anomalies can provide actionable insights because of their deviation from the average in the dataset.


Regression analysis is considered to be among the more advanced data mining techniques in CRM. Even though this approach also aims to find the dependency between different data items, it is different from correlation or association. Regression shows which variables are affected by other variables, but does not indicate which variables can affect others.

All in all, data mining is more than just running some queries on the data stored in the database. Various techniques, discussed above, help companies to restructure the data and  derive relevant actionable insights that may later be used to analyze and predict customer behavior.

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