Price Optimization – What Is It – How to Get It
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. A breakthrough in Consumer Behavior market science and eCommerce Big Data means that 4 new types of Price Optimization are now available to every industry that sells online.
Price Optimization … and how to get it
- Price Optimization is NOT the same as Price Monitoring.
- Price Optimization is NOT the same as Price Matching.
- Price Optimization is price setting according to the Willingness To Pay (WTP) of customers.
Until very recently it was only possible to calculate customer Willingness To Pay (WTP) for a small handful of industries. Both transaction data and test markets needed to be plentiful. Typically, 2 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.
But a breakthrough in market science, coupled with eCommerce Big Data, meant that Price Optimization is now available to every industry that sells online.
The breakthrough in market science was “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.
RADAR is eCommerce Price Optimization software. It is available as an integrated partner solution within the Price f(x) environment.
RADAR uses Market Simulation to calculate the Willingness To Pay (WTP) of customers. RADAR then forecasts demand and calculates profit-maximizing prices.
RADAR only needs the data an eCommerce platform automatically collects over the previous month. No price experimentation or price variation is ever required. But RADAR does need to know the Cost of Goods Sold (COGS) to optimize profitability.
Four (4) types of Price Optimization
There are, in fact, four types of Price Optimization:
- Single Product Pricing
- Reaction Pricing
- Portfolio Pricing
- 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. The Demand Curve shows how price can be used to generate the demand required to clear the products in inventory.
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 Cost of Goods Sold (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 problem when discounting price. Existing customers are more likely to switch to the discounted product than are new customers. Hence, the Demand Curve should also forecast same-store cannibalization. This allows you to maximize your total return across all the products in your store or brand.
The Hurricane Chart shows that matching price is rarely your “Best Response” to a competitor’s discount. Competitors are different. And your matching discount is more likely to cannibalize your other sales from existing customers considering more expensive items.
The Hurricane Chart ranks competitive products by Price Sensitivity (blue bars). That is, the competitive products that most impact your sales are sorted to the top. In the above case, 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 Portfolio Pricing makes small, scientific, up-down adjustments to related products in the price list— keeping the average price about the same.
Market Science 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 unused many high-value revenue opportunities.
You can think of this Portfolio Pricing as like “free money”. Profitability from each Product Line will increase without staring a Price War. Competitors won’t react as they see no change to the average price or to their own sales volume.
Over the long term, you want your pricing policy to match the customer’s perception of unique value that your brand and store provides compared to your competitors. This is called Strategic Pricing.
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, this 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.
RADAR is eCommerce Price Optimization software offering both Single Product Pricing and Multi-Product Pricing results.
RADAR represents a major upgrade in pricing science. The Demand Curve can now be calculated instantly without any price experimentation or price variation whatsoever. Profit maximization and demand forecasts can be accurately calculated with only a month of historical data. And the negative impact of same-store cannibalization is always considered.
RADAR automatically generates Demand Curves, Competitive Landscapes, Mind Share Graphs, Customer Flow Charts, Profit-At-Risk Analysis, Feature and Attribute Importance, Product Mix Optimization, Lean Inventory Management, New Market Entry Strategy, Keyword Optimization, and What If Analysis.
RADAR is available as an integrated partner solution within the Price f(x) environment.
About the author
Ted Hartnell is the Founder of Revenue Watch (http://radar.revenue.watch) and the Chief Architect of RADAR eCommerce AI. RADAR is Artificial Intelligence that can convert Big Data into profit.
Prior to RADAR, Ted was the Managing Director who brought PROS (Price and Revenue Optimization Software) to China and East Asia.
Before PROS, Ted was at a Goldman Sachs company developing Wall Street’s high-frequency pricing and trading platforms.
Ted has an Engineering degree and Law degree from Sydney University, and an MBA from UC Berkeley. Ted’s post-graduate research in Market Science was conducted at Dartmouth College.