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November 09, 2015
Quick wins with Price Optimization
By: Haresh Gopalan & Billy Hou
Quick wins with Price Optimization

In today’s crowded market place, with shrinking margins and ever expanding product variety, retailers are determined to turn every stone in order to achieve their profitability objective. Instead of looking at the product related information and carrying out supply chain related cost reductions, we recommend focusing on customer behavior around a product and fine tuning the price.

Price optimization is the analysis done by an organization to understand how customers respond to different prices for its products and services. Conversion of traffic to sales by changing the prices dynamically or periodically is the main objective of price optimization models. Retail today is not just about traffic, it is about the targeted and relevant traffic as we described in better forecasts start with understanding traffic.

Online E-commerce retailers are seemingly the most suited for price optimization because of the massive amount of data collected around the product and customer traffic as well as the ability to easily test new pricing strategies. However, any company from equipment rentals to industrial manufacturers can make use of these techniques to drive up their profit margins.

Price Optimization models investigate how traffic and demand varies at different price levels. They then associate that data with information on cost and inventory levels to recommend prices with the most common objective of maximizing profit. There are many off the shelf solutions that can perform automated price optimization but building a decision support model that is specific to the company and organic to its business, is the key to maximizing profits.

As an example of the risk in automated pricing, the book “The Making of a Fly" was listed at $1.7 million and that price went up from there as described in How A Book About Flies Came To Be Priced $24 Million On [Name Redacted]. We assume that this issue has been fixed, but for some businesses automation is not necessary and you still need some human involvement in the process to understand the finer points of the business and its customers.

In Three steps towards price optimization success we outlined the following steps:

  1. Predict the price to volume relationship by leveraging both internal and external data, using a variety of machine learning techniques.

  2. Use the predicted price-volume curves as an input into a price optimization model to maximize profit or revenue while meeting volume and margin targets.

  3. Companies need to facilitate effective change management, by leading with a detailed current state assessment and integrating the analytics decision support into their processes.

We will delve more deeply into some of the opportunities presented by this process:

Identify Low Hanging Fruits

In the simplest terms, this approach looks for outliers in transactions, products, customers, sales representatives and market segments. By analyzing outliers, we can identify abnormal pricing behavior that could be corrected for immediate benefit capture. Based on our past projects, an analysis such as this can typically help spot:

  • Sales representatives who are quoting low prices in order to meet bonus targets
  • Customers who receive an inappropriate price for their volume
  • Products that are mis-priced compared to the market

The key here is to eliminate any deviant behavior in the system that is misaligned with the current pricing strategy.


The next step is to examine the current product portfolio and customer base. This involves performing statistical analysis on both product and customer data and understand the relationships between products. A typical output from this analysis on the product side will identify complementary and competing products for each product in the portfolio.

On the customer side, segmentation is vital for developing a variable pricing scheme. By identifying core groups of customers and their buying habits, you can target them accordingly by offering them prices that will help win their business over the competition.

These segmentation results are two of the key inputs to the price optimization model. The idea is to segment your products and customers and perform targeted pricing so that they buy the products. Without proper segmentation, dynamic pricing can fail. It might trick the consumers in thinking that they will get the better deal later making them perpetual deal seekers rather tahn buyers at a fair price.

Determine Price Elasticity

Price Elasticity

Simply put, price elasticity of demand is the extent to which price affects demand. It answers the question "if the price goes up by x, how much will the demand shrink?”. To build a price elasticity model, we have to first determine the factors which drive the price. Then we build a price elasticity model using these factors to predict the price for each product and customer segment. A typical output for this analysis will be a set of price elasticity curves, which could be in the hundreds or even thousands, for each product and customer segment combination.

Optimize the Price

The goal of the price elasticity study is to predict the price at which revenues and margins are maximized. To do that, we need to use optimization techniques such as Mixed Integer Programing or Linear Programing to sort through thousands of price elasticity relationships and identify the best price to charge which maximizes the overall profit (or other objective). The form of the optimization is determined by the underlying structure of the pricing problem which could involve satisfying the operational constraints such as available inventory, capacity etc.

Understanding the products, customers and rules specific to your business provide a good foundation for an improved pricing process. Executing the steps above towards price optimization will lead towards acheiving the goal whether it is increased revenue, better market share or profit maximization.

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