What are some of the criteria and factors that influence your market segmentation decisions?

This is an "AI-powered collaborative article" that does include the writings of Al Ries and Jack Trout who literally wrote the book on how consumers’ perceptions of market segmentations and how brands are positioned (and repostioned) relative to competitors. The Book: “Positioning: The Battle for Your Mind.”

When it comes to our market segmentation decisions, we prioritize a multidimensional approach. Demographics such as age, income, and education level guide us in tailoring our products/services to specific needs. Geographical considerations enable us to adapt to regional preferences and cultural nuances, ensuring our offerings resonate locally. Behavioral patterns offer valuable insights, shaping targeted strategies aligned with customer preferences. Psychographics, including values and lifestyles, allow for a more personalized connection. Technological trends inform our online presence, staying aligned with our audience's preferences. Monitoring the competitive and regulatory landscape ensures strategic adaptability.

Some of the criteria and factors that influence market segmentation decisions include demographic characteristics, psychographic profiles, behavioral patterns, geographic location, purchasing behavior, industry trends, and product/service usage data.

When considering market segmentation, it's crucial to define your purpose clearly. Ask yourself why you're segmenting your market and what you aim to achieve. For instance, if your goal is to uncover new opportunities and niches, you might focus on factors like unmet needs, pain points, or customer feedback. On the other hand, if you're aiming to refine your pricing and positioning strategies, you could consider criteria such as willingness to pay, perceived value, or purchase behavior. By aligning your segmentation criteria with your objectives, you can gather insights that enhance your market intelligence, improve customer experiences, and gain a competitive edge.

> Age, gender, income, education, and location. > Lifestyle, interests, values, and attitudes. > Purchase frequency, usage patterns, brand loyalty, and price sensitivity.

You need to reflect on what you think will be the main factors of any segmentation. For a fast food company, it may be demorgraphics like young men vs Mums/Moms. For a b2b business, it might be frequency of purchase. The analysis may throw up a variety of different segments but you, as the client, should think about what you expect to see as the main criteria.

Diversity is integral to our market segmentation strategy. Recognizing and celebrating the diverse needs, preferences, and lifestyles within our target audience allows us to create inclusive and resonant campaigns. By understanding the unique characteristics of different demographic groups, we tailor our products and messaging to reflect the richness of our customer base. This not only strengthens our brand's relevance but also fosters a sense of belonging among our diverse customer segments. Embracing diversity in market segmentation is not good business; it's a commitment to understanding and serving the varied tapestry of our consumer landscape.

Uno de los factores más importantes para mí al momento de una segmentación es conocer a fondo cuales son pos gatillos que disparan la motivación de compra de un producto o servicio de un cliente. Para esta investigación utilizo herramientas como un mapa de empatia, un buyer persona, entrevistas de profundidad acompa?ado de un atomic research y hasta un estudio aplicando la técnica de los Jobs To Be Done; estos me ayudan a comprender bien no sólo quién es el cliente, sino qué es lo que lo motiva a comprar, a descubrir insigths que me ayuden a perfeccionar el mensaje y hasta desarrollar segmentaciones psicográficas que se adapten a clientes específicos. Una segmentación no sólo es demográfica ya que se puede segmentar absolutamente todo.

For instance, if your goal is to identify new niches, you might focus on criteria like unmet needs or customer feedback. On the other hand, if you're looking to refine pricing strategies, factors such as willingness to pay or purchase behavior might be more relevant. By aligning your segmentation criteria with your objectives, you can derive actionable insights that drive market intelligence and competitive advantage.

I've traditionally used a large scale quantitative survey to segment a customer base. But you may also have large volumes of data either from a loyalty scheme or from your CRM database. Credit/Debit/Checking card spend analysis is allowing many organisations now to segment their customers into meaningful groups. This data includes demographics, spend levels and frequency with your brand, with your competitors but also with a range of other retailers.

In the era of Artificial Intelligence, leveraging diverse data sources is paramount. Beyond traditional datasets, incorporating real-time, unstructured data from social media, customer interactions, and IoT devices enriches AI models. Quality, labeled data is the backbone, ensuring accurate training and model performance. Collaborative efforts with external data providers and partnerships amplify data variety. Ethical considerations and privacy compliance are integral, to maintaining trust. Continuous adaptation to evolving data landscapes and emerging sources sharpens AI's predictive capabilities. In AI times, the fusion of structured and dynamic data fuels innovation, allowing us to unlock deeper insights and enhance decision-making.

I've used cluster analysis or factor analysis. But I have always found it helpful to create several options for segments. Do we prefer this 3-segment option based on age? Or this 4-segment option based on geography? Or this 2-segment option based on frequency and loyalty? By looking at a menu of options with colleagues you can decide which will land best in the organisation. Analyses can and should be run several times dialling up or suppressing the importance of different variables.

In the realm of Artificial Intelligence, the most effective analysis methods are rooted in a blend of machine learning algorithms, deep learning, and natural language processing. Harnessing the power of predictive analytics enables us to extract meaningful insights from vast datasets. Ensemble methods, combining diverse models, enhance accuracy and robustness. Explainable AI ensures transparency, crucial for understanding complex models. Continuous learning through reinforcement and unsupervised learning refines AI systems over time. Embracing a multidisciplinary approach, where data scientists collaborate with domain experts, maximizes contextual relevance.

The utility is essential. Will people in the organisation believe the results? Will they embrace and move ahead with them? I recently worked on a segmentation based on high value, loyal customers and a long tail of lower value, less loyal customers. We had other possible segments available but the energy in the organisation was for this frequency-based approach as so many people felt passionate about this already.

In market division, it's crucial to predict and tackle the obstacles and restrictions that might come up. Some of the common mistakes include data accessibility and accuracy such that the segmentation process can be disturbed. There is also a risk of data private life and safety which make it necessary to manage properly in order for customers’ information not to be compromised. Another complication of handling and interpreting the figures is the possibility of misclassification among partitions.

It is tempting to have lots of complex segments but in reality most organisations can only cope with one or two segments. The best and most successful organisations I have worked with use just one or two segments. A very successful womens fashion retailer had one segment called "Emma" and they had a laser focus on her. A fast food retailer I worked with had "Young men" and "Mums/Moms". I am neither of those but when I go in to their restaurants with my young son, the Mum offers (great kids meal, coffee, safe environment) are great for us. When I go alone, I can enjoy the big greedy burger and fries they make for the "Young men" even if I am no longer young myself!

Uno de los factores más importantes para mí al momento de una segmentación es conocer a fondo cuáles son los gatillos que disparan la motivación de compra de un producto o servicio de un cliente. Para esta investigación utilizo herramientas como un mapa de empatía, un buyer persona, entrevistas de profundidad acompa?ado de un atomic research y hasta un estudio aplicando la técnica de los Jobs To Be Done; estos me ayudan a comprender bien no solo quién es el cliente, sino qué es lo que lo motiva a comprar, a descubrir insights que me ayuden a perfeccionar el mensaje y hasta desarrollar segmentaciones psicográficas que se adapten a clientes específicos. Una segmentación no solo es demográfica, ya que se puede segmentar absolutamente todo.