The battle between dynamic pricing and the bots that will defeat them

As enterprises connect the dots between enormous amounts of data, all of us are characterized on an individual basis, paving the way for dynamic pricing.

This practice harkens back to yesteryear when a shopkeeper changed prices on the fly if the patron appeared to be able to pay more. Of course, this still happens to many of us when buying a car – at least in the United States! But as the retail world shifts more and more to an online, omnichannel experience, there’s a question on whether prospects can be sized up accurately.

The data that is being assimilated allows for unprecedented insight into us, as consumers. What if an online retailer could tell I was browsing on a Mac versus a PC? Could they assume that I had a higher earning profile and charge me more?

Dynamic Pricing: Will technology Change This Age-Old Practice?

More likely, it will become easier to bring better-quality prospects to your products and close the deal quickly. This approach is all about throughput: Increasing sales at variable pricing to generate more revenue/profit in the same period.

In a frequently cited example, dynamic pricing is used, with great effect, in the airline industry. Before we take a look at this, it’s worth recapping a few things about the industry’s economics. In its most recent forecast, the International Air Transport Association (IATA), projects industry-wide, net post-tax profits rising from $12.9 billion in 2013 to $18.7 billion in 2014. For context, IATA’s 2014 forecast would be the most profitable year the industry had experienced over the last 15 years – with the second-highest year being 2010 with $17.3 billion in revenue.

To some, this may appear to be a sizeable profit. But for a $745 billion industry that is notoriously cyclical, it represents a profit margin of only 2.5%. And from the IATA’s perspective, this is a fragile margin amid a risky business environment. With fuel accounting for anywhere between 29%–51% of operating costs across the industry, the number of flying passengers (known as load factor) is a key metric to follow. In its adoption of dynamic pricing, the industry has embraced a view that pricing for customers is fluid within regulatory constraints, enabling them to maximize profits to stay on the right side of profitability.

What about the store environment? A large European retailer thinks that dynamic pricing could help it move perishable goods. The ideal scenario requires that fresh meat and fish is sold based on demand so the last filet of salmon, for example, is sold exactly five minutes before the store closes. This approach has an impact on inventory, profitability, and society, and a goal to avoid destruction of produce.

There are many benefits when it comes to dynamic pricing for retailers and enterprises. But for every action, there is an equal and opposite reaction. In the airfare war, there’s Hopper – a price-prediction algorithm that serves to answer one question: Should I buy my flight now, or should I wait? The company achieved capability by archiving billions of airfare records to build out an algorithm that predicts when to buy a ticket. Sounds like a familiar topic, right?

These emerging realities are what we will see played out across many industries. With the volume of data available and the power of ubiquitous computing available to many startups with great ideas, it is possible to counteract dynamic pricing. Without a doubt, this will be an escalating technology arms race that promises to be fascinating viewing for all of us.

For more on the digital economy and its impact, check out the research paper “Live Business: The Digitization of Everything” on the Digitalist Magazine online.

Dinesh Sharma is the Vice President of Digital Economy at SAP.