An Integrative, Methodologically Shrewd Approach to Pricing Decision Support
Pricing strategy becomes more challenging in an environment where customers have greater access to real-time information about far-ranging options and are able to develop situational buying strategies that reflect a more dynamic set of priorities. Advances in technology are already bringing some of those same dynamics to markets more traditionally associated with stable pricing, including durable and high-ticket goods.
A limber and astute approach to pricing requires skilled selection and application of appropriate techniques when asking customers for “pricing permissions” and modeling the implications of their willingness to pay. Our pricing decision support spans the full range of industries we serve and is always guided by a foundational understanding of specific market dynamics. To that, we bring:
Sales Data Analytics. Advanced analytic capabilities for mining existing sales data to discern historical sensitivities and inflection points... Read More
Pricing Survey Techniques. Adept pricing survey research that effectively matches markets and pricing problems to various test methodologies, each of which can be useful under the right circumstances… Read More
In-Market Price Testing. Design and execution of in-market price tests, where relevant data are available to build robust sales models based on real-world experimental designs… Read More
Price Optimization Modeling. Powerful price optimization applications with our Farsight® suite of predictive models, including elegant choice-based simulators designed to help marketers detect price sensitivities in response to significant uncertainty… Read More
A world-class portfolio of analytic tools allows us to mine historic sales data, where available, for insight on sensitivities and inflection points at the aggregate level and within key segments. With appropriate sales and survey data, we can develop revealing, accurate, and actionable models that predict the response to pricing strategies, including how price cuts will cannibalize or stimulate sales of portfolio products, how they will impact competitors, and what are the optimal market conditions for a price increase.
Propensity Analysis (an area in which we’ve done pioneering work) enables us to control more effectively than traditional Regression for many confounding factors (e.g., macroeconomic and seasonal variables) that can obscure the relationship between price changes and demand. We can also integrate social media data, using advanced text analytics, to enrich the understanding of how price changes impact sales.
Given sufficient data, we can assess the stability of models over time and, consequently, how confidently they can be used to predict the outcomes of particular pricing actions. While available insights are ultimately limited by the nature of historic data, this kind of retrospective exploration often provides important insights that complement and inform the design of more forward-looking pricing research and in-market testing techniques.
Direct assessment of customer demand in the context of pricing research poses unique measurement challenges because “willingness to pay” feedback is arguably in opposition to customers’ own self-interest.
- Selection of appropriate survey techniques requires an appreciation of the proper role for monadic vs. comparative test methodologies, and a perspective on the special application of conjoint-based approaches that co-vary product attributes along with price.
- Interpretation of the model outcomes must reflect a point of view about the role and magnitude of survey bias and overstatement at all points along the sensitivity curve.
- Guidance in applying the survey insights requires understanding of the structure and culture of each market.
- Decades of experience have given us a well-considered point of view on all these issues, enabling us to select survey methodologies that fit the pricing problem and interpret research outcomes through the lens of that experience.
When performed in conjunction with other pricing analyses, in-market price testing helps create a comprehensive pricing framework grounded in real-world outcomes. We review existing sales channels and processes to identify market opportunities that are conducive to price testing, and then design a statistically robust matrix of test scenarios for implementation. Extensive analysis of test data is conducted to determine the impact of price movements on product and portfolio sales, including cross-product elasticities, or more subtle price movements over a defined timeframe. Price response curves are calculated to project sensitivities and evaluate net profitability implications, taking account of potential competitive response patterns to map market-wide pricing dynamics.
Our Farsight® Forecasting suite of predictive analytics allows us to create pricing simulators that anticipate market reactions to changes in price and pricing structure for existing products and new products, including innovations that require customers to rethink price-value in entirely new ways. Built on a platform of price sensitivity research data (and, where available, the results of extensive sales data analysis), these market simulators allow users to estimate the corresponding sales revenues, share, penetration, and profitability for specified scenarios. Price optimization applications embed the model with additional analytic capabilities to support profit-maximization goals (or other defined objectives) without requiring extensive trial-and-error testing. Pricing simulators and optimizers can be developed as "stand-alone" applications for evaluating pricing strategies, or can be integrated with product configuration research to help product developers.