TYPOLOGY

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Better understand and segment thanks to statistical typology

Typology is not just a segmentation technique: it is a strategic key for innovation, adapting the offer, personalizing the customer relationship and supporting decision‑making. Why are these methods now essential assets for business teams?


1. Introduction: Categories to understand and act

Human beings have always sought to classify: to group in order to understand reality, their customers, their markets. Typology materializes this need by creating homogeneous groups from varied data, thus making it easier to read complex phenomena.


2. Typology: definitions and stakes

Typology refers to an approach that aims to define or study “types” in order to analyze complex realities. Each “type” summarizes common characteristics: consumer profiles, product usage, purchase motivations, etc.

Here are some marketing issues that can rely on a typology to better meet customer expectations:

1. Understand your customers’ expectations, needs, and behaviors
2. Adapt the offer (product, service) to each target group
3. Drive product or service innovation to address new needs by customer group
4. Optimize communication and loyalty‑building
5. etc.


3. Typology serving innovation and marketing

a. In product or service innovation

Example: An urban mobility company wants to launch a new e‑bike. A typology survey reveals:

  • The “Eco‑mobiles” (environmentally conscious, short urban trips)
  • The “Pragmatics” (looking for autonomy, practicality, cost of use)
  • The “Connecteds” (interested in tech, app, geolocation)

Each type guides the design, the marketing message, and the sales channel: a single product created, but differentiated communication and sales strategies.
Typology then becomes a driver of innovation: you create

i. for what already exists (historical segment),
ii. for what is changing now,
iii. and for what is emerging (foresight, trends).

b. In customer satisfaction studies

Example: A food service company surveys 3,000 customers. The typology distinguishes:

  • The “Ambassadors”: high expectations, very satisfied, strong positive word‑of‑mouth
  • The “Neutrals”: little involvement, rational choice
  • The “Critics”: several dissatisfactions, detailing product or service weaknesses, and communicating about these negative points

Usage:

  • “Ambassadors” act as positive relays;
  • “Critics” help target the key areas for improvement.
    You no longer manage customers according to an average, but according to differentiated expectations. Typology increases the granularity of the customer strategy.

4. The foundations of typology: variables and distances

a. Which variables?

  • Quantitative: height, income, number of purchases, ratings, Net Promoter Score (NPS)
  • Qualitative nominal: gender, occupation, region, brand choice
  • Qualitative ordinal: satisfaction level, purchase rank, tenure

b. How to measure similarity?

Distance, a central concept, is used to assess which group each individual/statistical unit belongs to.

Type of distance Use Example
Euclidean: d ( A , B ) = ∑ ( A i - B i ) 2 Quantitative variables Difference in income, satisfaction
Manhattan: d ( A , B ) = ∑ | A i - B i | “Walking‑path” distance Average basket, purchase frequency
Gower: mixed Mixed data Age, region, habits

The choice of distance has a direct impact on the relevance of the groups: this is one of the secrets of a high‑performing typology.


5. Factor methods and preparation

Data are often multivariate and correlated. Before segmenting, they are “projected” into a simpler space using a factor analysis such as Principal Component Analysis (PCA), Correspondence Analysis, etc., in order to:

  • Remove “noise” and stabilize the groups
  • Retain the most salient information (Kaiser rule, scree plots)
  • Make visualization easier (biplots, maps)

Example:

In marketing, a PCA (Principal Component Analysis) makes it possible to visualize customer profiles based on their behaviors, expectations, and product/service usage, on two axes for instance: “Price & intensive use”, “Innovation & comfort”.

Positioning via a factor method precedes the typology algorithm and ensures stable results: each group is coherent and “readable”.


6. Main typology methods: explanations and illustrations

a. Hierarchical Ascendant Classification (HAC)

  • You first create as many groups as there are individuals
  • You progressively merge the closest groups
  • You cut the tree at the optimal level (dendrogram cut) to obtain the segments

For the company: the number of segments is not fixed upfront but determined a posteriori. You thus explore the deep structure of the market or customer base.

b. Partitioning with k‑means

  • Choose the number of groups
  • Assign each individual to the nearest centroid
  • Readjust until convergence

For business teams: highly relevant for large databases, fast segmentation, adapted to CRM/ERP environments (customer bases, sales history).


Whatever algorithm is used, and whatever optimal solution is identified, these solutions highlighted by segmentation algorithms must always be systematically challenged by business teams, by the people who will work with this typology and keep it alive over time.


c. What makes a good typology?

  • Internal homogeneity: individuals within a given type are truly similar on the key variables.
  • Between‑group heterogeneity: types differ clearly and significantly from one another.
  • Stability/robustness: results do not change completely as soon as you slightly modify the sample or parameters.
  • Interpretability: types are easy to describe, name, and explain; they “make sense” to teams.
  • Operationality: the typology supports concrete decisions (targeting, offer, messaging, prioritization, etc.) and can be technically used in the client’s decision‑making and operational systems.

d. What are the conditions for building a good typology?

  • Well‑defined objective from the start: what will the typology be used for, for whom, in which use cases?
  • Thoughtful choice of data: relevant variables, possibly transformed, noisy or redundant variables filtered out.
  • Iterative work and dialogue with end‑users to adjust the number of groups, and to validate understanding and operational relevance.

7. Concrete case: typology applied to product innovation

Imagine a cosmetics company surveying prospects about a new cream.

  • Variables: age, purchase motivations, beauty routine, values, budget
  • Methods: PCA + k‑means
  • Typology results: “Naturals”, “Avant‑gardists”, “Traditionalists”

1. For marketing: each type receives a personalized offer, tailored messaging, and targeted campaigns.
2. For R&D: guides development prioritization (innovative ingredients, format, channels).
3. For sales teams: segmentation of the sales force (arguments, pitch, customer expectations).


8. Advanced typology and opening to machine learning

Classical methods (HAC, k‑means, CA, MCA) serve as a foundation for more advanced techniques:

  • DBSCAN: detection of robust groups even in noisy data
  • Gaussian mixture models
  • “Big data” clustering in high dimension

These techniques lie at the heart of unsupervised AI and enable “massive” analyses in digital marketing, e‑commerce, real‑time satisfaction through chatbots, etc.


9. Business value: why typology transforms added value

  • Marketing: fine‑grained segmentation for targeted campaigns, marketing automation, and ROI maximization
  • R&D: innovation oriented toward field reality, guided prototyping, anticipation of expectations
  • Sales: tailored offers, sales force allocation, differentiated follow‑up, loyalty‑building

Typology is the missing link between raw data and strategic business action.


10. Summary: best practices and key watch‑outs

  • A good typology always starts with in‑depth, well‑thought‑through reflection on the objectives expected from this typology
  • Given the objectives, carefully select the variables that are useful for the analysis
  • Prepare the data (cleaning, dimensionality reduction)
  • Choose the appropriate method (HAC for exploration / k‑means for operational use)
  • Test the stability of groups across several algorithm runs and multiple datasets
  • Ensure business readability of the results: each segment must resonate with the field and be operational!

11. Going further: typology and segmentation strategy

  • Think in terms of “evolving segmentation”: groups change with the market and over time; a typology must be challenged regularly.
  • Integrate typology into regular analysis cycles: pre‑ad and post‑ad tests, barometers, competitive intelligence, etc.
  • Showcase the typology to stakeholders: visual interpretation, business storytelling, operational deployment, etc.

12. Conclusion: typology as a pillar of modern business analytics

Typology offers unmatched analytical depth to segment economic reality, anticipate, personalize, and innovate. As a key enabler for marketing, innovation, and R&D, it turns data into added value. Its great strength is to bring out profiles, give relief to diversity, and transform complexity into actionable opportunities.

Beyond merely capturing a snapshot of behaviors, a robust typology becomes a true steering tool: it clarifies strategic trade‑offs, guides product or service evolution strategies, and helps prioritize investments on the most promising segments. By tightly connecting statistical methods, a fine understanding of audiences, and business constraints, it creates a common language between data, marketing, top management, and operational teams. Well‑designed, updated, and shared, typology is no longer an isolated deliverable, but a living strategic asset at the heart of the company’s decisions and innovations.


Want to go further with your own data?

If you already conduct marketing studies or drive innovation through surveys, you probably have the raw material needed to derive real value from an advanced typology analysis.

At Stat & More, we support our clients end‑to‑end:

  • From defining the expected objectives to implementing TYPOLOGY algorithms tailored to the study’s goals.
  • From structuring the data to analyzing the TYPOLOGIES produced.
  • From producing detailed reports to delivering PERSONAE that illustrate each group highlighted in the typology.
  • From analyzing study results to formulating operational recommendations that integrate your business and brand strategy constraints.

To learn how TYPOLOGY can be applied to your own marketing objectives, we invite you to visit our website and contact us:

Would you like to discuss a concrete case, build a typology from your data, or deploy a typology aligned with your marketing strategies? Contact us today to discuss your challenges and jointly build a TYPOLOGY that fits your needs.


To explore typology methodology and its applications in more depth, see our previous article on the Stat & More blog.


REFERENCES :

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✔ Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27(4), 857‑871. https://distancia.readthedocs.io/en/latest/Gower.html

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✔ MathWorks. (n.d.). Cluster using Gaussian mixture models. MATLAB & Simulink documentation. https://www.mathworks.com/help/stats/clustering-using-gaussian-mixture-models.html

✔ Escofier, B., Pagès, J. (2008). Analyses factorielles simples et multiples : objectifs, méthodes et interprétation. Dunod.

✔ Saporta, G. (1990). Les méthodes d’analyse d’enquêtes. In Les méthodes d’analyse de données (chap. 5). https://pdfs.semanticscholar.org/d9ac/f767dd80e72088c6c53b1253cb52b4530a56.pdf

✔ Chesneau, C. (2013). Éléments de classification : classification hiérarchique ascendante (CAH) et méthodes de partitionnement. Lecture notes, Université de Caen. https://chesneau.users.lmno.cnrs.fr/classif-cours.pdf