Technology and the internet of things has revolutionized the retail market. Consumers can now shop on-the-go, order from home, and automate the ordering and delivery process. Before e-commerce, purchasing data gathered from brick-and-mortar stores was several steps removed from actual consumer actions. Today, value is being created by capturing and reliably associating purchasing data to unique consumers. Companies need this data to harness the true power of predictive analytics. But are customers ready to ride the data wave?
In 2015, Amazon surpassed Walmart as the most valuable retailer in the United States by market capitalization. Since 1994, the company has thrived under a standard online retail business model, leveraging its buying power and financial resources to control the marketplace. Amazon has been successful because price, selection, and convenience have been at the core of their value proposition. Together with their easy-to-use, self-service platform and expertise in logistics, Amazon has managed to change the face of retail.
The Cost of Variability
Amazon’s current online retail model requires that customers place orders online or thru their smartphone. After an order is placed, products sitting in warehouses are mobilized and packaged for shipment. From purchase to delivery, customers can track their order status. Unfortunately, customers have become accustomed to placing small orders. As a result, Amazon’s logistics costs have skyrocketed. Several steps have been taken to control these costs. In 2007, Amazon introduced a subscription program to minimize variability and to aggregate orders into monthly shipments. Called “Subscribe and Save,” the program offered consumers a 5-15% discount to opt into automatic reordering of items. The problem with this model was that it required a customer to guess the frequency at which they needed products replaced. Unsurprisingly, customers experienced periods of product starvation and overstock due to inaccurate demand forecasting. In response, Amazon has chosen to improve its own forecasting models.
Mastering the Recurrent Purchase
Enter the Amazon Dash Button. Each Dash Button is linked to a specifc product, e.g. Tide detergent, and a customer’s Amazon account. The customer first places the Button next to the appropriate product and presses it when they run out. Pressing the button automatically reorders the product. The power of the Dash Button lies not in sales, thought it does remove friction in the shopping experience, but in the data it collects. The Dash Button is unique in that it creates value by extracting an individual consumer’s shopping habits. Amazon is using this information to optimize demand prediction algorithms to better forecast upcoming purchases. This widget brings Amazon one step closer to their patented vision of “anticipatory shipping,” where products are delivered to customers before they even place an order.
Challenges and Opportunities
Enhanced demand prediction will certainly help Amazon deliver on its customer promise by reducing logistics costs and increasing the convenience factor. However, not all customers are ready for change. Some worry about privacy and the threat of hackers. What if your neighbor stops your Quilted Northern Ultra Plush toilet paper order? Or what if a hacker unlinks your Tide Dash and you instead get 100% Pure Wolf Urine? Yes – Amazon does sell bottles of 100% Pure Wolf Urine. Others fear automation. Some may think the Dash Button is simply a forcing mechanism to pressure customers into buying high margin, Amazon-approved brands. These are valid concerns and something for Amazon to think about as they develop next-generation Dash technologies.
Big data and machine learning is driving innovation in all industries and the Dash Button is a colorful view of our inevitable automated future. I anticipate that we’ll soon see single-use, built-in order buttons on all consumer products. Gone will be the days of 1-click Ordering. Soon, common household items will replenish themselves seemingly out of thin air. Hello future world.
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