Analytics Blog

Optimal Pricing Is in the Eye of Corporate Strategy

An optimal suite of great products offered at acceptable prices is an integral part of a company’s strategy.

But what sounds like a pretty simple proposition is not that easy in practice. How does a company develop optimal products? And, more importantly for the current discussion, how does it optimally price them? The answers to these questions are embedded within the overall company strategy. A company decides who and what it wants to be (see the recent strategy blog for more on this subject). From this foundational decision emerges the optimal product-pricing strategy, the approach that a company takes to maximize share and/or profit.

Understanding Pricing Strategies

Pricing strategies support the overall corporate strategy—the who and the what. Many companies have a multi-tiered distribution strategy, for example. The product manufacturer sells to distributors, who sell to retailers, who, in turn, sell to consumers. These companies often adopt cost-plus pricing, a fairly straightforward strategy. First determine all costs involved in the production of the product. Then decide how much margin is acceptable. Finally, combine the two amounts. Distribution points that are downstream from the product manufacturer add their margins until the product reaches the consumer, ostensibly at a reasonable price.

Other companies want to become the market-share leader in a particular category. This overall strategy corresponds to pricing products to maximize the number of consumers who buy (i.e., maximize units sold or demand). “Me too” products, which are highly substitutable for others already on the market, follow this strategy by setting prices below that of competitive products. Companies may offer volume discounts on single products or bundles of two or more products to generate demand.

Companies innovating in high-tech spaces seek to maximize product-line profit quickly after introduction. In the early days of a truly new product, these companies will charge a higher price and then lower the price gradually as competitors enter the market. This approach yields high profit in the short-term to finance longer-term new product innovation.

Yet another strategy is to set prices to maximize customer-perceived value. Set higher prices for products that are more valued by customers and set lower prices for less-valued products. Products that occupy a niche or specialized market have a better chance of being highly valued, of course, but value can be created in virtually any category.

The customer-perceived value approach can be extended to pricing products differently based not only on the product value but on the customer value (i.e., price customization). Purchasers who are willing to pay more for a product can theoretically be charged a higher price for the same product. A cautionary note: there must be compelling and legal reasons to charge different prices for the same product. Failing to adequately justify price customization can lead to customer alienation (witness the controversial time- and geography-based pricing models used by some travel companies).

Some industries, such as transportation and hospitality, are time- and capacity-sensitive and they employ complex, dynamic algorithms to manage pricing. These organizations use margin-maximization strategies that account for limited supply, such as a finite number of seats on an airplane. If there are empty seats, for example, it’s better to drop the price and obtain a less margin than to maintain price levels and realize zero margin. If there is a high demand at certain times, ticket prices may increase to encourage customers to consider alternatives (e.g., to take an earlier flight).

Assessing Pricing Strategies

After compiling a shortlist of pricing strategies, it is important to vet these approaches quantitatively. There are several methods of pricing feedback that range from using existing data to obtaining new information from customers.

Econometric-Demand Modeling. This method capitalizes on the large body of well-grounded economic and econometric theory. With sufficient data, the method delivers high-quality, unbiased estimates of price elasticity. This technique relies on secondary data, such as web analytics, employment rates, weather patterns, company sales data, competitive pricing data, promotion dates, and even media-mix information.

Demand modeling has some disadvantages. Price elasticity is not projected reliably to price points outside the price range in the historical data. Rapidly changing conditions mean that historical data may not reflect current markets. The growth of e-commerce, for instance, dramatically changes the nature of demand and impacts price elasticity.

Choice Modeling. Survey-based trade-offs overcome some of the drawbacks of econometric-demand modeling. An experimental design (a plan that dictates which products are shown and at which prices) is used to create survey screens. These tasks replicate real-world buying scenarios where a respondent considers a set of competitive alternatives with pricing and makes a choice about what, or how many, to buy.

When a representative sample of respondents has completed the survey, the data is used to estimate a choice model. The choice model is also an econometric model, with one major difference: it does not rely solely on the past, but instead attempts to predict the future. Model results are used to estimate a demand curve and price elasticity. Different pricing strategies can be assessed by changing price and forecasting the impact on margin and customer acceptance.

Stated Preference. These methods are not model-based, but instead rely on self-reported purchase intentions. One of these methods (see a previous blog on pricing research for more details) is called the Gabor Granger approach. In this technique, a purchase-intent question is asked for a price point. This price can be selected either randomly or from the middle of the price range. If the respondent answers “definitely” or “probably” then purchase intent for a higher price is asked. If the respondent answers that they would not purchase, purchase intent is asked for a lower price, and so on.

From this data, the percentage who would buy at each price point produces a demand curve that can be used to estimate price elasticity. Plotting expected margin yields the price or price range that maximizes profit and/or customer acceptance.

Pricing strategies should simultaneously encourage product purchase, promote customer goodwill, and, ultimately, maximize profit. Evaluating potential pricing strategies with historical or future-looking methods is critical because missteps alienate customers and damage margins. Successful implementation depends on how well the pricing approach matches corporate strategy. Companies must first decide who and what they want to be and then use pricing to support this overall vision. Optimal pricing is truly “in the eye” of corporate strategy.

Elizabeth Horn

Senior VP, Advanced Analytics

Beth has provided expertise and high-end analytics for Decision Analyst for over 25 years. She is responsible for design, analyses, and insights derived from discrete choice models; MaxDiff analysis; volumetric forecasting; predictive modeling; GIS analysis; and market segmentation. She regularly consults with clients regarding best practices in research methodology. Beth earned a Ph.D. and a Master of Science in Experimental Psychology with emphasis on psychological principles, research methods, and statistics from Texas Christian University in Fort Worth, TX.

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