Firms want to acquaint customer behavior as much as possible, but as the number of customers
accumulated in large quantities and customer information has become increasingly complex, more
advanced technologies and tools are needed to manage and analyze customer information so as to
find out the valuable knowledge of management decision-making. Data mining technology provides
a good technical support for CRM to analyze large amounts of complex customer data and explore
customers’ value.
Data Mining in CRM
CRM collect and accumulate a large number of market and customer information for the firms to
build a huge data warehouse. The key to data mining is to distinguish truly valuable information and
find the association between them from massive data.
Every firm wants to quickly and accurately extract the most important and valuable customer
information through the use of certain tools and instruments. After a deep analysis for a huge data
warehouse, data mining techniques can find hidden information and knowledge conducive to
business operations, thus improve the competitiveness of the firm. Data mining can help firms
manage customer life cycle stages, including acquiring new customers, making existing customers
create more profits, maintaining valuable customer, etc. It can do in-depth analysis of customer
needs, to meet firms’ needs of customer relationship in individual market segments. So the firms are
able to determine customer characteristics, provide them with targeted services and increase
business opportunities.
Application Fields Of Data Mining For CRM
Data mining can filter customer information, and mining implicit, unknown and potentially valuable
knowledge on business decisions and rules from a huge number of customer data. To help firms
identify new opportunities, predict success in business strategies and make quick decisions. The
following aspects of its application are particularly prominent.
Customer character analysis
In addition to customers’ address, gender, age, occupation, income, education and other basic
information, access, such as hobbies, marriage, spouse, health, home environment and other
features can help firms get a more detailed understanding of customers, observe their behavioral
regularities and thus better develop customer strategies to improve their campaign response speed.
The main idea is to use classification and clustering techniques, divide customers into different group
with different demand and transaction practices according to their age, gender, income, trading
behavior, etc. And finally arrive at customers’ concern, targeted to the development of personalized
marketing strategy.
Customer loyalty analysis
Customer loyalty analysis is to analyze and categorize upscale customers, stable customers, valuable
customers, more consumption demand customers, etc. To help firms make customers persistent and
stability analysis and respond quickly to customer needs, so that each customer will get a highly
personalized service. Statistically, firms must pay much higher cost to get new customers than to
keep old customers. The gap is recognized to be more than six to eight times, no matter what
business they focus on. Meanwhile, according to 80/20 principles of marketing, that 20% of
customers contributed 80% of sales, more strategies should be adopted to develop high
consumption customers’ loyalty.
Cross marketing analysis
The previously customer information may contain the key or even critical factors to determine
customer behavior. Using data mining techniques, firms can get the key factors affecting customer
purchasing behavior from the customer information especially former purchase behavior, and build
prediction model to predict customer future purchase behavior. Association analysis is often used to
help companies find some implicit, subtle and great commercial value of relationship. This mining
process is generally divided into two steps: First, find all the frequent item sets whose frequency
should be at least equal to the minimum support frequency, thereby identify all related
merchandises that may be purchased together; Then, generate strong association rules according to
the frequent item sets. These rules must meet the minimum threshold of confidence; thereby
identify all related merchandises that are very likely to be purchased together. To sum up, firms can
discover hidden relationship between seemingly independent events; find factors that affect
customer behavior through data mining technology. And then target to expand marketing strategy
and promote other products in a timely manner.
Customer acquisition and churn analysis
The growth and expansion of the firm need continuously maintain old customers and draw new
customers. Through classification, clustering, decision tree and other techniques, it can extract mass
customer information, identify potential customer, and determine what kind of customer are most
likely to loss and what features they have. Thus build customer churn prediction model to more
accurately identify easily lost customers. This would help firms making plans to take appropriate
marketing measures in advance to hold old customers.
Tracking evaluation
Through clean and centralize, customers’ feedbacks are automatically store in data warehouse. So
the firm can track customer behavior, analyze customer satisfaction, credit rating and so on, in order
to evaluate and optimize the existing strategy, such as take different credit terms on different credit
rating customers. This could maintain customer loyalty and avoid unnecessary risks at the same
time. By tracking the evaluation to ensure that the firm customer relationship management to
achieve the goals, also establish a good customer relationship.
