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The Future of Pricing Strategy and Revenue Growth Management

Life is oddly complex. This makes even everyday predictions  like the results of soccer matches, the weather, or financial markets  seem very difficult. Luckily, there are often simple rules that can provide strong guidance.

If you bet on a 2:1 victory of the stronger team in a soccer match, you have a better chance of being more accurate than the experts on TV. In meteorology, the forecast “tomorrow will be just like today” is right for about 2 out of 3 days and was, until very recently, better than most professional predictions. In finance, simply buying the market portfolio will beat most other investment strategies.

Optimizing prices for revenue growth or profit is harder than you might think

Unlike other predictions, pricing is challenging. In pricing, we are interested in understanding what product offerings and prices would optimize revenue or profit. Here, cost-plus pricing is a simple rule that produces decent and reliable results. Despite being ridiculed by many pricing professionals, cost-plus pricing is still widely and successfully applied by decision makers around the world, according to Harvard Business Review. To beat it, pricing professionals have thrown in many different ideas that typically put more emphasis on the customer and competition, rather than on the product and its costs.

Two prominent examples are value-based pricing and price elasticity-based pricing. Yet, both often fail to perform better than cost-plus pricing, because they build on oversimplified core assumptions.


Value-based pricing leaves room for a variety of errors

In value pricing, a group of customers is typically replaced by ‘the customer’ who is the average of the customers in a segment to make the market situations easy to understand. The joke about the statistician who missed the rabbit first to the right, then to the left, and then claimed that the rabbit is dead — on average — is well known.

Similarly, the first mistake pricing managers make is tailoring their products and pricing to some hypothetical average customer who does not exist in the real world.

The second problem arises from focusing solely on value and ignoring costs.

Particularly, in the case of slim margins, this can be incredibly wrong. The Harvard Business Review explained value pricing by taking into consideration two competitors, A and B, in the big-screen TV market segment, each offering one TV model. Competitor A determined that because of customer preferences, her offer is worth $150 more than B’s offer. By pricing relative to the next best offer, which is B’s TV at $799, A determines a value-based price of $949. If B reduces the price to $399, then A must follow and price at $549. There is a definite possibility that this process can easily turn into a loss for A if costs are not considered. 

Value pricing very often misses a consideration of how profit or revenue are affected by the price difference between products (here, A’s and B’s TV) at different price levels.

Certainly, value pricing can be done better — and it often is — e.g. if it is based on a conjoint study and market simulation. However, conjoint studies are very costly, take a long time, and are thus only applicable in limited situations . In addition to this, they are inflexible and cannot easily be adjusted to answer new questions.

Using the price elasticity to compute volume reactions to price changes is another example of dangerous conceptual oversimplification.

Frequently, such calculations implicitly assume that elasticity is constant, or that the price demand function is linear across a larger price range. In a market with just one product, this might be acceptable. In a market with multiple products and competitors, such assumptions are grotesquely wrong. If a price is increased above that of a close competitor, the elasticity can easily jump from -2 to -10.

Price elasticity is good for educational or recreational pricing exercises and back-of-the-envelope estimates, but not for actual price decisions, especially where real money is involved. 

The differential calculus that the elasticity calculation is based on, was used to calculate the motion of the moon with pen and paper 1,500 years ago. Astrophysics have long moved forward and now rely heavily on computer simulations. Therefore, it is now time for pricing to also make the next step and leave pen and paper behind.

Pricing needs more precise tools for the future

For a true leap towards the future, pricing needs to be built on a precise model of the underlying purchase and market dynamics. Take Nate Silver’s comparison of progress in weather and earthquake forecasting in his 2012 best-seller “The Signal and the Noise”  for example. Despite what you may think, weather forecasts have become increasingly better over the past decades. Why? Because meteorologists have developed an increasingly better computer model of weather dynamics  to, for instance— simulate the movement of clouds or the build-up of storms. The more precise the model and the faster their computers, the better their predictions. 

On the other hand, earthquakes remain notoriously unpredictable. The tectonic dynamics that cause earthquakes are still largely unknown. Perhaps because they happen deep under the surface of earth and are hidden from our sight. The history of science teaches us that a clear understanding and modeling of the underlying dynamics is the starting point of improvement.

Similarly, to get pricing right, we need to get the customers’ behaviors right.

Just like the weather dynamics in meteorology, we need a model of customer behavior. However, much like in geology, today’s pricing concepts are mostly blind to the underlying dynamics responsible for customers’ purchases, and thus are not improving.

Unhappy with this situation, we developed Buynomics

Buynomics is a technology that makes full use of recent advances in large-scale computer simulations, big data analyses, and artificial intelligence (AI) to model the buying behavior of customers. Buynomics evaluates market data such as sales figures, price information, expert knowledge, and market studies — and combines these with the knowledge from our customer behavior database. Using AI-powered algorithms, these insights are used to invigorate virtual customers that behave just like their real-life counterparts. This gives pricing professionals full transparency on how to steer and optimize sales, revenue, and profit.

Buynomics can beat conjoint based pricing in established markets with available market data. Buynomics predicts more precisely how price and product choices affect sales, and it pinpoints the best pricing strategy to optimize revenues or profits. As a true software solution, Buynomics can economically price large portfolios and not just selected high-value products.

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Paul Hanke
Post by Paul Hanke
October 26, 2022

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