Building the Segmentation framework for your everyday campaigns

In a previous post, I highlighted how segmentation and personalized content form the backbone of any successful campaign policy. In this blog, let's dive deeper into defining the segmentation framework.

When asked if their marketing efforts are segmented, most teams would say yes. However, the issue often lies in the use of ad hoc segments that lack clear ground rules, leading to confusion and inefficiency.

A strong segmentation strategy starts with understanding the data you currently have and mapping out the data you’ll need in the next 3, 6, or 12 months to improve your campaigns. For example, let’s consider a unisex, youth-focused fashion brand named Echo and see what data they should aim to capture over time:

1. Essential Segments: Gender, Location, and Age:

• Gender and Age allow you to determine whether to promote trendy yet affordable clothing or premium athleisure wear.
• Location helps tailor messaging based on climate, which directly influences the types of clothing customers purchase.

2. Time-Based Segments: Occasions and Preferences:

• These could include interests in public and personal events, spending capacity, and lifestyle preferences such as sports, clubbing, or dancing.

3. Derived Segments: Affinities and Sensitivities:

• Identify users’ category affinity, product preferences, brand loyalty, and price sensitivity.

4. Customer Type: Best, Rest, and Next:

• The Best Customers have the highest Customer Lifetime Value (CLTV), while the Next and Rest categories help you identify where to focus your growth efforts.

Laying down what you need to know about your customers allows you to systematically capture relevant data over time and choose the right customer engagement platform that can help you derive insights from data. 

A simple yet effective starting point for any CRM team is to use RFM (Recency, Frequency, Monetary) analysis as a base for segmentation. You can then layer additional filters like age, gender, location, and behavior. An engagement platform that offers a customizable RFM model is key, as it allows for adjustments based on your specific business needs. 

RFM creates nine core segments, which can be simplified into three crucial categories: Loyal, Hesitant, and At-Risk (Dormant) customers. These serve as the foundation for your campaigns, with other filters layered on top. Here are a few examples: 

1. Loyal customers who haven’t purchased in the last 90 days.
2. At-risk customers who are male and based in Mumbai.
3. Hesitant customers with an affinity for athleisure products and are price sensitive.

Based on these segments, you can personalize your offers, discounts, and communication frequency. As your marketing strategy matures and you start tracking CLTV, you can transition from RFM to segmenting your customers into Best, Next, and Rest categories, making this your foundation for targeting. 

While the above focuses on defining segments for your existing customers, for users who have never made a purchase, you can apply the RFD model – Recency, Frequency, and Duration – to segment them effectively. Additionally, leveraging behavioral data such as browsing patterns, product views, add-to-cart or wishlist actions, acquisition source, and channel engagement will enable you to target these users with the most relevant campaigns. 

The goal isn’t to create overly complex segments but to establish a strong foundation for your day-to-day campaign strategies. I'd love to hear your thoughts on how segmentation can be improved, so feel free to share in the comments below.