Data Mining and Marketing
HOW TO INCREASE REVENUES AND PROFITS FROM MARKETING? USE DATA
Nowadays marketing and computer technologies provide unlimited
possibilities for collecting and storing data from interviews,
surveys and other sources. This could be precious information, that
can help to increase ROI, improve
customers relationships management(CRM), reducing marketing
campaigns costs, etc.
For example, to be successful, companies must be proactive and
predict what a customer needs. Customer profiling provides the
basis for starting what marketers call a "dialogue" with customers.
Arranging customers classes allows to increase response rate for
direct marketing campaigns, targeting a direct marketing campaign
to similar individuals.
But is it possible to manually deal with vast amounts of data,
collected in databases and to quickly respond to customers wants
and needs? How to receive the most of information in minimal time?
When you start asking such questions, it is time to get acquainted
with data mining software more closely.
The key is to ask the right question. Lets take a direct marketing
campaign as an example.
EXAMPLE OF DATA MINING IN MARKETING
What kind of data can be helpful for cUSTOMERs profiling?
To profile customers, you can use your current customers
database or results of surveys / interviews. This can be
information about their purchases, hobbies, everyday needs, and of
course, more personal information about their age, sex, marital
status, personal income, etc. All this information can be easily
analyzed with data mining software.
How should be the customers data stored for receiving best results
FROM DATA MINING?
The best way to store customers data are structured databases.
This will allow to easily use the most important profile parameters
during the data mining process. For example, you might be
interested in targeting your company for a certain age and sex, in
this case the database should contain such information. Besides,
the database should contain the key information, for example: is
client interested in your product (yes / no / not sure / don't know
about the product, etc), clients attitude to the kind of
advertising you are going to use (negative / positive / neutral).
After presuming the customers classes, it is important to create a
database which will contain information about all representatives
of customers groups. Only in such case data mining software will receive a summary
of portraits for every group.
How can clients be classified (profiled)?
Classes should be created depending on the object of the
customers database analysis.
If you tend to unearth the portrait of a customer, who is interested
in a certain product, you should set parameter(database table
field), containing information about it , as the class field. If we
return to the example above, we will receive 4 classes:
- not sure
- don't know about the product
Combining obtained data with an analysis of clients attitude to
advertisement types will allow to create a targeted marketing
campaign, based on results of data mining.
How to apply the results of data mining IN MARKETING?
Now, after data mining the customers database, the new
information can be used not only for targeted marketing campaigns,
but for fast interaction with each new client of your company.
After receiving some information about a new client, you will be
able to classify the customer, predict his expectations and needs
for mutual understanding between customer and company. As a result
you increase the probability of retaining customers in the face of
competition, increase ROI and profits.
Examples of application of data mining in marketing
Banks use new database marketing techniques to identify the best
customers. This allows to target customers for loan campaigns, to
forecast customer retention and to track direct marketing
Many telephone companies use data mining for analysing customers
calling patterns. As a result, they are able to recommend the best
plan for each new client from the very beginning of interaction.
Database marketing analysis procedures and techniques are a must
for keeping up with the vast amounts of data now available. The
data mining technique is not hard to understand and use, especially
keeping in mind the precious data on customers, customer buying and
behaviour patterns, and other valuable information that can have a
fundamental impact on companies revenues and profits.
H. Smirnov-M (c)