The days leading up to and after a Super Bowl can yield mountains of useful big data for manufacturers and retailers alike, but this...
The days leading up to and after a Super Bowl can yield mountains of useful big data for manufacturers and retailers alike, but this won’t help you score winning sales immediately. To successfully capitalize on the gold mine of data a company can collect during a Super Bowl season, there are several factors that need to be considered.
Implement a Plan
Utilizing the valuable data that you collect this year will allow you to create and implement a plan for the following year. Analyze the data to find trends and answers to different questions that arise, such as why did this store sell out and why was this product so popular? An effective strategy for next year can be built from analyzing the large sets of data from before, during and after this year’s Super Bowl. If you only analyze data from during the Super Bowl, then you are missing a few key pieces of the puzzle.
Think Outside the Box
Successfully using big data to solve business problems requires creative thinking. For example, competitor advertisements can impact the sales of your products. Consumers may go to the store to purchase the product they saw in an advertisement but then see yours lying on the shelf next to it. Look at all of the advertisements that ran around the Super Bowl season to see how they may have affected yours and your competitor's sales. By finding which advertisements were a success for each brand and which ones positively - or negatively - impacted sales, you can use that information to formulate a stronger advertising and marketing strategy for the following year.
Improve Demand Forecasting
Demand forecasting is pivotal to the success of a retailer and manufacturer, and it isn’t easy to do with all of the predictable and unpredictable factors that potentially impact production demands. However, using the data from the most recent Super Bowl enables a company to increase their accuracy of next year’s production needs. Demand forecasting is absolutely critical in preventing empty shelves and optimizing manufacturing. Empty shelves during the Super Bowl season will equate to a large loss in potential sales.
In order to win the next Super Bowl season, you should have already gathered data to be planning for the following year. Through analyzing the data you’ll be able to optimize a business and avoid any surprises; get manufacturing optimized for next year; prepare advertisements; and a data scientist will know exactly how to analyze the data; what information needs to be pulled from it; and how to utilize the data to find tangible business solutions.
Ironbridge Software was featured on the cover of Crain’s Chicago Business Jan. 16 issue! Michael Dickenson, CEO of Ironbridge Software, was interviewed for a...
Ironbridge Software was featured on the cover of Crain’s Chicago Business Jan. 16 issue! Michael Dickenson, CEO of Ironbridge Software, was interviewed for a story about the evolution of the role of a ‘data scientist.’ Click here to read the article.
“Data Scientists” have been the unsung heroes behind corporations since the 60’s, but suddenly, they hold the title of “America’s Hottest Job.” The data...
“Data Scientists” have been the unsung heroes behind corporations since the 60’s, but suddenly, they hold the title of “America’s Hottest Job.” The data scientist is not a new job, in fact, the early data scientist evolved from a statistician. Michael Dickenson, Ironbridge Software CEO, speaks about his tenure in this field, as well as why it holds such a high value that is sought after today.The Early DaysIn the ‘80s, Michael Dickenson was working for a company that was a leader in business intelligence (BI). It was the experience he gained in this position that led him to create his own company and build a product combining computer technology - a brand new concept! - with Michael’s BI know-how. It was this very product that was purchased by A.C. Nielsen Company, which was one of two corporations that collected large sets of data and published it to the consumer goods industry. Michael’s product increased data collection from thousands of data points monthly to hundreds of millions, thus creating what is now known today as “Big Data.”The Modern Age of Big DataToday’s data scientist has the capability to analyze unbelievably large sets of data, nimbly navigating the information to pull out solutions that will increase business efficiency. This is especially prevalent in consumer packaged goods (CPG), one of the most efficient industries in the world. The ability to take incomprehensible data and turn it into game-changing business solutions is obviously valuable.The Qualities of a Data ScientistWhat began as a BI specialty has too often become miscommunication between this entity and the world of information technology (IT). While both are essential to the success of a business, they often clash when it comes to working together due to their vastly different nature. Fortunately, data scientists are able to provide a bridge in communications between the two fields, making sense of the data that is collected from IT and turning it into solutions for BI. Syncing up BI and IT is largely beneficial to any company and analyzing data and finding solutions is an essential aspect of being a data scientist.
The data scientist is an amalgamation of many different skills and disciplines: speaking across business entities; identifying client and business pain points; and learning how to collect, analyze and understand large sets of data. The position may have evolved over the years, but, at it’s core, a data scientist must keep up with the latest technologies and industries involved.