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Bino Assignment Data Mining in CRM
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Transcript of Bino Assignment Data Mining in CRM
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Bino Joseph (M090041MS) Page 1
DATA MINING IN CRM
SUBMITTED BY
BINO JOSEPH
M090041MS
INTRODUCTION
Customer relationship management enables companies to improve their profitability by
having a better relationship with their customers. It enables a firm to identify profitable
customers and maintain a good relationship with them and delight them by individualized
offerings. The core of CRM is to understand customers which in turn maximises customer
lifetime value, customer retention, loyalty and profitability. In order to have a successful CRM
companies must be able to manage the customer life cycle effectively by matching products and
campaigns to prospective and existing customers.
Till a few years back the main purpose of CRM software was to manage customer
information so that the whole process is simplified for the organisation. This is called operational
CRM, where the focus is on storing customers information in databases that presents aconsistent picture of the customers relationship with the firm. This information is used in sales
force automation and customer services.
These days analytical CRM has replaced operational CRM. Data mining is a very popular
technique used in analytical CRM. Data mining can be defined as the automated process of
detecting relevant patterns in a huge database. The SAS Institute (2000) defined data mining as
the process of selecting, exploring and modelling large amount of data to uncover previously
unknown data patterns for business advantages. Data mining uses well-established statistical
and machine learning techniques to build models and predict customer behaviour. It helps
marketers to better understand the customer behaviour. For example, through data mining it was
found that beer and diapers are bought together from a supermarket. Hence, the decision to place
these to items close to each other. Such patterns which are not obvious are identified by data
mining.
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PURPOSE OF DATA MINING IN CRM
Data mining is an important component of the CRM framework and software. Data mining is
used for the following purposes in CRM.
1. Customer ProfilingIdentifying patterns in customer database and this can be applied to the database
containing prospective customers. This is useful for better customer acquisition. For
example, choosing customers who are likely to buy a product and sending them the
product catalogue.
2. Targeted MarketingPromotions are targeted at customers who are likely to respond to it. Promotions are
altered to suit the needs of the customer.
3. Market-basket AnalysisIn retail market, helps retailers to understand what products are purchased together. This
helps in deciding stock positioning and how to display the items for sale.
4. Customer RetentionRetaining existing customers by offering attractive promotions based on the data mining
result obtained as to which customer is likely to leave for a competitor. It is far less
expensive to retain an existing customer than acquiring a new customer.
5. Fraud DetectionIt is useful for banks, stock exchanges and insurance companies for detecting fraudulent
transactions.
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DATA MINING METHODOLOGY
The basic steps in data mining for making CRM effective are as follows:
1. Define business problemEach CRM application will have some specific business objective. The model is to be
built based on this. An effective problem statement would be to include measurement
criteria for the CRM project results.
2. Build marketing databaseThis constitutes the core data preparation. This is the most time consuming step as it
consists of repeated iterations.
3. Explore dataA variety of numerical summaries including averages, standard deviations are to be
gathered and the distribution of data is to be studied. Graphical tools and visual aids can
be used to get new insights into the otherwise usual data.
4. Prepare data for modellingThe final step in data preparation consists of 4 steps. Initially the variables on which the
model is to be built is chosen. It would be better to feed all the data into the data mining
tool and find out what are the best predictors. But practically speaking this is not viable
since the time consumed to produce a model with so many variables is really huge. Thesecond step would be to construct new predictors from the raw data. Next a sample or
subset of the data is chosen on which the model is to be built. A properly selected sample
will ensure that the model is accurate and robust. Finally the variables are transformed in
accordance with the algorithm that has been chosen.
5. Model buildingModel building is an iterative process and alternate models that are most suitable in
resolving business issues have to be explored. Many of the CRM applications use
supervised learning protocol. The customer information for which the outcome is already
known is taken. This is split into two and on one the model is trained and the other data
set is used to test this model.
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6. Evaluate modelThe results of are evaluated based on its accuracy and lift (the improvement achieved by
using this particular model). A prediction that nobody will respond is 99% accurate but
100% useless. It is preferable to look at the return on investment as a measure of the
success of the model.
7. Incorporating data mining in CRM solutionCustomer interaction can be classified into two: they contact company (inbound) or
company contacts them (outbound). The deployment of data mining in CRM applications
depends on the type of customer interaction. For outbound interactions the profiles of
good prospects shown by the model are matched with the profile of the people to whom
the advertisement, mail or campaign would reach.
In case of inbound interactions like internet order the data mining model is embedded in
the application and actively recommends an action.
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CLASSIFICATION OF DATA MINING TOOLS
Data mining tools are classified into 3 types based on their functionalities:
y Description and visualisationy Association and clusteringy Classification and estimation
DESCRIPTION AND VISUALISATION
This helps in understanding a given data set by detecting the hidden patterns in the data;
especially complex and non-linear patterns. This is usually performed before modelling.
Summary statistics like measures of central tendency and dispersion and graphical
representations like plots, pie charts are all common description tools. Visualisation tools are
high on graphic elements and are also highly interactive. For example, the rotating multi-
dimensional plot allows users to define the multiple variables as well as direction and angle of
rotation to understand complex relationships. These tools can be used to study relationships
among variables, to understand people, products and processes. These tools are used as a step in
constructing better models for data mining.
ASSOCIATION AND CLUSTERING
Market basket analysis is a perfect example of association tool in data mining. It
identifies which items are purchased together. The association tool too identifies which variables
go together. The result of this can be used in store layout, discount and promotion decisions,
items bundling etc.
Clustering requires that a particular group contains items that are similar and items from
different groups are dissimilar. The common clustering tools used are cluster analysis and self-
organising map. Customer segmentation is a major application of clustering tool.
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CLASSIFICATION AND ESTIMATION
Data mining involves prediction most of the time. Classification can be defined as the
prediction of a target variable that is categorical in nature. The prediction of a target variable
based on metrics is estimation. Data mining tools like multiple or logistic regression, neural
networks and decision trees are used to construct prediction models. Logistic regression is
similar to regression and is a traditional statistical method. Neural networks are patterned on the
human brain that has highly inter-connected neurons. This is very useful recognising patterns in
data.
Observations are divided into mutually exclusive and exhaustive subgroups for decision
tree based predictions. The solution is a graphical tree-like structure which is used to model
complex interactive and non-linear relationships.
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APPLICATIONS
CRM covers a range of functions and data mining is not the core for all of these
functions. Functions like marketing, sales force automation, lead generation can be improved. In
order to analyse CRM data, the exploration has to be done from different angles and has to be
looked at from various aspects. This requires the application of different types of data mining
techniques and their application to different slices of data in an interactive and iterative fashion.
Many companies in order to outsmart their competitors are opting for data mining and this is
used as tool to identify new customers and lower costs.
Data is generally obtained from different sources. If it is CRM data it can come in from
various departments of the organisation. Hence before an actual data mining is performed the
data from multiple sources has to be integrated to remove duplication. Integrated mining of
diverse and heterogeneous data is required.
There is a lot of inconsistency in data due to missed hits, crawlers etc. This causes
problems in cleaning the data. Patterns discovered by data mining are often considered as
hypotheses that need to be tested on new data using rigorous statistical tests for actual acceptance
of the results.
Current customer models are built based on the purchase patterns and click patterns of
customers at web sites. These are very shallow and do not have an in-depth understanding of thecustomers and their individualities. Predictions based on such models tend to be wrong.
ACQUIRING NEW CUSTOMERS VIA DATA MINING
Identifying prospects and converting them into customers is the first step of CRM. Data
mining helps in managing the costs and improving the effectiveness of a customer acquisition
campaign.Banks and credit companies use direct mail campaigns to acquire new customers. Getting
people to fill up an application for credit card is just the first step. The bank must decide whether
the applicant is a good risk and accept them as a customer. It is generally the poor credit risks
who are more likely to accept the offer than good credit risks. The Big Bank and Credit Card
Company (BB&CC) uses data mining to improve the returns on acquiring customers. The cost of
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mailing is about $1.00 per piece for a total cost of $1,000,000. Although this wont precisely
identify the eventual credit card customers, but will help in focusing marketing efforts much
more cost-effectively.
At the outset BB&CC did a test mailing of 50,000 and analysed the results, building a
predictive model of who would respond (using a decision tree) and a scoring model for credit
cards (using a neural net). The two models were merged together to identify good credit risks
and those who are most likely to respond to the offer. The model was applied to 950,000 people
of which 700,000 were chosen for mailing. The result was that 9,000 acceptable applications for
credit cards were obtained.
INCREASING THE VALUE OF EXISTING CUSTOMERS
CROSS-SELLING VIA DATA MINING
In order to retain the existing customers they must be provided more value for their
money. It is a well known fact that getting new customers is much more expensive than retaining
the existing ones. By cross-selling more value can be added to existing customers and data
mining is used for this purpose.
Let us take the example of Guns and Roses (G&R) which specialises in selling antique
mortars and cannons as outdoor flower pots. Usually around 12 million homes receive their
catalogue. When a customer calls to place an order, the caller is identified using his caller ID or
his phone number is asked and this is looked up in the database. This provides G&R an excellent
opportunity to cross-sell things. But they are reluctant to take it up for the fear of the suggestion
failing or irritating the customer.
With the use of data mining this has completely changed. Using the customer information
in the database and what is ordered by the customer, the model tells to the sales executive what is
to be recommended. The model even suggested who are likely to be offended if such
recommendations are made. This was done through a small telephone survey and people who
declined to participate in the survey were considered to be the ones who would not be interested
in cross-selling. This was verified by making recommendations to a small subset of people who
had refused to participate in the survey. But their assumption was not warranted. A second model
that would predict which offer would be most acceptable was also developed. G&R better
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understood their customer needs through data mining. The cross-selling models helped in
increasing their profitability by 2 percent.
PERSONALISATION VIA DATA MINING
When each customer is recognised by a companying on an individual basis it adds more
value for the customer. Big Sams clothing exactly did this by greeting customers by their name
once they have registered in the website. Based on the customers past transactions, new products
that are likely to be of use for the customer are suggested in a subtle manner.
Their initially site did not have any of this greeting by name. An online version of their
catalogue was put and this did not take advantage of the various sales opportunities available
online. Data mining boosted their sales on the website. Catalogues usually group items by types
so that it is easier for the user to select. But in case of online stores the product groups aredetermined by choosing items that will complement the product in consideration. Big Sams site
can not only look at the item you are going to purchase but also can look into your shopping cart
and recommend things that will complement it.
Big Sams used clustering to identify the product groups based on what customers
purchased together. Some of these groups were very obvious like pants and shirts, while others
gave new insights like books about snake bit kits and desert hiking. These groups were used to
make suggestions to customers when one item of the group was purchased. A customer profile
was developed to identify those customers who would be interested in purchasing the new
products that were periodically added to their catalogue. This solidified their relationship with
the customer.
This personalisation effort did well to Big Sams with significant and measurable
increase in repeat sales and average size of a sale.
RETAINING CUSTOMERS VIA DATA MINING
Acquiring a new customer is more expensive than retaining existing customers for every
company. Data mining can be of help in this field by identifying those customers who are
profitable to the organisation and keep them with the company by offering incentives.
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The customer retention programme of Know Service, an Internet Service Provider (ISP)
is a classic example for this. They had a very high attrition rate of 8% per month. Replacing
these customers required $200 for each and considering they had one million customers the cost
of replacement is very high. Know Service needed to predict the customers who are likely to
leave. For this they needed to select variables from the customer database that would help in this
prediction.
They also needed to identify the profitable customers based on calculations of
profitability or customer lifetime value. A model to build profiles of their profitable and
unprofitable customers was developed. This model enabled them to not only retain customers but
also enabled them to identify customers who would become profitable in the future but who are
not yet profitable now.
Using data mining they understood the customer profiles of people who were likely to
churn. Based on this they even developed programs that would entice people to continue with
Know Service. The churn project used three models. One model identified people who were
likely to leave, the second model identified profitable customers who had to be retained and the
third one tried to entice potential churners with appropriate offers. This reduced their churn rate
to 7.5% from 8%.
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DRAWBACKS OF DATA MINING
It is well understood that data mining has wide variety of applications in customer
relationship management. Along with this there are some drawbacks in using data mining for
CRM. An exhaustive mining of a large data set will definitely throw up some interesting patterns
but these may not be a result of the consumer behaviour but could be just some random
relationship. Therefore the patterns found might not be useful in improving sales.
Data mining can be used extensively for predicting based on modelling but this will not
help in assessing the effectiveness. Using data mining for fishing in the hope of finding patterns
will be unsuccessful.
For a data mining application to be successful the user not only has to be well versed with
the domain area of application but also must be knowledgeable about the data mining
methodology and tools. Considering these things a data mining team must possess the following:
1. Domain knowledge2. IT and data mining knowledge and skills3. Statistical and research expertiseEven if all the above mentioned limitations are tackled still a data mining project could
be a failure due to reasons like lack of management support, inadequate data mining expertise,
unrealistic expectations of users and improper project management.
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CONCLUSION
CRM initiatives have become popular with the advent of technologies like data
warehousing and data mining applications. For small businesses CRM comes naturally, but as
businesses expand this becomes more and more difficult for the organisation. In such a situation
technology driven CRM comes to the rescue. Here the behaviour of the customer is predicted
based on the information available like previous transactions, customer details etc. Data mining
can lead to important insights that will help companies to have a closer relationship with their
customers. The route to a successful business requires that customers and their requirements are
well understood and data mining if used appropriately can be very helpful in this.
REFERENCES
Chye, K. H., & Gerry, C. K. (n.d.). Data Mining and Customer Relationship Marketing in the
Banking Industry. Singapore Management Review .
CRM and data mining. (n.d.). Retrieved December 15, 2010, from CRM2Day:
http://www.crm2day.com/content/t6_librarynews_1.php?id=EpFEkVEFuVdgxAUUNG
Data Mining Defined. (n.d.). Retrieved December 15, 2010, from Information Management:
http://www.information-management.com/infodirect/19991220/1744-1.html
Edelstein, H. (n.d.). Building Profitable Customer Relationships with Data mining.
Xu, S., & Qiu, M. (2008). A Privacy Preserved Data Mining Framework for Customer
Relationship Management.Journal of Relationship Marketing, 309-322.