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Author: Alexandra Wiegel, Tax Business Intelligence Analyst In-2C-14px.png
Company: Comcast Corp

Awards Category: Best Business ROI


A Corporate Tax Department is not typically associated with a Business Intelligence team sleekly manipulating and mining large data sources for insights.  Alteryx has allowed our Tax Business Intelligence team to provide incredibly useful insight to several branches of our larger Tax Department. Today, almost all of our data is in Excel or csv format and so data organization, manipulation and analysis have previously been accomplished within the confines of Excel, with the occasional Tableau for visualization. Alteryx has given us the ability to analyze, organize, and manipulate very large amounts of data from multiple sources.  Alteryx is exactly what we need to solve our colleague’s problems.

Describe the problem you needed to solve

Several weeks ago we were approached about using Alteryx to do a discovery project that would hopefully provide our colleagues further insight into the application of tax codes to customer bills. Currently, our Sales Tax Team uses two different methods to apply taxes to two of our main products respectively. The first method is to apply Tax Codes to customer bill records and then run those codes through software that generates and applies taxes to each record. The second method is more home-grown and appears to be leading to less consistent taxability on this side of our business.


Given that we sell services across the entire country, we wanted to explore standardization across all our markets. So, our Sales Tax team tasked us with creating a workflow that would compare the two different methods and develop a plan towards the goal of standardization and the effect it would have on every customer’s bills.


Describe the working solution

Our original source file was a customer level report where the records were each item (products, fees, taxes, etc.) on a customer’s bill for every customer in a given location. As it goes with data projects, our first task was to cleanse, organize, and append the data to make it uniform.




The next step was to add in the data from several data sources that we would ultimately need in order to show the different buckets of customers according to the monetary changes of their bills. Since these sources were all formatted differently and there was often no unique identifier we could use to join new data sources to our original report. Hence, we had to create a method to ensure we did not create duplicate records when using the join function. We ended up using this process multiple times (pictured below)




And so, the workflow followed. We added tax descriptions, new codes, and other information. We added calculated fields to determine the amount of tax that should be owed by each customer today, based on our current coding methods.








After we had layered in all the extra data that we would need to create our buckets, we distinguished between the two lines of business and add in the logic to determine which codes, at present, are taxable.




For the side of our business whose taxability is determine by software, you will notice that the logic is relatively simple. We added in our tax codes using the same joining method as we did above and then used a single join to a table that lists the taxable codes.




For the side of our business whose taxability is determine by using our home-grown method, you can see below that the logic is more complicated. Currently, the tax codes for this line of business are listed in such a way that requires us to parse a field and stack the resulting records in order to isolate individual codes. Once we have done this we can then apply the taxability portion. We then have to use this as a lookup for the actual record in order to determine if a record contains within the code column a tax code that has been marked as taxable. Or in other words, to apply our home-grown taxability logic is complicated, time consuming, and leaves much room for error.




Once we stacked all this data back together we joined it with the new tax code table. This will give us the new codes so that the software can be used for both lines of business. Once we know these new codes, we can simulate the process of the software and determine which of the new codes will be taxable.




Knowing whether or not codes are taxable helps us hypothesize about how problematic a geographic location may end up being for our team, but it does not tell us the dollar amount of taxes that will be changing. To know this we must output files that will be run through the real software.


Hence, once we have completed the above data manipulation, cleansing, and organization, we extract the data that we want to have run through the software and reformat the records to match the necessary format for the software recognition.





We created the above two macros to reformat the columns in order to simply this extensive workflow. Pictured below is the top macro. The difference between the two resides in the first select tool where we have specified different fields to be output.




After the reformatting, we output the files and send them to the software team.





When the data is returned to us, we will be able to determine the current amount of tax that is being charged to each customer as well the amount that will be charged once the codes are remapped. The difference between these two will then become our buckets of customers and our Vice President can begin to understand how the code changes will affect our customer’s bills.


Describe the benefits you have achieved

Although this project took several weeks to build in Alteryx, it was well worth the time invested as we will be able to utilize it for any other locations. We have gained incredible efficiency in acquiring insight on this standardization project using Alteryx. Another benefit we have seen in Alteryx is the flexibility to make minor changes to our workflow which has helped us easily customize for different locations. All of the various Alteryx tools have made it possible for the Tax Business Intelligence team to assist the Tax Department in accomplishing large data discovery projects such as this.


Further, we have begun creating an Alteryx app that can be run by anyone in our Tax Department. This frees up the Tax Business Intelligence team to work on other important projects that are high priority.

A common benefit theme amongst Alteryx users is that Alteryx workflows save companies large amounts of time in data manipulation and organization. Moreover, Alteryx has made it possible (where it is impossible in Excel) to handle large and complicated amounts of data and in a very user friendly environment. Alteryx will continue to be a very valuable tool which the Tax Business Intelligence team will use to help transform the Tax department into a more efficient, more powerful, and more unified organization in the coming years.


How much time has your organization saved by using Alteryx workflows?

We could never have done this data discovery project without using Alteryx.  It was impossible to create any process within Excel given the quantity and complexity of the data.


In other projects, we are able to replicate Excel reconciliation processes that are run annually, quarterly, and monthly in Alteryx.  The Alteryx workflows have saved our Tax Department weeks of manual Excel pivot table work.  Time savings on individual projects can range from a few hours to several weeks.


What has this time savings allowed you to do?

The time savings has been invaluable.  The Tax Department staff are now able to free themselves of the repetitive tasks in Excel, obtain more accurate results and spend time doing analysis and understanding the results of the data.  The “smarter” time spent to do analyses will help transform the Tax Department with greater opportunities to further add value to the company.

Author: Jennifer Jensen, Sr. Analyst In-2CRev-28px-R.pngand team members Inna Meerovich, RJ Summers

Company: mcgarrybowen 


mcgarrybowen is a creative advertising agency that is in the transformation business. From the beginning, mcgarrybowen was built differently, on the simple premise that clients deserve better. So we built a company committed to delivering just that. A company that believes, with every fiber of its being, that it exists to serve clients, build brands, and grow businesses.


Awards Category: Best Business ROI


Describe the problem you needed to solve 

Mcgarrybowen creates hundreds of pieces of social creative per year for Fortune 500 CPG and Healthcare brands, on platforms including Facebook and Twitter. The social media landscape is constantly evolving especially with the introduction of video, a governing mobile-first mindset, and interactive ad units like carousels, but yet the capabilities for measuring performance on the platforms have not followed as closely.


Our clients constantly want to know, what creative is the most effective, drives the highest engagement rates, and the most efficient delivery? What time of day, day of week is best for posting content? What copy and creative works best? On other brands you manage, what learnings have you had?


But, therein lies the challenge. Answers to these questions aren’t readily available in the platforms, which export Post-Level data in raw spreadsheets with many tabs of information. Both Facebook and Twitter can only export 90 days of data at a time. So, to look at client performance over longer periods of time and compared to their respective categories, and derive performance insights that drive cyclical improvements in creative – we turned to Alteryx.  


Describe the working solution

Our Marketing Science team first created Alteryx workflows that blended multiple quarters and spreadsheet tabs of social data for each individual client. The goal was to take many files over several years that each contained many tabs of information, and organize it onto one single spreadsheet so that it was easily visualized and manipulated within Excel and Tableau for client-level understanding. In Alteryx, it is easy to filter out all of the unnecessary data in order to focus on the KPIs that will help drive the success of the campaigns.  We used “Post ID,” or each post’s unique identifying number, as a unifier for all of the data coming in from all tabs, so all data associated with a single Facebook post was organized onto a single row.  After all of the inputs, the data was then able to be exported onto a single tab within Excel.


After each client’s data was cleansed and placed into a single Excel file, another workflow was made that combined every client’s individual data export into a master file that contained all data for all brands.  From this, we can easily track performance over time, create client and vertical-specific benchmarks, and report on data efficiently and effectively.


Single Client Workflow



Multi-Client Workflow



Describe the benefits you have achieved

Without Alteryx, it would take countless hours to manually work with the social data in 90 day increments and manipulate the data within Excel to mimic what the Alteryx workflow export does in seconds. With all of the saved time, we are able to spend more time on the analysis of these social campaigns.  Since we are able to put more time into thoughtful analysis, client satisfaction with deeper learnings has grown exponentially.  Not only do we report out on past performance, but we can look toward the future and more real-time information to better analyze and optimize.

Author: Omid Madadi, Developer

Company: Southwest Airlines Co.


Awards Category: Best Business ROI


Describe the problem you needed to solve 

Fuel consumption expense is a major challenge for the airline industry. According to the International Air Transport Association, fuel represented 27% of the total operating costs for major airlines in 2015. For this reason, most airlines attempt to improve their operational efficiency in order to stay competitive and increase revenue. One way to improve operational efficiency is to increase the accuracy of fuel consumption forecasting.


Currently, Southwest Airlines offers services in 97 destinations with an average of 3,500 flights a day. Not having enough fuel at an airport is extremely costly and may result in disrupting flights. Conversely, ordering more fuel than what an airport needs results in high inventory and storage costs. As such, the objective of this project was to develop proper forecasting models and methods for each of these 97 airports in order to increase the accuracy and speed of fuel consumption by using historical monthly consumption data.


Describe the working solution

Data utilized in this project were from historical Southwest Airlines monthly fuel consumption reports. Datasets were gathered from each of the 97 airports as well as various Southwest departments, such as finance and network planning. Forecasting was performed on four different categories: scheduled flights consumption, non-scheduled flights consumption, alternate fuel, and tankering fuel. Ultimately, the total consumption for each airport was obtained by aggregating these four categories. Since data were monthly, time series forecasting and statistical models - such as autoregressive integrated moving average (ARIMA), time series linear and non-linear regression, and exponential smoothing - were used to predict future consumptions based on previously observed consumptions. To select the best forecasting model, an algorithm was developed to compare various statistical model accuracies. This selects a statistical model that is best fit for each category and each airport. Ultimately, this model will be used every month by the Southwest Airlines Fuel Department.




In addition to developing a consumption forecast that increases fuel efficiency, a web application was also developed. This web application enables the Fuel Department to browse input data files, upload them, and then run the application in an easy, efficient, and effortless manner. Data visualization tools were also added to provide the Fuel Department with better insights of trends and seasonality. Development of the statistical models has been finalized and will be pushed to production for use by the Southwest Airlines Fuel Department soon.






Describe the benefits you have achieved

Initially, the forecasting process for all 97 Southwest Airlines airports used to be conducted through approximately 150 Excel spreadsheets. However, this was an extremely difficult, time-consuming, and disorganized process. Normally, consumption forecasts would take up to three days and would have to be performed manually. Furthermore, accuracy was unsatisfactory since Excel's capabilities are inadequate in terms of statistical and mathematical modeling.


For these reasons, a decision was made to use CRAN R and Alteryx for data processing and development of the forecasting models. Alteryx offers many benefits since it allows executing R language script by using R-Tool. Moreover, Alteryx makes data preparations, manipulations, processing, and analysis fast and efficient for large datasets. Multiple data sources and various data types have been used in the design workflow. Nonetheless, Alteryx made it convenient to select and filter input data, as well as join data from multiple tables and file types. In addition, the Fuel Department needed a web application that would allow multiple users to run the consumption forecast without the help of any developers, and Alteryx was a simple solution to the Fuel Department's needs since it developed an interface and published the design workflow to a web application (through the Southwest Airlines' gallery).


In general, the benefits of the consumption forecast include (but are not limited to) the following:


  • The forecasting accuracy improved approximately 70% for non-schedule flights and 12% for scheduled flight, which results in considerable fuel cost saving for the Southwest Airlines.
  • The current execution time reduced dramatically from 3 days to 10 minutes. Developers are working to reduce this time even more.
  • The consumption forecast provides a 12-month forecasting horizon for the Fuel Department. Due to the complexity of the process, this could not be conducted previously using Excel spreadsheets.
  • The Fuel Department is able to identify seasonality and estimate trends at each airport. This provides invaluable insights for decision-makers on the fuel consumption at each airport.
  • The consumption forecast identifies and flags outliers and problematic airports and enables decision-makers to be prepared against unexpected conditions.

Author: Cesar Robles, Sales Intelligence Manager 

Company: Bavaria S.A.


Awards Category: Best Business ROI


Describe the problem you needed to solve

In September 30th 2015, a gossip widespread through whatsapp reduces our Pony Malta sales to 40% of normal levels. The social networks’ gossip that impacts a brand destroys brand equity and creates distrust in our customers. Our company executes a 1st stage plan that helps to spread the gossip in the first weeks to more customers increasing the crisis. In Colombia no brand had suffered an attack like this before.


Describe the working solution

The Alteryx solution was develop to design and decision tree that define which customers has an relevant impact in sales volume in 5 groups that allows define differentiated protocols to recover our sales in a healthy way. These 5 groups were:


Citizens: Actual Customers without any impact related to social network crisis.
Refugees: Customers that reduce significantly (<50%) his rate of sales related to social network crisis.
Deportees: Customers that didn’t bought our brand related to social network crisis.
Pilgrims: Customers with doubts about our products related to social network crisis.
Aliens: New customers without any impact related to social network crisis.


Our gap in crisis was 180k PoS (Point of Sales) impacting 92 KHl (Kilo-hecto-liters)


This workflow runs monthly and uses multiple sources of information in SQL server related to Customer properties and historic sales levels. We report some results in Excel and Java applications to define our performance in recovery actions. Actually we are migrating to in database process to optimize the algorithm performance and use Tableau to manage our visualization process.



Figure 1. Decision Tree description



Figure 2. 1st Quarter Deportees results



Figure 3. 1st Quarter Refugees results



Figure 4. 1st Quarter Citizens results



Figure 5. Numerical Distribution Initial and End State



Figure 6. Blending Workflow



Figure 7. Decision Tree workflow



Figure 8. Hierarchy and Priority workflow


Describe the benefits you have achieved

The project defines a new way to customer segmentation in our company. We use the same algorithm to define not only crisis contingence, also we used to brand expansion and price control process including geographical variables and external info of our providers (Nielsen, YanHass, Millward Brown).


The solution had not been implemented before Alteryx. An estimated time saving show us that initial state needs 2 or 3 weeks to develop compared with 4 or 5 days that we used in Alteryx (We just used it 1 month ago in the firs solution). Right now our response time is less than 2 days in similar solutions.


In Business terms, we achieve to recover 100k PoS (approximately 25% of all Colombia Market) and increase our sales in 75% of normal levels in the first 3 months. In August 2016, we recover our normal levels of sales with the trade marketing actions focused support by Alteryx workflow.