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Tableau > Case Studies > Online Retailer Digs Into Revenue Change with Web Analytics and Tableau
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Online Retailer Digs Into Revenue Change with Web Analytics and Tableau

Technology Category
  • Analytics & Modeling - Big Data Analytics
  • Application Infrastructure & Middleware - Data Visualization
Applicable Industries
  • Retail
Applicable Functions
  • Sales & Marketing
Use Cases
  • Demand Planning & Forecasting
Services
  • Data Science Services
The Challenge
The online retailer experienced a significant jump in revenue growth, which doubled. However, the cause of this increase was unknown, leading to concerns. The company had to analyze various factors, including the events in a typical conversion funnel, to understand the reason behind the revenue spike. The traditional conversion funnel elements were not applicable in 2008, and it was discovered that revenue was growing because people were buying higher-priced items. The company then sought to determine the root cause of this increased revenue per unit.
About The Customer
The customer in this case study is an online retailer that experienced a significant increase in revenue growth. The company uses web analytics to understand the behavior of its customers and the factors that influence their purchasing decisions. The retailer is committed to providing an excellent experience for its loyal customers and is interested in understanding the patterns of new customers versus loyal customers. The company uses Omniture’s SiteCatalyst software to download and analyze various reports, and Tableau for data visualization.
The Solution
The company used web analytics and data visualization tools to analyze the factors contributing to the revenue spike. They downloaded almost every report that Omniture’s SiteCatalyst software had to offer and combined them into a massive Excel file. From there, they proceeded to look for the causal factors in the spike. They analyzed the events in a typical conversion funnel, from initial site visit all the way to purchase. They also analyzed the revenue patterns generated by new customers vs. loyal customers. The analysis revealed that the jump in revenues was due to loyal customers ordering more when they came to the site.
Operational Impact
  • The company was able to identify the cause of the revenue spike.
  • The analysis revealed that loyal customers were comfortable with the site, the products, and the service and were thus ordering more.
  • The company identified that to continue to grow revenues, they should focus on maintaining an excellent experience for loyal customers.
Quantitative Benefit
  • Revenue growth doubled.
  • Loyal customers were ordering more when they visited the site.

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