Marketing Analytics Using R and Excel

Learn how marketing analytics measures and optimizes ROI by consolidating data from blogs, social media, impressions, and click-through rates to guide future marketing decisions.

Explore marketing metrics across product, price, and promotion to benchmark performance, quantify opportunities and cannibalization effects, and forecast revenue using analytics and data preparation.

Explore marketing planning metrics to evaluate industry structure, identify major players, and assess your relative position using market share, unit and revenue shares, and concentration ratios.

Explore market effectiveness through market share, market penetration, and brand penetration, and learn to apply the BCG matrix: star, cash cow, question mark, and dog, to assess growth and strategy.

Explore customer service metrics such as satisfaction surveys, Net Promoter Scores, Voice of the customer, and customer tone analysis, alongside profitability, product, and customer value metrics to guide marketing analytics.

Outline the sales channel management metrics from manufacturing to customer, covering sales, customer value, buying power, inventory turnover, stock levels, promotions, and digital metrics like impressions and click-through rate.

Apply marketing analytics to increase debit card usage by segmenting customers, predicting segments, and delivering targeted offers and promotions, using data preprocessing, normalization, and missing-value handling to improve transaction insights.

Apply autism metrics to segment customers by recent activity, then use clustering and machine learning to tailor offers and boost debit card usage for existing and new customers.

Explore a bank case study on net promoter score, distinguishing promoters from detractors, and show how service delivery and perceptions drive customer loyalty.

Analyze how customers rate banks using NPS, examining loyalty programs, product ranges, website and mobile banking, and the expectation–perception gap across branches.

Develop a regression model to determine the dependent variable customer service index from independent variables, identifying convenience and related factors as the most significant drivers, with 72% model accuracy.

Analyze the customer service index and net promoter score across public and private banks using marketing metrics. Evaluate promoters, detractors, and sample size considerations to derive actionable insights.

Explore how conjoint analysis helps identify which laptop attributes—price, brand, operating system—drive Indian consumer choices, using cluster-based segmentation to tailor marketing strategies.

Apply machine learning clustering to airline customer data to segment markets, personalize loyalty messages, and optimize marketing ROI using R and Excel with loyalty program variables.

Cluster 4000 airline customers into five groups with k-means after descriptive statistics and data preprocessing, revealing each cluster's average balance and miles to inform targeted strategies.

Learn how marketing analytics use machine learning to predict telecom churn, distinguishing voluntary and involuntary turns, and apply logistic regression and decision methods to retain customers.

Examine a logistic regression model for telecom churn using demographic and usage variables, including international plan, voicemail, minutes, calls by time of day, and customer service calls.

Explore predicting telecom customer churn using logistic regression, computing predicted probabilities, and evaluating accuracy, plus exploring a decision tree to identify influential variables.

Explore product-service bundling with machine learning, market basket analysis, and association rules to recommend cross-sell bundles, boost share of wallet, and enhance e-commerce customer experience.

Explore how an association algorithm uses transaction data to build item sets, compute support, form two-item combinations, and derive if x then y to boost wallet share.