What is Customer Segmentation?

Customer segmentation, also known as market segmentation, describes the process of identifying groups (or segments) of a company’s customers that are similar in terms of one or more specific characteristics or factors. The goal of this categorization is to optimize marketing to each group, such that individual customers receive the most appropriate and relevant communications, and so as to maximize the value of each customer to your business.

The potential characteristics or factors that can be used to segment customers are nearly unlimited, but the most common (and easily accessible) include: 

• Personal characteristics such as age, stage of life (retired, new parents, students, etc.), gender, marital status
• Geographic factors such as location, urban/suburban/rural areas
• Buying behavior, including purchase history (value, frequency, type of products purchased) and responses to marketing communications or social media promotions

Other factors may provide more-detailed information about customers, but it may be necessary to use additional tools to access this information; we will touch on these toward the end of this article.

Why is Customer Segmentation Important?

Customer segmentation is not only important, but vital, in order to optimize your marketing strategies, maximize a customer’s value to your business, and improve customer experience and satisfaction.

With such an unimaginable variety of people and personalities in the world, a potential market is usually not characterized singularly nor easily. It becomes important to know your target customer base in order to make sure your communications are both effective (attractive, action-promoting) and appropriate (non-offensive, timely, and relevant). The segmentation process begins by grouping customers and potential customers into customer segments with similar characteristics, such that you can communicate to all of the individuals in that segment efficiently, yet still effectively and with a sense of personal attention, without actually contacting each individual separately. Your marketing tactics thus become more effective and efficient, which will not only save you time and money, but will enhance the benefits as well.

Types of Customer Segmentation Models

As mentioned, in theory there are nearly uncountable potential ways to segment your consumer base. However, certain factors or characteristics have been tried and tested and have been shown to be actually useful. The particular consumer segmentation model that will be of most benefit your business will also depend on the specifics of your products/services and customer base. With that said, below are some of the most common customer segmentation models, which should be useful, at least to some degree, universally. These can also be combined to bring in advantages from different models, or to create entirely new and narrower customer segments.

• A priori segmentation: This technique involves making assumptions about consumers based on publicly available characteristics, such as age, location, income, etc., rather than using more detailed market research. The obvious benefit is that it is simple and quick to obtain such data, and it is generally easy to make inferences or assumptions based on conventions or stereotypes. The main drawback is that it can be overly simplistic, and this decreases the likelihood that you will end up with optimal segments.

• Needs-based segmentation: With this strategy, customer groups are formed according to their specific needs (or drivers) that they may have expressed for a particular product or service. These needs are identified through primary market research, which adds an additional step to the process, but confers the huge advantage of relevancy, as customer segments are more appropriately designed based on the individuals’ relationship to your product/service rather than on more general characteristics such as age.  For example, rather than assuming that all new parents are looking to buy disposable diapers (as could be inferred under an a priori segmentation approach), you can market to a group of people who have already shown interest in purchasing disposable diapers, excluding those who may be set on alternatives such as reusable cloth diapers.

• Value-based segmentation: This model groups consumers based on their potential economic value (to your business). Factors such as disposable and discretionary income would be relevant, as would the value or volume of previous orders and purchase frequency. The customer segments can then be targeted in a more relevant way, offering products or services that are both desirable and within reasonable reach of the individual customers.

• Cluster-based segmentation: A cluster analysis in customer segmentation uses mathematical models to identify groupings of customers by revealing the smallest differences between customers in each group. This has the benefit of letting the data “speak for itself,” without using predetermined ideas or thresholds to characterize individuals, and allows segmentation based on a multitude of factors simultaneously.
• RFM based segmentation: RFM, an acronym for recency, frequency, and monetary, is a segmentation technique that uses objective and quantitative scales to classify customers, providing intuitive (easy to interpret) outputs, and without requiring complex software or external data scientists. Recency describes the time elapsed since a customer’s last interaction with your brand, where more-recent customers are generally more likely to be open and responsive to communications. Frequency is self-explanatory; customers who interact with your brand more frequently (through purchases, social media, etc.) can be thought of as being more engaged. The last word of the acronym, monetary, reflects monetary value, which is typically inferred as the amount they have spent over a given period of time. We can see that these metrics are both useful, in terms of providing relevant insights, and intuitive, in that they are easy to understand and interpret. Hence the popularity of RFM-based segmentation! For a comprehensive guide to RFM segmentation, see RFM Segmentation.

Below is a table highlighting the factors or characteristics taken into consideration under each of these customer segmentation techniques, along with some of their main advantages and disadvantages.

Common Customer Segmentation Methods

Benefits of Customer Segmentation

We have already touched on some of the key benefits of accurate customer segmentation, including optimizing your marketing endeavors, maximizing customer value, and improving customer satisfaction. Nonetheless, the benefits do not end there. Here we will elaborate on four other key benefits of having a well-segmented customer base.

• Collecting feedback for your product: With markets of many products and services becoming increasingly saturated, it is vital for you to stand out and assert yourself as the optimal provider for the needs of your customers. Knowing who is interested in your brand, and why, you will be better equipped to incorporate feedback in order to ensure that your products or services meet the ever-changing needs of your customers. By having such feedback grouped by consumer segment, you will be even better able to tailor to and meet individual needs.

• Increased conversion rates through personalized marketing messaging: Knowing your customer base well will be of incredible value as you will be able to offer that personal touch, communicating to each of them in a way that they find enticing, useful, and attentive, rather than impersonal, uninterested, or even rude/bothersome. Satisfied customers who receive timely and relevant marketing communications from you and who feel emotionally connected to and supported by your brand, or in other words who have a positive customer experience, are more likely to return for successive purchases and to recommend your brand to friends and family. Under the same context, this strategy will help you maximize your efficiency in finding and attracting new customers, rather than looking in the wrong places or using tactics that are simply irrelevant to the target group.

• Focus on higher-value sales opportunities: Here we can think in terms of maximizing a customer’s (or potential customer’s) value to your business. It is helpful to think in terms of customer lifetime value (CLV), the long-term value that customer can bring in terms of revenue, rather than focusing myopically on the short-term. In other words, it will be more valuable to your bottom line to attract and retain a loyal customer who makes frequent or high-value purchases than to simply catch someone for a one-off sale. Beyond the improved aspect of personalized communication and service, appropriate segmentation means that you can focus your marketing efforts on higher-value potential sales (higher-value segments), improving the return on the time and money you invest in marketing by making it more targeted and relevant to those customers or segments who are more likely to make larger purchases.

• Optimized customer experiences at scale: Just as how not every customer has the same needs, a “good customer experience” will be defined differently by different people. When you understand your different customer segments, you are better equipped to offer them the experience they desire. As we have explained previously, customer experience is an important factor in choosing who to do business with, and being able to offer some level of personalization even from the get-go will make potential customers feel understood and appreciated, and prevent you from alienating them due to misunderstandings or inappropriate communications. 
  
  

Customer Segmentation and Machine Learning

In addition to the models previously presented, machine learning algorithms have the potential to uncover some of the more hidden insights and details that may be difficult to identify otherwise, allowing you to create even narrower or more specific groupings. With these tools, you can unlock a whole new level in your segmentation process based on further criteria.

Conversational AI tools can also be used to service different segments of customers at scale, as we do here at Ada. Machine Learning and natural language processing (NLP) models identify consumers’ intent and enable businesses to provide unique experiences for each customer.

Finally, machine learning can also be applied to identify how a specific segment is performing and then weight marketing activities more heavily toward that segment. All-in-all, AI opens up a whole range of new and advanced possibilities for marketing and customer service that can help your business stand out and stay ahead of the game.

Useful Customer Segmentation Tools

Luckily, there are numerous tools available to help you in collecting data and characterizing and segmenting your customer base. There are others that will essentially do this for you, and others will even incorporate other aspects such as customer support and content management. Here we will touch briefly on some of the top tools. Links are included so you can click around to investigate anything that sticks out as being potentially useful for your business.

• Google Analytics: A web analytics service provided by Google that monitors website traffic. This can be a great tool to dive deeper into the demographics visiting your website and prepare and plan for appropriate segmentation.
  
  
• HubSpot: Provider of software tools for customer relationship management (CRM), social media marketing, live chat, customer support, search engine optimization (SEO) and other services designed to help your business with marketing and customer support. 
  
  
• Kissmetrics: Offer “advanced product and marketing analytics” to gain deeper insight into consumer behavior.
  
  
• Segment: A customer data platform (CDP) that you can use to collect and control customer data.
  
  
• Piwik Pro: Piwik offer an analytics suite to analyze customer journeys across various websites and apps, with an emphasis on privacy and data security. 
  
  
• Optimove: AI-assisted tools to create more in-depth customer segmentation and assist with customer relationship management. 
  
  
• Adobe Analytics: Multi-source data to generate detailed insights, with extensive analytics options.
  
  
• Experian: Experian plc state that they are “gathering, analyzing, combining and processing data... to better understand and meet the needs of your customers.” 
  
  
• Sprout Social: Sprout Social, a social media management platform, claims to be a leader in usability, return on investment, customer support and satisfaction, and usability.
  
  
• Qualtrics: Qualtrics offer products for capturing and acting on customer and product experience insights in one integrated platform. 
  
  
• Mailchimp: An all-in-one online marketing platform that can be used to create marketing campaigns, collect and analyze customer response data, and much more.
  
  
• Yandex: Beyond operating the most-used search engine in Russia, Yandex also offers Yandex.Metrica, a free all-round web analytics platform to gain insights into your customer base.
  
  
• Ada: Ada enables businesses to provide personalized customer experience at scale with innovative AI technologies, improving customer experience and freeing up live agents for when they are really needed.