Business to Business (B2B) price optimization is applicable to many industries, such as package delivery services (UPS), telecommunications services (bundled services for voice, data, and video communications), industrial products, energy services, construction, even travel and hospitality.
But what is the key ingredient for any company hoping to utilize B2B price optimization analytics? The answer: the sales model should be a packaged offering with some degree of custom pricing built in. Also important is that the company must negotiate a few hundred to hundreds of thousands of deals annually. These deals serve as the basis for estimating the market response model and can be categorized as “won” or “lost.”
With those requirements satisfied, implementing B2B price optimization analytics can work effectively. The goal: Maximize the contribution by estimating the optimal price to quote to customers.
There are several important considerations along the way, such as sales compensation and incentives (which are often misaligned), pricing performance measurement (which means looking historically at how well the company has been pricing), marginal costs, risks associated with winning or losing a deal, price sensitivity of volume (a company can win a deal, but the customer may buy less volume if the price is too high), and finally what price to show sales (such as target price, price range, stretch price).
Effective B2B price optimization analytics should accelerate sales by reducing the number of exception pricing requests, i.e., salespeople attempting to offer discounts. If done right, these analytics can standardize the exception pricing process, while incorporating channel and account considerations.
Companies also must keep in mind their competition, strategy and market share. For example, they may have different competitors on different deals. They also must be aware of the interplay between price optimization and price strategy, and realize that price optimization sometimes may run counter to strategic market share objectives.
Other steps required for successful Business to Business price optimization analytics include:
- Compute the Market Response Model (MRM), which helps to estimate customer response and essential drivers of demand;
- Calculate the contribution function, incorporating all cost (net contribution), considers expected fulfillment (volume);
- Deduct opportunity costs of winning the deal and account for risk of losing the deal;
- Determine price quotes, factoring in optimality, target prices and guidelines for sales force discretion;
- Manage changes to the pricing business process, providing what-if capabilities to evaluate different scenarios.
Companies that harness predictive analytics will generate optimal pricing that will keep them ahead of the competition. By doing so, they can drive organic revenue growth and profit gains while transforming their B2B sales culture.
The below graph illustrates how B2B pricing analytics works. Combining the win probability and contribution functions, produces an expected contribution function, from which we can prescribe the optimal price to quote.