Customer data analytics using Python

Explore the fundamentals of data analytics as an umbrella term that uses raw data to draw conclusions and reveal patterns in consumer behavior and team performance, marketing and supply chains.

Learn the four analytics types—descriptive, diagnostic, predictive, prescriptive—and how they reveal what happened, why, what will happen, and how to act, as shown in the data-to-wisdom pyramid.

Explore customer analytics using python to uncover insights that drive timely offers and revenue growth. Identify profitable channels, optimize retention and engagement, and enhance the journey from awareness to advocacy.

Leverage predictive analytics to model and forecast future customer behavior, target prospects, solve problems, and retain customers with data-driven insights from rich data sources and large-scale processing.

Explore the predictive customer analytics process, from identifying data sources and building a data pipeline into your data warehouse, through data wrangling, modeling, testing, deployment, monitoring, and continuous refinement.

Identify and leverage diverse data sources: customer, products and services, agents, and channels, to feed predictive analytics models with attributes and interaction events signaling customer behavior.

Define goals and collect the right data to build a customer analytics framework. Clean, manipulate, and analyze data with statistical tools and visualize insights to drive action.

Explore the customer lifecycle as a relationship continuum where acquisition starts the journey and long-term retention, service, and selling other products drive revenue.

Apply predictive analytics to every stage of the customer lifecycle, from acquisition through attrition prevention. Optimize channels, timing, pricing, upsell opportunities, and proactive service.

Use python to estimate customer lifetime value with linear regression, train-test split, and correlation analysis on six months of revenue to predict customer lifetime value for new customers.

Learn how collaborative filtering uses user, item, and affinity score data to recommend products and media, with practical calculations demonstrated on Amazon and Netflix.

Identify affinity scores through collaborative filtering to recommend products based on user purchases, using pandas in Python and a Jupyter notebook workflow.

Explore how analytics boost new customer acquisition and retention through Nike and Wal-Mart case studies, using predictive modeling, personalized data, and direct-to-consumer insights to optimize inventory and buying propensity.

Discover how Netflix leverages customer analytics to boost retention and return. Identify how developing a user persona, collecting customer interaction data, and a robust feedback system enable personalized recommendations.

Explore customer data in Python by loading a dataset in a Jupyter notebook, linking it with pandas, and inspecting rows, columns, and basic statistics, including null checks.

Visualize your data in Python with matplotlib by importing the library and generating histograms and scatterplots to explore relationships between website visits and purchases.

Analyze marketing and customer data with python to explore impressions, ctr, and CPA, visualize patterns with pivot tables and heat maps, and optimize paid-search ROI.