Jay LeonardThe internet has revolutionized the way consumers shop so significantly that online sales account for more than 8 percent of all retail sales in the United States. According to Forrester, the growth of ecommerce is expected to outpace sales growth at brick and mortar stores over the next five years, reaching $370 billion in sales by 2017. By that time, ecommerce is expected to account for a full tenth of all retail sales in the U.S.
This was no accident. Online retailers have thrived in the past decade because of the development of data mining and analytics. But this isn’t the end for physical retail locations. Through business-specific predictive analytics, traditional retailers can also benefit from this predictive technology.
Retail predictive analytics enables a retailer to have the right product, at the right place and the right time, proactively, before a customer walks through the door. This ensures that many of the perceived disadvantages of purchasing from a traditional retail outlet all but disappear.
Let’s dig a bit deeper into how and why predictive analytics works in the retail industry.
- Retail predictive analytics helps retailers create advance demand forecasts. The key to inventory management is having the right product quantity at the right time, and at the right place. Retail predictive analytics empower retailers with a reliable forecast of demand that calculates proper orders reconciled with costs and budgets. Furthermore, predictive analytics helps retailers work out appropriate allocation and replenishment quantities, taking into account existing inventory, expected demand, frequency of shipments, custom business rules, and more.
- A predictive analytics solution optimizes pricing strategies. Price is one of the main factors influencing the quantities of products consumers buy. Ultimately, the amount of products sold at a given price determines total revenue and profits for those goods. The price strategies for different products can be based on factors including cost, competitor’s prices, inventory targets, brand reputation and store traffic. In each case, the optimal price can be calculated through a predictive system that factors in all of these variables in its algorithm.
- Inter-store inventory balance can be achieved through predictive analytics.It’s not unusual for a product to sell out in a particular color or size (SKU) in one store, while a nearby location has a surplus of that exact item. Inter-store inventory balancing via predictive analytics is an increasingly popular way for retailers to get aheadof imbalances. Incorporating all costs associated with the transfer – from logistics to store capacity, demographic diversity to the sizes and colors most likely to sell at the specific location – retail predictive analytics solutions anticipate the necessary transport of an item before the transfer becomes tedious or overwhelmingly expensive for the retailer.
According to a recent Gartner report, 70 percent of the most profitable businesses will manage their processes using real-time predictive technology by 2016. Online shopping and mobile technology has undoubtedly transformed the retail industry, but through intelligent retailing traditional brick and mortars can prevail in an increasingly mobile retail world.
This article was written for Business 2 Community by Jay Leonard.
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Very true. And most e-commerce enabled retailers are probably already using some kind of demand forecasting or pricing strategies. It’s up to the e-commerce platform providers to create user friendly software and hide the big-data hype terminology from the end-user.
Thanks for the comment Arnaud. E-commerce platforms usually have a lot of BI on user friendly dashboards. However, without integrating the solution to the rest of a Retailer’s supply chain systems, and processes, you are just playing the game of “guestimation”.
In today’s Omni channel environment, retailers need Integrated Solutions. Take a look at this solutions map:
http://www.retalon.com/wp-content/uploads/2012/10/Retalon-Solution-MAP-2012.pdf