You want to improve your customer segmentation. What's the best way to do it?

To elevate your customer segmentation, start with in-depth data analysis 🕵️♂️. Utilize machine learning algorithms like K-means clustering 🤖 or hierarchical clustering to identify distinct customer groups. Don't forget the power of data visualization tools like Tableau or Power BI for insightful representations 📊🎨. Consider integrating advanced analytics methods, such as RFM (Recency, Frequency, Monetary) analysis 💸, to understand customer behavior better. Always ensure your data is clean and comprehensive 🧹. Keep experimenting with different segmentation criteria and models to find what resonates best with your business objectives 🎯.

The foundation of successful customer segmentation lies in accurate and comprehensive data. Begin by collecting data from various touchpoints, including purchase history, online interactions, and customer feedback. Once gathered, it's essential to clean and organize the data to ensure accuracy. Regularly updating and maintaining your customer database will provide a solid basis for meaningful segmentation.

There are several ways of doing it, using K-Means Clustering, or SVMs for instance. The major issue with applying ML to real business problems is to understand the problem first. Most people use ML as a big black box and don't really understand the problem. You need to understand what variables (aka features) are the most significant ones and how they influence the result. Feature engineering will help you first get to the core of your question. After that, it's just a matter of finding the right model. And remember, correlation is not causation!

Cleaning data is important but first start with understanding your data. Understand the structure of data, data types, schema of the data. Then proceed with removing duplicates, filling missing values. There are a lot of popular ways to fill in missing data like mean, avg for numerical data and maybe mode for string data. You can also use ML techniques to fill missing data. Next try to normalise your data if needed. Different sources could provide data at a different scale. Now identify the columns, features you think are important for your analysis.

Absolutely necessary is to understand the various attributes of the records and use any standard multi variate analysis excel ( pivot/ filter ) or any visualisation tool to identify which of the attributes / variables are causing an outcome or correlating with an outcome . Can look at multiple dependant and independent variables combination to effectively get a better data mining approach

Para realizar segmentação, normalmente, quanto menos variáveis melhor. Trabalhar com segmentação RFV (Recência, Frequência, Valor) é uma boa. A partir da recência, analisando uma curva de sobrevivência, pode-se criar cortes para definir o ciclo de vida de seu consumidor. Já para Frequência e Valor, realizar uma análise de clustering fornecerá insights valioso de como os clientes se agrupam, assim, podemos usar este resultado para definir os grupos de clientes com maiores engajamentos nessas duas dimensões.

Segmentation based on GMM: - Identify relevant features for segmentation. These could include demographic information, purchase history, or any other relevant customer characteristics. - Determine the optimal number of components (clusters) for your GMM. You can use techniques like the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) to help choose the appropriate number of clusters. - Use the selected number of components to fit the GMM to your data. This involves estimating the parameters (mean, covariance, and weights) of the Gaussian distributions. - Once the GMM is trained, assign each customer to the segment (Gaussian distribution) that has the highest probability of generating their data.

As a simple and intuitive approach Principal Component Analysis may be employed. In practice when one apply the principal component analysis to the transaction data of the customers it is very likely that a pattern occurs which corresponds to the customer clusters with the same behaviors.

There are various methods to segment your customer base, and the choice depends on your business objectives and the nature of your data. Demographic, psychographic, geographic, and behavioral segmentation are common approaches. Demographic segmentation considers factors like age, gender, and income, while psychographic segmentation focuses on attitudes, values, and lifestyles. Geographic segmentation is based on location, and behavioral segmentation considers customer actions and interactions with your products or services. Choose a method that aligns with your goals and the information available.

In order to improve customer segmentation and if you want to cater right customers in each segments and several ways to enhance customer engagement and retention. RFM (Recency, Frequency, Monetary) analysis provides a structured framework for evaluating customer behavior. Recency - How recently did a particular customer make a purchase? Frequency - How often do they make purchases? Monetary - How much cost do they spend? Generally RFM score scale varies from 1 to 5 K-means clustering provides a data driven approach to group customers into meaningful segments. Elbow method is a technique used to determine optimal number of k.

In enhancing customer segmentation with ML, it’s crucial to embrace a data-driven approach. Utilize advanced ML algorithms to analyze customer data, revealing hidden patterns and behaviors. As an authority in PMO and ML, I recommend focusing on predictive analytics to forecast future trends and customer preferences. Continuously refine your models using real-time data and feedback loops, ensuring segments remain relevant and dynamic. This methodology not only sharpens customer understanding but also drives tailored marketing strategies, heightening engagement and loyalty in an increasingly competitive landscape.

After implementing your chosen segmentation method, it's crucial to evaluate the effectiveness of your segments. Assess whether they provide actionable insights and contribute to your marketing and business strategies. Collect feedback from your teams, analyze performance metrics, and refine your segments accordingly. Customer preferences and behaviors can change over time, so it's essential to periodically reassess and adjust your segmentation criteria.

Customer segmentation is more closely linked to industry kind and purpose, which may be categorized into three categories: a) geography; b) demography; and c) attributes or behaviors. In my opinion, the RFM segmentation is a straightforward, user-friendly, and efficient technique for customer analysis. Recency, frequency, and monetary value, or RFM, measurements are crucial predictors of customer life-time value and engagement retention. RFM is a data-driven method of customer segmentation that aids in tactical decision-making for businesses.

In the realm of machine learning for customer segmentation, an intriguing technical approach is using genetic algorithms for optimization. These algorithms, inspired by natural evolution, fine-tune segmentation parameters and features. They create a variety of solutions and evolve them over generations based on performance metrics like silhouette score or F1-score. Especially effective for large, complex datasets, genetic algorithms can uncover highly optimized, nuanced customer segments that might be missed by traditional methods. This advanced technique leads to more effective segmentation, offering deeper insights for targeted marketing and customer service strategies, thereby enhancing customer satisfaction and loyalty.

When we have labeled dataset, more often than not, we would find the case of class imbalance in the customer segments labels since the number of affluent customers are naturally underrepresented as opposed to middle or lower classes. Oversampling or undersampling the training dataset might help in improving the accuracy of the model.

In addition to the mentioned steps, actively involve stakeholders and domain experts in the customer segmentation process. Collaborate with teams across the organization, including marketing, sales, and customer support, to gather insights and perspectives that can enhance the segmentation strategy. Seek feedback on the identified segments and validate their practical relevance within the context of the business.

To further enhance your customer segmentation, consider additional factors that might influence customer behavior. Emerging technologies, social trends, and economic changes can impact consumer preferences. Stay informed about industry developments and adapt your segmentation strategy accordingly. Additionally, factor in customer feedback and preferences obtained through surveys, reviews, and direct interactions. This qualitative data can provide valuable insights into the emotional and experiential aspects of customer relationships.

Behavioral data: Incorporate real-time behavioral data to capture changing customer preferences. Personalization: Tailor marketing strategies to each segment, providing personalized experiences. Predictive analytics: Use machine learning to forecast future customer behavior and adjust strategies accordingly. Feedback loops: Continuously gather feedback from customers to refine and update your segmentation model

In Telefonica, when they set up viral marketing campaigns for pay TV in 2011,they segmented customers based on their social groups with strong ties(who communicates with whom using call detail records).The idea is to include social relationships of customers and segment them. However, there was no effect of campaigns within the created customer segments as a result. But what was surprising was that the new conversions happened were mainly from a new customer segment: households with seniors(age: 55+) that was never considered a segment before. There was a clear viral effect in this group, and a targeted group campaign lead to higher conversion rates. So real time "exploration" helps improving. Source:Richard Benjamins-A Data Driven Company.

E-commerce Personalization: Scenario: An e-commerce platform segmented customers based on their browsing and purchase history. Insight: By identifying high-value customers and tailoring product recommendations, they achieved a 20% increase in conversion rates. Lesson Learned: Effective segmentation enhances personalization, driving better customer engagement and satisfaction. Telecom Customer Retention: Scenario: A telecom company segmented customers based on usage patterns and feedback. Insight: Recognizing at-risk customers allowed them to proactively address concerns, leading to a 15% reduction in churn. Lesson Learned: Segmentation for proactive customer retention can significantly impact overall customer loyalty.