Prices Must be Rational: For Sales Reps & Customers
“Price optimization” is generally used to estimate the price elasticity for each segment in order to find the relationship between prices and win rate, and then optimization techniques are used to maximize revenue and profit.
Although this mathematical approach to quantifying price response is intuitive and beneficial, by itself it lacks an important capability: do the individual prices make sense relative to each other? While optimized prices maximize profit and revenue (on paper), they must also be explainable by sales reps and appear rational to customers, which may entail aligning prices over one or more variables. For example, customers typically expect a better unit price when they order a higher quantity; however, a demand model based optimization alone cannot guarantee that this expectation will be met.
Likewise, one can argue that large customers are generally more price-sensitive than small customers, so the data-driven elasticity should be able to capture this fact. Indeed it does most of the time, but only when the historical transactional behavior reflects the expected pattern
In business price rationality mostly likely requires price alignment over several dimensions, such as:
- Customer Size: Large, loyal customers expect to receive better prices than cherry-picking, causal customers.
- Product / Service Levels: Higher performing premium products should be priced higher than lesser alternatives.
- Lead Time: Customers who place orders in advance expect to receive better prices than customers who place orders at the last minute.
Transaction-Specific Pricing Strategies in Complex Environments
When more than two dimensions require rationality, conflicts are very likely to arise among them. How much should you quote a large customer when upselling them on a premium product? What is the best pricing strategy when a large customer places a large order at the last minute?
Once we have optimized prices based on demand model, one naïve approach to rationally aligning them is to define and prioritize the rules based on backward analytics, and apply them sequentially to adjust broad price targets.
First of all, considering innumerable customer-product-market combinations and the speed of change, this heuristic approach fails to ensure margins and price relationships line up for every combination of selling circumstances.
Secondly, even if a complete set of rules for all circumstance can be created, this approach cannot guarantee 100% rationality, as it always sacrifices low priority rules in cases of conflict.
A Lack of Price Rationality Can Weaken Customer Faith in Fair Pricing
But if these rules are violated just 20% of the time or less, does it really matter? The answer is yes. A lack of price rationality will eventually be recognized by sales and customers, weakening their faith in price targets and initial quotes.
Any amount of irrational pricing, once discovered, means lack of justification and conviction about the “right price." Without conviction, your salespeople will fall prey to more frequent and aggressive negotiation from customers who feel obliged to push back on every order to ensure they’re getting the best price. On the other hand, having a solution that is rational in all dimensions gives everyone confidence that prices make sense relative to the specifics of the quote, which minimizes negotiations, over discounting, and exception requests.
At Zilliant the price optimization model not only includes demand model and elasticity computation but also has all required price alignments in the same model and solves everything simultaneously. This approach guarantees all price recommendations for any circumstance are rationally aligned and also optimum for the business strategy.
About the Author
Amir Meimand is Zilliant Director of R&D, pricing science, where he designs and develops pricing solutions for customers and performs research in which he applies new methods to improve the current solutions as well as develop new tools. Prior to joining Zilliant, Amir helped design and develop a promotion planning and pricing platform for B2C retailers. Amir holds a dual Ph.D degree in industrial engineering and operations research from Pennsylvania State University. In his doctoral work, he applied operations research concepts to dynamic pricing and revenue management.More Content by Amir Meimand