It’s hard to ignore the tremendous rate of growth that online retailers have experienced in the past 10 years. The sharp rise in Internet enabled devices such as tablets, netbooks, smartphones, and readers have turned e-Commerce into a lucrative industry. There are more and more retailers popping up online every single day. How has this phenomena affected brick-and-mortar retailers, and how can they remain competitive with online retailers?
Pure online retailers such as Amazon.com are able to amass billion-dollar empires that include movies, books, toys, electronics, and much more without opening a single location. Product manufacturers like Apple & Dell that found a way to bring products to the masses by selling directly to their base. Even traditional Brick-and-Mortar giants have found an effective way to use utilize this channel such as Wal-Mart, BestBuy, Target and more. There is no doubt that online retailers have taken a bite out of traditional brick-and-mortar. Just ask Blockbuster, or Borders.
But what has made online retailing so attractive? What does it provide that traditional retailers are lacking?
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Online retailers have many advantages. By being able to seamlessly mix warehouse inventory with drop shipments they can offer a broader product category range without experiencing a heavy cost. Moreover, most online retailers are almost instantly global. The saying “location, location, location!” does not apply on the Internet.
These advantages are inherited in online retailers, but there is something even more important that has changed the game in the past few years, and that is data & analytics. Online retailers are entrenched in their data. They are able to know exactly who has visited their stores, for how long, what products they were looking for, what their previous consumer behavior and purchase history is, and how much money they spend in your store.
These kinds of strategies allow online retailers to get away with things traditional retailers can’t. For instance, an online retailer can claim that a certain product is in stock on their website even when it’s not in the warehouse. The customer won’t know the difference and it will be shipped within days from half way across the world. On the contrary a traditional retailer must have the product on the right shelf where the customer can find it. Moreover, online retailers can customize your shopping experience based on your consumer history down to the price of the product, related products, and accessories causing higher gross margins and a higher turnover rate.
So how can traditional retailers compete with this trend?
The answer lays in Retail Predictive Analytics. A good predictive analytics engine will track your entire retail supply chain taking every single factor that affects your business into account, and suggest the optimal retail strategies. For example, how much inventory to order, how to allocate to stores, when to replenish, how much to price at, what the best markdown strategy is, or which merchandize to transfer between stores. The best part is that predictive analytics technology is forwards looking, which means you can make these decisions in advance. You can literally ensure that the right product is at the right place at the right time.
With a solution such as Inter-Store Inventory Balancing, a predictive analytics engine will highlight opportunities for you to move slow selling inventory from one store to another store where there’s a demand for it, and where the retailer will sell it at a full price. Sounds simple, but it’s actually quite complex and extremely effective.
Once you consider a retailer with 100 stores, and 1000 SKUs, the distances between stores, combinations of size, color, or styles, merchandise pricing, and store-shelf capacities you’re dealing with millions of combinations. Retail predictive analytics can analyze all of these factors and come up with the optimal inventory transfers. Brick-and-mortar retailers that have used this approach reported to have reduced inventory costs by 40%, and increased comparative store sales by up to 20%.
Another two great predictive analytics tools are the Price Optimization and Markdown Management solutions that make sure you’re not just proactively optimizing your prices, but that you’re also taking inventory levels, promotional events, vendor pre-packs, assortment rules and many other factors into account. You certainly can’t offer every single customer that walks into your store a customized shopping experience with their own price, but you can make sure that you are maximizing your revenue, and responding to seasonal trends without going under/over stock. Additionally retail predictive analytics helps traditional retailers manage their suppliers, vendor compliance, micro-merchandising, allocation & replenishment, event promotion, and corporate planning.
With the amount of data that traditional retailers are collecting it is certainly possible to replicate some of the advantages that have made online retailers so lean and profitable in the past few years. According to a recent Gartner report: “by 2016, 70% of the most profitable companies will use predictive analytics.”[1] To learn about how to get started with retail predictive analytics, you can read: “5 Considerations When Buying A Retail Analytics Solution”.
Yan Krupnik is the Marketing Manager for Retalon. Since 2002 Retalon has optimized pricing, inventory management, merchandising, planning, procurement and marketing operations for retail organizations in a variety of industries. Retalon products range from task-oriented solutions to a common analytic platform, resulting in tangible optimization of the supply chain and significant measurable benefits for the entire organization. For more information go to www.retalon.com.
