4 Key Frustrations for Retail Analytics
According to a recent CDW survey, there are 4 key frustrations that the retail sector experiences when attempting to leverage their data that powerful data analysis software can help them to overcome. Below are the 4 areas that retail analytics struggles with and how Big Data analysis can help to ease their frustrations.
Outdated Information - Information comes into a company through multiple channels, sales, marketing, customer service, or others. It can be difficult for the right information to reach decision makers in a timely manner as to be useful. Fast, accurate analysis is necessary to make decisions in real time. Businesses that analyze their incoming data sources with AI powered analytics can achieve faster time to insight on large volumes of information, allowing the decision makers to have the right insights at the right time.
Siloed Information - Data coming in through different channels also presents the problem of information being locked into completely different silos that don’t communicate with one another. The need to analyze marketing, sales, and customer service data separately and make leaps in logic as to how they fit together is neither accurate nor efficient. Data tools can unify the information from every channel to provide clarity into the overall picture. This let’s decision makers identify areas of need and act upon them.
Reliance on IT - Legacy analytics relies heavily on IT to organize and distribute data analysis to relevant departments and key individuals through generated reports such as spreadsheets, charts, and graphs. The data analytics tools on the market today are able to automatically generate dashboards that visualize the artificially intelligent analysis. The best data analytics tools do the heavy lifting for the user, creating not only better results, but also greatly reducing the time to achieve them.
Difficulty Translating - Even with data in hand, it can be difficult to take analytical results and put them into a format that is easily digestible. In the same way that automated analysis and visualization can speed up the process, they also allow almost any relevant individual in the company to use the results in a clear, defined manner. Also, clean visualizations and interactive dashboards allow users to apply their own knowledge to gain better insight into the data.
Time to insight and accurate analysis of the entire data set continues to be a struggle for businesses of all kinds. For retail, especially, difficulty leveraging their structured and unstructured data can mean the difference between revenue growth and closing doors. Big Data analytics tools, like ours here at Stratifyd, can use artificial intelligence and machine learning to drive faster time to insight with increased accuracy over large volumes of data both structured and unstructured. By utilizing the best data analytics tools, retail companies can gain better understanding into their customers to drive impactful change and improve customer experience.