An Actionable User Segmentation Guide 2021 | Propellor.ai

User Segmentation Propellor

User Segmentation Introduction

User segmentation means using data and not just looking at it? Most decision-makers struggle with the deluge of digital data that comes their way. Companies collect data such as purchases, clicks, scrolls, likes, video plays, file uploads, order cancellations, subscriptions, etc., to get a clear picture of their user behavior. However, just collecting data and not using it effectively is a case of missed opportunity.

User segmentation is a type of analytics that classifies users according to their behavior in an app or a website and scans for patterns among different cohorts

It helps growth, marketing, and product teams learn how different prospects and customers are likely to use their product, how engaged they'll be, how much revenue they will generate, and how long they might remain customers. 

Let's take a more solid look at what it is.

 

User segmentation allows the development of user profiles that list each segment's characteristics, behaviors, and interests. Users can also specify different ways of accessing their data collected from a diverse set of data sources. 

Today's businesses need to think through how to manage today's new wave of data and use it better to personalize offers and messages to ever-narrower customer segments. User segmentation helps track consumer preferences and behaviors at increasingly granular levels and enables rapid response to opportunities or threats. E.g., a Direct to Consumer (D2C) brand can get early signals around a user segment that might be susceptible to churn and lead to the loss of recurring revenue. 

Today's digital-savvy brands also use different types of user segmentation to improve their shopping experiences, offer better recommendations, increase cart size, and build an intuitive user flow that matches the needs of their ideal customers.

Types of User Segmentation Strategies

At a high level, there are four types of user segmentations -

  • Demographic Segmentation helps determine which users you should be targeting based on attributes like Age, Gender, Asset Ownership, Brand Ownership, etc. 
  • Psychographic Segmentation identifies users' essential attributes like Personality traits, Lifestyle, etc. 
  • Geographic Segmentation uses attributes like Latitude, Longitude, Zip Codes, Communities, Cities, States, and Countries.
  • Behavioral Segmentation is more helpful in optimizing your product experience basis attributes like usage, engagement, purchase behavior, etc.
Demographic Segmentation (Who is the target audience)

A whole series of demographic changes across markets means there is no such thing as the stability of customers. User tastes are becoming more varied, flexible, and demanding. Today, a total market now comprises a series of submarkets, each with its characteristics and each requiring a different sales approach. 

Demographic Segmentation helps create Buyer Personas that bring alive the Typical User Profile across cohorts.

Psychographic Segmentation (Why)

Psychographic Segmentation enables personalization for consumers at scale, and this enhances the consumer experience significantly. Consumer lifestyles drive consumer motivations towards products, and hence studying consumer lifestyles through psychographic Segmentation is imperative.

Geographic Segmentation (Where)

Geographic Segmentation is extensively used to take differentiated actions based on micro geographies, unlocking massive value for businesses.

Behavioral Segmentation (How)

To hone their insights, companies collect 1st party, 2nd party, and 3rd party data to identify upticks in demand, where new customers are coming from, evaluate which customers in their existing user base have increased spending and where lapsed customers have gone.

The top use case where behavioral Segmentation helps take decisions are

  • Enhance the User Experience
  • Calibrate Conversion Funnels
  • Retarget Customers on other websites
  • Increase Customer Loyalty
  • A/B testing new Features

Role of technology in Segmentation

The right tools that collect the correct type of data transform it fast and make it available to business users for Segmentation pave the way forward for Digital Brands. The ability to effectively plan, identify, and regulate sectors will require developing "smart" technologies that use artificial intelligence (AI) - or deep learning- algorithms to create a process with an accuracy greater than human experts through observation and reasoning. Smart machines are capable not only of understanding complex processes but also of information about each other and individuals involved in them, which enables efficient communication between partners within one sector using simple tools provided by AI applications.

How to implement customer segmentation?

User Segmentation Propellor

Step 1: Analyze stages of the customer life cycle and track your users.

You will be able to build rich product profiles like Salesforce, Zendesk, or other leading software development platform companies in the market with a simple dashboard built on Open Source technology from Google Apps Service. You need to understand what customers care about – that is exactly how their entire web experience will evolve during the coming years.

Step 2: Create micro-segments with effective customer cohorts

We need to implement micro-segments with effective customer cohorts, not just a particular brand. Use the insights gained from your data to create new programs that target customers based on their interests and needs," said Greg Van der Kamp, CEO of Hootsuite.

Step 3: Consume automated segments based on a proven model.

At Propellor, we use AI-based machine learning models that can easily segment customers using a much wider variety of data, including browsing behavior, prior purchases, demographics, household data from third parties, and more. The outcome is much smaller cohorts of customers with clearly defined attributes that are customer-based, including common characteristics, interest, and intent.

What is the main challenge of customer segmentation in data analytics?

Data Quality - The main challenge in customer segmentation is data quality. Customer segmentation with data quality testing at the highest levels in each product category. We also believe this will result from a better understanding of essential customer needs and strengths. To maintain the data quality" is a critical component of an integrated strategy to help companies understand their customers' needs and ensure that they receive the best product for those in need.

To summarize, the User Segmentation tool plays a vital role in the analytics industry. Knowing our customers better helps business scale faster as compared to other businesses. In this blog, we have covered the significant aspects of user segmentation. Business users who are looking to achieve substantial outcomes quickly should adopt a practical approach to user segmentation.

 

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Ralph Muller

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