Retain Long Term Customers by Using Market Segmentation

Several concepts are applied by marketing specialists in an attempt to attract and retain consumers. For customer retention, the STP process has arguably the most impact on results. This article aims to show how market segmentation can be used to keep customers coming back for a long time. 

Segmentation, Targeting, and Positioning

The STP process stands for segmentation, targeting, and position. It is a basic concept that is utilized globally by marketing teams and has a substantial effect on the marketing success of an organization. Without the STP process,  marketing strategies are likely to fail when using generic tactics. Note that although the STP process is divided into three parts, they are integrated to work in unison and create the best marketing strategy. 

The First Step in the STP Process Is to Segment the Market

Market segmentation refers to dividing or categorizing a market into homogeneous segments based on a common characteristic that the constituents of the market segment share. There are several standard methods of segmenting a market, but different organizations have different requirements and thus tend to divide the market slightly differently. This process inherently assumes that different market segments need different marketing strategies, which is the case most of the time. 

The Segmentation Process Begins By Grouping Customers and Demographic Profiles

In order for the masses to be separated into market segments, marketing teams start by grouping together different parts of a population according to shared characteristics already known about their demographics. For example, they could be divided according to age or gender. Of course, more specific market segmentations yield better results. 

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Market Segmentation for Customer Retention

These are two techniques that are currently being used by marketers that yield exceptionally better results than older market segmentation methods. 

Psychographic Segmentation: Let the Machines Do the Work

This is a technique that uses machine-learned data to help separate the market better. This data can be automatically updated as the market changes and attitudes change. Here’s a typical example: if a company has a social media account, they can use analytics to gather data about their consumers. This includes:

  • The time of day that their customers visit their social media
  • The posts (products) that the customers engage with more often (and the time of the day they prefer interacting with them). 

The benefit of using this type of market segmentation is that it can help organizations learn how to stay relevant to their target customers and make accurate predictions about the market. These predictions can be used to give the target market a better marketing campaign that is based on their desires and habits. Data-driven marketing campaigns can help reach customers based on their interests. 

Intent-Based Segmentation: Adding Context to Your Marketing Campaign

This is another technique based on data science, whereby the marketing specialist tries to predict user intent and from those predictions creates a marketing campaign accordingly. With this technique, there is more return on investment because the marketing budget can be spent on the right customers (or potential customers). Instead of casting a wide net and hoping to attract a wider audience that might contain the right consumer, the marketing campaign only sends out its messages to potential customers and customers that it predicts will use the intended product. 

This technique is incredibly useful and has many benefits:

  • It is cost-effective because money is only spent on marketing to the target market.
  • The technique creates the opportunity to further divide the market into micro-segments that can be separately marketed for. 
  • Curate customer engagement strategies to create a more relevant campaign.

Using market segmentation can greatly improve the marketing strategies and campaigns of an organization, making customer retention highly probable.

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