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Step-by-Step Customer Segmentation for Personalized Marketing

By Pini Yakuel, CEO of Optimove

In the old days, companies sent the same marketing campaigns to all of their customers. Later, marketers began to understand that even simple grouping of their customers allowed them to run more relevant, effective and profitable campaigns. More recently, sophisticated marketers and retention experts – particularly in Internet companies – have discovered that applying the latest technologies to this challenge delivers far more targeted (and profitable) campaigns.

The reason is clear: when each individual customer (or small groups of very similar customers) receives a personalized and highly-relevant offer, there is much more chance that the message will resonate with them. In a world of marketing overload, customers have little patience to hear what you’re telling them. Thus, it’s critically important to tailor your messages and incentives so that they are the most relevant and interesting for each individual customer.

At the heart of personalized marketing is accurate and fine-grained segmentation of your customers. Let’s take a quick look at how to do this.

Step 1: Segment your Customers into Lifecycle Stages

The first step is to divide your customers into distinct lifecycle stages. For most Internet businesses, the important stages are the New, Active and Churn stages. Customers in each stage behave differently and thus the way you market to them will necessarily be different.

  • You can define the New stage as customers who recently made their first purchase (or payment or deposit). These guys are checking you out, there’s no trust yet and they are relatively sensitive to their experience on your site.
  • Select your Active customers as those who have been active recently on your site (making a purchase, playing a game, executing a trade, etc.) beyond the period of time you’ve chosen to define customers as New.
  • Customers in the Churn lifecycle stage have been inactive for an extended period of time.

Even with this initial, high level of segmentation, you will be able to market to your customer base more effectively than you would if you sent the same offer to everyone. However, it’s the next steps that deliver the greatest value.

Step 2: Segment your New Customers into Actionable Sub-Segments

The best way to encourage New customers to become long-term customers is to give them a good experience. Beyond good on-site and customer service experiences, this is a good time to give them extra benefits or bonuses to make them feel welcome and appreciated.

Going deeper, you should segment the New group into two important sub-groups which will receive extra attention:

  • One-time-only customers – these are customers who never returned after their first purchase (or payment, deposit, trade, etc.). Your goal is to bring them back again. Typically, you want to send them particularly attractive offers to encourage them to return in the short term.
  • New with high potential – these are the customers with the highest potential long-term value. You can identify this group using customer lifetime value forecasting, which uses customer modeling technology to incorporate both behavioral and demographic data to predict how a customer will likely behave in the future. If you don’t have access to a customer modeling solution, a simpler (though less accurate) method is simply to take the top 5%-10% of New customers in terms of the amount of their first purchase/payment/deposit. You want to give this group VIP treatment; long-term incentive programs tend to keep them engaged for extended periods of time.

Step 3: Segment your Active Customers Using Cluster Analysis

Next, you want to perform periodic cluster analysis on your Active customers in order to identify small groups with similar behavior patterns. Commonly available software packages can help you perform cluster analysis using basic models, such as RFM (recency, frequency, monetary) – learn more about RFM here.

Your goal of RFM is to market to each cluster (customer group) in a way that will maximize their spend. Here are some examples to think about:

  • Your best cluster consists of those customers who have been on the site recently (R), engage in many transactions (F) and spend a lot (M). You want to give these customers VIP treatment to encourage them to keep coming back.
  • For customers who have spent a lot despite making few purchases (high M/F), you want to send them offers that encourage them to come back to the site more frequently.
  • For customers who spent an above-average amount (high M), but haven’t been back to the site for a while (medium R), you want to give them aggressive offers to bring them back. You can think of these as “risk of churn,” i.e., customers who might be at risk of never coming back.

Step 4: Differentiate between Differences among Churn Customers

You want to divide your Churn stage customers of each period into two distinct groups: those who made a single purchase/payment/deposit and never returned, and those who were more active for a longer period of time before disappearing.

The first sub-group is the most difficult group to reengage and thus it is usually worth sending them your most aggressive offers.

Some Internet businesses have the option to further sub-divide the second group into those customers who still have a remaining balance at the site (e.g., a credit at an e-retailer, a balance in a financial trading account, a deposit balance in an i-gaming site) and those who don’t. If this applies to your business, you should focus on the former group as they can often be reengaged with a simple reminder about the outstanding balance that they can still use on your site. The latter group will require more aggressive encouragement to return and make another transaction.

About the Author

 

Pini Yakuel has over a decade of experience in analytics-driven customer marketing, business consulting and sales. Pini is founder and CEO of a profitable and rapidly-growing start-up based in Tel Aviv. His passion for understanding what drives customer behavior led him to spearhead the development of Optimove, an advanced one-to-one marketing system used by Internet businesses in a number of verticals.

 

 

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