Review on three Pricing journals

 Following your review, share a 750-words reviewing the topic of PRICE.  The paper should be completed using APA formatting (in-text citations and references) – attach in a word document – no PDF Files. 

 Use subject headers for organization – Introduction, Conclusion and obviously specific headers to support content provided – minus 5 points if not provided 

 Reference three AUTHORED sources minimum (see below) – in-text citations must be made evident for the three articles assigned.  Reference page and citations must match 100%.  No points given if there is no in-text citation.  All author’s names must be credited in the in-text citations. 

PRICING

A Survey of 1,700
Companies Reveals
Common B2B Pricing
Mistakes
by Ron Kermisch and David Burns
JUNE 07, 2018

GLASSHOUSE IMAGES/GETTY IMAGES

Poor pricing practices are insidious — they damage a company’s economics but can go unnoticed for
years. Consider the case of a major industrial goods manufacturer that was struggling with low profit
margins, relative both to competitors and to its own historical performance. It traced much of the
cause to a mismatch between its sales incentives and pricing strategy. The manufacturer was

2COPYRIGHT © 2018 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED.

compensating sales representatives based solely on how much revenue they generated. Reps thus
had little motivation to hit or exceed price targets on any given deal, and most were closing deals at
the lowest permissible margin.

Like this manufacturer, many business-to-business (B2B) companies have a major opportunity to
improve their standing on price. To help companies understand the state of pricing capabilities and
how they figure into performance, Bain & Company conducted a global survey of sales leaders, vice
presidents of pricing, CEOs, CMOs, and other executives at more than 1,700 B2B companies. We
gathered their self-rating of 42 pricing capabilities and outcomes.

Roughly 85% of respondents believe their pricing decisions could improve. On average, large
capability gaps exist in price and discount structure, sales incentives, use of tools and tracking, and
structure of cross-functional pricing teams and forums.

What Pricing Leaders Do Differently
To understand which capabilities matter most, we studied a subset of top-performing companies, as
defined by increased market share, self-described excellent pricing decisions, and execution of
regular price increases. While different pricing capabilities may be important for a particular
situation, the analysis showed that top performers exceed their peers primarily in three areas. Top
performers are more likely to:

• employ truly tailored pricing at the individual customer and product level
• align the incentives for frontline sales staff with the pricing strategy, encouraging prudent pricing

through an appropriate balance of fixed and variable compensation
• invest in ongoing development of capabilities among the sales and pricing teams through training

and tools

Our analysis also revealed just how much excelling across multiple pricing capabilities pays off.
Among the companies that excel in all three areas, 78% are top performers, versus just 18% of
companies that excel in none of the three. Let’s explore why these three areas have such a strong
effect on pricing effectiveness.

Pricing to the Average Is Always Wrong
One-size-fits-all pricing actually fits no one. Yet it is not unusual for sales executives to admit that
their ability to tailor prices at the customer and transaction level is rudimentary, or that they are not
even aware of how much margin they make on deals.

By contrast, more-advanced companies tailor their pricing carefully for each combination of
customer and product, continually working to maximize total margin. They bring data and business
intelligence to bear on three variables for setting target prices:

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http://www.bain.com/publications/articles/is-pricing-killing-your-profits.aspx

• the attributes and benefits that each customer truly values, and how much value is created for
them

• the alternatives and competitive intensity in the industry
• the true profitability of the transaction after accounting for leakage in areas such as rebates, freight,

terms, and inventory holding

One North American manufacturer with margins that were highly dependent on raw material pricing
suffered from an undisciplined approach to pricing. A diagnosis allocated costs at the product and
customer level to determine true profitability. That diagnosis, which showed the manufacturer was
undercharging in many cases, provided the support needed to raise prices where appropriate in
subsequent contract negotiations, leading to an average 4% increase from that opportunity alone.
The company designated an executive to be accountable for related profit margin opportunities and
to track the status and effect of each price increase. As a result, the company improved earnings
before interest, taxes, depreciation, and amortization by 7 percentage points.

Bad Incentives Undercut the Best Pricing Strategies
Managers often criticize sales reps for losing a deal, but rarely for pricing a deal too low, so reps learn
to concede on price in order to close the deal. Moreover, companies rarely reward sales reps for
exceeding price targets, which means few reps take risks to push for a higher price. Misaligned
incentives push deals down to the minimum allowed price.

The antidote is to align compensation with strategic goals. Incentive plans benefit from following a
few principles:

• Clarify the objectives — be they revenue growth, share gains, margin gains, or others — and the
behaviors that will help meet the objectives.

• Make it foolproof. Help sales reps understand the payout calculation, simplify the quota structures
and supplemental incentives, and make the upside for outperformance meaningful.

• Ensure transparency. Sales reps should easily see the effect of a deal’s price on their personal
compensation.

• Track the results through regular reviews that flag areas where frontline staff might game the
system.

Returning to the case of the industrial goods manufacturer described earlier, the company also
overhauled its incentive program to balance revenue and profit. It created a pricing tool to make the
commission on each deal visible to sales reps — for instance, “If I raise the price by $2,000, I earn an
extra $700.” Sure enough, reps began to close higher-margin sales. These changes led to a 7%
increase in prices, which added almost 1 percentage point as part of a 3.5-percentage-point
improvement in margin overall.

4COPYRIGHT © 2018 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED.

Training and Tools — Often Afterthoughts — Can Have a Big Payoff
Top-performing firms invest in building the capabilities of the pricing team through training and
forums to share best practices. This runs counter to the norm at many B2B sales organizations, which
give little or no formal training on price realization.

Further, most companies can raise their game by adopting pricing software tools. Based on the
performance of historical deals, software solutions — whether in-house or from a provider such as
Vendavo or Price f(x) — can provide frontline reps with real-time pricing feedback based on the
characteristics of a deal under way. Using dedicated pricing software is associated with much
stronger pricing decision making, our survey analysis shows. Yet despite the proven value of pricing
software, only 26% of survey companies use it.

The value of developing capabilities became evident to a specialty chemical producer with lackluster
margins. The company had hundreds of different products, each with different competitors,
substitutes, and customer bases. Product and sales staff could not explain their pricing decisions, and
often resorted to a rule of thumb summed up by one product manager as, “I estimate I can raise the
price by four cents per pound.” Not surprisingly, she had raised prices by four cents per pound for
four straight years, leaving money on the table.

By analyzing the various products and their markets, the chemical producer found pricing
opportunities that enabled it to increase earnings before interest and taxes by 35% within two years.
Just as important, the company set out to raise its game on pricing capabilities. It created forums for
sharing best practices, trained product managers in doing fundamental pricing analysis, and trained
salespeople on how to have better pricing discussions with their customers. New dashboards
monitored progress toward pricing goals and flagged places where sales reps might be getting too
aggressive, or weren’t getting aggressive enough. Finally, the CEO reinforced these measures by
demanding that the product and sales teams report on pricing actions taken, as well as results, so that
effective pricing remained a high priority. The company established itself as a pricing leader in its
markets and continued to optimize margins, both by raising prices and, in selective cases, by
lowering prices to drive the right balance of price versus volume gains.

Regardless of a company’s starting point in pricing, there is significant value in building out the
capabilities highlighted by our survey analysis. The three areas discussed here have proved to be the
most important for upgrading tools, resources, and behaviors. That said, companies in almost all
industries have underinvested generally across pricing. The episodic “pricing project” approach
leaves companies well short of full potential. With meaningful margin upside at stake, managers
cannot afford to continue pricing by rules of thumb or by taking a one-size-fits-all approach to pricing
across entire segments of their business.

Ron Kermisch is a partner with Bain & Company’s Customer Strategy & Marketing practice.

5COPYRIGHT © 2018 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED.

David Burns is a partner with Bain & Company’s Customer Strategy & Marketing practice.

6COPYRIGHT © 2018 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED.

Copyright 2018 Harvard Business Publishing. All Rights Reserved. Additional restrictions
may apply including the use of this content as assigned course material. Please consult your
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institution. For more information and teaching resources from Harvard Business Publishing
including Harvard Business School Cases, eLearning products, and business simulations
please visit hbsp.harvard.edu.

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The Good-Better-Best Approach to Pricing.

MOHAMMED, RAFI

Harvard Business Review. Sep/Oct2018, Vol. 96 Issue 5, p106-115.
10p. 2 Color Photographs, 1 Chart.

Article

*PRICING
*DISCOUNT prices
*LUXURIES
*PROFIT
*CONSUMER psychology
*PRODUCT bundling

Companies often crimp profits by using discounts to attract price-
sensitive customers and by failing to give high-end customers reasons
to spend more. A multitiered offering can use a stripped-down product
(the “Good” option) to attract new customers, the existing product
(“Better”) to keep current customers happy, and a feature-laden
premium version (“Best”) to increase spending by customers who want
more. There’s nothing new about this concept, of course—think of the
different grades of fuel at any gas station and the varying packages
marketed by cable TV providers, to name just two examples—yet many
companies and industries have failed to embrace it. The author, a
consultant who has helped many organizations adopt G-B-B pricing,
presents a step-by-step guide to devising, testing, and launching the
strategy. Key steps include identifying “fence” attributes that will prevent
current customers from trading down from the existing offering; carefully
choosing features and names to create clear differentiation and value;
and setting prices using feedback from in-house experts and, when
possible, drawing on conjoint analysis and other market research.
[ABSTRACT FROM AUTHOR]

Copyright 2018 Harvard Business Publishing. All Rights Reserved.
Additional restrictions may apply including the use of this content as
assigned course material. Please consult your institution’s librarian
about any restrictions that might apply under the license with your
institution. For more information and teaching resources from Harvard
Business Publishing including Harvard Business School Cases,
eLearning products, and business simulations please visit
hbsp.harvard.edu. (Copyright applies to all Abstracts.)

Founder, Culture of Profit

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131356798

Business Source Premier

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The Good-Better-Best Approach to Pricing

Companies often crimp profits by using discounts to attract price-sensitive customers and by failing to give
high-end customers reasons to spend more. A multitiered offering can use a stripped-down product (the
“Good” option) to attract new customers, the existing product (“Better”) to keep current customers happy, and a
feature-laden premium version (“Best”) to increase spending by customers who want more. There’s nothing
new about this concept, of course-think of the different grades of fuel at any gas station and the varying
packages marketed by cable TV providers, to name just two examples-yet many companies and industries
have failed to embrace it. The author, a consultant who has helped many organizations adopt G-B-B pricing,
presents a step-by-step guide to devising, testing, and launching the strategy. Key steps include identifying
“fence” attributes that will prevent current customers from trading down from the existing offering; carefully
choosing features and names to create clear differentiation and value; and setting prices using feedback from
in-house experts and, when possible, drawing on conjoint analysis and other market research.

For decades the auto insurance industry operated on a simple assumption: Consumers are highly price-
sensitive, and most will buy the least-expensive plan they can find. But in the early 2000s Allstate conducted
some research that caused it to revisit that assumption. Price does matter, it learned, but there’s more to the
story: Many drivers worry about being hit with premium hikes if they’re in an accident. And drivers with clean
records want to be rewarded.

Armed with those insights, in 2005 Allstate launched Your Choice Auto. The program relied heavily on
modifications to a feature in the company’s standard policy (which it continued selling) called accident
forgiveness, in which drivers who went five years without accident claims would have no premium increase
after their first accident. It introduced a Value plan, priced 5% below Standard, that didn’t include accident
forgiveness. A new Gold plan, priced 5% to 7% above Standard, offered immediate forgiveness (no five-year
wait) along with a deductible rewards feature in which repair costs borne by the driver would decline by $100
for every year of accident-free driving. And at the highest end, a new Platinum plan (15% above Standard) also
included forgiveness for multiple crashes and a safe-driving bonus under which credits were issued for each
accident-free six months.

Consumers were enthusiastic: By 2008 Allstate had sold 3.9 million Your Choice policies and was selling
100,000 new ones each month. A decade later the pricing plan remains attractive: In 2017, 10% of customers
chose the Value plan, and 23% chose Gold or Platinum. The company has no doubt that Your Choice drove
significant incremental growth. “There were a lot of skeptical people in the company,” recalls Floyd Yager, one
of Allstate’s senior vice presidents. “But we demonstrated that car insurance doesn’t have to be about being
the lowest-price game.”

Your Choice is a classic example of Good-Better-Best (G-B-B) pricing. There’s nothing new about the concept
of adding or subtracting product features to create variably priced bundles targeted to customers of varying
economic means or those who value features differently. It’s been nearly 100 years since Alfred Sloan
introduced a “price ladder” to differentiate Chevrolets and Buicks from Oldsmobiles and Cadillacs, creating “a
car for every purse and purpose” and powering General Motors to overtake Ford. In the modern era, G-B-B
pricing is evident in many product categories. Gas stations sell regular, plus, and super fuel. American Express
offers a range of credit cards, including green, gold, platinum, and black, with varying benefits and annual fees.
Cable TV providers market basic, extended, and premium packages. Car washes typically offer several
options, separated by services such as waxing and undercoating.

Idea in Brief

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THE PROBLEM
Companies often crimp profits by using discounts to attract price-sensitive consumers and by failing to give
high-end customers reasons to spend more.

THE SOLUTION
A multitiered offering (typically with three options) can use a stripped-down product to attract new customers,
the existing product to keep current customers happy, and a feature-laden premium version to increase
spending by customers who want more.

THE IMPLEMENTATION
Key steps include identifying “fence” attributes that will prevent current customers from trading down from the
existing offering; carefully choosing features and names to create clear differentiation and value; and setting
prices using feedback from in-house experts and, when possible, drawing on market research.

Yet many companies and industries haven’t adopted tiered pricing-and there’s little rhyme or reason to which
have, which haven’t, and why. G-B-B is a strategy every company should consider. In my consulting work, I
routinely see it used to simultaneously attract new high-spending customers and price conscious ones,
dramatically boosting revenue and profits. (Disclosure: Among my clients is Harvard Business Publishing, the
publisher of this magazine.)

Although G-B-B is conceptually simple, implementation can be tricky. If new offerings aren’t constructed and
priced correctly, existing customers will trade down, hurting profits. In this article I outline why G-B-B can
benefit many firms. Then I present a step-by-step guide to devising, testing, and launching the strategy in a
way that boosts profits and reduces the threat of cannibalization.

Capitalizing on G-B-B
G-B-B’s benefits come from three approaches: offensive plays aimed at generating new growth and revenue,
defensive plays meant to counter or forestall moves by competitors, and behavioral plays that draw on
principles of consumer psychology, whatever the competitive landscape.

Going on the offensive. Offensive plays can help brands grow revenue in at least four ways. First, companies
can dramatically lift margins by creating a high-end Best version that persuades existing customers to spend
more or attracts a new cohort of high spenders. In my work with companies, managers consistently
underestimate customers’ willingness to spend and the number of customers who might upgrade to Best, even
at prices that were previously unthinkable. Across a range of industries, it’s not unusual to observe up to 40%
of sales landing on the Best option.

For example, visitors at Six Flags amusement parks can buy one of three Flash Passes (Regular, Gold, and
Platinum add-on options to the standard admission ticket, with prices varying by day and location) to bypass
lines and thus enjoy more rides. The Gold Pass, which costs as much as $80 a day on popular week-ends,
reduces waits by up to 50%; the Platinum Pass, which can reach $135, reduces them by up to 90%. “It’s
amazing, actually, how many people pay for this,” then-CFO John Duffey told analysts shortly after the new
passes were rolled out, in 2011. Many Flash Pass purchasers are existing customers who decide to upgrade,
but some are new customers who had previously been put off by the notoriously long lines for rides.

Second, and at the other end of the spectrum, a low-priced Good offering can make a product accessible to
price-sensitive or dormant customers for whom the existing product line (which typically then becomes a Better

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offering) is out of reach. And it can limit the need for discounts or sales on the existing product or service-a
crucial advantage, because frequent sales can erode long-term pricing power.

A low-priced Good offering can make a product accessible to price-sensitive or dormant customers, and it can
limit the need for discounts or sales on the existing offering.

Uber has shown continued creativity and success with its Good versions. The company began in 2010 as a
black-car luxury service, and it still offers several high-end options. But in 2014, hoping to lure price-sensitive
riders, it launched uberPOOL, in which riders share a car with strangers going in the same general direction.
Unlike the traditional uberX service (in which riders have a midsize sedan to themselves and go directly to their
destination), uberPOOL trips involve multiple pickups and drop-offs of other passengers, so there’s additional
travel time; in exchange, the service is priced as much as 50% below uberX. UberPOOL now accounts for 20%
of all Uber rides-and in some cities it accounts for more than half of all trips. The company has begun
experimenting with Express POOL, which costs 30% to 50% less than uberPOOL and requires riders to walk a
few blocks to a central pickup location. Uber’s story shows that even after implementing a G-B-B strategy,
companies should continue exploring innovations that might lead to new, lower-priced versions of Good.

A third way that G-B-B can increase revenue is through a new Best offering that boosts the entire brand. In
2015 Patrón Spirits debuted a line of Roca Patrón tequilas made by the tahona process, which uses a two-ton
wheel hand-cut from volcanic stone to extract juice from cooked agave. The result is a sweeter, earthier, more
complex spirit than tequila produced by automated means. Even at $69-plus a bottle, Roca Patrón has
exceeded sales expectations: It is projected to sell 60,000 cases in 2018, which would make it the world’s
seventh-best-selling premium tequila brand.

And the benefits go beyond that revenue: Sales of lower-priced Patrón tequilas have risen sharply. Lee
Applbaum, Patrón’s chief marketing officer, cites research showing that Roca has boosted perceptions of the
overall Patrón line as artisan-crafted (from 60% of consumers surveyed to 64%), made by a small-batch
producer (47% to 58%), and fitting an image people want to convey (59% to 65%). “The details of the
expensive and laborious way that Roca Patrón tequilas are manufactured create a brand halo that reinforces
important attributes. . .for the entire Patrón line,” he says.

Fourth, a lower-priced Good version can spark ancillary revenue from related or complementary goods and
services. Consider Apple’s SE phone, which sells for just $349 (roughly a third as much as the iPhone X).
Every SE sale stimulates additional revenue through purchases on iTunes and the App Store, payments for
iCloud storage space, and sales of cases, chargers, and other accessories.

Playing defense. Sometimes G-B-B isn’t about aggressively seeking new revenue-it’s about protecting a
brand’s exposed flank. When faced with a low-cost rival, many companies’ knee-jerk response is to drop
prices, but that’s often a mistake. When the price holds firm, 15% of sales, say, might be lost to a low-cost
competitor, but 85% of customers are still paying full price-whereas if the price is cut, 100% of customers will
be paying less. Another common response to cheaper rivals is to launch a “fighter brand”-a discounted product
with entirely new branding. Classic examples include Procter & Gamble’s Luvs diapers and Intel’s Celeron
computer chip. (See “Should You Launch a Fighter Brand?” HBR, October 2009.) That may work well, but the
resources needed to create a new brand can be enormous.

In many cases, creating a new Good product is a better defensive strategy. Two of my B2B clients (in financial
services and industrial parts) held significant market share and enjoyed healthy profit margins when new
entrants began offering inferior products at rock-bottom prices. Customers seized on the disruptive entry as an

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invitation to negotiate, threatening to defect from my clients unless granted a discount. Although reluctant to
lose any market share, both clients resisted the impulse to discount their core offering. Instead, they quickly
rolled out cheaper Good versions that closely matched the new entrants’ stripped-down products. When
offered those options, most customers backed off their demands for a discount and continued buying their
existing offering at full price; they had been bluffing and weren’t actually willing to trade down to a lesser
product. Implementing a Good version calls such bluffs-something a straight discount can’t do.

A caveat: This defensive maneuver can have mixed results. In 2015 Town Sports International, a chain of
fitness centers whose memberships averaged $40 to $90 a month, began losing customers to competitors
such as Planet Fitness, whose monthly fees are as low as $10. To fight back, TSI retained its existing
membership plan and prices while launching a new plan-priced as low as $19.99 a month-that excluded or
restricted some benefits, such as towel service and access to fitness classes. This staunched the membership
decline: TSI gained 64,000 new customers in 2015. But the stock price plummeted, same-club revenues fell,
and the CEO resigned. Still, the new Good membership may have been the best possible response in a tough
environment. By steering clear of a simple discount or a price war, TSI ensured that many members continued
to pay their existing monthly fees, and the company avoided a devaluation of its primary offering.

Drawing on consumer psychology. Some G-B-B strategies aren’t specifically aimed at luring new customers
or defending against competitive threats; they’re more-general responses to consumer psychology.

For instance, companies often jam multiple features and attributes into a single product, but this can confuse
and over-whelm customers. A G-B-B plan helps potential buyers focus on and understand features and think
about which ones they value-and how much they’re willing to pay for them. (See the exhibit “Helping
Customers Understand Good-Better-Best.”) An educational software company I worked with found that
customers didn’t really grasp its myriad product features. So it tested a G-B-B model that unbundled those
features, creating a Good offering (its core software), a Better one (the core software plus new electronic
exercises), and a Best one (the core software and exercises plus one-on-one tutoring). Customer research
showed that the three-tiered model helped people differentiate the company from competitors-and indicated
that half of potential customers would pay a premium for Better or Best. (Because of a sudden leadership
change, however, the G-B-B model was never implemented.)

G-B-B can also shift customers from a binary “buy/don’t buy” mentality to consideration of incremental value
and spending. This can work in two ways. First, customers prefer having choices to feeling under an ultimatum,
so three differently priced options can give them a sense of empowerment. Allstate CEO Thomas Wilson has
identified this as a key benefit of the Your Choice policies, explaining that they moved people away from simply
comparing Allstate’s prices with those of competitors. “If people [have] a choice in the conversation, they are
not likely to switch [to a competitor] for $25 or $50,” he said in a July 2005 quarterly call.

Second, when faced with multiple options, customers tend to decide more quickly whether they are going to
buy some-thing, using their remaining time to focus on what. Having made that mental shift, they typically treat
the Good version as a sunk cost, which makes them more amenable to upgrading. Salespeople exploit this
tendency all the time: For example, instead of detailing all the features of a $1,200 appliance, they emphasize
that “for only $200 more” than the entry-level $1,000 unit, a buyer gets lots of extra bells and whistles. Rental
car companies highlight the full-size sedan you could be driving for $12 a day more than the price of a
subcompact.

Companies can also use G-B-B to exploit the so-called Goldilocks effect: people’s propensity to choose the
middle option in a set of three. In his book Priceless, William Poundstone recounts how Williams-Sonoma

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reaped unexpected benefits after launching a fancy bread machine priced at $429. That high-end model
flopped-but sales of the $279 model (previously the highest-priced unit) nearly doubled.

A final argument for considering G-B-B relates to the real politik of instituting change. The simplicity of the G-B-
B strategy makes it highly compelling to senior executives. For change to occur at any organization, top
management must be committed, deploying political capital to sell others on the shift. Because managers have
experienced G-B-B as consumers, they can quickly understand its appeal. In my consulting work, I often
suggest other pricing strategies but wind up helping implement G-B-B because it’s the option managers find
the easiest to understand, explain, and get behind.

Brainstorming About Tiers and Features
When considering a G-B-B pricing structure, the first step is to decide how many product versions to offer. As
the name implies, the most common approach is three. In general, companies with a single existing product
will designate it (or something close to it) as Better, adding features to create Best and subtracting them for
Good. But if taking away features to create a Good offering isn’t feasible, companies can forgo that option and
simply offer Better and Best.

Companies with complex products or a long buying cycle may be able to justify more versions. But too much
choice is risky. In a well-documented study by Sheena Iyengar and Mark Lepper, researchers offered samples
of jam to shoppers in an upscale grocery store. When presented with six flavors, 30% of tasters made a
purchase. When 24 options were on the table, only 3% opted to buy. Researchers believe that when
consumers have too many options, they become confused or paralyzed with indecision-a phenomenon the
psychologist Barry Schwartz explored in The Paradox of Choice.

If a company is set on many offerings, it can be useful to group them in away that turns consumers’ decision
making into a two-step process. New York’s Metropolitan Museum of Art offers seven memberships. To
minimize confusion, it divides them into two categories: Members Count plans ($80 to $600) for people joining
primarily because they want to visit the museum, and Patron Circle memberships ($1,500 to $25,000) for those
whose primary goal is philanthropic. Grouping member-ships in such a way guides people toward a general
category; once there, they can examine the G-B-B options in each.

After a company has gotten a sense of how many tiers to offer, managers can brainstorm about the features to
include in each. Sometimes the decisions are obvious, but many of the best G-B-B plans draw on unexpected
features, as Six Flags did when manipulating wait times to create a consumer benefit for its Flash Passes.

To help companies consider a wide array of potential features and benefits, I use a tool called the Value
Barometer, which lists 13 common product attributes that can be added, dropped, or varied to create different
perceptions of value. (See the exhibit “Pump Up the Value.”) Companies typically begin by identifying features
of the current offering that vary or would be easy to vary, but the tool’s real power is its ability to help firms
come up with out-of-the-box options that could be increased, decreased, or tweaked.

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Pump Up the Value

Once the brainstorming is complete, a company can begin analyzing the potential features it has identified.
Three questions are key: Does the feature have mass appeal or low appeal? How would adding or subtracting
it affect the cost of producing the good or offering the service? And is it a “fence” attribute-one that constitutes
a barrier preventing existing customers from crossing over to something cheaper?

Many managers start by focusing on the Best option, because of its obvious potential for revenue growth (and
because imagining new high-end features is fun). But they should begin by identifying and analyzing fence
attributes-often the most challenging task in G-B-B implementation.

The goal of adding a Good offering is to pick up new budget-minded customers without losing revenue from
existing ones. (In a perfect world, not a single customer would move from Better to Good.) Indeed, one of the
biggest risks of shifting to G-B-B is that existing customers will migrate to the new lower-priced offering,
cannibalizing revenue and margins. Fence attributes prevent this, by making the downgrade a difficult,
unpleasant, or painful choice.

Examples of fence attributes abound. In cable television, ESPN, CNN, and HGTV are always included in
“extended basic” (the Better offering) because many existing viewers highly value at least one of those
channels, and losing access makes the idea of trading down to basic (the Good offering) anathema. Hotels
offer discounted “no cancellation” reservations; the lack of flexibility creates a fence for many travelers. During
a recent tour, the Rolling Stones sold seats for just $85, but those seats came with a catch: Concertgoers
wouldn’t learn their location until arriving at the arena. That was a significant fence for many fans, who would
rather stay home than sit in a poor location. And paperback versions of books previously published in
hardcover utilize an obvious fence: They appeal only to readers who don’t mind waiting a year or more for the
book. Companies seeking to implement Good offers must find similarly effective fences.

Defining and Pricing Bundles
To choose the fence attributes that will separate their Good and Better offerings, companies should look for
features that have both wide and deep appeal (meaning that most customers want them and consider them
vitally important) and are somewhat costly to produce. The combination of high appeal and high cost means

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that if the feature is part of the Better but not the Good offering, relatively few people accustomed to Better
(that is, existing customers) will consider Good-but those willing to do without the feature can enjoy a
significant discount. For instance, when the New York Times launched its digital subscriptions, in 2011, it
moved to a G-B-B model in which the physical paper (which many subscribers were loath to discontinue, and
which is costly to print and deliver) served as a fence attribute. That fence is effective enough to support a
hefty price differential: An all-access digital subscription currently costs $324 a year, whereas adding print
delivery brings the price to $481 and up, depending on location.

The same qualities-appeal and cost-that help companies choose fence features will also guide them toward
features that belong in Best. Those should similarly appeal to a wide segment of buyers, but ideally they will
cost relatively little to include so that the company can keep high margins on Best.

When Southwest Airlines created the Business Select package as its Best offering, about a decade ago, it
identified high-appeal/low-cost items such as priority boarding, extra frequent-flier miles, and free cocktails as
amenities worth including. Bundling those relatively inexpensive amenities in a premium package delivered
$73 million in incremental revenue in the offering’s first full year.

High-appeal/low-cost Best features are often less about the actual product and more about the customer
experience. For instance, quicker delivery time can be part of a Best offer. And in some industries, guarantees
or warranties can deliver high perceived customer value at little cost, depending on the hurdles that must be
overcome to redeem the guarantee or on the expected utilization rate. For example, the length of the warranty
is the major differentiator between Good, Better, and Best versions of car batteries-products that behave fairly
predictably. But some products, such as tutoring services and weight loss programs, require customer
involvement to achieve success. Because of that uncertainty, companies generally aren’t willing to guarantee
them, even as part of Best packages and even if consumers would highly value guarantees.

Helping Customers Understand Good-Better-Best
Once a company has created a multitiered offering, it needs to help customers understand the various options.
This comparison grid, from a website design and hosting firm, is effective for three reasons, as described in the
following annotations.
1. Limiting the use of features available with the Good option (pages, bandwidth, and storage) creates a
“fence” separating the truly price-sensitive from those willing to pay more.

2. There’s a nice consistency and progression between packages: Customers don’t lose anything as they
move up in price, and each level has three or four key differentiators.

3. The packages have been intelligently named. In particular, ‘”Business” clearly communicates the type of
customer who should choose the premium option. The 80% price difference between that package and
“Advanced” signals the company’s belief that business customers-who typically have greater needs and are
less price-sensitive-will be willing to pay significantly more.

When devising Best bundles, companies need to be realistic about the attributes they can include. During
brainstorming, it’s natural to dream big-but as dreaming turns to planning, vigilance is needed to weed out
features that may be difficult to execute well or that could delay the launch. It’s also important to be judicious
about the number of attributes. It’s tempting to throw all the latest and greatest features into Best, but this can
result in unnecessary complexity and an unrealistically high price.

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After completing the cost-benefit analysis of the various features, it’s time to design and assign tentative prices
to the G-B-B bundles. Two rules of thumb for design: To ensure sharp distinctions between offerings, no more
than four attributes should differ between Good and Better and between Better and Best. And it’s important to
maintain a consistent progression of benefits from Good to Better to Best-beneficial features in Good should be
retained in the higher-priced offerings so that every step up the ladder is a clear improvement.

Some rules of thumb can similarly help with pricing. Companies should pay close attention to the price gaps
between Good and Better and between Better and Best. In my consulting, I strongly advise against setting a
Good price that’s more than 25% below Better, and I recommend that the Best price should not exceed Better
by more than 50%. Although customers’ perceived value must be the North Star, companies must also
consider how many customers might opt for Good, Better, and Best and what the margins of each package will
be. As a starting point-before conducting customer research-many companies estimate that 10% to 20% of
revenue will come from Good, 25% to 50% from Better, and 30% to 60% from Best. The actual mix will depend
on how many attributes vary between versions, the degree of differentiation achieved, and the price spread.

It’s never too early to think about names for the G-B-B options; those are essential in helping consumers
quickly identify which version best meets their needs. Lisa Krassner, the chief member and visitor services
officer at the Metropolitan Museum of Art, says that the very clear names of the three Members Count options,
each delineating a particular benefit-With Early Views, With Evening Hours, and With Opening Nights-have
been key to the offerings’ success.

Bringing in Research
Many companies conduct formal research to see whether their intuitive sense of what customers want is on
target. The timing and scope will depend partly on organizational culture: Some data-driven companies do
several rounds of testing, starting soon after the brainstorming step, while other companies wait until they’ve
created tentative G-B-B bundles and prices. (Still others proceed without any formal research.) Regardless of
timing, companies can draw on three sources of data:

Expert judgment. Experienced executives, salespeople, and other frontline employees have a good
understanding of customers and their needs. They’ve watched people balk at prices, and they often have a
sense of when customers would pay more. When setting G-B-B prices, companies should collect and factor in
the views of these in-house experts. Although that may feel unscientific, my experience with clients shows that
in-house expert judgments often reliably predict data gathered during more-formal testing-and many
companies design and implement effective G-B-B strategies using only those judgments to drive bundle and
pricing decisions.

General market research. Basic insights can be gained by asking customers to respond to potential features
and prices in quantitative or qualitative surveys (the questions can be added to existing post-purchase
satisfaction surveys). Simplicity is crucial: A survey item might say, “We’re excited to roll out this premium
feature for $79. Would you be interested in making this purchase, and why or why not?” Modifying the
questions to test customers’ interest in a discounted Good product instead can yield insights into fence
attributes and the risk of cannibalization.

Conjoint analysis. This common research technique involves giving subjects a series of binary product
choices, each with different features and prices, and asking which they prefer. It can be a powerful tool: If the
choices are constructed well and enough data is gathered, researchers can gain a clear sense of which
attributes or features customers want, how much they will pay for each, and which are fence attributes. It isn’t
foolproof: As with any market research, results can be flawed or biased, particularly by the composition of the

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customer sample that responds. Still, especially for companies desiring strong quantitative evidence before
bringing a G-B-B strategy to market, positive results from a well-designed conjoint analysis can provide comfort
and affirmation.

Once research has helped a company finalize feature and pricing decisions, it’s time to launch the G-B-B
offerings. Early results should be watched carefully and adjustments made as needed. Compared with other
product attributes, pricing is often easy to alter on the fly.

MOST COMPANIES COULD implement some form of G-B-B. Every company already offers the equivalent of
a Better offering, and even if some firms can’t implement both Good and Best, many could gain new
customers, additional revenue, or both by adding either a Good or a Best to their lineup.

The companies with the biggest challenges in designing a full G-B-B lineup are those whose products have
few distinct features and/or features that can’t easily be modified, making it hard to identify effective fence
attributes and move down market with a Good bundle. In other cases, executives may be too fearful of
cannibalization (or skeptical about the effectiveness of fences to limit it) to sign off on a Good offering. (Some
B2B companies that decide against explicitly marketing a Good product may devise a compromise: quietly
offering a Good version to budget-constrained clients on a case-by-case basis, with the goal of establishing
new customers or saving existing ones and upselling them in the future.) Even if a Good option is not viable in
any form, exploring a G-B-B strategy may prompt companies to introduce a Best offering, which can deliver
new revenue.

As strategies go, shifting to G-B-B pricing may seem simplistic, but many companies have discovered that it’s
more powerful than it appears at first blush. Jim Roth, a senior vice president at Dell EMC, was in a fast-food
restaurant at Chicago’s O’Hare airport when he realized that the bundled value meals on the menu board
made it easier for him to order. That caused him to reflect on his own company’s pricing and bundles. Dell
EMC ultimately created Good, Better, and Best versions of its deployment support for B2B customers-and
found that customers buying those bundles generally spent three times as much as they had previously spent
on that type of after-purchase support. Dell EMC thus joined the many other firms who have recognized that G-
B-B could help them serve their customers better-and boost their bottom line.

PHOTO (COLOR)

PHOTO (COLOR)
PHOTO (COLOR)

~~~~~~~~
By RAFI MOHAMMED, is the founder of Culture of Profit, a consultancy that helps companies develop and
improve their pricing strategies, and the author of The Art of Pricing: How to Find the Hidden Profits to Grow
Your Business (Crown Business, 2005) and The 1% Windfall: How Successful Companies Use Price to Profit
and Grow (HarperBusiness, 2010).

Copyright 2018 Harvard Business Publishing. All Rights Reserved. Additional restrictions may apply including
the use of this content as assigned course material. Please consult your institution’s librarian about any
restrictions that might apply under the license with your institution. For more information and teaching
resources from Harvard Business Publishing including Harvard Business School Cases, eLearning products,
and business simulations please visit hbsp.harvard.edu.

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New Facts on the Marketing Performance of U.S. Farmers (Joy L. Harwood,
USDA-Economic Research Service, Presiding)

IMPLICATIONS OF COMMODITY PRICE BEHAVIOR
FOR MARKETING STRATEGIES

WILLIAM G. TOMEK AND HIKARU HANAWA PETERSON

Prices of agricultural commodities have sys-
tematic, dynamic components, and many
agricultural economists have thought that
forecasts of this behavior ought to aid mar-
keting decisions, thereby raising producers’ re-
turns. But, if commodity markets are pricing
efficient, then noncompetitive profit opportu-
nities should be quickly arbitraged away. Thus,
a debate exists about the magnitude of the
likely benefits of various marketing strategies.
Herein we address the question, what does
the literature about commodity price behav-
ior contribute to our understanding of the ex-
pected outcomes from alternative marketing
strategies?

Our brief answer is as follows. Spot prices
in U.S. commodity markets typically behave
in a way that is consistent with efficient mar-
kets; systematic behavior persists because of
the costs of arbitrage. Hence, price forecasts in
the public domain cannot be used to produce
returns which are consistently above the com-
petitive norm, although low-cost strategies to
manage price risk exist. Evaluating the evi-
dence on these topics is not easy, however,
because the price generating processes are
complex. Sometimes prices seem to behave as
if a market is inefficient. Advice based on anal-
yses using short samples should be used with
great caution.

The remainder of the article elaborates:
the first section reviews conceptual models of
price behavior, while the next section covers

William G. Tomek is professor emeritus at the Department of
Applied Economics and Management, Cornell University. Hikaru
Hanawa Peterson is assistant professor at the Department of Agri-
cultural Economics at Kansas State University.

The authors acknowledge Wade Brorsen’s helpful suggestions.
The usual caveats apply.

This article was presented in a principal paper session at the
AAEA annual meeting (Providence, Rhode Island, July 2005).
The articles in these sessions are not subjected to the journal’s
standard refereeing process.

empirical analyses that link price behavior to
marketing strategies. Then, the performance
of selected marketing programs is compared,
using simulated prices from a model of a grain
market. The discussion abstracts from differ-
ences in farmers’ risk tolerances, opportunity
costs, tax management strategies, and other
idiosyncratic factors, which influence farmers’
marketing decisions. Also, individual farmers
can own grain with attributes that command
a premium over a representative price series,
such as number two yellow corn, while this ar-
ticle is necessarily about such prices.

Models of Price Behavior

Commodity price behavior over time is a mix-
ture of systematic and random factors. Spot
prices exhibit autocorrelation with occasional
spikes. For the grains, the variance and skew-
ness of (say) monthly probability distributions
tend to increase as the marketing season pro-
gresses and inventories decline. This behavior
is more or less accommodated by concep-
tual models in the literature (for a synthesis
relevant to grain markets, see Williams and
Wright), and as noted in the next section, many
empirical models of this behavior exist.

Peterson and Tomek (2005a) provide a
model, in which decision makers have ra-
tional expectations, that mimics the behav-
ior of monthly corn prices. Storage is carried
out by rational arbitragers whose expected
profit depends on the expected appreciation
in price and storage costs. Prices are autocor-
related, consistent with the incentives needed
to carry inventories from month to month and
from one crop year to the next. The model al-
lows prices to decline as the new harvest ap-
proaches, even though inventories are positive,
by including convenience yield.

Amer. J. Agr. Econ. 87 (Number 5, 2005): 1258–1264
Copyright 2005 American Agricultural Economics Association

Tomek and Peterson Price Behavior for Marketing Strategies 1259

The random components of prices are as-
sociated with the arrival of new information
about current and expected supply and de-
mand conditions, but the monthly probability
distributions have systematically changing mo-
ments, associated with the seasonality of inven-
tory changes and of information about growing
conditions. An upward spike in prices can
occur given a combination of changes in expec-
tations related to a larger demand, smaller cur-
rent inventory, and/or decline in the planned
crop. In this case, the resulting (unexpected)
price appreciation is far larger than the cost of
carry, but this is the outcome of a combination
of unforeseeable events and not of a market
imperfection.

The price of a futures contract at current
time t is the expected value—in essence a
forecast—of the cash price at contract matu-
rity, time T, conditional on the information
available at t.1 Since the conditioning informa-
tion can change by large amounts with the pas-
sage of time, the time t price can change rad-
ically as maturity approaches; futures prices
can be unbiased, but imprecise forecasts. The
variance of futures price changes is not a con-
stant; rather the price changes have time-to-
maturity and seasonal components (e.g., An-
derson; Fackler and Tian), and like spot prices,
they are likely not normally distributed.

Prices of old- and new-crop futures are usu-
ally correlated because inventories link the
crop-years, but when inventories are tiny, a
disconnection can occur (Tomek and Gray).
High current prices reflect small supplies rel-
ative to demand. Low prices for future de-
livery of the new crop reflect large expected
production relative demand. It is not possible
in this situation for producers to obtain high
prices for the forthcoming crop by selling old-
crop futures and rolling over these positions
into new-crop contracts (Lence and Hayenga).
Again, these varied price relationships in dif-
ferent years need not be the consequence of
market imperfections.

At harvest, the prices of a constellation of fu-
tures contracts, for different delivery months,
can be compared with the current cash price.
The differences are prices of storage, which can
be assured by hedging, subject to basis risk.

1 Futures prices, in contrast to cash prices, can follow a martin-
gale process; the expected price change is zero in an efficient fu-
tures market. (Risk premiums in grain markets appear to be zero
or tiny.) Testing for this property is tricky, however. A price series
may be constructed inappropriately, if the analyst uses only the
nearby contract prices, which are essentially the spot prices. The
martingale property pertains to prices for individual contracts.

Prices of storage vary with the passage of time,
depending on changes in the supply and de-
mand for storage. Regional prices also differ
and adjust to allocate inventories over space
(Benirschka and Binkley). The fact that a mar-
ket is providing incentives to store in some lo-
cations, but not in others, is consistent with the
operation of a competitive market. The returns
to storage can be less than the costs of storage,
in most locations, in an efficient market.

Analogous models exist for animal agricul-
ture (e.g., Mundlak and Huang; Rosen). Prices
are autocorrelated because of the dynamics
inherent in managing herds and flocks. At a
point in time, the economic problem is to allo-
cate the existing stock of animals between cur-
rent consumption and future use. The rate of
culling from the breeding herd can be varied as
can the allocation of young female animals be-
tween the breeding herd and slaughter. These
decisions depend on current prices relative to
(discounted) expected prices in the future, tak-
ing account of the relevant costs of maintaining
the herd, analogous to a cost of carry. Given
the price risk associated with arbitraging over
long-time periods, it is not surprising that cy-
cles in hog and cattle prices cannot be arbi-
traged away. An added complexity is that the
costs of arbitrage themselves vary with the pas-
sage of time.

Clearly, a variety of price behaviors are con-
sistent with pricing efficiency. Of course, some
spot and futures markets may be inefficient. At
a minimum, a few traders can have superior
private information and can earn a return on
the investment in this information. Such mar-
kets are semistrong, but not strong, form effi-
cient (Fama). In addition, the information flow
among traders may be slow (Grossman and
Stiglitz); a high proportion of traders may be
uninformed about new information; and con-
cepts from behavioral finance imply that prices
may have “psychological runs” (for a review,
see Park and Irwin). Perhaps profit opportuni-
ties are not always quickly arbitraged away.

In sum, spot prices for agricultural com-
modities have systematic components,2 but in
an efficient market price forecasts cannot be
used to produce returns that consistently ex-
ceed arbitrage costs. Prices in efficient futures
markets follow a martingale process and can-
not be forecast (footnote 1); rather, they are a

2 An implication is that spot prices for agricultural commodities
should not follow a random walk. Research that claims to have
found nominal price series that are integrated of order one is likely
mistaken (Wang and Tomek).

1260 Number 5, 2005 Amer. J. Agr. Econ.

type of forecast. But, markets may not be fully
efficient. Nonetheless, if they are semistrong
form efficient, arbitrage opportunities will be
short-lived.

Empirical Evidence

Econometric analyses of agricultural prices
attempt to capture the systematic, dynamic be-
havior of cash prices (Tomek), but few pub-
lished studies relate forecasts to marketing
decisions. Not surprisingly, private consultants
do not publish the precise foundation for their
advice. Extension economists do provide fore-
casts and marketing advice through newslet-
ters and websites, but the evaluation of their
forecasts is not easy (see below). It is even
more difficult to evaluate whether farmers
have benefited from using the advice (Brorsen
and Irwin).

Both point and interval forecasts have not
been particularly precise (Allen; Isengildina,
Irwin, and Good), and the risk associated with
an imprecise forecast is a component of the
cost of using it. An imprecise forecast may
be the result of using conditioning informa-
tion that subsequently turns out to be wrong
rather than the result of a poor model and/or
sampling error. Further, a precise forecast in
one time period does not necessarily imply a
precise forecast in a subsequent period. Fore-
casts from econometric models apparently
cannot outperform those implied by futures
market quotes (Just and Rausser), and techni-
cal analyses that seem profitable in one period
probably are not in subsequent periods (Park
and Irwin).

While market imperfections might persist
that can be forecast and provide superior arbi-
trage profit opportunities, the evidence to this
effect is small. An important exception is the
analysis of futures prices for corn and soybeans
by Wisner and colleagues (e.g., Wisner, Blue,
and Baldwin). They argue that the prices of
soybean and corn futures contracts for harvest-
time delivery, observed the prior winter and
spring, are biased upward. This is particularly
true in a year when the prior harvest is rela-
tively small and current prices are high. Thus,
“. . . hedge positions were placed routinely in
the fourth week in February [in December
corn] after short-crop years . . . ” (p. 294) on the
expectation that the price would decline. The
“forecast” is qualitative, based on historical av-
erage performance of prices from a twenty-two
year sample, but it is linked to specific advice.

Whether or not this advice will continue to be
profitable is uncertain (Zulauf and Irwin); the
rule would have resulted in losses in 2003, but
gains in 2004.

Other studies of pricing inefficiency exist in
the futures markets literature, and they can be
linked to speculative strategies. Tests of effi-
ciency have problems, and results are mixed.
For example, in applying a particular trad-
ing system to a 1986–93 sample for different
futures contracts, Olszewski found that “[the
system] generated the best profits for cotton,
corn, and Japanese yen” (p. 701). But, based
on additional analysis for corn, Olszewski con-
cluded that “no statistical evidence [existed]
that the trading system generates profits other
than by chance” (p. 702). Schaefer, Myers,
and Koontz found that the live cattle futures
market was not strong form efficient, but in
concluding they write, “ . . . the predictability
of price changes dies out quickly . . . , and we
have not demonstrated that this predictabil-
ity can be used to generate profitable trading
rules that take transactions costs into account”
(p. 450). Zulauf et al. obtain diverse results
for soybean prices, which are influenced by the
model specification; a price-level model found
significant bias, a percentage price change
model did not.3 We are predisposed to con-
clude that the seeming inefficiencies found in
empirical analyses are likely the result of id-
iosyncrasies of the sample and model spec-
ifications and hence that futures markets in
the United States are likely semistrong form
efficient.

Although the underlying foundation for
marketing advice provided by private consul-
tants is not available, the AgMAS project at
the University of Illinois has evaluated the out-
comes of the advice given by many consultants.
One conclusion is that it is difficult for any advi-
sor to provide above-average returns on a con-
sistent year-to-year basis; good performance in
one year does not predict good performance in
the next year. This is an expected outcome if
markets are pricing efficient.

A similar evaluation of marketing programs
developed by extension economists would be

3 Park and Irwin provide a survey of the profitability of techni-
cal analyses for many different asset prices, although relatively few
applications relate to agricultural commodities. Results reviewed
by Park and Irwin are mixed: technical rules sometimes appear
profitable, but it is usually unclear whether the estimated returns
exceed transaction costs. Typically the rules did not remain prof-
itable beyond the sample period. Also, if markets are not strong
form efficient—a few traders have superior, private information—
such results by definition are not in published studies.

Tomek and Peterson Price Behavior for Marketing Strategies 1261

interesting, inter alia, because some of them
suggest that technical analysis is a useful de-
cision tool. Moreover, websites for these pro-
grams contain quotes from producers stating
that the marketing program has increased their
returns. But, given the seeming inconsistency
of technical analysis with price theory, one
wonders whether the above-average results
are sustainable.

Forecasts, however, have the potential to
help decision makers earn a competitive profit
in an efficient market. An example is to use
a forecast of basis convergence over a stor-
age interval to make a decision about storing
and hedging (Working). At harvest, the cur-
rent price of a distant futures contract can be
compared with the cash price; this basis should
narrow as contract maturity approaches; and
the magnitude of convergence can be forecast
from relatively simple models. The standard
error of forecast is an estimate of basis risk.
This information can be an input into a deci-
sion to store and hedge, or not. A few such
models exist in the literature (e.g., Heifner;
Lence, Hayenga, and Patterson; Hranaiova
and Tomek), but we are unaware of economic
evaluations of them.

An extensive line of research provides esti-
mates of optimal marketing portfolios, based
on the relationships among prices in cash,
futures, and options markets (Tomek and
Peterson). The optimum is usually defined as
minimizing risk, where risk is measured by the
variance of returns, and empirical estimates
find optimal portfolios that shift risk, at low
cost, though they do not increase average re-
turns. But, most farmers’ marketing decisions
differ from the estimated optima (Harwood
et al.). This is likely because the objective func-
tions used in the analyses are inappropriate
specifications relative to individual farmer’s
situations. The functions often exclude realis-
tic potential alternatives in the portfolio and
their costs, and the assumption of variance
minimization is likely inappropriate (Collins).
Yet, if objective function specifications could
be made more realistic, this research could be
useful to decision makers.

A somewhat related line of research has
used short samples (perhaps ten to twenty
years of prices) to compute returns from differ-
ent marketing strategies with the objective of
finding the alternative that gave the largest av-
erage return. The “optimal” alternative is then
recommended to farmers as the best choice to
be used in future (out-of-sample) years. Such
an analysis is analogous to data mining, which

in our view, is a dangerous basis for mak-
ing marketing recommendations. The results
in the next section help illustrate the danger
and more generally the problems of inter-
preting the results from alternative marketing
strategies.

Simulating Marketing Strategies
in an Efficient Corn Market

As implied above, the academic literature on
pricing efficiency often conflicts with the mar-
keting advice given by private consultants and
extension economists. These differences are
difficult to reconcile using relatively short sam-
ples. One approach to a better understanding,
if not a reconciliation, of the different views is
to analyze large samples obtained by simulat-
ing market prices.

Thus, to compare marketing strategies,
monthly prices of corn were simulated to rep-
resent central Illinois (Peterson and Tomek
2005a). The model assumes an efficient mar-
ket, and the monthly probability distributions
of the simulated prices have changing mode,
variance, and skewness similar to the distribu-
tions of cash prices in the 1990s. Prices of May
and December futures contracts are simulated
under the assumption that they are the con-
ditional expected values of the spot prices at
maturity. Prices are generated for forty-year
“lifetimes” to illustrate the varying behavior
that could be faced in different lifetimes. The
long run, or expected, outcomes are based on
10,000 forty-year periods.

Alternative Strategies

The base strategy is marketing the entire crop
at harvest. This is a common base for compar-
ing alternatives. The individual farmer is as-
sumed to be a price taker, who controls the
quantities marketed per month.

In this context, strategy 1a diversifies by sell-
ing the corn crop in equal amounts in the cash
market over nine months (see Peterson and
Tomek 2005b). Strategy 1b diversifies by pre-
selling 40% of the crop in December futures
and the remainder in the cash market after
harvest.

Strategy 2a routinely sells the entire ex-
pected crop in May, using the December
futures contract, completing the hedge in
December when the entire crop is marketed.
An alternative, 2b, is a conditional hedge,
which is intended to approximate the advice of
Wisner, Blue, and Baldwin. The crop is presold

1262 Number 5, 2005 Amer. J. Agr. Econ.

Table 1. Returns from Alternative Marketing Strategies, 10,000 Forty-Year
Lifetimes

Difference from Basea Proportion

Mean SD Above Triggeredc

Strategy (c/ per Bushel) (c/ per Bushel) Baseb (Percent)

1. Diversification
a. Spot only −3.0 −2.4 31 NA
b. Spot/futures −7.0 −11.9 31 NA

2. Preharvest hedges
a. Routine in May −1.1 −12.0 51 NA
b. Conditional −1.2 −17.3 52 50

3. Storage hedges (Nov–May)
a. Routine −2.4 −12.1 45 NA
b. Conditional 3.8 −3.5 52 33

4. Speculative −0.1 −1.4 19 6
Source: Adapted from Peterson and Tomek, (2005b), table 2.
a Base (harvest-time sales only): mean = 256 c/ per bu.; SD = 45 c/ per bu.
b Proportion of years within a forty-year period where the return exceeded the base return, averaged over 10,000 replications.
c Proportion of years within a forty-year period when the strategy is triggered, averaged over 10,000 replications.

in February, using December futures, when
supply is small relative to demand. Otherwise,
the crop is sold in May, as in 2a.

Strategy 3a routinely stores the crop and
sells May futures in November, completing the
hedge in May. This is done whether or not the
basis in November provides an incentive to
store. An alternative, 3b, is to store and hedge
only when the basis signals that it is profitable
to do so; otherwise, the crop is sold at harvest.

Finally, a speculative strategy (number 4) is
considered. The crop is sold at harvest, but
positions are taken in May futures selectively
based on forecasts of the May price made in
November using a regression model. Although
the simulated prices are based on an efficient
market, they sometimes appear to have a sig-
nificant upward or downward bias in regres-
sion models fitted to ten-year subperiods. Thus,
if a statistically significant bias is found in a par-
ticular period, a speculative position is taken
in year eleven in May futures based on the ex-
pected price change. Returns from the crop
sale are adjusted for the speculative gain or
loss.

Empirical Results

The long-run results are consistent with the
performance of an efficient market, as they
should be. Because of the time-to-maturity ef-
fect of the variance of futures prices, the pre-
harvest sale of all or a part of the crop reduces
the variability of returns (strategies 1b, 2a, 2b,
and 3a in table 1). Returns are adjusted for
hedging costs, and hence average returns usu-
ally are lower than for the base case. Also, for

strategy 3a, hedging locks in basis convergence
that does not cover the cost of storage in some
years.

If the harvest-time basis provides an incen-
tive for storing (which occurred in about 33%
of the years, on average), then hedging assures
the return (strategy 3b).4 This strategy has little
influence on the variability of returns, because
the crop is sold at harvest whenever fore-
cast basis convergence does not make storage
profitable.

One possibly surprising result is that diver-
sification of cash sales (strategy 1a) has only a
small effect on the variability of returns. This
occurs because sales are being deferred from
harvest time, when prices are less variable,
to months later in the marketing year, when
prices are relatively more variable. We are re-
minded that diversification does not “automat-
ically” reduce the variability of returns.

Deeper insights are obtained by examining
the results from the individual forty-year peri-
ods. Although space constraints limit our abil-
ity to characterize the diversity of results, those
presented demonstrate that a farmer can face
very different outcomes from a given market-
ing strategy, depending on the particular cir-
cumstances that occur within the time period
in which the strategy is used. Prices occurring
in one finite period are only a sample of the
many possibilities that could have been gener-
ated by a given market structure.

4 Recall, prices need not provide for a positive return to storage
in every year in every location. The simulated prices should be
interpreted as pertaining to a location (Illinois).

Tomek and Peterson Price Behavior for Marketing Strategies 1263

For example, although diversification of
cash sales is a poor alternative when appraised
by the average performance over 10,000 dif-
ferent lifetimes, the mean return from diversi-
fication exceeded the base return in over 30%
of the years in the average forty-year period
(table 1). Since this is the average outcome
over 10,000 replications, a nontrivial probabil-
ity exists that diversification would “pay” in
thirteen or more years of a farmer’s forty-year
lifetime. Also, since diversification produces
a wide range of standard deviations, as does
the base case (not shown), it is possible to find
forty-year periods in which diversification does
reduce variability relative to the base strategy.
The opposite is also true; diversification can
perform poorly in the large majority of years
faced by a farmer. On average, it is not partic-
ularly helpful in managing risk.

Speculation has little effect, on average, over
10,000 simulated lifetimes, but the returns to
speculation ranged from −$2.41 per bushel in
one year to $2.99 in another. Speculative rules
can appear profitable, even over forty years, al-
though not in the long run. The probability of
finding “bias” can exceed the nominal proba-
bility of type I error, associated with commonly
used t-tests, as the error terms of the regression
models are not normally distributed.

The simulations also illustrate the difficulty
of evaluating the Wisner, Blue, and Baldwin
strategy, which is conditional on supply–
demand information known in February. Their
condition was triggered in 50% of the years in
an average forty-year sample. This contrasts
with 31% in their 1975–96 sample, a frequency
which is near the low that can occur in our
simulations. We find that the early sale of the
expected crop reduces the returns variability
(strategy 2b), but with a slight decline in av-
erage returns. The returns from this strategy,
however, exceeded those from the base case
in about half of the forty years in the aver-
age sample. This strategy can seem successful
a high percentage of the time in an efficient
market.

Concluding Comments

An efficient commodity market can generate
highly diverse price behavior with the pas-
sage of time, and consequently the relative
performance of alternative marketing strate-
gies can vary over different samples. A strategy
that is inferior, on average, can perform rela-
tively well in a particular period. Indeed, this

happened in 30%–50% of the years (depend-
ing on the strategy) in an average simulated
forty-year lifetime, and outcomes in particular
periods vary substantially around this average.

Another conclusion is that it is exceedingly
difficult to discriminate between efficient and
inefficient markets and to determine whether
or not profitable arbitrage opportunities exist
that will persist. The sampling error associated
with estimates from short data series, com-
bined with the consequences of data mining,
raise serious qualifications about the value of
technical analyses and of comparisons of mar-
keting programs. If grain markets in the United
States are semistrong form efficient, then rec-
ommendations based on seeming market inef-
ficiencies have little validity.

The evidence is clear, however, that routine
hedging can often reduce the variance of re-
turns, at a relatively small cost. In addition, it
is possible to develop models to forecast ba-
sis convergence and basis levels. Such analyses
should be viewed as helping producers earn a
competitive return to their production or stor-
age decisions, and our challenge is to make
these analyses more relevant to producers.

Our skepticism about the robustness of em-
pirical results has grown steadily with our ex-
perience as price analysts. Nonetheless, we
hope that this article will not only discourage
some past practices, but encourage fresh think-
ing about analyses underlying recommended
marketing programs. Useful results require
careful scholarship.

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