Segment your customers to serve them better
Customer Segmentation Tools
Why are customer segmentation tools important?
Customers enjoy shopping experiences that cater to their individual wants and needs. Shoppers in the market for any sort of product will gravitate toward retailers that serve their needs best and avoid those who don’t.
Because of this, it’s in the best interest of businesses to improve the customer experience as much as possible. Meet customer needs consistently and effectively and they’ll shop with you more and make you more money.
However, not every customer has the same needs. Change things to make one person happy and another may end up switching to a competitor. Even determining and focusing on the wants and needs of a majority of buyers would ignore a huge part of any business’s customer base and mean the loss of many potential sales.
One possible solution is personalization—providing options, perks, and other custom benefits on a person-by-person basis for improved brand engagement, lifetime value growth, and customer satisfaction. Aspects of the shopping experience can be adjusted regularly depending on who’s shopping so that each feels their needs are being met.
Unfortunately, many businesses have a very large number of customers, and listening to everyone and giving each shopper a fully personalized experience is practically impossible. The solution is something called customer segmentation. This takes all your customers and sorts them into groups or customer personas based on similar preferences. These larger groups can be more effectively served and marketed to, and with fewer segments to worry about, more can be done to please each customer segment.
Today, finding the customer segments marketing will target is achieved by customer data and experience software programs such as Customer Data Platforms (CDPs). These tools can have a huge impact on the success of all kinds of businesses, and as they come into wider use, knowing how they work and what they do is going to become increasingly important.
In this article, we’ll look at the different types of customer segments you can identify to unpack customer insights and build personalized customer journeys, cover the basics of how customer segmentation tools work, go over some strategies and customer segmentation examples, and mention some of the best market segmentation tools currently available.
Types of customer segmentation
Customer segments are created by grouping together people who share similar interests, needs, and preferences. In segmenting your base, you can either create several bigger groups with fewer shared characteristics or many small ones which are more uniform.
The first step is to figure out what kinds of people actually shop at your business. A general rule is that people with shared characteristics will have similar expectations about shopping. This means the average customer segmentation template will start off with groupings based on age group, location, income bracket, vocation, household composition, gender, and even groups from third-party data sources such as Experian’s Mosaic. These groups will tend to shop for the same kinds of things, patronize a shared set of businesses, and have similar spending habits. Many people will fall into multiple different groups, and this is actually a good thing, as several different marketing campaigns can end up being relevant to a single individual.
Of course, these types of customer segmentation are just a few major examples, and in reality there are infinite ways to sort and categorize individuals and their preferences. For example, you could also segment customers by the recency, frequency, and monetary value of their past purchases using a proven method called RFM segmentation. Each business will cater to a different customer base, and, as we’ll explain below, determining the most effective way of segmenting your base is the key to success.
Customer segmentation analysis
Choosing the right groups to market to can be done by educated guess or pure intuition. By far the most effective means of categorizing shoppers, however, is with market segmentation tools and techniques involving customer analytics software.
Customer data platforms (CDPs) are extremely versatile and can be used to find patterns in just about any collection of information. The first step is to gather as much data as possible on what customers purchase, how often they shop, what they buy, and various pieces of info about who they are.
Once you have a large pool of data, you need to combine this data into a single customer view in order to use it effectively. Customer analytics tools can then search through this info and look for similarities in these behaviors and correlations between actions and personal characteristics. Software can’t decide how to draw boundaries around these shared qualities, but your team can look at the patterns that have been found and make much more intelligent choices about what the most solid and defined customer segments are. For example, you might identify your highest-value customer segments and use them to build lookalike audiences for your acquisition campaigns.
These segments and the behaviors of the people in them will change over time, so keeping these programs running constantly can allow your team to track and measure the impact of your business activities on actual customer behavior. In this way, customer segmentation analysis allows business leaders to better understand the real-world dynamics within their customer base and constantly adapt to meet the changing needs of their shoppers and improve their overall effectiveness.
Customer segmentation models
Different groups of customers are effectively visualized by creating customer segmentation models. These give a bird’s eye view of how different groups are interacting with your store, how they are responding to different parts of the customer experience, and how they change as time goes on.
The most important kind of customer segmentation model is created by analytic prediction programs. These predictive software tools take massive amounts of data about customer purchasing habits and personal info and use advanced statistical methods to determine how these people are likely to behave in the future. AI and machine learning technologies are a core part of many of these programs, allowing them to create models of extreme precision and accuracy.
For example, you could use predictive analytics tools to understand which customer segments have the highest likelihood of churn so you can take action to re-engage customers and ultimately improve retention rates. Additionally, you could use customer segmentation to identify customers with gifting motivations for more effective retail holiday marketing.
These segmentation models are simplified expressions of the movement of hundreds, thousands, or even tens of thousands of shoppers. Models and their parameters can be tweaked by your team in different stores, locations, or branches of a business so that you can better understand the specific groups you deal with. As already mentioned, segmentation models also allow minute changes to be detected right as they emerge, helping you look out for groups that are merging, splitting, or experiencing high churn rate. Any problems can be quickly addressed, and the effects of any new initiatives or tactics can be seen almost immediately, allowing you to double-down on marketing successes or quickly pivot to mitigate risks.
Customer segmentation strategy
The first step in data-driven customer segmentation is to set your analysis programs to work looking through all the data you have about customer behaviors and personal info. From there, your team can begin to define the most promising and consistent customer segments to deal with. Typically, we’d recommend starting with the highest-value customer segments, or the ones who’ve spent the most money and made the most frequent purchases with your brand. Click here to learn how to measure customer lifetime value in order to do value-based segmentation.
After that, other software tools can track how each group behaves and create models so that real-time changes can be monitored and predictions can be made about how the data will look in the future.
The most important part of any customer segmentation strategy, however, comes when your team uses these data and actionable insights to plan out ways of effectively marketing to different segments. Ad campaigns will be created to target the specific wants and needs of individual groups. This can take the form of either physical banners and promotions placed in brick-and-mortar locations or digital ads placed on your store website or other places on the web. Discounts, memberships, and even new product lines can be deployed to encourage customers to shop with and choose you over competitors. The end goal of this whole process is to make customers happier, as satisfied customers will be more eager to spend and more loyal in the future.
Customer segmentation examples
To better understand how this process works in the real world, some retail customer segmentation examples and case studies will be useful.
A discount clothing store has spent several months gathering purchase data from customers and pairing this with personal info. Management realizes that one of its largest demographics is older customers, aged 55 and above. They also notice that this group tends to shop infrequently, however, and only makes small purchases. The company institutes a senior discount which offers older customers lower prices if they come in to shop on certain days of the week. In the coming months, they see that more seniors visit their store on these days and end up making larger purchases than they had originally.
An outdoor retailer has a number of locations spread throughout the U.S. It already has a seasonal rotating ad campaign in place which advertises different kinds of clothing and equipment relevant at different times of year. Their software is set up to gather information on the kinds of activities different customers are most likely to engage in, and finds that in stores located in the vicinity of mountains, snow gear sales are high all year. In order to take advantage of this, the company decided to run ads about skiing, snowboarding, and other snow-related sports year-round. This ends up drawing in more customers overall, and winter lines become best sellers in these areas.
These customer segmentation examples are just a taste of the impact that customer segmentation tools can have, and businesses of all types can benefit from investing in it. Click here to learn more about measuring the impact of customer segmentation using tools like CDPs.
Customer segmentation software
With all the uses customer segmentation software can be put to, it’s no surprise there’s an abundance of companies out there offering tools and solutions for businesses to better cater to the wants and needs of their customers—or, to move from channel-centric to customer-centric business models.
Some of the biggest names in this kind of tech are Salesforce, Informatica, SAS, and Lexer. Most of these companies offer dedicated customer segmentation analytics solutions focusing on specific aspects of the process—data gathering, compilation, analysis, prediction, visualization, activation, and measurement.
Lexer is unique in being one of the only companies to offer a complete suite, one which can handle all steps of the process for every team, including marketing, retail, and service. Having a single versatile system as your all-in-one hub for customer insights and segmentation can be quite advantageous, as it eliminates the need to tweak many separate programs to be compatible with one another. This makes Lexer a perfect choice for anyone just starting out in data analysis and for companies already engaged in it but looking for a more streamlined and holistic solution that combines all of their data from every source and system into one, easy-to-use platform.
📄 Customer Data Platform (CDP)
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📄 Customer Engagement Platform
📄 Customer Intelligence Platform
📄 Retail Data Analytics Solutions
📄 Customer Experience in Retail