Segmentation Analysis: Definition, Benefits, Examples

Segmentation analysis is the process of dividing a broad consumer or business market, normally consisting of existing and potential customers, into sub-groups of consumers based on some type of shared characteristics. This method allows businesses to target specific groups of consumers more effectively by tailoring marketing strategies, products, and services to meet the distinct needs of each segment. Segmentation analysis is a broad term encompassing methods like Latent Class Analysis, k-means clustering, and hierarchical clustering. It is integrated into other analytical approaches such as Conjoint Analysis and Latent Class Analysis, all of which aim to understand consumer preferences and behaviours.

Segmentation is closely tied to both marketing and product development. In marketing, segmentation allows for more precise targeting, ensuring that promotional efforts resonate with the specific needs and preferences of different customer groups. For product development, customer segmentation analysis insights guide the creation of products that are designed to meet the unique needs of various market segments, leading to higher customer satisfaction and loyalty. Together with Key Drivers Analysis, which identifies the main factors influencing customer behaviour, segmentation analysis provides a holistic approach to aligning products and marketing strategies with consumer expectations.

Segmentation can be categorised into several types, each focusing on different criteria to classify consumers. The primary types include demographic, psychographic, behavioural, and geographic segmentation. Each type offers unique insights and applications, making them valuable for different strategic purposes, though often segmentations are a blend of several of these.

Techniques from Survey Weighting can be incorporated into segmentation modelling to limit or even nullify the effects of certain groups of cases on the segmentation. This might be useful if, say, we want to build the segmentation using one group of cases, whilst still estimating segment membership for another. The use of appropriate weighting also adjusts data to better represent target populations.

Benefits of Segmentation Analysis

Segmentation analysis offers several key advantages that can significantly enhance business performance:

1. Enhanced Targeting and Personalisation

Segmentation enables precise targeting by identifying specific groups within the market. Tailored marketing messages and personalised offers resonate better with each segment, increasing campaign effectiveness and conversion rates.

2. Improved Customer Retention

Understanding customer segments helps develop strategies that cater to their unique needs, enhancing satisfaction and loyalty. Personalised experiences foster stronger relationships, leading to higher retention rates.

3. Optimised Product Development

Insights from customer segmentation analysis guide product development to align with the preferences of various segments. This ensures that products are relevant and appealing, leading to higher adoption rates and customer satisfaction. This approach is similar to how Conjoint Analysis can inform product feature prioritisation.

4. Efficient Resource Allocation

Segmentation helps prioritise the most profitable segments, maximising ROI by focusing efforts on high-potential groups. This targeted approach reduces waste and improves efficiency.

5. Competitive Advantage

Identifying niche markets and underserved segments provides opportunities for differentiation. Tailored strategies for these groups can capture market share and offer a competitive edge.

6. Informed Strategic Planning

Segmentation provides insights for data-driven decision-making and strategic planning. Understanding segment behaviour and composition helps anticipate market trends and adapt to consumer needs, leading to effective long-term strategies.

In summary, segmentation analysis enhances targeting, customer retention, product development, resource allocation, competitive advantage, and strategic planning. These benefits make it essential for optimising marketing efforts and achieving sustained growth.

Market Segmentation and Customer Segmentation

The only distinction between Market Segmentation and Customer Segmentation is the population of the interest. Market Segmentation aims to segment the entire addressable market, including customers, competitor customers, prospective customers and considerers for the product or service of interest. Customer Segmentation focuses only on existing customers. 

As customers are a subset of the whole market, a good strategy is often to segment the entire market as this will then cover both groups, as it makes more sense for business strategy to focus on attracting new customers as well as retaining existing customers. Provided this is done in a way that delivers a large enough sample of customers it means that a market and customer segmentation can be achieved in one model.

Customer segmentations give a more nuanced segmentation capturing differences in patterns on underlying measures such as attitudes which are unique to existing customers rather than the whole market. This may be useful if the main goal is, say, a customer retention or loyalty program.

Segmentation Types: Demographic, Psychographic, Behavioural, Needs-based, Geographic

Understanding the different types of market segmentation is crucial for businesses to effectively target and meet the needs of various customer groups. Here’s an overview of the primary segmentation types and their unique characteristics:

Demographic Segmentation

Demographic segmentation classifies the market based on combinations of variables such as age, gender, income, education, and occupation. Demographic segmentation tend to be developed without the use of statistical algorithms. They are often produced for convenience by researchers and marketers as simple demographic variables are easy to measure or estimate. Sometimes they are derived from small scale qualitative research, such as focus groups. Demographic segments tend to have a poor relationship with attitudinal, behavioural and other variables of interest and we usually advise our clients to retain demographics just for profiling purposes rather than to define segments.

Psychographic Segmentation

Psychographic segmentation focuses on the psychological aspects of consumer behaviour, such as lifestyle, values, and personality traits. This type of segmentation helps businesses understand the motivations behind consumer decisions and tailor their strategies accordingly. These kinds of traits are usually more effective in a segmentation when combined with attitudes, needs and behaviours which are relevant to the category of interest.

Behavioural Segmentation

Behavioural segmentation analyses consumers based on their behaviours, such as purchase patterns, brand loyalty, and product usage. It helps businesses understand how consumers interact with their products and develop strategies to enhance engagement and retention.

Needs-based Segmentation

Needs-based segmentation focuses purely on differences in the needs of the group of interest. Special algorithms such as MaxDiff, and other types of choice models, can be used to indirectly determine these differences based on the choices made historically and using hypothetical choice exercises designed by researchers. This approach is akin to methods used in Conjoint Analysis for understanding consumer preferences.

Geographic Segmentation

Geographic segmentation divides the market based on location, such as local, regional, national, or global markets. It is significant for businesses that need to consider geographic factors in their marketing and distribution strategies. These more basic geographic factors can be considered as a type of demographic. 

Cluster Analysis and Latent Class Analysis in Segmentation

Cluster analysis is a term used for a range of alternative statistical methods used to group similar segmentation data points into clusters, identifying natural groupings within the data. This technique is widely used in market segmentation research and analysis to uncover distinct subgroups within a larger dataset. By examining these clusters, businesses can better understand the characteristics and behaviours of different market segments.

Latent Class Analysis (LCA) is a specific type of cluster analysis that segments data based on latent variables, which are not directly observed but inferred from the data. LCA assigns probabilities to each data point for belonging to different latent classes, providing a more complex understanding of the underlying structure. This probabilistic approach allows for more flexible and nuanced segmentation compared to traditional methods. This method is frequently employed alongside other advanced analyses like Survey Weighting and Key Drivers Analysis.

LCA is particularly useful in market and customer segmentation analytics, as it is very flexible in its underlying assumptions and data requirements. Traditional cluster-analysis methods are often very restrictive in their data assumptions and specifications. The additional flexibility in LCA tends to produce better fitting segmentation models and greater control to the analyst in terms of focusing segments on the real patterns of interest in the data.  

Segmentation Best Practices

Effective segmentation requires following industry-recognised best practices to ensure precision and relevance. Here are some key points:

1. Ensure Data Quality

Maintain high-quality, up-to-date segmentation data. Regularly clean and validate data to ensure accuracy and relevance.

2. Define Clear Objectives

Set clear goals for your market segmentation research efforts, whether it’s to enhance marketing, improve product development, or better understand customer needs.

3. Use Relevant Criteria

Select appropriate segmentation methods and criteria like attitudes, psychographics, behaviour, or needs to create meaningful and actionable segments.

4. Validate and Test Segments

Test segments with real-world data to ensure they are distinct, substantial, and stable over time, confirming the robustness of your model.

5. Continuous Monitoring

Regularly review and update your segmentation techniques to keep them aligned with changing market dynamics and customer behaviours.

Case Studies: Examples of Successful Segmentation Analysis

Segmentation analysis is a powerful tool that helps businesses understand and target their customer base more effectively. Here are three examples of how market segment analysis has been successfully applied by The Stats People:

1. Food Brand Persona Segmentation

A well-known food brand commissioned The Stats People to conduct customer analysis segmentation to understand different customer attitudes towards food treats. By identifying personas such as ‘Staples,’ ‘Bonders,’ ‘Hedonists,’ ‘Guilt Trippers,’ and ‘Temples,’ the company was able to tailor its marketing strategies effectively. This segmentation helped the brand develop targeted marketing campaigns that resonated with each specific group, enhancing engagement, and driving sales. For more details, visit Segmentation of Target Personas.

2. Fashion Retail Segmentation

A leading men’s retailer wanted to understand the various groups that made up male shoppers. After meeting with board level executives from the retailer’s business, The Stats People assisted them in developing an effective research and questionnaire design to capture differences in underlying attitudes to clothes, shopping, fashion and style.

Guidance provided on the softer issues, such as balance of content, semantics and question order / rotation were as important as statistical experience. Several potential customer segmentation analytics solutions were generated, and The Stats People participated in a client workshop to present the profiles explaining the pros and cons of each. The client settled strongly on one model, now widely used within the organisation to recognise and win key groups as customers. Video profiles were developed for the groups to bring them to life throughout the business.

3. Segmentation of Migrant Doctors

We have developed several segmentations on behalf of IFF Research for the General Medical Council (GMC) among GPs and Specialist Doctors on (a) What it means to be Doctor – to understand differences in lifestyle, workload issues and career expectations; (b) drivers, motivations and experiences of migrating to other counties among existing and potential migrant doctors.

The latter focussed on perceptions of the political, media, and work environment for UK doctors, and the cultural, financial, and developmental drivers of migration. The purpose was to understand differences in the balance of push and pull factors. Both pieces of segmentation research focussed on work-life balance and satisfaction with career to drive out segments such as ‘System sceptics’, ‘Burnt-out’ and ‘Open to new opportunities’.

These case studies highlight how segmentation analysis can drive targeted strategies, improve customer satisfaction, and enhance operational efficiency across various industries.