Growth of Customer Segmentation Analytics with GenAI and ML

The best way to turn your audiences into loyal and valued customers is approaching them at the valid time, at the valid place, with the valid and relevant message.

Still many marketing-led businesses don’t have access to complete data to support this fundamental concept in deciding when and how to activate touch points of customers.

In the absence of this data, marketers are susceptible to losing possible opportunities within their target audience, missing audiences to the competition. 

To help companies increase their conversion opportunities, Express Analytics integrates multi touch attribution analytics with marketing mix modeling into a 360-degree approach to identify individual customers, targeting programs and relevant micro-segments that when initiated, can help these companies reach their revenue goals.   

What is Customer Segmentation Analytics?

Customer segmentation analytics is an activity of dividing audience groups within an analytics platform to produce more deeper audience data.

The best instance would be grouping audiences by country, where you have different subsets, like visitors in the US, UK, Canada, and India. 

Another instance would be grouping by device, where you have multiple customer subsets like visitors that browse your website on a tablet, smartphone, and desktop. 

The intention behind customer segmentation is to create productive data to fully understand your audiences so that you can keep making changes to your website, marketing campaigns, customer service, and mobile apps.  

Why Use Customer Segmentation and Advanced Modeling?

Uniform communication methods that don’t pay attention to customers have not been effective to use opportunities worth millions of dollars at the segment and individual levels. 

To stay active in a competitive market, businesses always try to come up with unified strategies that include marketing mix models, value-based segmentation, and attribution platforms. 

With this type of strategy, businesses can measure, capture, and extract value from this opportunity which was missed before.  

Most of the brands rely on predictive analytics and data management platforms to allow clients to get a unified set of brand drivers. 

In order to address media and local activities, almost all brands focus on elements like complete and broad reach media strategies. 

Engaging Your Highest Profitable Customers and Segments

By merging customer segment level data into marketing mix models, marketers can identify messages, promotions, media channels at the relevant time and at the relevant customers. 

They can depend on a marketing mix modeling platform to do this.

It has the potential to quickly generate thousands of business and client oriented models that capture the indirect and direct impacts of marketing investments using both customer segment level and customer data. 

By setting up the connections within the buyer’s journey for every specific target customer, businesses are allowed to make informed decisions and perfectly execute deliverables resulting in millions of dollars in gradual value. 

Customer Segmentation Analytics Uses in Business

Advanced analytics techniques in combination with advanced AI that can play a major role in the segmentation.

Various customer segmentation analytics tools produce useful insights that are used by businesses to target groups with suitable messages, including advertising and marketing strategies, and appropriate appeals. 

According to Rod Fontecilla, who was the chief innovation officer and partner at the consultancy Guidehouse until March 2024, “this capability to perform customer segmentation lets you choose where you would like to invest the money.” 

Marketing professionals are more familiar with customer segmentation analysis, but social, charitable, and political organizations, and product developers also use segmentation. 

The familiar method k-means clustering makes use of ML algorithms to separate observations into clusters. Data analysts define the number of segments or clusters used in the classification. 

According to Khater, “it’s simply a process of segmenting customers of similar attributes” and considering the algorithm doesn’t inspect the reasons a company wishes to establish clusters.

Khater says, analysts use another technique called look-alike modeling where ML algorithms can discover and classify users according to the demographics of a well-known group like business’s current users.

Businesses use customer segmentation optimization techniques to identify potential customers, and niche audiences who look like their present customers. 

Although algorithms are not the same in terms of their segmentation strategies, experts say that segmentation models are probabilistic and not deterministic. 

The clusters in segmentation analysis are classified according to the level of similarity between two or more sets of data rather than exact matches. 

Segmentation Vs. Personalization: Which is Better?

For proper personalization, marketers require segmentation. Customer segmentation analytics is a critical tool to address specific pain points and achieve a higher ROI. 

According to Fontecialla, the majority of the companies don’t have the data atmosphere needed for full personalization, nor would they like to spend on the necessary computer power. 

Segmentation can scale in a better way than personalization and segmentation produces better results that provide ROI to companies if it is enabled by intelligence and combined with the sets of quality data. 

In case, if users share demographic data, they may lose interest in the same services or products.

If companies and marketers thoroughly understand the concepts of personalization and segmentation and the differences between them, they can deliver customized experiences to ideal customers through these strategies. 

According to Gary Kotoverts, Chief Data and Analytics Officer, Dun & Bradstreet depends on segmentation to support marketing, sales, and other activities.

With the use of the analysis, teams can craft messaging and campaigns with a suitable degree of personalization for the targeted audience. 

Marketers make a big mistake by viewing both personalization and segmentation as competitive marketing approaches.

For instance, it is common for marketers to think that personalization includes thorough practices that overcome segmentation’s wider strokes.

Segmentation informs marketers whether they have to target customers in the initial phase. Hence, it should appear first before personalization. 

When to Use Segmentation in Business?

At the early phases of a campaign, marketers should implement segmentation strategy.

Once marketing teams gather more data, and form a story for their segments, they can develop a personalization content campaign. 

When to Use Personalization in Business? 

The intention of the customer is not always the same and changes every time they come into contact with the brand. Their intent will not be constant throughout a single interaction.

Hence, along with segmentation, marketers also have to understand what customers need. 

Marketers need to implement rules-based logic to consider different data points for identifying the intent of customers.

Personalization should happen every time when people engage with a brand, no matter what the content or platform is.  

So, segmentation or personalization: which is better? It’s not a simple question to answer.

These two strategies work better in hybrid and digital retail environments because marketers can impress customers by connecting to them deeply.

Segmentation lets marketers produce content for respective groups of audiences whereas, personalization is all about looking deeper into every individual within a segment independently. 

Further, the responsibility of businesses is to maintain the balance between these two strategies to optimize email strategy.  

How to Use Customer Segmentation Analytics to Create a Value?

Customer segmentation analytics offer no or less value in case a business does not take appropriate actions after identifying segments based on the business goals. 

According to Khater, “segmentation analysis can be done by understanding the objective of a business.”

Khater added that a business can utilize the outcome of its segmentation analysis to find customers who might not want to stay and reach them with promotional messages to retain them, thereby lowering churn. 

Analysis outcomes can be used by companies to enhance customer retention or acquire new prospects. 

Evolution of Segmentation with GenAI, Advanced Analytics and AI

GenAI, AI and advanced analytics can add more value to customer segmentation.

For instance, automation and intelligent tools are boosting data collection from client feedback mechanisms and customer surveys and analysis of demographic material along with buying trends.

According to Fontecilla, AI can produce more granular and accurate groups. Present AI systems have more capability to find the latest trends among customers.  

Conclusion

Segmentation is not rocket science. Therefore, companies go through the process of segmentation to grow in today’s competitive environment. The majority of the companies have decided to consolidate enough data to monitor sales, marketing, and client relationship metrics.

Through advanced analytics techniques merged with segmentation, businesses can determine underserved markets and convert them into brand loyalists.