Behind an e-commerce site selling many thousands of live events, with inventory from thousands of ticket suppliers who can and do change prices constantly, and all the historical data on prices for this and similar events, layer in customer bidding behavior and you have a big data opportunity on your hands. I will talk about the evolution of pricing at ScoreBig in this framework and the models we've developed to set our reserve pricing. These models and the underlying data are also used by our inventory partners to continue to refine their pricing. I will also highlight how having a name your own price framework helps with the development of pricing models.

Learn how leading retailers are developing key findings in digital data to be leveraged across marketing, merchandising, and IT.

With the latest release of SAS^® Business Rules Manager, decision-making using SAS^® Stored Processes is now easier with simplified deployment via a web service for integration with your applications and business processes. This paper shows you how a user can publish analytics and rules as SOAP-based web services, track its usage, and dynamically update these decisions using SAS Business Rules Manager. In addition, we demonstrate how to integrate with SAS^® Model Manager using SAS^® Workflow to demonstrate how your other SAS^® applications and solutions can also simplify real-time decision-making through business rules.

Before the Internet era, you might not have come across many Sankey diagrams. These diagrams, which contain nodes and links (paths) that cross, intertwine, and have different widths, were named after Captain Sankey. He first created this type of diagram to visualize steam engine efficiency. Sankey diagrams used to have very specialized applications such as mapping out energy, gas, heat, or water distribution and flow, or cost budget flow. These days, it's become very common to display the flow of web traffic or customer actions and reactions through Sankey diagrams as well. Sankey diagrams in SAS^® Visual Analytics easily enable users to create meaningful visualizations that represent the flow of data from one event or value to another. In this paper, we take a look at the components that make up a Sankey diagram: 1. Nodes; 2. Links; 3. Drop-off links; 4. A transaction. In addition, we look at a practical example of how Sankey diagrams can be used to evaluate web traffic and influence the design of a website. We use SAS Visual Analytics to demonstrate the best way to build a Sankey diagram.

Crowd sourcing of data is growing rapidly, enabled by smart devices equipped with assisted GPS location, tagging of photos, and mapping other aspects of the users' lives and activities. A fundamental assumption that the reported locations are accurate within the usual GPS limitations of approximately 10m is made when such data is used. However, as a result of a wide range of technical issues, it turns out that the accuracy of the reported locations is highly variable and cannot be relied on; some locations are accurate but many are highly inaccurate, and that can affect many of the decisions that are being made based on the data. An analysis of a set of data is presented that demonstrates that this assumption is flawed, and examples of the levels of inaccuracy that has significant consequences in a range of contexts are provided. By using Base SAS^®, the paper demonstrates the quality and veracity of the data and the scale of the errors that can be present. This analysis has critical significance in fields such as mobile location-based marketing, forensics, and law.