Business IntelligencePredictive Analytics

Predictive analytics provides value to retailers for stocking, shelving, supply chain

By October 25, 2016 No Comments

One of the must enduring and universal concerns for retail businesses is effective stocking and shelving. No matter the types of merchandise sold, the company's brand or the market segments targeted, maintaining the right number of products on shelves facing customers, in the stock room and in transit to retail locations is a vital consideration that has a direct impact on profitability and continued success. Organizations have to recognize the value that business intelligence software and predictive analytics provide in terms of answering these questions, helping retailers achieve highly effective stocking and shelving levels while also improving a variety of supply chain considerations.

"Predictive analytics helps businesses have data-based backing for their decision-making process."

Growth with analytics offers targeted, tangible improvements
An article in BizTech magazine detailed the stocking, shelving and supply chain concerns that are addressed through predictive analytics, noting that these solutions offer a more forward-facing path to success than was previously possible. While there's no doubt businesses gain value from more traditional forms of projections based on experience, industry trend reporting and forecasting and feedback from customers, a high level of analysis is missing in many of these approaches.

Intuition, educated guesswork and the informal use of past purchasing patterns and shifts in customer demand are all important tools in the overall effort to stock, shelve and ship products as effectively as possible. However, they can't be the only thing retailers rely on in the modern marketplace. Predictive analytics helps businesses have a solid, data-based backing for their decision-making process, and doesn't entirely preclude the use of intuition and educated guesses from experienced staff.

Companies can use data from past purchases to predict a wide variety of future customer behaviors. One of the examples cited by BizTech was technology retailers tracking major purchases to better forecast customers coming back to buy accessories and items with a limited term of use, like ink cartridges. There are a number of different dimensions that businesses can explore based on general purchasing data and, like the technology retailer example, more specific information closely tied to a specific retail sector or even an individual business.

Gathering data effectively
Collecting enough data is also a concern for companies, but there's good news on that front. Bloomberg reported data sensors and information storage prices fell in 2016, which means less spending is required to establish a predictive analytics solution or to take steps to improve the current platform used by a business.

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