In this first chapter, you’ll build a dataset for sales and expense analysis. You’ll combine multiple files, filter out invalid data, and create metrics for customers and orders. Using a self-join on sales data, learn to create a correlation matrix.
Use the correlation matrix to determine products that are frequently bought together.


Analyzing ecommerce data in Tableau

Build a product sales data source

Product costs exercise

Data clean up

Create calculated metrics for customers

Create order and shipping metrics

Data source for market basket analysis

Standardize attribute names and values

Market basket visualization

Data Exploration

Next, you’ll create metrics for the most profitable products and customers. You’ll standardize location names and build a map of sales by state, before exploring the shipping cost metrics. Finally, you’ll build a what-if analysis to display the impact of shipping higher quantities on shipping costs.

Ecommerce analytics

Add state-region mapping

Shipping distance

Sales by state exercise

Customer lifetime value by state

Metrics for what-if analysis

Shipping costs by product

What-if analysis

Ecommerce Analytics

In the final chapter, you’ll build multiple dashboards and present them as a coherent story. You’ll display the product and customer attributes that will result in the highest ROI for the upsell/cross-sell campaign. To round off, you’ll recommend specific actions to reduce shipping expenses.

Creating a cohesive story

Revenue by location and top sellers

KPIs in Big Angry Numbers (BANs)

Build an executive summary

Profit percentage treemap

Profit percentage exercise

Growth opportunities dashboard

Shipping costs dashboard

Present your story

Wrap-up

Visualize Your Analysis

Workbooks and Datasources

Metadata sheet

Are you ready to apply your Tableau skills to solve practical business problems? For ecommerce businesses, increasing sales and reducing expenses are top priorities. In this Tableau case study, you'll investigate a dataset from a fictitious online pet supply company called Munchy's.
You analyze the sales dataset and find out which products are frequently bought together and in what quantity. You will then make upsell and cross-sell recommendations by building a market basket analysis. Shipping costs remain a big expense for online retailers. You will consider strategies such as shipping more of the same product to reduce per-unit costs. You will understand where the customers are and what they buy, and you will make recommendations to the management for a new warehouse location.
 You'll answer these questions by creating calculated fields and various visualizations in Tableau, such as dual-axis graphs, highlight tables, and maps. You'll use filters and parameters to make your graphs dynamic, and then combine everything into a story to share your findings.

Are you ready to apply your Tableau skills to solve practical business problems? For ecommerce businesses, increasing sales and reducing expenses are top priorities. In this Tableau case study, you'll investigate a dataset from a fictitious online pet supply company called Munchy's.
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<h2>Use Your Tableau Skills With a Practical Case Study</h2>
First, you’ll analyze the sales dataset and find out which products are frequently bought together and in what quantity. You will then make upsell and cross-sell recommendations by building a market basket analysis.  
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Next, you’ll need to address the fact that shipping costs remain a big expense for online retailers. You’ll work on strategies such as shipping more of the same product to reduce per-unit costs. You will also need to understand where the customers are and what they buy to make recommendations to the management for a new warehouse location.
<h2>Analyze The Data and Visualize Your Findings</h2>
As you collate your findings, you’ll create calculated fields and various visualizations in Tableau, such as dual-axis graphs, highlight tables, and maps. Finally, you'll use filters and parameters to make your graphs dynamic and combine everything into a story to share your recommendations.


Analyzing Data in Tableau

In ecommerce, increasing sales and reducing expenses are top priorities. In this case study, you'll investigate data from an online pet supply company.

Case Study: Ecommerce Analysis in Tableau

Head of Business Intelligence

Analytics pipeline with Tableau

Our final deliverable

Sharing dashboard with Tableau Public

Congratulations!

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Visualize Your Analysis - Wrap-up

Wrap-up

1. Wrap-up

2. Analytics pipeline with Tableau

3. Our final deliverable

4. Sharing dashboard with Tableau Public

5. Congratulations!

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