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Introduction
How much to charge for a product or service is of paramount importance to any
business. There is always a tradeoff between price and how many units will be sold. But
this tradeoff can differ dynamically not only by product but also by the hour. Dynamic
pricing is a concept where prices vary within a very short time interval and, in some
cases, can also vary by customers. These changes in price are based on drivers such as
incoming traffic, available stock, competitor prices and other market factors. Algorithms
often set the price dynamically with an objective of maximizing revenue or profit.
Retailers that rely solely on judgment and manual processes cannot keep up with the
pace of change in the Internet’s price-transparent markets. Companies such as Amazon,
with a much higher technological maturity, have outpaced competitors that operate with
more traditional approaches to pricing. Amazon, through dynamic pricing, has at least 3
million price changes daily. Dynamic pricing allows Amazon to remain competitive with
24/7 price monitoring and price changes, as described here. To keep up, traditional
retailers must give up the outdated cost-plus and manual approach to pricing, and invest
in pricing technology immediately.
DYNAMIC PRICING
FOR ONLINE RETAIL

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History
In the early 80s, American Airlines pioneered a practice called Yield Management; a
concept which focused on revenue maximization through analytics based inventory
control. In 1985, they announced Ultimate Super Saver Fares which were priced lower
than the low cost airlines. By selling seats that would otherwise be vacant, at low prices,
American Airline’s revenue increased 14.5% and their profits were up 48% the very next
year, according to Revenue Management.
The hotel industry had very similar issues that the airlines faced: hotel rooms were
perishable inventory; there was low cost competition, and demand was highly variable.
Marriott International began working on a system that would provide forecasts of
demand and inventory recommendations for each of its 150,000+ rooms. Marriot’s
Demand Forecast system was adding $150M to its revenue by mid-1990s, as described
in Room at the revenue inn. By 2000’s, it was the retailers’ turn to start experimenting
with their version of Revenue Management.
Misconception
In the early 2000s, Amazon.com had been forced to apologize, issue refunds and
appease angry customers after it was found to have charged some people more than
others in random price testing of DVDs on its website. When people hear that a
company practices dynamic pricing, they tend to think that the company
opportunistically increases prices whenever possible. While this is partially true, people
tend to overlook the fact that dynamic pricing also enables companies to offer a product
or service at a lower price. Put differently, dynamic pricing is more of an objective
approach (i.e., revenue maximization or profit maximization) rather than a subjective
approach (i.e., price by customer segments and market). And this, in turn, enables a large
set of customers to be able to buy products and services that they otherwise would not
be able to afford.

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Getting Started
As in most projects, an effective pricing engagement requires both executive and
technical preparation.
Executive Preparation
A successful dynamic pricing initiative requires definite leadership pieces to be put in
place. Executive sponsorship plays a critical role as pricing is an activity which has a
direct impact on retailer's reputation and shareholder value. It is an active role that
requires regular attendance, availability for decision making and unwavering commitment
to the end goal. Change management is the tool that the executive sponsor will rely on
to ensure that the organization is ready, willing and able to embark on this journey and
to stick with it through success. Change management must span the entire process —
from visioning until several months after the implementation to ensure no momentum
gets lost. Business user readiness is an aspect that is important but often overlooked.
The technical team may build a great tool, but if the business user is not capable or
reluctant to use it, the efforts might go wasted. For an excellent review of managerial
best practices for a pricing project, refer to Gartner’s March 2015 report Prioritize Best
Practices for Successful Price Optimization Implementations.
Technical Preparation
Technical preparation encompasses a broad set of steps, requiring deep expertise in data
science and its application to business problems. These steps span from assessment of
current state of systems to development, testing and maintenance of pricing algorithms.
1. Data & Systems Assessment
Data gathering, evaluation and cleansing is often the longest step in the process once
management buy-in is secured. The types of data required can be classified, simply, into
internal and external data. In retail, internal data includes target margin, historical
transactions, discounts, number of page visits, stock level, etc. External data consists of
elements such as competitor prices and events. This stage is also a good time to think
about future IT systems scalability (like Hadoop) and computation capability (like parallel
computing).

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2. Algorithm Development
Dynamic pricing is never a ‘one size fits all’ process, which is why most commercial off-
the-shelf solutions either fail completely, or don’t give the desired ROI. Success with
dynamic pricing initiatives requires a solid knowledge of the business drivers,
understanding of readiness of data, systems and personnel, and capability to experiment
with various machine learning methods. Error! Reference source not found. shows the
key blocks and data flow in a dynamic pricing model.
2.1 Generating Price Elasticity Curves
The first step in the Algorithm Development is the phase where, using historical data,
various models are tested for their power to predict SKU level price-demand relationship.
Each model is a unique combination of factors, also called ‘predictors’, and learning
techniques. Funneling the various models to the final few requires an understanding of
the business (from commercial to marketing to fulfilment), and other drivers that
influence sales such as traffic, product segments, product lifecycles and intercompany
cannibalization. External predictors, such as competitor pricing, forecasts of events and
weather, could also be included. The analytical methods most often used in this phase
fall under the broad field of machine learning, where algorithms try predicting the future
based on what was experienced in the past.

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2.2 Cluster Optimization
Once the predictive knowledge of the price-demand relationship is extracted, the next
step is to recommend what levers need to be pulled to get the performance we want.
For this, we use prescriptive analytics, optimization to be specific. Depending on the type
of business, constraints such as current inventory and minimum margin can have a big
impact on price recommendations. In addition, there can be internal competition
between similar products (cannibalization). Optimization seeks to maximize the
performance achievable while not violating these various constraints.
3. Pilot
In this phase, we field test the First-Algorithms, and enhance them for live pricing. The
field test will provide statistics on current model performance to determine what
enhancements are needed. A/B testing is typically the technique used to measure model
performance. A/B testing refers to running two different pricing strategies on the same
product at the same time. There are some instances, however, where A/B testing can’t be
executed due to issues around IT capabilities, customer perception and regulation. In
those situations, other ways of testing such as creating control and treatment groups or
testing the same product for different time period can be used.
4. Algorithm Maintenance
The online retail industry is very dynamic; models that work very well today may perform
poorly in six months. Routine enhancements and modifications to the model are the
norm rather than an exception. Most common modifications include adding/removing
predictive factors (e.g., replacing average of weekly traffic with weighted average of daily
traffic), and changing the machine learning technique (e.g., using Decision Trees instead
of Linear Regression). The lack of the capability to use updated market knowledge or
knowledge about current events to enhance your pricing models is yet another reason
why commercial off-the-shelf tools fail to deliver ongoing value.

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Results
There are many constraints that influence a company’s pricing strategy and policies. This
requires deep expertise in the specific market including knowledge of the customers,
products and competition that often requires merchandiser or other sales involvement in
the process. The technology that can be developed around price optimization cannot
typically replace this expertise and is often best deployed as a decision support system
for these functions. Therefore, incorporating it into the current processes and allowing
the experts to weigh in is an important part of the success of this type of effort. When
deployed and executed effectively, OPS Rules has observed as much as 15%
improvement in, both, revenue and profit.
As described earlier, dynamic pricing doesn’t always mean increasing the product price. It
also means decreasing the product price when we can, either to simply be competitive
or to capitalize an under-tapped segment. It is because of this capability, OPS Rules has
also observed a two-digit increase in the number of units sold and in the proportion of
assortment sold – both indicators of earning new customers and an increasing market
share.

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Case Study: Dynamic Pricing at Rue La La
Background: Rue La La is an online fashion sales company offering limited-time
discounts (‘flash sales’) on designer apparel and accessories. One of Rue La La’s main
challenges is pricing and predicting demand for items that they sell for the first time.
Pricing too low would reflect in quick sell-outs (stock-outs) and pricing high would result
in too much leftover inventory. Rue La La approached OPS Rules chairman, Professor
David Simchi-Levi’s, for a dynamic pricing solution as described in this overview of the
project.
Approach: The team built a model based on machine learning algorithms to predict
demand for new items and to estimate lost sales. This was followed by optimization in
order to take into account competing styles when setting prices.
Process: Every day the price optimizer suggested recommended prices for the sales of
new styles that are starting the next day. All styles for a sale event are priced together to
ensure global optimization. The run which takes about an hour to complete sends out an
email to the managers with recommendations for prices for the exposure styles for next
day’s events. Manual price checks are performed to ensure that the recommended prices
are within normal ranges and competitive with external competitors.
Benefits: This new approach to pricing showed average increase in revenues of 10% for
the five different product categories that were tested. The work won the team the 2014
INFORMS Revenue Management and Pricing Section Practice Award.

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Conclusion
In a price-transparent industry like retail, traditional pricing methods are outdated and
incompetent. And while there are commercial off-the-shelf pricing tools available, they
do not deliver the expected ROI because they fail to capture the uniqueness of the
company and of the product. While data availability has never been a problem for retail
businesses, the ability to harness this data to understand the relationship between price
and demand has been rudimentary at best. But recent advances in machine learning
techniques and optimization have made available powerful new ways of harnessing this
data. One such way, dynamic pricing, uses a mix of predictive and prescriptive analytics.
When planned and executed effectively, along with right change management, dynamic
pricing has demonstrated more than 10-15% increase in revenue and profits.
About OPS Rules
OPS Rules is the leading analytics and optimization firm focused on helping companies
identify and capture hidden opportunities in their supply chains and operating
models. We bring fresh intellectual property to clients based on the work of Professor
David Simchi-Levi from MIT. We implement a three-step methodology - Analyze,
Innovate, Transform - to ensure sustainable business outcomes. Our blog features
material on Supply Chain and Operations Analytics topics of interest.