Technological change across every industry is unprecedented. Devices such as smartphones are ubiquitous and an integral part of people’s lives. Predictive analytics and the ability to combine data elements to predict the future holds latent potential for the retail industry. With data-mining tools, predicting what lies ahead is attainable, and with big data within the retail sector, its potential becomes unfathomable.
Every industry is sitting on a pool of information and data that could be used for predictive purposes. The companies that control the keys to data and intelligence will be prevalent in the market. According to a blog post by Mckinsey, analytics-driven decisions are more successful than the decisions driven by instinct. Analytics will help companies unlock new opportunities for growth.
The retail industry has a plethora of data in verticals like point-of-sale transaction, consumer-based loyalty information, physical store size and demographic characteristics, social media metrics, competitive intelligence, and more. Combining these verticals of data in unique ways can help in predictions that catapult the retail industry. Whether it is forecasting demand or personalizing customer offers, using big data can conjure up interesting insights.
Continuing to do the same thing such as aligning store navigation intelligence, neighborhood demographics, and shopper loyalty data may not help retailers in making additional sales. For example, if a grocery store was designed to target stay-at-home moms, but the neighborhood now comprises of older adults. The store can use predictive analytics to model itself according to the needs of older adults and examine the ROI of the investment. Furthermore, predictive analytics can help companies look at the success ratio of specific product launches.
Examining assortment data alongside customer loyalty intelligence, and point-of-sale transaction details decision-making process can be refined along with placement, timing, and promotion. Analytic modeling can significantly improve buying decisions and bolster performance. Combining historical learning and new enriched data elements can help companies drive new results.
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