Guest Post by Jessie Brandrick 

Digitalist Magazine reports that it wasn’t until 2017 that a significant number of entrepreneurial companies decided to adopt business analytics into their operations. Since then organizations have collected data to help with marketing strategies and improve customer service. Data analytics has become such a vital tool that businesses that don’t use it can quickly fall behind their competitors. It is for this reason that the analytics industry is growing at a record pace. Maryville University’s industry evaluation for data analytics graduates reveals that the market will exceed $95 billion by 2020. As the numbers show, where once data analytics was considered a luxury, now it is considered a necessity by most businesses, including the retail industry. 

There are four main kinds of analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive focuses on what happened, diagnostic answers why it happened, predictive helps businesses “see” the future, and prescriptive use data results to recommend the next best course of action. In the retail industry, descriptive and predictive analytics are the most effective.

Information Age points out that descriptive analytics allows organizations to see what is happening in real time and collect that data in one hub. The site notes that: “this data helps businesses to spot potential issues with current care procedures, key trends in demands and to improve the overall efficiency of the customer hub.” Retail trends move very quickly and descriptive analytics allows managers to keep on point with consumer habits.

Nikki Hallgrimsdottir’s feature on how retailers use data to create new products looked at how Coca-Coca was able to create a new flavor using prescriptive analytics. They set up a freestyle fountain dispenser that allowed customers to customize their flavors. The mixtures were collected and analyzed, with Cherry Sprite coming out a clear winner and subsequently, they made it available to the consumer market. Using prescriptive data analytics, retail companies can remove a lot of the guesswork that comes with developing new products. This leads to a much higher chance of a successful product launch. 

Lithium Technologies chief scientist Dr. Michael Wu told Information Week that predictive analytics doesn’t predict one possible future, but rather “multiple futures” based on the decision maker’s actions. Predictive analytics is especially useful in e-commerce, especially for brick and mortar retailers. Forbes notes that malls are currently hard at work to come up with a way to create an experience that cannot be replicated online.  New and emerging digital goggles can give executives new angles on how to analyze the data. Through predictive analytics, malls can create a one-of-a-kind personal experience for shoppers. Data that includes shopping history and product preferences allows retailers to make personalized recommendations in store with the aid of employees. 

On the flip side, it can also help prevent PR disasters. For instance, Costco has been collecting customer data since 2014 and was able to warn every purchaser who bought contaminated plums within a 24-hour time frame. This same data also helped identify a brand of salami that had caused widespread food poisoning. This shows how retailers can use data to not only improve the level of service they provide to their customers but also stop any damaging incidents that may hurt the company. 

As the above examples show data analytics is now a vital part of any successful retailer’s business strategy. And although all four types of analytics make a difference in the retail industry, descriptive and predictive analytics are the most valuable. As the world becomes more connected, expect the reliance on analytics to increase as a direct result.

Camcode, a worldwide manufacturer of bar code labels, recently reached out to Amjad to be part of their panel of Inventory Management Experts and asked him to pitch in on advice for warehouses looking to save money. It may not come as a surprise that Amjad points to data analytics as a powerful factor in optimizing inventory to save money:

“Prescriptive Analytics is the most powerful tool in optimizing inventory…”

The explosion in the availability and accessibility of data is creating new opportunities for better decision making in applications of inventory control. Prescriptive analytics starts by predicting consumer demand and then using Machine Learning to recommend the optimal inventory levels to make the most profitable use of warehouse space. Demand is the key uncertainty affecting inventory decisions, which presents a huge opportunity to leverage transactional data combined with large-scale, publicly available data such as web search queries, reviews, and social media chatter to optimize inventory and improve warehouse profitability.

Head on over to the Camcode blog to see 26 other tips on Inventory Control Methods for Saving Warehouses Money:

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