As consumers have 24 by 7 information access and more options for purchase decisions, retailers have shorter decision windows to act.
However, retailers can apply technology to automate and assemble much more data to act upon in near real-time.
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To stimulate that thinking, consider the following retail big data examples.
These retail big data examples can be extrapolated in many ways — from using weather patterns to predict in-store sales to combining data from web search trends, website browsing patterns, social networks and industry forecasts to predict product trends, forecast demand, pinpoint customers and optimize pricing and promotions.
Understanding the correlation between your product sales and otherwise undetected factors such as the weather, pop culture, social media trending, your competitors and consumer sentiment can allow you to tap into these environmental events with specific actions that lead to improved financial performance.
Retailers that leverage big data will design products that are more embraced by consumers, better anticipate and respond to market shifts, and engage consumers with predictable results.