Pricing is the fastest and most effective way for a company to realize its maximum profit potential. Yet, companies, in the B2B arena, can do a lot more to leverage the potential of data and analytical horsepower to optimize their pricing performance. Companies we come across still raise prices across the board, discount excessively and lack the structure and initiatives needed to recover the millions left on the table every day. Most B2B negotiations take place F2F, between two human beings, each with varying negotiation abilities and with their own biases, fears, uncertainties and agendas. More often than not, these fears, biases and personal goals creep into the price proposals made by these people. Establishing a Customer Target Price, in and off itself, is a relatively complex pursuit. Innumerable internal and external factors need to be taken into consideration – Costs, Demand, Competitors, Substitutes, Elasticity, and Customer Negotiation Power & Profile. This not including the thresholds and constraints the buyer may have. It is unreasonable, if not impossible, to expect humans to make these pricing choices with complete accuracy and sans bias.
Given its high dimensionality and probabilistic nature, it is observed, that Pricing is one of those use cases – that yields itself very well to applying data science and Machine Learning (ML) solutions. These methods enable pricing to become a lot more specialized, customized and thus, more geared to achieve desired results.
These price-optimization algorithms are designed to study a humungous amount of data, detect patterns and provide a price prediction per deal. This price recommendation approximates the customer willingness-to-pay based on historical data and reliable external data, and factoring in numerous price-drivers from size of the deal, regional differences, product pricing power, customer aggressiveness and other implicit nuances not always obvious to humans. This intelligence can be made available to sales in real-time (via APIs into CRMs or billing/quoting software) and can power negotiations with high-quality intelligence. A manufacturing company that we work with has driven a margin increase of almost 9% in a course of 2 years by providing sales with intelligence from predictive pricing algorithms.
Transactional level price recommendations can empower sales. To make pricing a strategic driver of growth – organizations need to have the ability to steer pricing to meet business objectives. Objectives could range from higher market share to higher profitability and may be different per business, region, product or customer group. An organizations data can reveal this optimum between margin and conversion/volume. Running simulations can give an indication, for example, of what the penalty on volume/conversation will be should a company pursue price increases. The ability to assess the impact of pricing decisions, based on data and quantitative facts, reduces uncertainty and gives confidence on what course to pursue. We recently helped an energy company adopt a more aggressive market penetration strategy to win back lost customers from competition. Using simulation and scenario analysis, they were able to estimate the penalty on margin from steering sales to increase conversion. Once the new strategy was activated, they saw conversion go up by 13%, as was expected.
In closing, using predictive analytics in pricing does not give one a crystal ball to predict the future, nor is it necessarily a case for automation. It is a means to ‘Empower & Augment’; the aim is not to replace the account/sales manager– but to empower and enable him/her with intelligence to get the right price, and not leave money on the table.