ABOUT THE AUTHOR
! Joe Mancini
Sr. Director of Product
! Follow me on Twitter
Joe recently joined the AgilOne Product team after leading
the Client Success Organization where he was responsible
for keeping AgilOne's clients happy. Joe joined AgilOne from
Hewlett-Packard where he was responsible for America's
marketing strategy and analytics within the personal
computer division. Prior to HP, Joe was a management
consultant with A.T.Kearney
Joe's an avid cyclist and runner and has completed more
than 25 half marathons. He loves traveling and has visited
over 30 countries with his wife, and favorite camera in hand.
TABLE OF CONTENTS
! INTRODUCTION | 4
! BENEFITS | 5
! TRADITIONAL PROCESSES | 6
! BEHAVIOR BASED CLUSTERS | 9
! PRODUCT BASED CLUSTERS | 11
! BRAND BASED CLUSTERS | 12
! CONCLUSION | 13
Clustering refers to using algorithms to filter for patterns in customer
data. It relies on machine learning to go through thousands or millions of
data points & discover optimal correlations that a person wouldn’t have
found or looked for. For example, an AgilOne user analyzed their
customers shopping habits through the use of machine learning and saw
that certain people who bought active wear also buy sunglasses. Of
course, additional customers buy sunglasses, but this finding helped this
end user target sunglasses towards active people.
Clustering enables marketers to send more targeted marketing
messaging to increase effectiveness and delight customers.
Traditional methods often use inadequate segmentation methodologies
and techniques that rely on human intuition and guesswork. Clustering,
on the other hand, uses machine learning algorithms to create customer
Clusters are formulated using mathematical models which can analyze
previous customer interactions to reveal insights into customer behaviors
& the forces driving those behaviors.
Since they are mathematically calculated, clusters are remarkably stable.
Their descriptions don’t usually change for a given merchandising
strategy and business model. This stability allows customers to move
freely amongst clusters when their behavior changes, and more
interaction data is added.
With stable clusters, marketers don’t have to constantly spend time
figuring out who to market to, and can actually focus on producing
relevant marketing content.
First Acquired Product
The rationale behind segmenting customers according to their first acquired
product is that a customer is likely to buy products that are similar or
complementary to the first item they bought. For example, if a bookseller's
customer's first purchase were textbooks, the bookseller would target the
customer with study guides and textbooks in their follow-up marketing
However, estimating a customer’s preference using first purchase alone
neglects other potential products a customer may need. This retailer's
customer might need notebooks, pens, and pencils to go with those books. If
the bookseller relied only on First Acquired Product they wouldn’t cross sell
and could end up burying their customers with impertinent offers.
The most traditionally used segmentation strategy is based on
demographics—age, gender, income, education, etc. Demographic data is
used to guide the creation of marketing material. For example, you would
use gender data to see if a customer buying a baby toy is a father or
mother; and then send them either ‘Mom’ or ‘Dad’ messages.
Although messaging can be customized to demographics, marketers
shouldn’t use demographic data alone to estimate a customer’s
tendencies. The best way to use demographic data is to use it in
conjuction with some of the more advanced segmentation methods
discussed in this pocket guide.
If you don’t have your customers’ demographic information, AgilOne’s data-
augmentation can help you determine gender, age, income level and
geolocation as well.
Recency, Frequency, Monetary Value (RFM)
RFM creates segments based on how recently, how frequently, and how
much money a customer spent in a given period. Customers are then
assigned a ranking based on their RFM, which is used to create segments.
The problem with RFM is that it’s not as accurate as predictive, and a lot
more complicated. It requires marketers to keep track of a lot of
segments, called cells, manually to try to determine who is most likely to
buy. Compare this to using a predicitive likelihood to buy model,
discussed in this guide, where software automatically assigns a likelihood
to buy ranking to each cusomter. On top of that, predicitve segments are
at least 30 percent more accurate than RFM.
While it can be used for short-term gains, often times users end up
leveraging the segments that provide them the most results more than
they should and neglecting the segments that don’t provide strong
results. This can lead to list wear out and opt-outs.
Behavior-based clustering is used to unearth different types of shopping
behavior or patterns automatically. For instance, customers who only buy
with heavy discounts may be great targets for inventory-clearing sales,
whereas customers who typically pay full price would be better targets
for a sneak-peek promotion of a new product line.
AgilOne’s algorithms can help identify completely new behavior-based
clusters using unsupervised algorithms; or using supervised algorithms
to correlate the traits and buying behavior:
• Average order size
• Days between orders
• First order revenue
• Order variety
• Discount sensitivity
• Order frequency
• Total items
• Total orders
• Number of returns
• First order products
• Order seasonality
• And much more
Airlines’ frequent flyer programs are a great example of how behavior-
based cluster characteristics such as order frequency, days between
orders, order seasonality, discount sensitivity and others help airlines
differentiate business travelers from leisure travelers. For airlines, this is a
critical segmentation tool that not only helps the customer side of
business but also guides their logistics and development side.
Likewise, other retailers can also utilize behavior-based clusters to take
targeted actions to increase sales. Here are some representative
examples of behavior-based clusters that can help retail marketers
customize their messaging:
Full price, infrequent buyers
Buyers who return products frequently
Buyers with Few orders, mostly on discounts
Buyers with High value first order, high order frequency
These cluster names could help you start to think about different
ways you can market to behavior-based clusters of your own customers.
Besides behavior-based clusters we often want to look at how customers
can be clustered based on the types of products they tend to purchase.
A product cluster can be broad or very specific. For example, some
customers may buy ONLY sweaters, whereas another cluster of
customers buy mostly active wear. The latter cluster may include
different subsets of clothing types such as outerwear, swimwear, &
sportswear. It’s important to recognize which types of clusters are
relevant and which are not, and that’s not something that can be done
easily by a manual segmentation scheme.
Finding powerful and optimum clusters is not something that can be
Similarly, customers can be clustered according to brand preferences as
well. Understanding a customer’s attraction to certain brands and how
they interact with it can reveal the centrality of those brands in the
purchasing behavior of customers.
These algorithms can also help reveal other brands that a new customer
may like by comparing his preference to an existing brand cluster. For
example, let’s say our algorithms had revealed that customers who liked
Tahari also liked Calvin Klein and Rebecca Minkoff. Armed with this
information a marketer could effectively cross sell Tahari and Calvin Klein
to customers when they buy Rebecca Minkoff.
Customers are not alike. Every customer wants something different and
everybody will respond differently to your marketing efforts. However, if
you could organize those customers into groups with similar behaviors,
product & brand preferences, and expectations you can customize and
better target your marketing messages .
Traditional methods relied on demographics, RFM and first-acquired
product to understand customers but they don’t paint an accurate picture.
These methods rely on human intuition and guesswork to group
customers using characteristics that don’t always correlate to buying
behavior. Clustering uses machine learning & alogrithms to find patterns,
between customer behavior & revenue, that a person wouldn’t even think
to look for.
Clustering is a powerful tool but output quality highly depends on your
data quality. So it’s important to make sure that your customer profiles are
complete and that your data is clean.
If you would like to learn how to clean and augment your data, or use any of
the clustering methods mentioned above to find better customer segments,
contact AgilOne at 877-769-3047, or visit www.AgilOne.com.
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