Evolution of customer segmentation and future prospects with generative AI

Our guest author delves into the evolution of customer segmentation, exploring the impact of generative AI on marketing's pivotal components.

Marketing and finance have remained the two crucial components in the success of a business. What has changed over the past few decades are the methods and tools used in marketing, largely to adapt to the ever-evolving economic landscape, the digital revolution and the subsequent changes in customer behaviour.

One critical aspect that has seen substantial evolution is customer segmentation, which is the art of dividing a diverse consumer base into manageable groups, enabling businesses to determine which customers to target and how to engage them effectively.

However, as the line between customers and consumers continues to blur, the division between customer and consumer data remains challenging despite the introduction of advanced technology and data management platforms. So one is led to ask, "What does the future hold for customer segmentation, especially with advancements in AI?"

To answer this question, it is essential to understand customer segmentation, its evolution and how marketing professionals can use it more effectively.

Traditional segmentation and its evolution

Customer segmentation has its roots in traditional marketing, where businesses rely on basic demographic data such as age, gender, and location to categorise their customers. This rudimentary approach offered limited insights and often led to generalised marketing strategies.

Companies usually categorise their customers into segments such as Segment 1, Segment 2, Persona 1, Persona 2, and so on. These were typically defined using demographic data, but as times change, so have the methods of segmentation.

The journey of customer segmentation started with the rise of digital marketing and data analytics, which enabled businesses to tap into the wealth of online information.

It began with search engines like Google, which allowed companies to monitor user behaviour and tailor their marketing strategies accordingly. As technology progressed, the focus shifted from understanding consumer needs to delivering a seamless, personalised experience.

However, behavioural sciences and behavioural intelligence have exploded into a multidimensional, omnichannel view of data. Now there is a tug-of-war between traditional demographic segmentation and the dynamic, real-time AI-led solutions that can categorise individuals based on their actions and behaviors.

Dynamic clustering

Today, the approach to segmentation is undergoing a significant transformation. Instead of relying on static segments, companies are increasingly adopting dynamic clustering methods. These clusters adapt and change in real-time based on the data they receive.

Engagement with them is also no longer based on a predetermined strategy. Instead, it is driven by the latest data and trends. Content recommendations, for instance, from platforms like YouTube and Netflix are not based solely on what you watch. Instead, it considers your recent viewing behaviour and combines it with what a broader audience is watching.

This dynamic content recommendation is a prime example of dynamic clustering-based segmentation.

The role of evolutionary data

In the evolving world of data and segmentation, the key lies in the evolution of data and how it’s handled. Evolutionary data, which is particularly relevant to consumer sciences and marketing, is about creating a dynamic learning system that continuously adapts through experiences. ChatGPT, a popular application, employs an evolutionary and reinforcement learning algorithm.

Much like a child, this system learns through experience. It is essential to feed it information gradually to shape its understanding. This takes time, but it is gradual, ongoing, and adaptable.

The importance of explainability

Explainability becomes a crucial aspect of managing the relationships within this evolving system. A multidimensional, evolving data graph will be complex and interconnected. In order to navigate this complexity, it’s essential to use elements of Generative AI to provide linguistic insights that will help make sense of the data and create actionable strategies for various clusters. This can be achieved through:

Correlation

It measures the degree of relationship between your objective function (the goal you want to achieve) and the various factors or variables that play a role in achieving that objective. It quantifies the strength and direction of the relationships between these elements.

Causation

This method goes a step further by explaining why something happens, unveiling the cause-and-effect relationships between variables. However, it takes a lot of work for models to grasp causation fully. They might provide a linguistic theory, but a straightforward cause-and-effect relationship can be elusive in the ever-evolving data landscape.

Clustering, which is a key technique in segmentation, can be made more understandable with Generative AI. Data science jargon can be converted into actionable insights that campaign managers can use to devise strategies more effectively for each cluster.

Customer data vs consumer data

Dealing with a new generation of consumers, such as Gen Z and millennials, whose needs and expectations differ vastly from those of previous generations, can be difficult. One of the key challenges in this evolving landscape is the differentiation between customer and consumer data. Both types are essential for understanding and connecting with the target audience, but the methods of collecting, managing, and categorising them have shifted over time.

Solving data complexity and storage issues

Organisations are embracing the complexity of data by treating it as a knowledge graph, where relationships and connections are as meaningful as individual data points. This approach allows businesses to understand the causality behind consumer behaviour and create more accurate and effective customer segments.

Data warehouses and customer 360 platforms were popular data storage and management choices in the past. However, concerns over data leaks and breaches prompted the development of dedicated platforms, like Customer Data Platforms (CDPs), to ensure more secure and effective data handling.

The future of segmentation will rely on more than just technology to solve complex problems. Segmentation is deeply rooted in understanding the nuances of customer and consumer behaviour. A black-box approach is insufficient because questions about why certain segmentation strategies are used and how cluster models behave will always persist.

Moreover, the marketing landscape is changing rapidly. The impending death of third-party cookies and the emergence of stricter data governance policies and regional regulations will emphasise the importance of first-party data strategies. As a result, businesses will have to blend the reliability of first-party data with evolving segmentation methodologies.

(Our guest author is Praveen Sathyadev, Vice President (Analytics & AI) at Course5 Intelligence)