How To Harness the Power of AI for Customer Segmentation

For many businesses, customer segmentation is as beneficial as it is challenging.

Breaking down your customer base into smaller groups or segments based on their shared characteristics allows for more personalized marketing and better engagement.

But customer segmentation requires good customer data, the knowledge to segment and then target customers appropriately, and the ability to bring your customers through more nuanced buyer journeys.

Artificial intelligence (AI) and machine learning provide many tools that can help. These technologies make the segmentation process more efficient and help businesses make more informed, data-driven decisions.

In this article, we discuss how you can harness the power of AI to improve your customer segmentation process.

Table of contents:

• Fundamentals of customer segmentation
• Traditional customer segmentation challenges
• How AI transforms the segmentation process
• Benefits of AI in customer segmentation
• How to implement AI in customer segmentation
• 4 AI tools that can help enhance customer segmentation

Fundamentals of customer segmentation

Customer segmentation is a method businesses use to divide their customers into segments or categories of similar characteristics. This way, companies can better understand and meet the unique needs and wants of their different customer groups.

Traditionally, the segmentation process has leaned on demographics, psychographics, and behavioral data. Demographics consider age, gender, and occupation, while psychographics delve into attitudes, interests, and lifestyles. Behavioral data, on the other hand, focuses on the ways customers interact with products and services.

While these traditional metrics are still used, advancements in technology let marketers uncover deeper motivations behind customer choices by tracking their buying journey.

Sentiment analysis reveals customers’ feelings toward products or services, helping businesses pinpoint which customer segments to concentrate on to enhance sales. Additionally, assessing customer lifetime value enables marketers to identify and categorize the most valuable customers.

Regardless of the metrics chosen, customer segmentation leads to more personalized experiences and marketing messages for each segment, potentially improving conversion rates and sales.

Traditional customer segmentation challenges

While traditional customer segmentation helps businesses achieve success, it also comes with some challenges that have to be overcome.

Manual data analysis

Traditional customer segmentation is time-consuming. It involves manually collecting data from numerous sources, including surveys, websites, and loyalty programs. Sifting through this vast array of data to find valuable insights requires time and effort.

Even when done well, the sheer amount of data that has to be processed makes finding connections difficult when done manually.

Basic data sources limitations

Relying on basic data sources like demographics provides only half the picture of customers. Customers may also provide inaccurate information in surveys, which affects the quality of marketers’ decisions. Businesses might have difficulty differentiating between false and factual data.

Basic data sources also feature attributes that are irrelevant to specific segmentation processes. This makes it possible to segment customers in ways that aren’t useful, and forces business stakeholders to dig deeper to extract meaningful information.

Difficulties with real-time customer data

Effective customer segmentation requires continuous access to real-time data so companies can respond quickly to changing customer needs and take advantage of new sales opportunities.

However, most data sources in traditional customer segmentation don’t output real-time information. Because businesses make decisions based on past records, they miss current market trends.

Growing data sets

As customer data grows, traditional data storage solutions may fail to accommodate scaling data sets. Processing and extracting valuable insights from this much data also poses a challenge.

How AI transforms the segmentation process

Artificial intelligence makes it easier for businesses to analyze vast amounts of data and find actionable insights. AI-powered tools keep track of data, provide real-time updates, and facilitate better decision-making. They also automate numerous tasks in the segmentation process, allowing marketing teams to focus on other core activities.

AI-based platforms support the visualization of customer groups using advanced data science techniques. For example, AI visualizes customers based on their geographic location, age, gender, or interests. AI-powered tools also clean and process dynamic data so marketers have quality and dependable information.

These are some of the essential technologies powering AI customer segmentation:

• Machine learning algorithms. Machine learning (ML) algorithms are the foundational elements of AI applications. They enable systems to analyze extensive data sets, highlight patterns, relationships, and trends, and undertake tasks like segmentation with precision.
• Deep learning. Similar to ML algorithms, deep learning models identify intricate relationships within data by using neural networks.
• Natural language processing (NLP). NLP allows sentiment analysis to understand what customers are saying about their products or services. It also helps with other tasks like language translation, speech recognition, text classification, and text summarization.
• Chatbots and virtual assistants. Conversational AI tools like chatbots address customer inquiries and provide information, improving customer satisfaction and engagement. Through these interactions, they collect valuable data on customer preferences, behaviors, and feedback, which is crucial for creating more refined and effective customer segments.

AI is transforming customer segmentation across many marketing areas, including:

• Predictive analytics. AI tools can anticipate customer behaviors and interests, allowing for segmentation based on current and prospective conditions.
• Customer profiling. AI can gather information from different data sources autonomously and use it for segmentation.
• Personalization. Artificial intelligence can create marketing content and campaigns tailored to specific customer segments.
• Cross-channel integration. AI can combine data from different customer touch points, helping organizations get a detailed overview of their customers.

Benefits of AI in customer segmentation

While AI isn’t perfect, it is demonstrably improving operations in many industries, including marketing. We explore some benefits from integrating AI into customer segmentation processes.

Real-time data analysis

AI tools offer real-time data analysis, allowing organizations to make timely and informed decisions. This immediacy enables businesses to adapt to changing scenarios, seize opportunities, and refine marketing strategies as market conditions evolve.

Ability to handle large data sets

AI-powered segmentation tools are powered by machine learning algorithms capable of quickly analyzing large and complex data sets and identifying patterns and trends. These tools make sense of both structured and unstructured data.

Enhanced customer experience with better-targeted marketing campaigns

Artificial intelligence can identify unique customer behaviors, habits, interests, and preferences. Organizations can use this information to tailor marketing campaigns to specific segments.

For instance, businesses can determine which products and services particular customer groups like and elevate the customer experience by focusing on these areas.

Boosted conversion rates

AI tools increase conversion rates by delivering products that align with the needs and preferences of specific customers. They also facilitate dynamic pricing, enabling marketers to adjust prices according to rapidly changing market demands to help customers make the decision to purchase.

Streamlined marketing efforts by targeting the right audience

AI segmentation tools analyze customer data and provide information for creating detailed customer profiles. This makes it possible to identify the right target audience and high-value customers, allowing organizations to focus their efforts on where they’re likely to have the greatest impact.

How to implement AI in customer segmentation

Implementing AI in customer segmentation involves a series of thoughtful steps, from understanding business needs to continuous monitoring and optimization of AI systems.

AI implementation process:

1. Understand your business needs
2. Collect relevant data
3. Choose the right machine learning models
4. Train and test the model
5. Integrate with existing systems
6. Monitor and optimize

1. Understand your business needs

When looking to implement AI into your operations, consider your business needs to understand how AI will fit in. This determines the types of AI tools and data you’ll need for a successful AI implementation.

Identifying challenges in customer behavior, customer experience, and marketing efforts narrows down the areas where AI will be most beneficial.

2. Collect relevant data

The accuracy of AI models depends on the quality of data used for training. As a result, you need to be equipped with the right data points before using AI.

When collecting data, consider different sources to get the most relevant and up-to-date information. Start by extracting the data from traditional customer relationship management (CRM) systems and databases. Next, consider digital touch points on e-commerce and social media platforms to gain more understanding of your customers’ online interactions.

Collecting data is usually a time-consuming process. Automated tools like Bright Data and Scrapingdog scrape data from various online platforms. Alternately, work with data scraping experts on Upwork to find the right training data.

3. Choose the right machine learning models

Once you have relevant data sets, the next step is to find the right ML models. Examples of machine learning algorithms include linear regression, classification, clustering, transfer learning, and dimensionality reduction.

ML models are tailored for different roles in the customer segmentation process. Traditional algorithms are more suited for specific tasks, while deep learning models are more capable of addressing complex problems.

Consider collaborating with machine learning experts on Upwork to help you find a suitable model to refine your customer segmentation techniques.

4. Train and test the model

AI models are trained on large data sets to perform different roles like personalization and content generation. They can only achieve these functionalities if they have access to accurate and reliable data. Clean, process, and transform your data to make it suitable for training.

While initial results can provide insights into customer preferences and behavior, you’ll need to continuously refine the model based on testing outcomes to improve its performance.

5. Integrate with existing systems

Once the training process is complete, you can move to the implementation stage. You likely have existing systems like CRM and marketing platforms, and you’ll want to ensure your AI technology is compatible with them.

The AI model should integrate seamlessly with these systems to streamline your marketing process. A successful integration leads to several benefits, including real-time insights, better decision-making, and improved customer segmentation.

6. Monitor and optimize

Monitor AI performance regularly to address challenges as they arise. You should update AI models to keep up with changing customer behaviors, market trends, and business goals. This ensures you always have access to valuable insights to make data-driven decisions.

4 AI tools that can help enhance customer segmentation

Let’s dive into some AI-powered tools to help you automate the customer segmentation process.

AI tools for customer segmentation:

• Peak
• Klynk
• Heap
• Optimove

Peak

Peak is a customer segmentation tool that can help you understand your customer base better and deliver personalized experiences. It automates your communication process to attract new customers and engage those you already have.

Peak has features like segment maps that categorize customers into different groups based on shared attributes and smart segments that alert you of changing customer needs.

Best for: Analyzing different customer profiles and segments

Pros:

• Brings information from different data sources into a centralized location
• Offers more than 35 AI attributes for segmenting customers
• Compatible with existing CRM systems

Cons:

• Pricing not publicly available
• May have a learning curve with its tools and features

Pricing:

• Peak’s prices are not publicly available. Contact the sales team for a demo and custom quote.

Klynk

Klynk creates customized marketing campaigns, engages customers, and generates personalized content. You can also use Klynk to craft emails with the right tone and subject headlines to boost conversion rates. It has a copilot bot that assists in automating marketing, sales, and customer experience processes.

Best for: Running automated marketing campaigns

‍Pros:

• Can engage and communicate with customers directly from the dashboard
• Features a bot for crafting email marketing campaigns
• Supports running multiple campaigns concurrently

Cons:

• Can be expensive for small business teams
• Still under development, so generated content may contain errors

Pricing: Klynk offers a free plan for individuals. Paid individual plans start at an introductory price of $19 per month (no annual billing). Teams plans start at $599 per month (no annual billing).

Heap

Heap analyzes your digital marketing touch points, such as social media and websites, and segments users based on their online interactions. It provides heat maps and charts to help you visualize user behavior and take action. It also features an intuitive dashboard that keeps track of your key metrics.

Best for: Segmenting users based on their online behavior

Pros:

• Allows you to collect in-depth information through the customer journey
• Supports other CRM systems like Salesforce
• Monitors changes in customer segments and triggers alerts

Cons:

• Segments can be complex to set up and use, especially for beginners
• Segments can be inaccurate if data is not clean or complete
• Can be slow to update if you have a lot of data

Pricing: Apart from the free version, Heap offers Growth, Pro, and Premier plans for a custom quote.

Optimove

Optimove focuses on improving brand loyalty, helping users plan and deploy multiple customer-focused campaigns. It supports multichannel communication, ensuring your messages reach out to audiences on different platforms. It also provides a bot that analyzes customer data to identify opportunities for increasing revenue.

Best for: Delivering personalized marketing content to different market segments

Pros:

• Processes and combines customer data, making it more accessible
• Ability to create customized content and layouts unique to different segments
• Performs historical, behavioral, and predictive modeling to create richer customer profiles

Cons:

• Pricing not publicly available
• Requires a lot of data to be effective
• Can be complex to set up for beginners

Pricing: Optimove has Build, Grow, and Scale plans, which are available for a custom quote.

Optimize customer segmentation with AI

AI streamlines your customer segmentation process by enhancing personalization, detailing customer profiles, and improving customer retention. AI customer segmentation tools analyze large data sets, enabling you to be more productive in your work.

To help you get the best of AI tools, consider working with consumer segmentation professionals with AI experience on Upwork.

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Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.

Prices are current at the time of writing and may change over time based on each service’s offerings.