translating pricing theory into reality

Theory to Practice: Translating Pricing Theory into Reality

Suresh Acharya, VP of Product Management

Copyright 2013 JDA Software Group, Inc. - CONFIDENTIAL

• Overview of RM• Media Industry• Ratecard Optimization• Dynamic Placement• Questions

Agenda

2

Copyright 2013 JDA Software Group, Inc. - CONFIDENTIAL 3

Overview of RM Systems:Perishable Inventory


Overview of RM Systems:Demand Shaping

4

Uni

ts S

old

Service Date

Observed Demand Unconstrained Demand CapacityLegend:

Excess Demand

Days Left

Uni

ts S

old

Capacity

RM systems work by predicting future demand, based on booking history and current booking trends

By understanding the inventory constraints, an RM algorithm decides which subject of the remaining bookings should yield out or displace

RM systems work by predicting future demand, based on booking history and current booking trends


• 1 Price• Maximum achievable revenue of

$2,500

100

PRIC

E

DEMAND

$100

$50

$11 50

DILUTED DEMAND

UNACCOMMODATED DEMAND

100PR

ICE

$100

$80

$60

$40

$20

$11 20 40 60 80

DEMAND• 4 Prices• Broader market segments can be

addressed• Produces $4,000 in revenue

Overview of RM Systems:Customer Segmentation


Price & Inventory Optimization

6

Inventory

Price Sensitive Forecasts

Willingness to pay

CompetitiveData

Optimal Price Recommendations

Optimization


Similarities:Media and Other Industries

Perishable inventory Segmentation for efficient inventory usage Fixed or inflexible inventory Purchase date vs. service date Competition affects rates Integration with transactional systems


Distinctions:Special Considerations in the Media Industry

What is being directly purchased is not what the buyer is buying

Stochastic nature of yield Small number of buyers Different delivery vehicles can

be used to satisfy shortages Splitting of inventory for Sales

and Promotions Pre-emptibility


Challenges in Media:

Constant changes in the selling vehicles

Rotational buyers vs. “cherry-picking” buyers

Changing delivery platforms Imbalance of revenues

between delivery platforms Ever increasing number of

selling items


Ratecard Building

The basis of selling is to determine at what price can something be sold to make the most revenue.

There are many groups that have a vested interest:o Finance: Here are our budget targetso Programming: Here is what we have to sello Research: Here is the data behind the inventoryo Sales: Here is how we should sell ito Revenue Planning / Management: Here are the statistical

trends


Ratecard Modification

Once rates are set, the next set of questions begins:o Should I change the rates?o Which rate(s) should I change? o For all time periods and by how much?o What relationships should be maintained between different

selling vehicles?o What is the overall revenue impact?o What are different scenarios that need to be considered?


Ratecard Optimization

12

Inventory

Demand Forecasts

Stewardship / ADU Req

Business Rules

OptimizationJDA Media Rate Optimizer generates optimal ratecards based on a

holistic evaluation of forecasts, remaining inventory and business rules


Media RM ProblemMatching Demand and Supply

Demand Supply

In addition to the optimal assignment of requests of spots, there are many complicating rules in media that make the problem very difficult


Demand Advertisers buy eyeballs;

i.e., the attention of audience

The size of audience depends on program, week, and target demographics

Demand is price-sensitive Demand comes with

complex and many times conflicting restrictions

Supply Network sells advertising

time The value of advertising

time depends on program and week

Network is limited by capacity and level of sale objectives

Opportunity to transfer inventory between Sales and On-Air Promotions

Media RM Placement ProblemMatching demand and supply


Dynamic Placement:Optimal Utilization of Inventory

Many problems include those in transportation involve the assignment of space against available capacity to maximize overall value


ManualPlacement

Lost Impressions

Improved Efficiency

More Units &ImpressionsRemaining

OptimalPlacement

Dynamic Placement: Under the HoodMinimize opportunity cost of

placement subject to …– Audience requirements– Daypart Mix– Flighting– Program quality constraints– Program inclusions/exclusions– Program/time block availability– Demand– Frequency– Category conflicts– Spot length mix– “Sellability”


Dynamic Placement:Different Realities

In certain media industries, it is possible to reassign spots on a periodic basis.

In other industries, each proposal must be assigned with little to no opportunity to re-assign.

JDA Commercial Proposal Optimization solution enables the optimalassignment of ad requests to spots to maximize overall profitability


Dynamic Placement:Heuristic vs. Optimization

Rule of Thumb-based solve methodologies are known to produce inferior recommendations –costing companies millions of dollars.

Media RM problems are often categorized as difficult combinatorial problems.These types of problems require sophisticated optimization technology.


Selling the right productto the right customerat the right time

for the right pricethrough the right channel

RM is a process of maximizing revenue from a “perishable” product through a combination of

pricing and inventory control.

How can you go WRONG with Revenue Management?

Theory to Practice: Translating Pricing Theory into Reality

More Info: www.jda.com/revenuemanagement

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