Customer Segmentation Analytics Masterclass 2024

Explore customer segmentation analytics to identify groups by demographics, geography, behavior, and psychographics. Apply predictive analytics and machine learning to forecast trends and tailor data-driven marketing and sales campaigns.

Analyze customer data to identify segments and tailor targeted marketing campaigns for sales, marketing, analytics, product management, and entrepreneurship. Apply segmentation analytics to maximize the value of each audience segment.

Explore customer segmentation analytics across pre-sales, sales, and after-sales analytics, and learn to prepare tools, apply supervised and unsupervised learning, and analyze a real-world case study for targeted campaigns.

Learn the fundamentals of customer segmentation, identify segments by demographics, geography, behavior, and psychographics, and apply predictive analytics and machine learning to targeted marketing and sales.

Discover how sales analytics uses data to reveal insights into sales performance, customer behavior, and market trends, and to optimize pricing, inventory, and marketing strategies.

Leverage pre-sales analytics to collect and analyze customer data for lead scoring, predictive analytics, and segmentation, using surveys, website and social media analytics, with pricing analytics to optimize conversions.

Leverage data analytics to optimize the sales phase by analyzing sales data, forecasting revenue with predictive analytics, and improving the sales pipeline and dashboards through cross selling strategies.

Harness after sales analytics to boost customer satisfaction and retention through customer satisfaction surveys, text analytics, real-time dashboards, churn risk analytics, share of wallet, and social listening.

Collect and analyze customer data to identify segments by demographics, behaviors, and preferences, then tailor marketing and sales strategies in presales to boost engagement and satisfaction.

Explore data analytics tools: Anaconda, Jupyter Notebook, and Python, along with packages like NumPy, pandas, matplotlib, and scikit-learn. Learn environment management, interactive exploration, visualization, and data manipulation and analysis.

Download and install Anaconda, then launch Jupyter Notebook. Create notebooks, manage cells, and run Python code with print, variables, and mathematical functions for mastering sales data analytics.

Download the latest Python version of Anaconda for your operating system, then run the installer, accept the license, choose an install location, and optionally add Anaconda to your path.

Learn to set up and use Jupyter notebook in Python via Anaconda Navigator, create notebooks, manage cells, run code, and leverage file, edit, and kernel controls.

Master sales data analytics by installing Anaconda, launching Jupyter Notebook, and writing Python code in notebooks and cells to define variables, run computations, and explore print and math functions.

Explore analytics basics and machine learning algorithms—supervised regression analysis and unsupervised clustering—to analyze large sales data, uncover patterns, and segment customers for targeted marketing and higher conversion.

Explore labeled and unlabeled data and how supervised and unsupervised learning drive customer segmentation, with examples from purchase patterns, browsing behavior, and marketing optimization.

Explore how supervised machine learning uses labeled data to build predictive models for unseen data. Apply decision trees and linear regression to forecast sales and inform strategy.

Explain how simple linear regression uses least squares to fit a line y equals m x plus b, and predict revenue from marketing budget.

Explore evaluation metrics for supervised learning, including accuracy, precision, recall, and f1 score for classification, and mean squared error and r-squared for regression, plus receiver operating characteristic curves.

Explore unsupervised machine learning techniques like k-means and PCA to uncover hidden patterns in sales data, segment customers by purchasing behavior, and reveal drivers of sales trends.

Master k-means clustering to segment customers by satisfaction and loyalty using euclidean distance and centroids, with standardization and the elbow method to choose cluster count, and derive actionable retention strategies.

Evaluate unsupervised learning with clustering metrics such as silhouette score and Kilinski, Zaruba's index; visualize results via scatter plots and dendrograms; apply PCA or t-SNE with reconstruction error.

Use supervised regression to predict sales from price, advertising, and product features. Apply unsupervised clustering to reveal customer segments for targeted marketing and data-driven sales optimization.

Learn how k-means clustering segments 200 customers by age, income, and spending score to tailor marketing strategies and drive growth through measured retention and revenue.

Explore collecting b2b and b2c sales data from crm, pos, web and social analytics, then apply k-means on a 200-customer dataset to segment by age, income, and spending score.

Explore data with python tools like pandas and seaborn to load and inspect a csv. Describe statistics, check for missing values, and visualize distributions to prepare for clustering with k-means.

Perform two dimensional clustering of customers by age and spending score using k-means and the elbow method to determine four clusters, visualized with a scatter plot.

Cluster customers by annual income and spending score using k-means, choose five centroids via the elbow method, and interpret distinct groups such as lower opportunities, well-off shoppers, and high earners.

Explore 3d clustering of customers using age, income, and spending score, determine six clusters with elbow method, visualize with interactive 3d scatter plot, and export the clustered data to csv.

Identify four customer clusters: frugal, senior, young, and junior; and five income-based groups, then tailor affordability, quality, exclusivity, and convenience to drive spend.

Personalize marketing strategies based on customer segmentation to boost engagement, sales, and loyalty, using targeted messaging, location-based marketing, and predictive analytics.

Define marketing goals and evaluate strategies with customer segmentation analytics, measuring KPIs like customer acquisition rate, retention rate, conversion rate, and customer lifetime value to optimize high value segments.

Apply k-means clustering to segment customers by age, income, and spending score, using a real-world 200-customer data set, and personalize marketing to boost retention, loyalty, and revenue.

Explore segmentation fundamentals and analytics insights in the customer segmentation analytics masterclass to drive growth by targeting and acquiring customers, using predictive analytics and data visualization.

Master customer segmentation analytics to tailor offerings and marketing campaigns for different customer groups. Make data-driven decisions to boost sales and marketing success.

Celebrate completing this course and apply your tools and knowledge to excel in customer segmentation analytics and sales data analytics, advancing your professional development as a lifelong learner.

Master customer segmentation analytics and the full sales cycle with TMA's pre-sales, sales, and after-sales courses on pricing, campaign analytics, social media analytics, revenue forecasting, and customer lifetime value analytics.

TMA members club is an exclusive LinkedIn community for students, offering early access to TMA insights, research updates, events, discounts, and opportunities to shape future projects through networking.

Dive into advanced sales data analytics techniques to extract insights from complex data sets and drive informed business decisions, while unlocking exclusive discounts on additional courses.