Competition based pricing: Monte Carlo methods in B2B & B2C pricing
Competition based pricing: using Monte Carlo methods in both B2B & B2C PRICING
(Note – this article was originally published by Ranson Pricing on 25th January 2017. You can find the original article here as well as numerous other informative articles.
In the mobster film Casino, smooth operator Ace Rothstein says “the cardinal rule is to keep them playing and to keep them coming back – the longer they play the more they lose, and in the end we get it all”.
In games like roulette, dice and slot machines each player’s winnings are subject to the luck of the draw.
But since casinos serve many players, profits average out. Monte Carlo methods, named after the famous gambling hub, aggregate similar random effects to model deterministic outcomes and can be extremely effective in competition based pricing.
Why consider competition based pricing such as Monte Carlo methods?
Offering goods for sale at a particular price point comes with a certain probability of success for each potential customer.
Whatever the price level there will always be some customers who decide to buy and some who do not. In the B2C environment, this means that a certain proportion of a large number of customers can be expected to buy.
Monte Carlo methods help determine the competition based pricing level that optimises profitability given the large number of customers each buying relatively small amounts.
In B2B things are slightly different in the sense that each Client will be buying significant amounts of the product in question (B2B markets where end users that are businesses behave like individual consumers are considered B2C for the purposes of this article).
But Monte Carlo methods are still helpful for facilitating the management decision of what competition based pricing to offer in a competitive tender process as they can estimate (a) the probability of success of one particular contract at each price point and (b) optimise revenue over a number of contracts tendered for over the medium to long terms.
What are the essential components of a Monte Carlo model
At the heart of a Monte Carlo model is a random number generator (“RNG”). Best practice in this area involves going out to a quiet, dark place to measure atmospheric noise and commercial solutions are available. But don’t forget that Excel has an RNG and in many cases this is probably sufficient.
In a pricing context, RNGs can be used to create and evaluate a wide range of scenarios and see how likely it is that each will materialise. Whether you use a hundred, a thousand or a million such scenarios will depend upon the nature of your business, the data you have available and the tools at hand.
But at Ranson Pricing we recommend keeping it simple, especially at first, as stakeholders will need convincing of the technique’s merits before further development incorporating more sophisticated statistics is likely to be of value.
What does a Monte Carlo model’s output look like?
At Ranson Pricing we believe that best practice in B2C Monte Carlo modelling leads to a “cloud” of probabilities showing the chance of achieving particular market shares at each price point.
In B2B, Monte Carlo models can be used for short term and long term benefits.
In the short term, they can show the probability that a bid will succeed at each individual price point. In the long term they can highlight pricing strategies that optimise profitability across a large number of contracts.
Who can use Monte Carlo methods?
Monte Carlo methods are applicable to pricing in almost any industry, even (especially!) when this type of method is not common practice. Airlines, hotel operators, other travel service providers, retailers, entertainment venue operators, manufacturers, professional service providers, pharmaceutical developers, software houses all, like Ace Rothstein have customers who play again and again.
Monte Carlo pricing is certainly not for beginners – and is a huge step up over market strategies such as setting the price based on total cost markups or price matching.
This technique is certainly in the realms of advanced strategic pricing and is a pricing method dependent on your target audience.
Like other techniques in pricing, Monte Carlo methods can help secure and build revenue streams to profitably deliver products and services that only get better over time.