The e-commerce industry is making moves to defend its margins. The ability of online retailers to balance price with profitability will determine who wins in volatile markets.
What is the biggest driver of online buying decisions? Do an internet search for whatever you want to buy and it’s obvious: listings show the product and compare its price.
Of course there are other factors: is this a retailer I trust, how long will delivery take, how much will delivery cost, do they stock other items I want? I might be persuaded to pay more than the lowest price if I’m concerned about one or more of these secondary factors, but all else being equal it is price that matters the most.
The challenge for online retailers is how to compete on price without giving away most or all of their margin. This challenge is becoming more acute because, as I argued in my previous blog post, profit is becoming a bigger priority than growth as the e-commerce industry struggles with volatile market conditions: higher wages, disrupted supply chains, ballooning delivery costs, and surging inflation.
This results in a paradox: how can an online retailer maximize its profit margin at the same time as winning the sale by offering compellingly low prices?
Beer and diapers
Perhaps you’ve heard the story of the supermarket that discovered that it could sell more beer when it was stocked alongside diapers. It’s a somewhat apocryphal allegory about why young fathers intent on buying beer or told to buy diapers (depending on which version of the tale you read) end up buying more of the other product. Why? Because the supermarket had identified a sales correlation between these products and arranged its displays to take advantage.
This is a simplistic example of hidden profits unlocked by data analysis, but it highlights an important truth. There are all sorts of complex relationships in shoppers’ behaviors that are not obvious or logical, but can be revealed through careful analysis of data.
Despite the dot-com bubble bursting, we’ve seen two decades of steady growth in online shopping (aside from a brief pause that trailed the 2007-8 financial crisis). Perhaps that explains why the industry’s attitude of growth first, profit later has lingered. And there’s plenty of to play for: 80 percent of total retail sales still take place offline.
At Hypersonix, we know these kinds of correlations exist because identifying them has been our business for around four years. From e-commerce stores, to supermarkets, to fast food restaurants, we’ve worked with brands that carry broad product ranges and sell to large numbers of consumers. Our core proposition and the purpose of our artificial intelligence technology is to reveal unobvious relationships in data that yield higher profits.
The margin in the machine
Sophisticated consumer markets mean ever greater breadth and volumes of data, driving the advancement of analytics software. Today, technology identifies not just product correlations but anchor purchases for entire baskets of goods. It shows the effects of shopper loyalty and the potential for personalized prices. It peers into seasonal and contextual effects that inform daily, even hourly price adjustments.
I don’t think that there is any doubt that setting prices with this level of detail and alacrity is beyond human ability. The data volumes are too big, the relationships are too intricate, and timescales are too tight. The question is what kind of help – what category of data analytics technology – is needed?
There is no shortage of solutions to choose from. They range from simple and quite basic rules-based engines to cutting-edge behavioral analytics and optimization systems. If your e-commerce business carries hundreds/hundreds of thousands of product lines or options, sells to hundred of thousands of consumers (or more likely some combination of these variables) then you’ll need something pretty smart – tools that don’t only surface pricing insights, but offer predictions that enable you to look ahead as you manage margins.
A prediction for the future
Predictive analytics is the current state-of-the-art for e-commerce pricing. Inside the biggest players, technologists, analysts and data scientists have been at work honing this category of AI for some time. It’s part of the reason why, in a price sensitive market, the e-commerce giants have become so gigantic. Predictive analytics, decision intelligence, and price optimization represent the current state-of-the-art for ecommerce pricing. Inside the biggest players, technologists, analysts and data scientists have been at work honing these categories of AI for some time. It’s part of the reason why, in an inherently price-sensitive online economy, the ecommerce giants have become so gigantic.
Will predictive analytics and pricing models lead to a winner-takes-all scenario? I don’t believe so. The big firms have understood that it is just so easy for consumers to compare prices online. They’ve invested heavily and engineered their businesses to exploit this. How your e-commerce business can do the same is the topic of my next blog post.