Which Type of Segmentation Is Best by ns42j3

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									Which Type of Segmentation
Is Best?
By Neil Mason, ClickZ, Feb 16, 2010
Sponsored by Omniture
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One of the things I like about my job at a
customer experience consultancy is that I'm
surrounded by people with a very different
outlook on life than Web analysts. Our user
experience consultants, who tend to have
backgrounds in behavioral psychology, are
great at using qualitative research techniques
such as lab testing, eye tracking, and
ethnographic studies to get into the mind of
users and to understand what makes for a
good or bad experience. That's obviously a
different set of skills and tools from our
quantitative, analytical approach to solving
problems using vast quantities of data. Each
approach complements the other:
quantitative data is good for asking the
"what" and "when" type questions, and
qualitative techniques are good at helping to
understand the "why."


Every now and then we get into one of those
interesting conversations about which
approach is best for solving a particular type
of problem. Last week, one of these
conversations turned to the topic of
segmentation and which types are best for
addressing particular issues. Segmentation,
one of those popular words used a lot these
days in the digital marketing world, usually
means different things to different people.


Segmentation is the process of creating
groups of individuals (customers, Web site
visitors, prospects, etc.) that have something
in common. Importantly, what one group has
in common is then different to the other
groups. Segmentation's purpose is to make
you, your marketing communications, your
Web site experience, your product offering,
and so on more relevant to these different
groups. But how are these groups defined?
There are three main ways:


          Demographic segmentation


          Behavioral segmentation


          Attitudinal segmentation


Segments can be defined by demographics,
i.e., based on who someone is. Typically,
classical marketing approaches use
demographics as the basis for segmentation
and then targeting. Demographic
segmentation in online can also be useful. For
example, "gender" can be a useful
segmentation split because people can
behave very differently online depending on
whether they are male or female. So, to be
able to segment your audience by gender,
age, income, and more can be useful.


Another approach to segmentation is
behavioral segmentation. This is not
classifying people according to who they are,
but on the basis of what they do. This
segmentation approach is very popular in
digital marketing because it's quite easy to
understand how people behave thanks to the
loads of available behavioral data. Again, it
can be a very powerful technique to group
people according to different behavioral
criteria and to use that knowledge to improve
the effectiveness of campaigns or to present
different Web site experiences. For example,
the way that people behave when they first
visit a Web site is often very different from
the way they behave on a subsequent visit.
What's more, their needs are also often
different on follow up visits. So, why not
present that visitor with a different
experience? Behavioral segmentation lies at
the heart of personalization.


Finally, attitudinal segmentation is about
classifying people not according to who they
are, or what they do, but about what they
think. Attitudinal segmentation is about
getting into the minds of customers and
understanding what makes them tick. People
of different genders and ages may have
similar needs when it comes to interacting
with products and services; they may be
trying to pursue the same goal or trying to
achieve the same outcome. Often, attitudinal
segmentation is used for the development of
"personas," which are tools to help designers
get closer to the people they are designing
for.


So, which type of segmentation is best? Well,
of course, the answer is that "it depends."
What problem are you trying to solve? What
will you do with the segments when you've
got them? The other questions then are:
"What data do I need?" and "Where do I get
the data from?" I'll be looking at the answers
to these questions next time. Till then...
             Which Type of Segmentation Is Best
             for You?
                 ANALYZING CUSTOMER DATA
             By Neil Mason, ClickZ, Mar 2, 2010
             Columns | Contact Neil


In my last column, I took a look at the meaning of segmentation and the different types of
segmentation strategies available to digital marketers. There are three main types of segmentation:
demographic segmentation, behavioral segmentation, and attitudinal segmentation. But which one is
best? It really depends on what problem you're trying to solve.

Demographic segmentation strategies have traditionally been used by marketers for targeting.
Customers or prospective customers are classified according to different demographic criteria and are
then selected for different types of marketing activities or communications. Often predictive models
can be used to predict which segments are most likely to respond to which types of campaigns based
on their previous history. The ability to identify potentially lucrative segments and then target them
can be powerful and result in much higher returns on marketing investment.

Demographic segmentation can be useful for digital marketers, but it depends on the type of data on
customers and prospects that can be collected. Strategically, it can be important to understand which
type of people are likely to be interested in your product or service or to shape your product or service
to appeal to particular demographic segments. Data for developing the segmentation might come from
existing customer databases or, if you don't have a customer database, it might need to be collected
using other sources such as online surveys. Integrating survey data with Web analytics data could
help you to understand, for example, conversion rates among different demographic groups. Media
planning tools can then be used to refine the acquisition strategy orientated around those
demographic groups with the highest potential.

Using demographic segmentation approaches can be as useful online as they are offline, but collecting
data can be a problem. However, we are generally not short of data on how people behave online and
so behavioral segmentation approaches can be powerful and easier to adopt. Behavioral segmentation
lies at the heart of most personalization and behavioral targeting techniques, whether they are based
on relatively simple rules-based approaches or more complex models and algorithms. The data for
behavioral segmentation are readily available in your Web analytics system and these days most Web
analytics tools give you the ability to cut data a number of different ways. So, there really is no excuse
to not start segmenting your audience or customers based on how they behave on your Web site or
how they interact with you over a period of time.

Some simple behavioral segmentation strategies can be very powerful. Optimizing landing pages
based on source of acquisition is a simple but effective behavioral segmentation approach. Creating
different experiences based on the number of times that someone has visited the Web site is another.
One of the classic behavioral segmentation strategies is recency, frequency, monetary (RFM) analysis.
Developed originally by catalog retailers, RFM customers are categorized according to how recently
they transacted with you, how frequently they have done that in the past, and the monetary value of
those transactions. The high recency, high frequency, high monetary value group represents your
most valuable customers, (for example an airline's Gold Card customers) and the way that you would
market to them would be different than other groups. On the other hand, a new customer (high on the
recency scale but low on the frequency scale) presents a different opportunity and the key thing is to
get them to buy or transact again.

The challenge of applying online behavioral segmentation approaches is to manage data across
different systems either doing that manually or by having more integrated solutions. This is becoming
easier for digital marketers as many of the Web analytics providers have interfaces to other marketing
systems (such as e-mail tools) to enable these types of behavioral segmentation strategies to be
implemented.

One limitation of behavioral segmentation is that while you might know what works, there may not be
a lot of insight into why it works and, consequently, how it might be improved. Attitudinal
segmentation involves getting in the mindset of your customers and understanding what makes them
tick. This allows you to potentially develop different strategies for different people based on their
attitudes and opinions about your product or service rather than how they interact with you. This type
of segmentation lends itself to applications such as design work, where you are trying to develop
solutions that are appropriate for different groups of people based on their needs, goals, and
ambitions.

Although we have data on behaviors in abundance in our digital marketing world, we rarely have
abundant data on our customers or visitors. As with demographic data, we need to collect that data
from sources such as surveys (or to get really deep insight, other techniques such as in-depth
interviews or focus groups). As a result, the data that feeds into attitudinal segmentations is more
sparse than that for behavioral segmentation approaches, but it can be richer.

So, which is best? As with most analytical techniques, it depends on what problem you are trying to
solve. For developing acquisition strategies, demographic segmentation techniques can be useful. For
improving design and conversion, attitudinal segmentation feeding into persona development can play
a role. And for improving retention and customer lifetime value, classic behavioral techniques such as
RFM can be powerful. Whichever approach you use though, there really isn't any excuse these days
for carrying on with a one-size-fits-all digital marketing strategy.

								
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