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Retailers Tap Into Data Analytics for Amazing Customer Experiences
Published on 11/11/2016 | Use Cases
Big data analytics is one of the decade’s biggest buzz phrases. One place it can really deliver value for retailers is in data-driven decision making.
Today’s retailers have a wealth of technology at their disposal to gather, analyze, and use many kinds of data to understand markets, improve satisfaction, and be more profitable.
Predictive analysis is a powerful tool that helps retailers realize the fourfold dream of right product, right message, right person, and right time. Predictive analytics solutions enabled by Cloudera and Intel mine insights from past and present sales and customer data to anticipate those customers who are most likely to purchase.
The result is increased demand planning precision, optimize marketing campaigns, and make more strategic decisions by understanding customers’ past buying habits and predicting future spending patterns.
Data-Driven Decisions in Retail
Leaders in the retail industry have embraced the practice of data driven decisions by investing in analytics packages and the analysts who perform queries for strategic questions. They gain a competitive edge by applying analytics to functional areas such as marketing, operations & customer intelligence. Analytics can help retailers stock popular items based on real-time trends, know when to run promotions, and match customers to the most relevant products.
So what does a data-driven decision making culture look like? Ideally, every person in an organization is trained in the practice of using data and equipped with the appropriate tools to inform their tasks. This applies equally to the category manager who is optimizing planograms to the store associate who restocks the shelf. To limit the practice to within the confines of high-end analytics will hamper your ability to compete with the pure play on-line retailers and survive digital disruption.
Analytics teams must both strive to improve their craft as specialists as well as implementing automated tools such as dashboards and alerts for lay personnel. This is not something accomplished overnight, rather, it is a journey with a number of focal points including assessment, culture and the application of usage models.
You can find the original article here.