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Price Optimization – What Is It – How to Get It

May 17th, 2019 (Updated 06/20/2023) | 5 min. read

By Ted Hartnell

Price Optimization – What It Is and How to Get It

Understanding the relationship between your pricing strategy, that of your competitors and your customers’ willingness to pay those prices was once exclusively the domain of large corporations. But with a recent breakthrough in artificial intelligence, powerful, actionable pricing insights have become accessible to anyone selling online. And that means you, too.


Meet Price Optimization, Your New Best Friend

Before I make introductions, it is important to explain that Price Optimization is not the same as Price Monitoring and Price Matching. Price Optimization is price setting by the Willingness To Pay (WTP) of customers. Due to the recent advances in consumer behavior market science and e-commerce, Big Data means that not one but four new types of Price Optimization are now available to you. We’ll talk about them later in the piece.


Price Optimization… and How to Get It

Until very recently, it was only possible to calculate customer Willingness To Pay (WTP) for a handful of industries. Both transaction data and test markets needed to be plentiful and, typically, two years of transaction data from 200 test markets was required. Only large supermarket chains (with many locations) and airlines (with many routes), along with a few other industries, could supply such data.

That was before “Market Simulation” – an Artificial Intelligence (AI) technology that uses Game Theory to quantify Consumer Behavior. Market Simulation creates a software replica of a living market that can be tested and optimized.

Four Types of Price Optimization

Back to our four types of Price Optimization. They are:

  1. Single Product Pricing
  2. Reaction Pricing
  3. Portfolio Pricing
  4. Strategic Pricing

Single Product Pricing – Inventory and Revenue

Single Product Pricing calculates independent Demand Curves for every product in the market. The Demand Curve is the forecasted quantity sold at every price point. It shows how price can be used to generate the demand required to clear products in inventory.


Demand Curve


In addition to forecasting inventory, the Demand Curve forecasts revenue at every price point. This can be used to set prices that meet revenue targets.


Single Product Pricing – Profit and Promotion

The Demand Curve can also be used to set the Profit-Maximizing Price and optimal promotional discount of individual products.
When COGS is known, the Demand Curve calculates profitability at every price point. The profitability curve is used to maximize the return from a promotion.

Cannibalization is the biggest concern when discounting price. Existing customers are more likely to switch to the discounted product than new customers. Hence, the Demand Curve should also forecast same-store cannibalization. This allows you to maximize your total return across all the products inventoried.


Reaction Pricing

Price fx- What is PriceAnalyzer


The Hurricane Chart shows that matching price is rarely your “best response” to a competitor’s discount. Competitors are different, and matching them is more likely to cannibalize sales from existing customers who might be considering more expensive items.


Hurricane Chart


The Hurricane Chart ranks competitive products by Price Sensitivity (blue bars), with the products that most impact your sales sorted to the top. In this example, you would only need to lower your price by about 8% (orange bars) to protect your lost revenue if your top competitor were to discount by 20%.


Multi-Product Portfolio Pricing

When there are many related products in a portfolio, then a multi-product pricing strategy is needed.
Multi-Product Portfolio Pricing can increase profitability across a portfolio of products without losing customers and market share.


Multi-Product Chart

Multi-Product Portfolio Pricing makes small, scientific, up-down adjustments to related products in the price list— keeping the average price about the same. This ensures that the increased profitability from those customers willing to pay more is greater than the lost profitability from customers who switch to your cheaper products. Large profit increases of 8% to 20% are possible because, in the past, there was not a good way to find the optimal prices of all products in a portfolio. Single Product Pricing would optimize one product but cannibalize others. This left many high-value revenue opportunities underutilized.


Strategic Pricing

Over the long term, you want your pricing policy to match the customer’s perception of the unique value that your brand and store provides versus your competitors’. This is called Strategic Pricing.

You can think of this Portfolio Pricing as “free money.” Profitability from each product line will increase without starting a price war and competitors won’t react, as they see no change in average price or in their own sales volume.


Value vs. Competiton Chart


Strategic Pricing is another Multi-Product Pricing technique. But unlike Portfolio Pricing, it involves raising or lowering all prices in each Product Line by the same amount. In addition, unlike Portfolio Pricing, Strategic Pricing will very likely trigger a competitive reaction.

Hence, Strategic Pricing needs to predict a Best-Case, Expected-Case and Worst-Case Competitive Reaction. For example, if you lower all your prices for a given Product Line, then the Best-Case scenario is that none of your competitors match the lower prices. The Worst-Case scenario is that all competitors match. Considering competitive reaction allows you to strategically set more profitable and sustainable prices, this is a positive outcome that constantly keeps you one step ahead of your competitors.

Ted Hartnell

Chief Architect and Developer , Scientific Strategy

/ Ted was at a Goldman Sachs company developing Wall Street’s high-frequency pricing and trading platforms. Ted has an Engineering degree and a Law degree from Sydney University, and an MBA from UC Berkeley. His post-graduate research in Market Science was conducted at Dartmouth College.

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