Customer Segmentation Definition and models

Customer Segmentation Definition and models

Customer segmentation in marketing is defined as the art of finding homogenous groups of customers and dividing them into one subset. Thus, by planning marketing strategies according to a group’s specific likes and dislikes, customer segmentation eases up developing marketing strategies and sets a clear path towards tailoring efforts for each group.

Customer segmentation has become an all-encompassing arsenal, expanding from less-digitized around-the-corner shops to exciting and life-changing eCommerce online shops. It has even penetrated the archaic banking system and, today, you can find a whole interesting directory of search results under the label “customer segmentation in banking” on the web.

Why The Urgency in Customer Segmentation?

Well, why not? Have you ever found yourself cooking up excellent marketing ideas, carefully planning and coordinating the teams, and yet somehow things not going the way you wanted them to? And after a long time of careful studies, you realized you’ve targeted the wrong people?

If that’s been giving you sleepless nights, then you’ve missed out on the game-changing customer segmentation practice. Customer segmentation stops the company from throwing its resources into the wind and eventually itself into chaos. It saves a lot of the marketing teams’ time and energy and bestows upon them divine marketing light!

Customized customer segmentation strategy shows you how to interact with groups of customers with the same preferences, behavioral patterns, and mindsets. Additionally, by breaking down similar individuals, it provides a plethora of vital and groundbreaking information. The data extracted from customer segmentation models can give you the edge that eluded your grasp for so long.

Here are some more advantages to incorporating a customer segmentation strategy:

  • Tailor-made data-driven marketing decision making
  • Pinpointing the right approach channel (whether it’s social media, on-side advertisement, email marketing, etc.)
  • New opportunities will present themselves as dedicated groups and show their specific alterations in products and services.
  • Improving sales
  • Creating a better functioning communication line with customers

Customer Segmentation Techniques

There are different types of customer segmentation techniques. Here are core customer segmentation techniques:

1.    Behavioral Segmentation

2.    Psychographic Analysis

3.    Social Media Segmentation

4.    Geographic Segmentation

5.    Firmographic Segmentation

For a marketer to lay out the above-mentioned techniques, there are first some data that need cultivating. Marketers are tasked with so. They bravely dive into the customer base and data reconnaissance for a better marketing strategy design.

Demographic Data

Cracking down customers based on their common demographic data enables companies to further understand them.

Every group of similar people possesses some unison characteristics and personal traits. These are easy-to-crack statistics like age, gender, and education which play a massive role in custom-making products and services. You can use them to better understand the key market elements and how to address them.

The demographic data is usually collected from customers. Yet, it’s also achievable through third parties like marketing agencies or governmental bodies.

Behavioral Data

Customers go through a rollercoaster of emotions on a daily basis before making a purchase. Here, behavioral data extraction comes into play and traces the behavioral factors for a purchase.

Unlike demographic data, collecting behavioral data isn’t zero and one (male or female data). They shift from a person to another and from time to time, rendering it one of the more demanding techniques for customer categorization.

The crucial point of obtaining behavioral data would be timing. By realizing the exact time of purchase, companies can target customers with timely marketing programs. Afterward, it’s just a game of sitting back and letting the successful behavioral program run its course.

To create meaningful marketing insights, first, there are some questions that you have to go through:

  • Whether a customer has previously bought your product.
  • How they interact with your channels.
  • How regularly they buy from you.
  • At what intervals do they typically buy from you.
  • Whether they have displayed loyalty towards your brand.

After gathering the questions, it’s up to marketers to study the customers and come up with the best time for multipronged marketing action.

Geographic Data

Geographic data means splitting up the groups based on where they live. The parameter for dividing the audience is the terrain and the local geographical situation. Also, the climate variants make up for solid and consistent geographical data.

The way to take advantage of geographical data is also easy peasy. For example, paint the picture of sun-stricken residents of a semi-arid area or storm-frozen inhabitants of a cold region. It just makes sense to offer them products or services that contradict their current situation. A cold Slurpee for the desert people or a trip to more hospital spots and warming garments for the group dealing with the cold crept under their skins.

Geographic data manipulation changes under various circumstances, and the past two pandemic years proved that. Many service providers changed their way of thinking and providing services according to the Covid-19 pandemic because it has also been a geographical disaster!

Technographic Data

Technographic data and techniques are more suited for B2B scenarios, focusing on businesses and handling technology. Nevertheless, it still verifies as an impactful customer segmentation strategy. 

Marketers use this data to show which devices are more popular with customers. As a result, marketing strategies can be shapeshifted to benefit these customers as they get notified of the services and products faster and easier.

If you’re opting for this segmentation method, you’re caring about your customers’ technological preferences. You’re showing a sign of advanced digitalization by developing applications that run faster on customers’ phones, faster notifications, and overall, a more user-friendly app catered to the devices in their hands.

Also, don’t forget about payment through cell phones. You need to make it a priority. Without smooth phone transactions that don’t take a whole lunchtime to finish, customers will abandon your app.

To sum it up, make a better app and simpler checkout transition.

Psychographic Data

Psychographic data is the gateway to a customer’s mental system when it comes to shopping. It outlines their thoughts, insights, priorities, and passion. In a simpler world, the emotional connection they feel towards buying a specific product or service.

Rounding off your marketing efforts based on psychographic data is exceptionally beneficial only if done correctly. It’s a highly complex procedure. Forming groups through psychological preferences and mental cues that push customers to click order checkout is not, by any means, a mundane task.

Marketers feed off conducted surveys (after or before the purchase) to mark the psychological checkpoints customers pass through before getting attached to products and services. While it sounds straightforward on paper, reviewing these explorations can become costly and take a lot of time, something you don’t want to happen in your company.

Social Media Segmentation

Social media segmentation is the act of differentiating various groups on social media platforms (not users with phones), both within or across multiple ones.

The reason behind this is that platform users – in today’s seemingly unending sea of connecting apps – don’t exude the same traits, thoughts, and lifestyles. They don’t come from the same background and don’t react the same way. Social media like Facebook cater to some people who might find Twitter not so good and vice versa. LinkedIn users tend to be work-centric, and other contents of unfamiliar nature don’t attract them. In contrast, Instagram users might find these posts or news much to their delight.

Splitting up customers by their social media platform preference isn’t only bound to the digital space either. It’s heavily affected by the users’ geographical locations as well. Whereas users in the Middle-East might spend a lot of time on Instagram, who’s to say the same thing for tech-savvier web-surfers someplace else who prefer YouTube?

Consequently, it’s safe to say segmentation via social media contains, to some extent, the demographic and even behavioral data inside its framework.

Firmographic Segmentation

Before tackling the format, first, we have to get technical and literate. The word Firm refers to a broad spectrum of organizations from non-profits to governmental bodies and entities to LLCs or PCLs – though the term is commonly associated with non-profits. Despite the meaning debate, the firmographic segmentation model gathers all these companies under its umbrella and gives us more detailed information over B2B customers.

Firmographic segmentation points to the classification of B2B customers based on their shared company values, organizational attributes, and characteristics. It owes its existence to other types of customer segmentation models but casts itself apart as being only applicable to the working force of a firm. Here are some aspects of firmographic segmentation:

  1. Industry
  2. Location
  3. Company Size
  4. Status
  5. Performance
  6. Executive Title

So far, we’ve only been rambling on about the five major segmentation models, and for a good reason; they’ve paid their dues. However, some other segmentation models have woven reliable results.

1.    RFM Customer Segmentation

2.    Segmentation via Clustering

These two other segmentation techniques have definitely secured a spot on our list.

RFM Analysis and Its Importance

A common practice of subcategorization of a body of customers is familiarizing ourselves with these three principles: “Recency, Frequency, and Monetary Value,” or RFM for short.

As stated on the “CleverTap” website article, RFM factors illustrate these facts:

  • the more recent the purchase, the more responsive the customer is to promotions
  • the more frequently the customer buys, the more engaged and satisfied they are
  • monetary value differentiates heavy spenders from low-value purchasers

These are critical traits and valuable data for any company. Together, frequency and monetary value provide not only crucial information but also spin “customer’s lifetime value.” On the other hand, recency explores customer retention concepts, also known as a measure of engagement.

Benefits of RFM Analysis

As far as eCommerce customer segmentation is concerned, conducting an RFM model is one of the best ways at disposal to analyze the customer database. It provides a pretty convincing bulk of advantages for any company that takes them in. 

  • Personalization: By creating influential customer segments, you can make relevant, personalized offers.
  • Improve Conversion Rates: Personalized offers will yield higher conversion rates because your customers engage with products they care about. 
  • Improve unit economics
  • Increase revenue and profits

RFM in Real Life 

Now, there’s a good deal of real-life examples of successful RFM analysis that catapulted a company’s sales and brought it fame and celebration since RFM has been around in the dark ages of email marketing.

“That being said, one instance of RFM analysis stands out.”

In 2014, Delta Airlines marketers were desperately seeking a new rewarding method that could help with its customer base, and they hit the jackpot.

They created a foundation where travelers would be rewarded for the miles they have spent on the airships rather than the money they’ve spent on the tickets. For example, a discount maniac who constantly booked slashed deals but traveled a lot would rack up more free flights compared to a high-value yet scarce flying customer. 

That’s not to say that the latter customer isn’t essential. On the contrary, the airline’s per-dollar model made these ranks of customers satisfied. However, looking into the RFM model and the frequency and recency of booked flights help the company’s marketers develop tailor-made messages and marketing schemes to make the other group happy.

Meaning of Customer Segmentation via Clustering

So far, human sciences have had the upper hand in the fight for main customer segmentation methods. However, when the talk’s about so many people with various numbers, mathematics is bound to show up and pose a new way. 

Segmentation via clustering is mathematically dismantling a larger body of people into smaller ones with the most negligible variations — a mathematical model trying to group people who show minor deviations. These groups are usually called “customer archetype” or “personas.” 

Similar to other methods, segmentation via clustering aims to accurately divide customers for more personalized and custom-made marketing actions. One of the predominant cluster analysis methods is the “k-means cluster analysis,” or the scientific segmentation. 

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