What Is Data Segmentation?

Data segmentation is the process of breaking information into smaller, well-defined groups to make it easier to sort and analyze. By exploring subsets of data, you can glean relevant information that aids in decision-making. 

Often used in sales and marketing, data segmentation helps companies understand their core audience. Companies can target specific groups of customers with relevant messaging and customized product offers. 

With 89 percent of companies considering personalization crucial to their success, this marketing skill is in high demand [1].  

Types of data segmentation

Advances in technology allow companies to collect customer information. Many collect, capture, and store key pieces of information on individual customers, and data segmentation turns the many, many data points into actionable information. By creating tailored campaigns, fueled by segmented data, brands can increase engagement, build brand loyalty, and drive sales. 

To achieve these goals, brands use common types of data segmentation, which include:

• Demographic segmentation: Dividing data by demographic factors like age, gender, income, or occupation.

• Geographic segmentation: Separating groups by location. Segmentation could occur by time zone, state, city, or school district.

• Behavioral segmentation: Splitting groups by actions taken, like a person's purchase history or online browsing habits.

• Psychographic segmentation: Grouping people by attitudes, interests, and values. 

What is data segmentation used for?

Data segmentation has many applications, but typically its goal within marketing is to connect with customers on a more relevant level. Using the common types of data segmentation gives brands a starting point to leverage data in a more effective way. Data segmentation helps companies achieve goals, like: 

Improving efficacy

Connecting with small segments of a population should increase your ability to drive results. Rather than trying to gain traction by marketing to the masses, you improve the efficacy of your campaigns by focusing on smaller groups that are more likely to engage. 

Driving customer retention

With tailored messaging towards existing customers, you can improve retention and brand loyalty. Customers tend to buy from companies if they feel a sense of inclusion and community. 

Strengthening lead generation

Companies can leverage data to identify their most promising leads. Data collection helps companies build customer profiles, which can help the sales team identify prospects that are most likely to convert. 

Generating a stronger product differentiation

As companies compete in crowded markets, setting your product apart from others becomes more important. Through data segmentation, you can focus on key features that are vital to each group rather than focusing on general characteristics to cater to the masses. With targeted messaging, product differentiation becomes more apparent to customers. 

Who uses data segmentation? 

While several industries use data segmentation, it's most commonly associated with marketing and sales. Typically, business strategists, marketers, and data analysts use the process to gain insight into a customer base to create personalized campaigns that drive results. 

Pros and cons of using data segmentation

As with any technique, data segmentation comes with pros and cons. People often equate data segmentation with personalization. By identifying niche customer groups, you can speak directly to their needs. Improved personalization has beneficial side effects, including elevated customer satisfaction and loyalty. 

Through segmentation, companies can also tailor advertising campaigns. With a defined audience, thanks to data collection and analysis, brands can carefully select audiences that mimic their ideal profiles. Identifying similar audience traits and behaviors gives companies a better chance at connecting with—and selling to—customers. 

Data segmentation has limitations too, including a high upfront cost to gather and analyze data. Companies may need to invest in data collection tools or platforms to help them mine, or interpret, the information—like Hubspot, Experian, or Sprout Social.

Segmentation works well with strong data, but response rates can fall if gaps exist. Companies need a reliable method of collection and analysis to ensure the decisions made are effective. 

Data collection and segmentation have ethical implications as well. Consumers have increasing concerns about how companies use personal data, which spurred several pieces of legislation globally. This includes the European Union's General Data Protection Regulation, which protects consumer's right to privacy and establishes common rules for data usage and record keeping. 

Getting started in data segmentation

To leverage data segmentation, marketers often follow a prescribed set of steps, which include: 

Identify data sources.

To start, identify where data is coming from. For instance, a company can task its internal team of marketers to collect information from customers. Additionally, a data collection platform could be deployed, or you can purchase data from third parties. 

Set goals based on customer personas.

Customer profiles or personas provide an example of your ideal customer, listing a variety of data points. Use data to compile a customer persona and set marketing goals. For example, a company might hope to achieve a 10 percent conversion lift in sales by targeting a specific persona. 

Define your marketing campaign.

Select a persona and a corresponding segment of your audience. For instance, an athletic brand's customer persona might be elite athletes over 50 years old who are avid cyclists. Using that information, you can segment your audience using these demographic and psychographic data points.

Brainstorm a campaign that speaks to this audience. Include marketing deliverables, timeline, and budget. With an outline in place, marketing teams can add finishing details until the campaign is ready to roll out. 

Analyze your campaigns.

Take some time to review your campaigns and key performance indicators (KPIs) to gauge success. Compare the results against your goals, gather feedback from your internal team and customers, and make notes for your next campaign. 

Getting started with data segmentation on Coursera

Data segmentation, predominantly used in marketing and sales, allows companies to engage with customers and drive key performance indicators—like engagement, loyalty, and sales. Segmentation is the first step toward personalized, relevant marketing campaigns and aids many companies in their pursuit of cultivating customer relationships.

If you're interested in learning more about this marketing technique, classes like Google Digital Marketing and E-commerce provide in-demand skills that include engaging with customers to build loyalty.