Customer Segmentation 101
Introduction
For the last couple of weeks, I was tasked to research about what are our options to implement customer segmentation and why we should.
About Formaloo CDP:
Formaloo Customer Data Platform (CDP) collects, analyzes, and unifies data from all data sources in order to grow customers’ loyalty.
I’ve read around 50 articles about customer segmentation and its appliance on Machine Learning and I’ve learned the ways we can segment customers. I’m here now to wrap my 2-week research in this article.
What is Customer Segmentation?
Based on Shopify’s article: Customer segmentation is the process of dividing customers into groups based on common characteristics so companies can market to each group effectively and appropriately.
Basically, there are 2 perspectives for this purpose: What, why, and how customer segmentation should apply for B2C companies and the same for B2B companies. The main difference between these two is the type of data material you have to use for your segmentation purpose:
B2B:
• Industry
• Number of employees
• Products previously purchased from the company
• Location
B2C:
• Age
• Gender
• Marital status
• Location (urban, suburban, rural)
• Life stage (single, married, divorced, empty-nester, retired, etc.)
So the rest of the article will be about segmenting their customers. If you want to read more about B2B customer segmentation, there are so many good articles about this subject on the internet. Just try searching “b2b customer segmentation” on Google.
For the rest of the article to be meaningful, I’ll need to explain the “tag” definition in Formaloo Customer Data Platform:
In Formaloo, each customer, which can enter the platform from different data sources, has specific fields. One of these specific fields is Tags. Tags can be generated from specific information about the customer. For example, if the customer was added to the CDP after filling out a Formaloo Form Builder form, that specific customer will have a tag with the name of the form, survey, etc. he/she filled that moment.
So how are these tags related to our customer segmentation purpose?
“An Example of Segmentation using form data: Generating Smart Tags for Each Customer Based on their Segments”
Different Types of Customer Segmentation
So let’s dive into different types of customer segmentation available for B2C companies. Each of these models will answer a specific question about our customers. The general question is “Who wants to buy What and Why?”.
Demographic Segmentation
The first (and maybe the most common) is demographic segmentation.
Demographic data on its own provides some insights about customers. It answers the Who are our customers but not the Why questions — e.g. why customers buy product x?
So it can contain information like age, gender, income, education, religion, etc. about our customers.
For example, a restaurant can target Females (Gender) between the age group 20–30 (Age) living in big cities like New York City. (Location), who is single (Marital status).
Behavioral Segmentation
Behavioral segmentation is based on What your customers do and therefore likely to do again. To improve accuracy, you must leverage all of their behavioral data across various touchpoints and channels so that you can build weighted algorithms based on patterns of behavior over time.
It can contain information such as usage frequency, brand loyalty, etc. for each customer.
For example, a customer can be tagged as high-spender or low-spender according to their buying habits.
Other examples are:
- Occasion: Segmentation based on purchases for a specific occasion such as weddings, Christmas, or Halloween.
- Usage: Segmentation based on the frequency of their purchases.
- Thought Process: Segmentation based on the driving force behind their purchase decisions.
Also, the lack of behavior such as an incomplete survey form or an abandoned cart allows you to re-engage consumers using personalized messaging.
Psychographic Segmentation
Unlike demographics, transactional, or behavioral data, psychographics gives us an idea of Why a certain customer chose to buy a product.
It can contain information such as each customer's lifestyle, personality traits, values and attitude, social status, etc.
For example, retrieved from QuestionPro, imagine a luxury mobile-manufacturing brand that specializes in customization.
- These mobiles are not available for people from every class. A certain standard of living and family income is essential to be able to purchase an expensive mobile that is customized for each customer.
- By using psychographic market segmentation, the marketing team of this mobile-manufacturing brand can divide the target market according to their social status first and then based on lifestyles, attitudes, or personality.
- They can also evaluate the same variables for their competitor’s target market as well for the better selection of a market for their branding activities.
Segmentation Based on Different Formaloo Form Builder Fields
Using Formaloo Form Builder you can create unlimited different forms and surveys. You can create anything from simple contact forms to call for papers or even create an advance application process for your attendees or even an ordering form with your own payment gateway.
So let’s investigate some examples in which we can use different form field types to segment customers based on them:
- Choice Type Fields: As we show them in charts, we also can give each customer their selected choice field option as a tag.
- Like/Dislike: Convert the question as tag and use like/doesn’t like.
- Example Tags: Likes Going to Movies & Doesn’t Like Going to Movies (or prefer watching at home)
- The same applies to Yes/No
questions.
- Score Fields (NPS, Star, etc.):
We can calculate the average scores on a dataset and assign each customer a tag like this: We know that TEDxTehranWomen was an interactive TEDx event about Women. So, Tags can be: If Score is above 5: Does Like TEDx Events, Does Like Interactive Events, Care About Women Rights. - Date/Time Fields: Can be used in behavioral segmentation and assign tags based on different date/time segments. e.g. tags based on birthday month or birthday season
- Short/Long Text Fields: Can discover tags using NLP.
Usage of RFM Method
So what is RFM and why am I try to implement a section about it here? To answer that I would like to quote from our CEO at Formaloo, Farokh Shahabi Nezhad:
Whatever result we can get from our data to have a better segmentation, we must consider!
RFM is one of them. RFM is a method used for analyzing customer value.
RFM stands for three dimensions:
• Recency — How recently did the customer purchase?
• Frequency — How often do they purchase?
• Monetary Value — How much do they spend?
For example, a service-based business could use these calculations:
• Recency = the maximum of “10 — the number of months that have passed since the customer last purchased” and 1
• Frequency = the maximum of “the number of purchases by the customer in the last 12 months (with a limit of 10)” and 1
• Monetary = the highest value of all purchases by the customer expressed as a multiple of some benchmark value
Alternatively, categories can be defined for each attribute. For instance, Recency might be broken into three categories: customers with purchases within the last 90 days; between 91 and 365 days; and longer than 365 days. Such categories may be derived from business rules or using data mining techniques to find meaningful breaks.
Based on the information we collected till now (maybe they’re demographical, behavioral, or psychographic) we can use it to calculate an RFM score which can help us through a better segmentation process. So consider it!
What obstacles do we have?
According to the image above, you can see companies (mostly small and medium businesses) are struggling in cases like lack of good data quality or lack of understanding customers’ behavior, so our goal is to cover this lack using our powerful customer data platform (with the help of data mining and machine learning).
If you want to try a demo of our CDP, feel free to fill the contact form below:
https://en.formaloo.com/contact/
Conclusion
So this was what I’ve learned about customer segmentation. I’ve also researched the different implementations of customer segmentation using machine learning algorithms but I won’t discuss them here. I will talk about the implementation methods in a separate article. Happy Segmenting Your Customers!
Thank you for reading this story, I would love to hear your feedback and your experiences regarding it. If you want to contact me or ask me any questions, here is my LinkedIn, I would be happy to hear from you.
