Big data’s next step towards providing deeper business insights is real-time data analytics. Companies that have access to daily sets of big data can...
Big data’s next step towards providing deeper business insights is real-time data analytics. Companies that have access to daily sets of big data can identify problems and find a remedy before additional sales are lost. While analyzing gathered data is the first step towards a more efficient business, taking it one step further into near real-time analysis can reduce the lag time between the rise of a problem and the implementation of a solution.
Instant Insights Leads to Instant Solutions
Through real-time insights, companies can establish alerts to quickly investigate potential problems when metrics fall below a predetermined threshold. Knowing there is a problem is the first step towards a solution, and problems can take on many forms. For example, real-time data can provide a simple fix such as a competitor’s sign being placed in front of a competing products shelf facings to an out-of-stock. Thankfully, analyzing daily data sets opens the door to being able to adjust strategies on-the-fly and implement immediate solutions.
Out-of-Stock and Out-of-Luck
Avoiding out-of-stock situations is critical to increasing sales and efficiency. Proper demand forecasting can help foresee periods of increased demand, but that is only one piece of the solution. A reduction of sales may be the result of the dreaded out-of-stock situation. These issues may arise from a period of high demand such as cold remedies selling out during an unexpected outbreak, or sudden spikes in media attention. Real-time big data can give a first alert of when and where increases in demand are occurring.
New Product and Promotion Rollouts
When introducing a new product to the market, it is essential to track and assess the success of the introduction. Obtaining and analyzing daily data is the best way to determine if a product rollout is happening according to plan. Any number of reasons can cause a product to fall short of its expected demand, however, realizing there is a problem is the first step towards fixing it. If for some reason, the pricing, display or advertisement was not executed as agreed, it is crucial to understand where the link is broken. Analyzing the daily data of individual sale points during a product rollout period can help pinpoint where the systematic problem lies, whether it’s at the retailer or supply chain.
Running a detailed analysis and tracking the results of a promotion are essential to maximizing efforts and sales strategies. Data gathered from individual transactions can help gauge success of various promotions and how it helped increase profits, customers or brand loyalty.
Big data insights in real time is the next step for the ever-improving world of big data. Analyzing data as it's collected allows a business to reach solutions to problems immediately rather than weeks or months later when sales opportunities have already been missed. Reducing the frequency of out-of-stocks and other forms of sales losses will directly improve the bottom line of a business. Analyzing data sets in real time is the key to preventing a leak in sales.
Ironbridge Software was featured in the American Marketing Association as a data scientist in their big data article. Michael Dickenson, CEO of Ironbridge Software,...
Ironbridge Software was featured in the American Marketing Association as a data scientist in their big data article. Michael Dickenson, CEO of Ironbridge Software, was interviewed for the story about his tenure in the ever-evolving data scientist career. Click here to read the article.
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.