Types of User Segmentation
User segmentation has taken new forms with its increased applicability in product marketing such as customer journey mapping
and retention analytics
. Apart from the general categorizations on technical aspects, users are also separated by their behaviors, needs, and values.
1. Geographic Segmentation: Certain regions respond to your product/service in a different way than others do. These responses may be based on cultural phenomena or the usefulness of your product to them. It can also help you understand how significant your product is in many regions, and thereby focus on promoting it better in places you would like more business to be generated.
2. Demographic Segmentation: Based on basic information that individual users share with you, such as their age, gender, location, language preferences, title, etc., you can segment them into different cohorts. This will help you see common trends such as age-specific preferences when using a product or better suit your product to the needs for a specific job role.
You will be able to collect demographic data through first-party data
collection methods and by maintaining close customer relations.
3. Technographic Segmentation: Technographic data can be particularly useful for organizations that sell software products. They can separate users based on the tech stack, infrastructure, and network used, to better serve all the different user groups.
Technical segmentations help companies understand the level of digital transformation that each of their customers is undergoing. This will help them in better product building and customer support. Technographic data is, without being said, more valuable to B2B enterprises and ISVs than B2C businesses.
4. Behavioral Segmentation: Segmenting users by their behaviors offers perhaps the most leverage among all categorizations, as customer behavior gives an in-depth understanding of how well customers interact with your product.
Behavioral segmentation can be based on user activities such as login frequency, usage of features, page views, tickets raised, and other activities. Consequently, they can be influencers for plenty of product changes, such as in the UI, addition of more features, and further targeting user groups based on interests.
An example for behavioral segmentation would be:
Who are the users who used the checkout feature and logged in at least twice every month?
5. Needs-based Segmentation: Customer needs are more complicated than what you see on the surface with classifications such as profession, gender, or income. Though a trickier sort of segmentation, needs-based segmentation tells you exactly what your customers’ needs are – whether they be functional, characteristic, emotional, or based on certain beliefs.
Acknowledging the needs of users will help them resonate with your product, where customers feel they are being listened to. This will motivate users to stay loyal to your brand as they feel more and more comfortable with the product benefits and interactions, such as the message and tone.
You can collect needs-based information through personal interactions, questionnaires, and advanced analytics data. Product analytics can answer some of your pressing customer-needs questions. Example:
What are the top items purchased by first-time users who started using my application, during their first 3 months?
6. Value-based Segmentation: This is helpful in analyzing the economic value of a user segment to the business. Value-based segmentation allows you to understand the cost involved in acquiring and maintaining users. This understanding will motivate you to target better and improve budgeting for marketing accordingly. You can categorize users on the basis of the longevity of their commitment to the product and focus your marketing on converting or retaining those segments accordingly.
An example of a value-based query with your product analytics application would be:
Who are the users who fall in the top 10% who had more than 100 sessions in my application and have made a purchase?
7. Psychographic Segmentation:
You can also segment users based on their ‘psyche’, that is, their likes, dislikes, opinions, sentiments, etc., specifically related to their interest in your product. Psychographic information can be collected using general surveys, NPS®
surveys, customer satisfaction ratings, etc.
How to Use User Segmentation to Generate More Meaningful Business?
User segments are helpful in formulating business strategies by basing your information on real data and customer expectations. Re-adjusting a few of your product specs or changing the way you advertise to your audience can increase revenue and boost business. If product purchases are below the reasonably expected limits, user segmentation can give you insights to take remedial actions.
Here are three ways in which user segmentation can help in meaningful decision-making in product marketing:
User segments can be compared to create correlations between cohorts to make associations that will benefit the business.
E.g., Compare demographic data with need-based segments to find trends in product usage and requirements.
Analyze customer behavior close to when a segment of users took a negative action to make sense of it.
E.g., Taking a long, hard look at website engagement statistics for your target user personas under each segment before they churned can tell you how to tweak your user experience.
Re-target existing and loyal users as it is more rewarding than going for new business prospects.
E.g., Make a list of users who have made repeat purchases to target them for special promotions.
Customers love having their applications and products customized and personalized to suit their unique demands. Expectations for personalized experiences have reached a new level with the advancement of analytics through machine learning and new product analytics tools. In fact, a survey of 15600 customers by Salesforce found that 66% of customers
expect companies to understand their needs and expectations.
However, are there ways to customize and personalize products even while respecting the user’s privacy? When customers are unwilling to share more about their personal preferences or when their privacy feels violated, customers seem to drop off from services. Personalization can backfire with an excessively large tech stack, especially for financial services
(where user privacy is paramount).
The reality is that your product usage analytics can bring in tons of information that is useful without being intrusive or creepy. Find out more about how you can make the best use of data analytics from our academy