Foundational pricing principles are essential for any organization looking to get serious about Pricing and Revenue Management. Once your company has instituted the foundational principles of pricing: measuring demand vs. inventory/capacity, measuring price sensitivity, and measuring competitor pricing positioning, your next step towards Pricing and Revenue Management sophistication should be to leverage predictive analytics capabilities across each of these areas.
A rules-based pricing strategy can be a great way to build an initial pricing capability. Leveraging Pricing and Revenue Management capabilities that use sophisticated analytics to drive pricing rules can help decision-makers get comfortable with the principles behind price optimization. The primary benefit to a rules-based methodology is transparency – decision-makers can easily understand why the price recommendation is being made.
Rules-based pricing allows a company to set up an analytically-driven pricing framework that produces revenue uplift and creates a competitive advantage. Listed below are five areas where predictive analytics can inform a rules-based framework to produce prices that drive business objectives:
- Price sensitivity – this helps to measure the price-volume relationship for various products, categories, or segments;
- Supply and demand – by measuring the supply and demand of certain products, this allows insight into both your current inventory and recent demand trends;
- Customer metrics – understanding your customers behavioral patterns, including metrics such as web traffic and customer ratings, helps to provide the right products, at the right time and for the right price;
- Strategic Objectives – is the company goal to penetrate new markets, increase market share or drive profit margins? Frameworks can be tailored to meet strategic objectives and produce the desired results;
- Competition – by looking at metrics such as competitive density, competitive price movement and/or competitive price volatility, you can add another lens to better position your products against the competition.
By incorporating analytics like these into a rules based framework, you can trace back why the recommendation is what it is – with a high degree of precision. For example, on a product with low price sensitivity, little remaining inventory, and medium customer importance, a rules-based pricing result may drive an increase in price.
A key decision-maker will see these factors, and will be more likely to accept that recommendation, as it clearly aligns with the organizational strategy, analytics, and web metrics. This transparency gives decision-makers more confidence to accept the recommendations.
The ability to systematically execute dynamic pricing that makes sense to pricing decision-makers and your customers is a great way to establish initial Pricing and Revenue Management capabilities, and generate strong financial returns and organizational buy-in.