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Data-Driven Customer Segmentation for Personalized Business Solutions – AI Time Journal


Photo by Chris Liverani on Unsplash

While segmenting consumers based on their traits, behaviors or preferences is a widely accepted business strategy, many organizations fail to use data to their advantage. How big of a difference does analyzing customer information make? It is often more influential than businesses initially assume.

What Is a Data-Driven Approach to Customer Segmentation?

Conventional customer segmentation divides individuals into groups based on shared behaviors, preferences, or characteristics. The main difference between it and its data-driven counterpart is accuracy — the latter can uncover hidden relationships between variables, making deriving precise insights from datasets more straightforward. 

Many businesses miss out on those insights because they don’t analyze customer information — even if they have vast amounts of it. According to McKinsey & Company, enterprises use less than 20% of the data they generate. 

A data-driven approach to grouping customers enables hyper personalization, allowing decision-makers to adapt their products, services or marketing strategies to group-specific needs. Whether they make pricing dynamic, tailor advertising or provide custom product recommendations, they benefit significantly. 

The Merit of Segmentation in the Age of Personalization

Using datasets to segment individuals into highly specific groups to personalize the customer experience offers several competitive advantages.

Increased Customer Retention

More people crave tailor-made customer experiences every year. The percentage of consumers reporting a company would lose their loyalty if it didn’t deliver a personalized experience increased to 62% in 2022, up from 45% in 2021. In other words, personalization is proven to improve retention and brand loyalty. 

Improved Marketing Effectiveness

Using data to segment a target audience into smaller groups enables real-time adjustments. Considering most small businesses fail due to a lack of market demand, this flexibility may be the deciding factor for long-term success. Used correctly, it can optimize marketing effectiveness and resource usage, expanding companies’ profit margins.

Heightened Consumer Engagement

Segmentation-based personalization makes consumers more receptive to sales, advertisements, and incentive utilization attempts. Research shows 63% of marketers in the U.S. saw increased conversion rates because of it. It substantially increases customer engagement, driving sales. 

Considerations for Data-Driven Customer Segmentation

There’s no guarantee that a data-driven approach will outperform its conventional counterpart. Misguided decision-making, infrastructure issues and poor preprocessing can make insight generation inaccurate and ineffective. If decision-makers want to secure competitive advantages, they must consider these factors.

Inaccurate information is one of the most impactful factors to consider. Irrelevant sources, duplicate values or improper transformation contribute to poor insights. Unfortunately, ill-advised decision-makers may not realize their mistake until their tracked metrics reveal their data-driven strategy performs worse than their conventional one. 

Even if organizations have enough high-quality, accurate information, they must be careful. Data silos can complicate data governance, enabling dataset errors and irrelevant information to influence insight generation. They can also fracture departmental decision-making, meaning marketers, manufacturers and designers will likely be disorganized and disoriented.

If all datasets are kept in one place, volume often quickly becomes an issue. The time and resource costs of analyzing vast amounts of information may not be worth it for overly specific, unimportant insights. This fact is especially true for those who collect and analyze data in real time since the process requires significant processing power and storage space. 

What Technologies Should You Use for Segmentation?

Data-driven customer segmentation relies on various software and tools for a reason. According to the U.S. Chamber of Commerce, 80% of small businesses with high technology utilization report positive profits, sales and employee retention growth. More often than not, it is a question of which to select, not whether or not to use one. 

Artificial intelligence is among the latest and best tools for segmentation-based personalization. It helps companies overcome accuracy and analysis-related obstacles. Machine learning models are particularly beneficial because they enable predictive analytics. Decision-makers can forecast demand this way. 

Integrations with data visualization software or customer data platforms can improve insight relevancy and make AI output easier for non-technical professionals to understand. A user interface or shared dashboard has the same effects. This way, teams can secure board buy-in or clearly explain their technologies’ impactfulness. 

The Best Data Science Techniques for Segmentation

Some data science techniques are better for data-driven customer segmentation than others.

  1. RFM Analysis

A recency, frequency, monetary value (RFM) analysis reveals how recently individuals made a purchase, how often they do business with a brand and how much money they spend. Organizations can use it to identify loyal or high-value segments. 

  1. Clustering

Clustering creates groups of individuals with similar characteristics, preferences or behaviors, making it an ideal data science technique for data-driven segmentation. Since it uncovers non-obvious clusters, it remains a useful tool for existing groups.

  1. Time Series Analysis

With a time series analysis, decision-makers can segment their target audience based on purchasing behavior over time. By inputting the frequency and variation of customers’ spending habits, they can uncover the underlying cause of trends to better divide individuals into groups. 

  1. Decision Trees

A decision tree can quickly uncover hidden patterns and relationships. It is most useful when businesses have a large selection of customer attributes and extensive knowledge of how they want to segment individuals.

  1. Factor Analysis

Since a factor analysis explains the variability and correlations among a large set of variables by condensing them into a smaller number of factors, it is ideal for grouping customers. It allows decision-makers to facilitate their understanding of relationships between individuals.

How Important Is Personalization to Your Customers?

Modern consumers value tailored experiences because they live in a digital age. They subconsciously expect websites and apps to know what they want out of every interaction — and they’re often impatient about it. Using information to segment them for personalization may soon become the norm.


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