Customer Loyalty
This article is about customer loyalty in general. If you are looking
for information on loyalty programs (points, etc.) click
here. If you want to read a case study describing how incredibly
profitable well-designed loyalty programs can be, click
here.
Customer loyalty describes the
tendency of a customer to choose one business or product over another for a
particular need. In the packaged goods industry, customers may be described as
being "brand
loyal" because they tend to choose a certain brand of soap more often than
others. Note the use of the word "choose" though;
customer loyalty becomes evident when choices are made and actions taken by
customers. Customers may express high satisfaction levels with a company
in a survey, but satisfaction does not equal loyalty. Loyalty is
demonstrated by the actions of the customer; customers can be very satisfied and
still not be loyal.
Customer Loyalty has become a catch-all
term for the end result of many marketing approaches where customer data is used. You can say
Relationship Marketing, Database Marketing,
Permission Marketing, CRM,
or 1-to-1 Marketing and what
you are really talking about is trying to increase customer loyalty - getting
customers to choose to buy or visit more.
Increased customer loyalty is the end result, the desired benefit of these
programs. All of the above approaches have two elements in common - they
increase both customer retention and
the LifeTime Value of customers.
Customer loyalty is the result of well-managed
customer retention programs; customers who are
targeted by a retention program demonstrate higher loyalty to a business.
All customer retention programs rely on communicating with customers,
giving them encouragement to remain active and choosing to do business with a company.
You want customers to do something, to take
action. You want them to visit your website, make a purchase, sign up for
a newsletter. And once they do it for the first time, you want
them to continue doing business with you, especially since you probably paid big money to get them to
do business with you the first time. You don’t want to pay big money the second
time. You want to create a "loyal" customer who engages in profitable
behavior.
Customer data and models based on this data can tell you which customers are
most likely to respond and become loyal, no matter what kind of
front-end marketing program you are running or how you "wrap it up"
and present it to the customer. The data will tell you who to promote to, and how to save precious marketing dollars in the process of
creating customers who are loyal to you longer.
For example, let's say you look at your most loyal
customers and find on average they buy or visit at least once every 30
days. So you begin tracking these customers, and discover 20% of them
"skip" their 30 day activity. In addition, 90% of the 20% who
skip never come back. You are watching the erosion of customer
loyalty right before your eyes.
And it's too late to do anything about it, because they're already
gone. You will waste a tremendous amount of money trying to get them
back. You have to develop a way to identify high loyalty customers who are
at risk, and take action before they leave you.
This is accomplished by using the
data customers create through their interactions with you to build simple models or
rules to follow. These models can be your early warning system,
and will alert you to situations like the
"30 day skip" example above in time for you to do
something before the customer defects. Behavior models cause
the data to speak to you about the loyalty status of the customer before it's
too late.
This site and the Drilling Down book are
about
teaching you how to build and use these models yourself in 30 minutes
with an Excel spreadsheet. If you want to increase
sales while reducing the costs of marketing to customers, you
have to get this book.
What would you like to do now?
Find
Out Specifically What is in the Book
Learn
Customer Marketing Models and Metrics (site article
list)
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with Free customer scoring software at:
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