On the surface, times have never been better for online retailers, as was spectacularly highlighted by the 2015 holiday shopping season. Online holiday sales grew 9 percent to $105 billion, according to the National Retail Federation.
Why, then, aren’t more retailers celebrating? First, Amazon secured nearly 51 cents of every additional $1 spent online in 2015, according to Macquarie Research. Everyone else was a distant third, or worse.
Second, it was expected that almost one-third of everything sold online over the holidays would be returned to the retailers who sold it – about six times the rate of returns for bricks and mortar retailers. These returns erode much more than sales, with the cost of shipping, processing, and restocking or disposal taking a large bite out of already thin profits.
So unless you are Amazon, or offer that elusive item everyone wants and no one returns, there is a lot of work to do in 2016. Never has retail e-commerce been more volatile, transparent, or skewed in its market share gainers. While many factors have contributed to this state of affairs, perhaps none are more critical than price.
Historically, the industry has set its prices using fairly straightforward financial and pricing standards, such as category role (the role the retailer wants the category to play within their store), gross margin targets, and own price elasticity (the customer’s sensitivity to price changes). That simply is no longer cutting it in today’s online environment.
The volatile and transparent nature of retail e-commerce calls for an innovative, more dynamic approach to price setting. To truly compete and win, every online pricing decision must dynamically, rapidly and continually consider:
- Relative price sensitivity: Classic price elasticity measurement is derived based on the relationship between a product’s price and customer demand. In such a highly competitive and transparent marketplace, this measurement is at best incomplete, at worst inaccurate. It is not simply the price you set for the item that drives demand, it is that price relative to competing market offers that truly reflects the price-demand relationship.
- Product tradeoffs: When a customer sets out to purchase an item they typically make one of three choices – buy the item, don’t buy the item, or buy something else. Measuring that third behavior enables a full understanding of the impact your prices have across your portfolio of goods. Doing so can uncover tipping points when, for example, a higher-priced item is so far above a lesser-priced substitute item that customers disproportionately trade down thus dampening organic revenue growth.
- Cart abandonments: Congrats! You got the customer to place items in their “shopping cart” – your online checkout line – only to have them subsequently delete the item or never check out. Although pricing is often the reason for abandonment, online retailers need to know as much as they can about what went wrong. Data analysis can determine whether they made other choices, left the site altogether, returned to purchase later at a better price, etc. Less abandonment = more organic revenue growth.
- Page views: More critical than ever is the measurement of where online shoppers are landing on your website. How long do they stay? How often do they move from browsing to buying? Which page views translate into the most sales and how should that factor into your pricing calculus?
- Return Rates: Stuff gets returned. That’s the life of a retailer, but online merchants suffer with more returns than their brick and mortar counterparts. To best manage this necessary evil, online retailers need predictive analytics to help anticipate returns and, more important, execute pricing tactics that consider those return rates.
- Customer sentiment: While this remains perhaps the most nebulous metric of all, many companies – not just retailers – want to know more about how social media impacts sales and how, if at all, it should determine pricing. This calls for deeper analysis of customer reviews and complaints (and even the occasional compliment). One of the difficulties with this metric is that shoppers often say one thing but do another. They complain on social media about the very same thing they purchase time and again. And when was the last time a customer posted on Facebook or Twitter that they believed they paid too little for a product? The bottom line is that while it’s not prudent to base pricing decisions solely on tweets, customer sentiment must be measured, monitored and factored into the new pricing equation.
- Inventory on hand: More inventory data must be mined so that e-commerce companies fully understand how much product is available at any given moment, how fast it’s moving, and how pricing impacts sell through rates. At a bare minimum, pricing decisions made in concert with demand and supply insights can help retailers avoid customer-infuriating stock outs and margin-crushing markdowns. In the future, these metrics would be an essential input into enabling true dynamic pricing.
Retailers still haven’t fully figured out how to compete online in the most successful way, especially when it comes to dynamically pricing their products to align with changing customer, product and competitive variables. Evolving the pricing equation to consider these innovative price setting metrics offers the best chance for online retailers to drive organic revenue growth.