With tight margins and increased competition, savvy online retailers are leveraging big data in ways that set them apart from the competition. It’s the only way to move forward in this data-driven world. When done right, big data can be leveraged by retailers to make more sales, enhance forecasting, refine supply chain efficiency, improve inventory management, and reduce costs. Let’s look at some of the effective big data use cases for retail. The one thing they all have in common is the need to ensure performance and reliability.
- Predict Customer Behavior: We’ve all experienced recommendation engines offering up suggestions with “you may also like.” But there’s a fine line between savvy and creepy, so sellers must tread lightly. Anyone who has heard the story remembers the infamous time big data went wrong for a major retailer. When Target used analytics to market to customers that data predicted were pregnant, the father of a teenager responded in anger that Target would send such ads to his daughter, who was still in high school. Target apologized, only to receive a return apology from the man, who found out his daughter was indeed pregnant. While extremely awkward, more importantly, people were understandably creeped out and became mistrustful of the brand. After all, if it was using its data to predict something as sensitive as pregnancy, how else was Target using data to target consumers?But when thoughtful and careful, companies can leverage data they have on customer purchases, behavior, loyalty cards, and online wish lists to effectively increase sales and marketing.
- Predict Customer Lifetime Value: Big data is invaluable when it comes to estimating customer lifetime value. And retailers are not limited to owned data; they can leverage and correlate their own customer metrics with social media, email clicks, browsing patterns and other behaviors.
- Improve Supply Chain Efficiency: Machine learning can be applied to stock and supply chain metrics combined with data from customer behavior, social platforms, industry reports and the media. These insights can be used to predict demand, manage inventory, optimize pricing, conduct targeted marketing, and even determine the best store layout and product placement.
- Perform Sentiment Analysis: Retailers can perform sentiment analysis using social media, review sites and other online data to drive strategy. Using language processing to track words – both positive and negative – can be extremely valuable for making improvements to marketing, customer service, and product development.
- Fraud Detection: Data breaches are an increasingly dangerous threat to brands, and retail organizations must be especially cognizant of the risks associated with consumers’ personally identifiable information (PII). In addition to securing the data its consumers trust them to protect, online retailers also need to protect their own financial interests. Big data from point-of-sale, return rates, and online activity feeds the platforms organizations leverage to monitor activity and detect fraudulent activity based on hidden patterns and anomalous behaviors, and algorithms predict future fraudulent activities.
Performance Management is Key
To ensure system availability for predictive analytics, retailers need a performance management solution that monitors the entire environment — the infrastructure as well as the applications. Pepperdata’s unified solution combines application performance management (APM) and infrastructure performance management (IPM) into a single, comprehensive platform to ensure uptime. Driven by AI and machine learning, Pepperdata continuously learns, identifying and addressing abnormalities in infrastructure and analytics application behavior to automatically detect and prevent performance roadblocks.
Pepperdata helps retailers optimize their applications and ensure they know what’s going on in their mission-critical systems. Only Pepperdata can correlate infrastructure and application event data to determine the root cause of an application slowdown and automatically tunes resources for optimum performance.