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The Price is Right...or is it?

Posted by Dr. Jay Weiner

Thu, Jan 28, 2021

Pricing Method and Strategy Blog Opener Jan 2021

An Overview on Pricing Research

Pricing a product or service correctly is critical to realizing profitability. Too often, pricing decisions are based on the cost to develop and produce the product or service. Price it too high, and no one will buy. Price it too low, and in addition to forgoing revenue, your buyers may question the quality.

One typical goal of pricing research is to understand price elasticity—the measure of the change in the quantity demanded of a product in relation to its price change. The typical micro-economics course presents the downward sloping demand curve.

price is right graphic

As the price is raised, the quantity demand drops, and total revenue falls. In fact, most products exhibit a range of inelasticity. That is, demand may fall, but total revenue increases. It is the range of inelasticity that is of interest in determining the optimal price to charge for the product of service. Consider the following question: if the price of gasoline was raised 5¢ per gallon, would you drive fewer miles? If the answer is no, then we might raise the price of gas such that the quantity demanded is unchanged, but total revenue increases.

The range of inelasticity begins at the point where the maximum number of people are willing to try the product/service and ends when total revenue begins to fall. Where the marketer chooses to price the product/service depends upon the pricing strategy. A company should have a strategy for pricing a product/service throughout its product lifecycle.

There are two basic pricing strategies:

  1. Price skimming sets the initial price high to maximize revenue. As the product moves through the product lifecycle, the price typically drops. This strategy is often used for technology or products protected by patents. Apple and Samsung, for example, price each new mobile phone high and as other competitors match performance characteristics, they lower price.
  2. Penetration pricing sets the initial price low to maximize trial. This pricing strategy tends to discourage competition, as economies of scale are often needed to make a profit. Understanding the goal, maximizing revenue versus maximizing share is part of the first step of pricing work.

The pricing researcher needs to understand this range of prices to make good strategic pricing decisions. There are many approaches to pricing research:

  • Blunt approach: You can simply ask, “how much would you be willing to pay for this product/service?” In this approach, you typically need a large number of respondents to understand purchase intent at a variety of price points.
  • Monadic concept: You can present the new product/service idea and ask, “how likely would you be to buy X product @ $2.99?” Monadic concept tests tend to over-estimate trial. This may be because prices given to respondents in a monadic concept test do not adequately reflect sales promotion activities. Respondents may think that the price given in the concept is the suggested retail price and that they are likely to buy on deal or with a coupon.
    Monadic concept tests also require a higher base size. A typical concept test would require 200 to 300 completes per cell. The number of cells required would depend on the prices tested, but from the results we often see, it appears that these cells tend to over-estimate the range of inelasticity. Providing a competitive price frame might improve the results of monadic concept tests.
  • van Westendorp’s Price Sensitivity Meter (PSM): The van Westendorp model is a good way to get at price elasticity and better understand the price consumers are willing to pay for a particular product or service. Developed by Dutch economist Peter van Westendorp, the underlying premise of this model is that there is a relationship between price and quality, and that consumers are willing to pay more for a higher quality product. The PSM requires 4 questions:
    • At what price would you consider the product to be getting expensive, but you would still consider buying it? (EXPENSIVE)
    • At what price would you consider the product too expensive and you would not consider buying it? (TOO EXPENSIVE)
    • At what price would you consider the product to be getting inexpensive, and you would consider it to be a bargain? (BARGAIN)
    • At what price would you consider the product to be so inexpensive that you would doubt its quality and would not consider buying it? (TOO CHEAP)
    The van Westendorp series does a reasonable job of predicting trial from a concept test without the need for multiple cells. This reduces the cost of pricing research and the likelihood that we do not test a price low enough. The prices given by respondents are believed to represent the actual out-of-pocket expenses. This permits the research some understanding of the effects of promotional activities (on shelf price discounts or coupons). The van Westendorp series will also permit the research to understand the potential trial at price points higher than those that might be tested in a monadic test.
  • Conjoint Analysis: Conjoint is often used in early product development to assess the value of including certain features into the product/service option. While this does provide some indication of what attributes or features consumers would pay more for, it does not do a good job capturing the true value of these features. To do that, we need to show consumers a competitive set of offers from which to choose.
  • Choice based conjoint and Discrete Choice allow us to test products in a competitive setting, and to get a truer read on price elasticity and willingness to pay for certain features.

Choosing the correct pricing methodology is often dependent on where you are in the new product development process. The closer to market launch, discrete choice models offer the best insight into the actual potential in the market. Early in the development process, the other techniques provide guidance on how to price the product and how to choose a pricing strategy. Whatever stage of development or pricing strategy the technique you choose should yield results that help you make smarter more confident marketing decisions.


Jay WeinerJay Weiner, Ph.D. is CMB's VP of Analytics & Data Management.

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Topics: advanced analytics, methodology, product development, data collection, Dear Dr. Jay, predictive analytics, Market research, technology, Best Practices, pricing

How Triathlon Training Makes Me a Better Market Researcher

Posted by Shira Smith

Wed, Nov 28, 2018

triathlon

From training to crossing the finish line, competing in triathlons is one of my favorite hobbies. So far, I’ve completed four sprint races, each consisting of a short swim, a 10 to 15-mile bike ride, and a three-mile run. Not everyone would consider this "fun," but I love it.

When I'm not an training, I'm a market researcher who likes to draw parallels between my personal and professional life. Here are three ways training for a triathlon is like managing a research project:

Scheduling is key

Triathlons are long multievent races that require a ton of preparation and training. Months before race day, I map out a detailed training schedule that allots time for each event (e.g., swimming, biking, racing) to ensure I’m well-prepared.

Managing a research project also requires a rigorous plan. Before the onset of each project, I develop a meticulous schedule that outlines every step, due date, and expectation, from project kick off to final reporting and delivery. This keeps my team and me on track and hitting our goals.

I also share this schedule with my clients so our teams are always aligned on how the project is progressing. It sounds simple, but it's critical to be transparent and ensure everyone's on the same page.

Be flexible when plans change

Even the best laid plans can go awry. Despite my planned training schedule, sometimes things come up and I must adjust. If it's downpouring on a running day, for example, I could instead go for a swim. If the pool is unexpectedly closed, I'll hop on a bike. Whatever the obstacle, I always find an alternative that keeps me marching towards my goal.

Unforeseen events can happen in research, too. The important thing is to flex and stay nimble so surprises won’t derail the project. So long as I stay focused and proactive, my team and I can pivot, overcome challenges, and keep the project on track.

Data consistency is also key

I track data to measure and improve my race performance. With the help of a sports watch, I can analyze my pace, heart rate, distance, elevation, cadence, and more. Tracking these metrics helps me see my progression over time and can help identify variables that may be impacting my performance. For example, I often run in the morning, so external variables (e.g., traffic and temperatures) are more consistent. Since my running environment is consistent (as much as it can be) I can be more confident my tracked pace is real.

Consistently tracking data over time is critical in market research, too. In brand trackers, for example, we’ll measure the same dimensions so we can accurately compare results wave after wave. This helps ensure our clients can refine the most compelling positioning, optimize brand and market communication, and then track influence on behavior over time.

I'm glad I found a hobby that I love, and I’m even more excited that it connects in so many ways to my job as a market researcher. I’m looking forward to growing both as a triathlete and as a market researcher – and I know if I plan, stay flexible, and remain consistent, I’ll be successful at both!

Topics: data collection, research design, project management

Why Standing up for the Census Still Counts

Posted by Athena Rodriguez

Wed, Nov 07, 2018

busy city street

Over a year ago, I wrote about the critical state of the U.S. Census. To recap: to stay within budget, the US Census Bureau planned to add online and phone data collection to the traditional mail and face-to-face fielding. As any good researchers would, they planned to test this new mix of methodologies using a series a field tests and an end-to-end test. 

After cancelling several field tests earlier this year, last month the bureau completed an end-to-end test in Providence County, RI, and are “ready to transition from a paper-based census to one where people can respond online using personal computers or mobile devices, by telephone through our questionnaire assistance centers or by using the traditional paper-response option.” Click here for an infographic with all the details.

Whew, right?  Not so fast—there’s still another problem. A big one.

Against the recommendation of the Census Bureau, the Secretary of Commerce, Wilbur Ross, is fighting to add a citizenship question to the 2020 Census.

In a memo sent to the DOJ, the bureau’s Chief Scientist and Associate Director for Research and Methodology, John Abowd, wrote the inclusion of a citizenship question would be "very costly, harms the quality of the census count, and would use substantially less accurate citizenship status data than are available from administrative sources.”

In response, opponents of the question, including the state of California, New York, and the American Civil Liberties Union, have filed lawsuits against the Federal Government—echoing Abowd’s fears that the citizen question would discourage participation and compromise the integrity of the census.

Despite a request by the Federal Government to postpone, the trial began on Monday, November 5, in New York City, and is expected to last two weeks.

As I wrote in my earlier blog, the US Census is critical to market research. It serves as the foundation for things like sampling plans, weighting data, sizing audiences, and determining who to target.

If the citizen question goes through, it may deter non-citizens from participating. This would seriously harm the quality of the data and pose a threat to the integrity of our industry—not to mention impact federal budgeting and the number of House seats.

As market researchers, it’s our duty to preserve the integrity of the US Census. Whether you support or oppose the citizenship question, I encourage you to pay close attention to how the decision plays out. We’re still a year away from the census, but what’s decided now could have far-reaching ramifications for our industry and country.

Athena is a Project Director at CMB who really hopes the next time she blogs it will be about a satisfactory resolution to this ongoing issue.

Topics: data collection, Market research

Does your metric have a home(plate)?

Posted by Youme Yai

Thu, Sep 28, 2017

baseball.jpeg

Last month I attended a Red Sox/Yankees matchup at Fenway Park. By the seventh inning, the Sox had already cycled through seven pitchers. Fans were starting to lose patience and one guy even jumped on the field for entertainment. While others were losing interest, I stayed engaged in the game—not because of the action that was (not) unfolding, but because of the game statistics.

Statistics have been at the heart of baseball for as long as the sport’s been around. Few other sports track individual and team stats with such precision and detail (I suggest reading Michael Lewis’ Moneyball if you haven’t already). As a spectator, you know exactly what’s happening at all times, and this is one of my favorite things about baseball. As much as I enjoy watching the hits, runs, steals, strikes, etc., unfold on the field, it’s equally fun to watch those plays translate into statistics—witnessing the rise and fall of individual players and teams.

Traditionally batting average (# of hits divided by number of at bats) and earned run average (# of earned runs allowed by a pitcher per nine innings) have dominated the statistical world of baseball, but there are SO many others recorded. There’s RBI (runs batted in), OPS (on-base plus slugging), ISO (isolated power: raw power of a hitter by counting only extra-base hits and type of hit), FIP (fielding independent pitching: similar to ERA but focuses solely on pitching, and removes results on balls hit into field of play), and even xFIP (expected fielding independent pitching; or in layman’s term: how a pitcher performs independent of how his teammates perform once the ball is in play, but also accounting for home runs given up vs. home run average in league). And that's just the tip of the iceberg. 

With all this data, sabermetrics can yield some unwieldy metrics that have little applicability or predictive power. And sometimes we see this happen in market research. There are times when we are asked to collect hard-to-justify variables in our studies. While it seems sensible to gather as much information as possible, there’s such a thing as “too much” where it starts to dilute the goal and clarity of the project.  

So, I’ll take off my baseball cap and put on my researcher’s hat for this: as you develop your questionnaire, evaluate whether a metric is a “nice to have” or a “need to have.” Here are some things to keep in mind as you evaluate your metrics:

  1. Determine the overall business objective: What is the business question I am looking to answer based on this research? Keep reminding yourself of this objective.
  2. Identify the hypothesis (or hypotheses) that make up the objective: What are the preconceived notions that will lead to an informed business decision?
  3. Establish the pieces of information to prove or disprove the hypothesis: What data do I need to verify the assumption, or invalidate it?
  4. Assess if your metrics align to the information necessary to prove or disprove one or more of your identified hypotheses.

If your metric doesn’t have a home (plate) in one of the hypotheses, then discard it or turn it into one that does. Following this list can make the difference in accumulating a lot of data that produces no actionable results, or one that meets your initial business goal.

Combing through unnecessary data points is cumbersome and costly, so be judicious with your red pen in striking out useless questions. Don’t get bogged down with information if it isn’t directly helping achieve your business goal. Here at CMB, we partner with clients to minimize this effect and help meet study objectives starting well before the data collection stage.

Youme Yai is a Project Manager at CMB who believes a summer evening at the ballpark is second to none.

 

Topics: advanced analytics, data collection, predictive analytics

Dear Dr. Jay: How To Predict Customer Turnover When Transactions are Anonymous

Posted by Dr. Jay Weiner

Wed, Apr 26, 2017

Dear Dr. Jay:

What's the best way to estimate customer turnover for a service business whose customer transactions are usually anonymous?

-Ian S.


Dear Ian,

You have posed an interesting question.  My first response was, “you can’t”. But as I think about it some more, you might already have some data in-house that could be helpful in addressing the issue.DRJAY-9-2 (1).png

It appears you are in the mass transit industry. Most transit companies offer single ride fares and monthly passes while companies in college towns often offer semester-long passes. Since oftentimes the passes (monthly, semester, etc.) are sold at a discounted rate, we might conclude that all the single fare revenues are turnover transactions.

This assumption is a small leap of faith as I’m sure some folks just pay the single fare price and ride regularly. Let’s consider my boss. He travels a fair amount and even with the discounted monthly pass, it’s often cheaper for him to pay the single ride fare. Me, I like the convenience of not having to make sure I have the correct fare in my pocket so I just pay the monthly rate, even if I don’t use it every day. We both might be candidates for weekly pass sales if we planned for those weeks when we know we’d be commuting every day versus working from home or traveling. I suspect the only way to get at that dimension would be to conduct some primary research to determine the frequency of ridership and how folks pay.

For your student passes, you probably have enough historic data in-house to compare your average semester pass sales to the population of students using them and can figure out if you see turnover in those sales. That leaves you needing to estimate the turnover on your monthly pass sales.

You also may have corporate sales that you could look at. For example, here at CMB, employees can purchase their monthly transit passes through our human resources department. Each month our cards are automatically updated so that we don’t have to worry about renewing it every few weeks.  I suspect if we analyzed the monthly sales from our transit system (MTBA) to CMB, we could determine the turnover rate.

As you can see, you could already have valuable data in-house that can help shed light on customer turnover. I’m happy to look at any information you have and let you know what options you might have in trying to answer your question.

Dr. Jay is CMB’s Chief Methodologist and VP of Advanced Analytics and holds a Zone 3 monthly pass to the MTBA.  If it wasn’t for the engineer, he wouldn’t make it to South Station every morning.

Keep those questions coming! Ask Dr. Jay directly at DearDrJay@cmbinfo.com or submit your question anonymously by clicking below:

Ask Dr. Jay!

Topics: advanced analytics, data collection, Dear Dr. Jay