Below are a few visualizations to provide insight into the type of analysis that we can provide and the deliverables we can offer.
Example 1: Transaction Analysis for a Retail or Service Business
The following visualization options provide a retail or service business with the tools needed to track key metrics over time. With this series, business users can easily identify trends in key areas and look at results compared to previous periods or budgets. The ability to compare actual results, prior period results, and budgets as well as the ability to drill down to a more detailed level of data to investigate variances is instrumental in answering questions and identifying topics for further research that previously weren’t on the radar. These charts represent a small sample of the potential analytical approaches using Tableau. The data was obtained from summary level tables exported into Excel from a point-of-sale (POS) system and then imported into Tableau for purposes of this presentation.
Example 2: What Are My Customers Doing?
The data set in this example contains 1.15 million rows of transaction data from more than 300,000 customers over a 3 ½ year period covering multiple locations. The data only contains a partial year for 2011 and ends in March 2015. This study provides insight as to when customers first became customers, how many times they have visited the business, what they have purchased, and how coupons/discounts have affected their behavior. These visualizations have drill down capabilities that allow subsets of the data to be exported for further examination. The visualizations are just a small sample of the types of analysis that can be performed.
Example 3: Monthly Statistics in Power Pivot
These tables provide a glimpse into two powerful tools from Microsoft’s Self Service BI suite, PowerPivot (an add-in that has the ability to analyze and visualize millions of rows of data within Excel) and DAX (Data Analysis Expressions Language). Data was extracted from the POS system, then queried within Access to transform the data into a suitable format to optimize the capabilities of PowerPivot, and then over 3 millions rows were imported into the PowerPivot data model where dozens of measures were created using DAX. Along with monthly statistics covering transactions and revenue, the customer stratification capabilities in some of these tables is particularly powerful and enlightening as a way to enhance the marketing process that can contribute toward increasing transactions and maximizing revenue. Most often these types of statistics are not available from your POS or ERP (enterprise resource planning) system. PowerPivot is an excellent tool to combine data from various databases without having to learn SQL or any other database language to get the job done. The data in the tables can also be viewed in various chart formats using another Microsoft BI tool, PowerView. These tools are a very cost effective option to the average small business.
Example 4: Transactional Analysis Uncovering Irregularities
This series tackles a more serious topic, employee theft, and is an example of how transactional irregularities are not always evident, particularly if the right reports are not available from your POS software. Discounted transactions, including voids and unclosed transactions, are the focus for this craft brewery. The first few visualizations show nothing out of the ordinary in either the relationship between gross and net sales or in the Discounted $ Amount per Staff. (Note: Jack is the owner and Morgan is responsible for happy hour promotions.)
However, once we start to apply a custom algorithm to assign a risk score to each transaction, we start to see variances that should be reviewed in further detail. In the third visualization, a trend comes to light that indicates Bernie as having some high risk transactions (remember, Jack is the owner and understandably discounts a high number of transactions). In the scatter plot, Bernie clearly emerges as an outlier, having the higher average discount across all transactions. Once we’ve identified Bernie as the main contributor to discounted sales, we can then investigate his actions more carefully. Through the last three visualizations, we learn that he discounts sales most often on Tuesdays and Saturdays at 3 pm and 9 pm and most commonly discounts 32oz and 64 oz growlers of Beer 1 and Beer 2. This visualization was created from over 750,000 rows of data that were imported into Tableau from an iPad-based cash register system.
Example 5: Great American Beer Festival Award Winning Craft Breweries
We did this one just for fun since we appreciate good craft beer. We downloaded award data from www.greatamericanbeerfestival.com for the last 10 years and looked at the geographic distribution of GABF awards across the US. The examples that follow include both single visualizations and dashboards encompassing multiple visualizations linked together by user action. The user actions and filters unfortunately aren’t active in this demo but it provides an idea of the options. Some of these visualizations would lend themselves for inclusion in marketing materials or annual reports.
Example 6: VA ABC Licenses in the Richmond, VA Vicinity
We created this dashboard in order to learn more about our local craft breweries and restaurants which have gained national attention. We downloaded the ABC license data for the state of Virginia from www.abc.virginia.gov and selected Hardywood Park Craft Brewery as a point of interest from which to view surrounding licensed businesses. It allows us to see the geographic distribution of all the types of businesses that have applied for ABC licenses. Tableau has the ability to blend in various levels of demographic and GIS data (County, Zip Code, Census Tract) and present it in a map format along with your underlying data plus show it in motion over a selected time period. We can search within a mileage radius from a particular point, by zip code or see where new licenses have been awarded within the selected time period. This type of visualization lends itself to many different industries and is helpful for site selection, potential sales opportunities, and related demographic research for a potential business evaluating its competitors and customer base.