Introductory Guide to Clustering

ABOUT THE AUTHOR

!   Joe Mancini Sr. Director of Product AgilOne

!   Follow me on Twitter @jmanSFCA

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.

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TABLE OF CONTENTS

!   INTRODUCTION | 4

!   BENEFITS | 5

!   TRADITIONAL PROCESSES | 6

!   BEHAVIOR BASED CLUSTERS | 9

!   PRODUCT BASED CLUSTERS | 11

!   BRAND BASED CLUSTERS | 12

!   CONCLUSION | 13

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INTRODUCTION

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.

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BENEFITSTraditional 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 segments.

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.

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TRADITIONAL MODELS

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 messaging.

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.

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Demographic

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.

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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.

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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:

BEHAVIOR-BASED CLUSTERS

• 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

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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.

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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 done manually.

PRODUCT CLUSTERS

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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.

BRAND CLUSTERS

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CONCLUSION

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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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