Commercial Pricing: – Worrying Trends Or Ripe For Disruption…?
Commercial pricing: Many of us imagine a typical accountant when we hear the word ‘Pricing.’ We picture lots of spreadsheets, bubble charts, and complex formulae.
Of course, excel price modelling has a role to play, as we’ll discuss later, but commercial pricing is more complex than that, and includes a great deal of customer value discovery, stakeholder management, price testing and PILOT trials (read: Team set up: Avoid 4 common mistakes when setting up a pricing team).
Scholarly articles have long had a technical lens on pricing, in universities across the world.
For years now, a wealth of highbrow academic articles have been written studying how we respond to prices and behave differently to prices in different conditions (i.e., price elasticity of supply demand, game theory and market equilibria).
However, commercial pricing as a science is a relatively recent development in B2B and B2C Australian organisations.
Is commercial pricing a science?
In the last 2-3 years in particular, there has been a data-driven movement influencing the commercial pricing discipline. The belief that price management is a science and that business decisions must be driven by rigorous analysis of data or else it is meaningless.
Many business leaders tell their teams now that “data should be at the heart of all their price decision making.” Many consultants are informing CEOs and executive teams that “big data can eliminate reliance on ‘gut feel’ pricing decisions.”
Businesses are evidently finding the proposition of commercial pricing as a science very appealing. Many business leaders have academic backgrounds in engineering / applied sciences.
But is it really true that commercial pricing is a science? Is it right to equate intellectual rigour with data analysis? Sometimes no is the answer because commercial pricing sometimes relies less on data analysis and more on imagination, experimentation and communication.
Commercial pricing also draws on a wealth of other disciplines, embracing ideas for cognitive science, economics, psychology, marketing, data science, math, statistics, as well as finance.
The benefits and tensions of such a wide scope can be found in daily commercial pricing practice and implementation, and are highlighted particularly in price trials, tests and experimentation or during iterative price optimisation cycles.
It is important to note that commercial pricing does not exist in a vacuum: market dynamics also has a role to play. The influence of the GFC (Global financial Crisis) on Australian manufacturing and distribution, for example, cannot be underestimated.
The growing importance of customer habits and experiences in product development and marketing. The ethics of artificial intelligence and data security.
Understanding what constitutes ‘ethical’ and ‘unethical’ customer research. Wide scale disruption across pretty much all Australian industries created by an online platform revolution.
Is commercial price data logic?
Is an experiment-based quantitative approach to commercial pricing really best for commercial businesses? Or does a more, human, discussion-based qualitative approach to commercial pricing and value discovery yield better results with stakeholders and customers?
It’s important to realise that the presence of data is not sufficient proof that pricing outcomes cannot be different. Data is not logic. In fact, many of the most profitable business moves come from bucking the evidence (i.e., GE, Lego, DuPont, Apple, Google).
Many business leaders, particularly those who apply design thinking and other user-centric approaches to innovation, recognise the importance of qualitative, observational research in understanding human behaviour.
Aristotle said:” Most of the things about which we make decisions, and into which we therefore inquire, present us with alternative possibilities….All our actions have a contingent character; hardly any of them are determined by necessity.”
Aristotle strongly believed that this realm of possibilities was driven not by scientific analysis but by human invention and persuasion even though he was one of the first to write about cause and effect.
Why commercial pricing should include imagination?
Perhaps the most fundamental issues for an evolving commercial pricing discipline are about free will and human agency. Does algorithmic pricing software sometimes remove choices that can radically change situations for the better?
As more businesses across industries and markets from retail, airlines, entertainment and energy adopt algorithmic commercial pricing because they believe pricing is a science, prices will increasingly be determined by the knowledge/data a company has about you, and the relationship between different, competing algorithms.
What can happens when businesses allow their application of the scientific method to go too far?
An example of crazy algorithmic commercial pricing decisions is Amazon’s book-selling services in 2011. For a short time, a book on evolutionary biology was priced at more than $US23M.
No human would ever price a book at this level, however the algorithm did because of the interaction between algorithms of two competing retailers. One was programmed to make sure that prices were 10% higher than those of a competitor, the other was designed to try and make any price 5% cheaper.
It took human sense and commercial logic to re-set the price. It took a pricing and revenue management team with insight into commercial pricing to solve what algorithms made extremely complex.
Even those who create algorithms acknowledge that sometimes their competitional reasoning is almost impossible to unravel – think Bitcoin.
According to a 2017 report by Digiconomics, worldwide bitcoin mining uses up more electricity than the country Serbia. Writing for Grist, Eric Holthaus calculated that by July 2019, the Bitcoin peer-to-peer network would require more electricity than all of the United States. And by November of 2020, it’d use more electricity than the entire world does today.
In the Amazon online book example, it took less than 1 second to rifle through huge amounts of big data (faster than any human could have observed) for these competing algorithms to reach this insane $23M price tag. However, it took a commercial pricing team 1 second to realise that this price tag was insane and re-set the system.
If we always rely on data then we inadvertently convince ourselves that new possibilities don’t exist. The imagination of new possibilities relies on us to break frame and believe in alternatives.
Scientific analysis of price data has maximised revenue and improved profitability for businesses. However, it should not drive every pricing decision. Sometimes businesses use science in contexts that actually require imagination and innovation, and not data.
The use of flexible pricing and algorithms in commercial pricing organisations is a clear indication that the egalitarian component to how we price and buy stuff is being disrupted right now.
Underlying these recent commercial pricing actions in the market is the belief that pricing is a science. It feels like businesses are excluding the possibility that commercial pricing is also a discipline that requires imagination – a very different process from analysis.
In following fact-filled articles, I will be discussing key questions such as:
To what degree are humans mislead by our innate biological biases and blind spots when we set prices?
Who’s to blame when price-setting algorithms work together to collude?
Is it entirely ethical to calculate the maximum price a customer is willing to pay without them even realising it?
How do can we create psychologically safe organisations in which people are willing to adopt new ways of thinking.
When we are continually outsourcing their roles and responsibilities to computers?
Is the future role of a pricing team gatekeeping algorithms with human ethical bias and real world logic?
For now, I would like to know how you make pricing decisions? Think about the last time your bucked the evidence and really challenged people to think differently about pricing.
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