Online retailers have access to far more data than their brick-and-mortar peers. In fact, brick-and-mortar retailers go to great lengths to collect data that eCommerce merchants get for “free”. But just because we have the data, it doesn’t mean we’re using it well. Big data is all well and good, but it’s just a collection of ones and zeroes without the will and the algorithms to turn it into useful information.
Many leading merchants are investing significant amounts of money and time into cracking the problem of algorithmic eCommerce. The dream scenario is retail operations that are almost completely automated. The potential benefits are huge, not the least of which is cost streamlining. At the simplest level, if we can buy at the best price and sell at the best price, we stand to reap considerable bottom-line advantages.
Unfortunately, determining what’s best is a difficult challenge because there are so many constantly changing variables. Monitoring the totality of the eCommerce environment and making predictions that can be applied to eCommerce operations is a task perfectly suited to machine learning and big data algorithms.
Gartner recently released a paper that discusses how algorithms can be used, today and in the future, to optimize eCommerce operations. Using Algorithmic Retailing to Drive Competitive Advantage explores four main areas to which algorithmic analysis can be applied. The four areas cover almost every part of an eCommerce business, but let’s have a closer look at a couple of major areas.
First is “Cost of goods sold”. This is the area most of us think about when we consider applying algorithms to eCommerce decision-making. It includes intelligent pricing, an area in which several companies already offer products. For a store with a large catalogue and a substantial number of competitors, automated smart pricing can make a substantial difference to competitiveness. Manual pricing at this volume is expensive and too slow for fast-moving markets.
Under the “Cost of goods sold,” Gartner also includes product selection, promotions, and inventory decisions — all of which impact the total cost to the company of buying and selling goods.
However, it’s not only the obvious “pointy” end of the eCommerce process that will be impacted by algorithmic decision-making. Many back-end processes are also amenable to automation, including IT — a significant cost-center for eCommerce merchants. There’s been substantial progress in this area too, especially around warehousing and distribution. Intelligent algorithms are being used today to determine maximally efficient warehousing and delivery — enabling retailers to have fewer products taking up warehouse space and reducing fulfilment times.
Gartner is perhaps a little optimistic in its claim that by 2020, the leading eCommerce merchants will be largely algorithmic, but there’s no doubt that online retails has embraced machine learning and algorithmic decision-making in a big way. Merchants that don’t invest in algorithmic eCommerce are likely to find themselves at an increasing disadvantage.