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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

Predicting Olympic Gold

Posted by Jen Golden

Wed, Feb 21, 2018

bobsled-team-run-olympics-38631.jpg

From dangerous winds and curling scandals to wardrobe malfunctions, there’s been no shortage of attention-grabbing headlines at the 2018 Winter Olympics.

And for ardent supporters of Team USA, the big story is America’s lagging medal count. We’re over halfway through the games, and currently the US sits in fifth place behind Norway, Germany, Canada, and the Netherlands.

Based on last week’s performance (and Mikaela Shiffrin’s recent withdrawal from the women’s downhill event), it’s hard to know for sure how America will place. However, we can use predictive analytics to determine the main predictors of medal count to anticipate which countries will generally be on the podium.

We’ll use TreeNet modeling to identify the main drivers of medal count based on previous Winter Olympics outcomes. For the sake of simplicity, we’ll focus on the 2014 Sochi winter games (excluding all Russia data which would skew the model!) From there, we can infer similarities between medal drivers for Sochi and PyeongChang.

Please note all these results are hypothetical, and not reflective of actual data!

To successfully run a TreeNet analysis, you need both a dependent variable (e.g., the outcome you are trying to predict) and independent variables (e.g., the input that could be possible predictors of the dependent variable).

In this case…

Dependent variable: Total 2014 Sochi Winter Games medal count
Independent variables (including data both directly related to the Olympics and otherwise):

  • Medal count at the Vancouver Olympic games
  • Medal count at previous Winter Games (all time)
  • Number of athletes participating
  • Number of events participating in
  • Number of outdoor events participating in (e.g., downhill skiing, bobsled)
  • Number of indoor events participating in (e.g., figure skating, curling)
  • Average country temperature
  • Average country yearly snowfall
  • Country population
  • Country GDP per capita

The Results!

Our model shows the relative importance of each variable calibrated to a 100-point scale. The most important variable is assigned a score of 100 while all other variables are scaled relative to that:

Olympic Medal Predictors.png

Meaning, in this sample output, previous medal history is the top predictor of Olympic medal outcome with a score of 100 while # in outdoor events and indoor events participating in are the least predictive.

This is a fun and simple example of how we could use TreeNet to forecast the Winter Olympic medal count. But, we also leverage this same technique to help clients predict the outcomes of some of their most complex and challenging questions. We can help predict things like consideration, satisfaction or purchase intent for example, and use the model to point to which levers can be pulled to help improve the outcome.  

Jen is a Sr. Project Manager at CMB who was a spectator at the Sochi winter games and wishes she was in PyeongChang right now.

Topics: advanced analytics, predictive analytics

CMB's Advanced Analytics Team Receives Children's Trust Partnership Award

Posted by Megan McManaman

Wed, Nov 01, 2017

jAYct.jpg

We're proud to announce that CMB’s VP of Advanced Analytics, Dr. Jay Weiner and Senior Analyst, Liz White, were honored with the Children’s Trust’s Partnership Award. Presented annually, the award recognizes the organizations and people whose work directly impact the organization's mission–stopping child abuse.

Jay and Liz were recognized for their work helping the Children’s Trust identify the messaging that resonated with potential donors and program users. Through two studies leveraging CMB’s emotional impact analysis—EMPACT, Max Diff Scaling, concept testing, self-explicated conjoint, and a highlighter exercise, the CMB team the Children's Trust identify the most appealing and compelling messaging.

“There is no one more deserving of this award than the team at CMB,” said Children’s Trust’s Executive Director, Suzin Bartley. “The messaging guidance CMB provided has been invaluable in helping us realize our mission to prevent child abuse in Massachusetts.”

Giving back to our community is part of our DNA of CMB and we’re honored to support the Children’s Trust’s mission to stop child abuse in Massachusetts. Click here to learn more about how the Children’s Trust provides families with programs and services to help them build the skills and confidence they need to make sure kids have safe and healthy childhoods.

From partnering with the Children’s Trust and volunteering at Boston’s St. Francis House to participating in the Leukemia & Lymphoma Society’s annual Light the Night walk, we have a longstanding commitment to serving our community. Learn more about CMB in the community here.

 

 

Topics: advanced analytics, predictive analytics, Community Involvement

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

Flying High on Predictive Analytics

Posted by Amy Maret

Thu, Jul 27, 2017

pexels-photo-297755_resized-1.jpgBuying a plane ticket can be a gamble. Right now, it might be a good price, but who’s to say it won’t drop in a day—a week? Not only that, it may be cheaper to take that Sunday night flight instead of Monday morning. And oh—should you fly into Long Beach or LAX? As a frequent traveler (for leisure and work!) and deal seeker, I face dilemmas like these a lot.

The good news is that there are loads of apps and websites to help passengers make informed travel decisions. But how? How can an app—say, Hopper—know exactly when a ticket price will hit its lowest point? Is it magic? Is there a psychic in the backroom predicting airline prices with her crystal ball?

Not quite.

While it seems like magic (especially when you do land that great deal), forecasting flight prices all comes down to predictive analytics—identifying patterns and trends in a vast amount of data. And for the travel industry in particular, there’s incredible opportunity to use data in this way. So, let’s put away the crystal ball (it won’t fit in your carry on) and look at how travel companies and data scientists are using the tremendous amount of travel data to make predictions like when airfare will hit its lowest point.

In order to predict what will happen in the future (in this case, how airfare may rise and fall), you need a lot of data on past behaviors. According to the Federal Aviation Administration (FAA), there are nearly 24,000 commercial flights carrying over two million passengers around the world every day. And for every single one of those travelers, there’s a record of when they purchased their ticket, how much they paid, what airline they’re flying, where they’re flying to/from, and when they’re traveling. That’s a ton of data to work with!

As a researcher, I get excited about the endless potential for how that amount of historical data can be used. And I’m not the only one. Companies like Kayak, Hopper, Skyscanner, and Hipmunk are finding ways to harness travel data to empower consumers to make informed travel decisions. To quote Hopper’s website: their data scientists have compiled data on trillions of flight prices over the years to help them make “insightful predictions that consistently perform with 95% accuracy”.

 While the details of Hopper are intentionally vague, we can assume that their team is using data mining and predictive analytics techniques to identify patterns in flights prices. Then, based on what they’ve learned from these patterns, they build algorithms that let customers know when the best time to purchase a ticket is—whether they should buy now or wait as prices continue to drop leading up to their travel date. They may not even realize it, but in a way those customers are making data-driven decisions, just like the ones we help our clients make every day.

As a Market Researcher, I’m all about leveraging data to make people’s lives easier. The travel industry’s use of predictive modeling is mutually beneficial—consumers find great deals while airlines enjoy steady sales. My inner globetrotter is constantly looking for ways to travel more often and more affordably, so as I continue to discover new tools that utilize the power of data analytics to find me the best deals, I’m realizing I might need some more vacation days to fit it all in!

So the next time you’re stressed out about booking your next vacation, just remember: sit back, relax, and enjoy the analytics.

Amy M. is a Project Manager at CMB who will continue to channel her inner predictive analyst to plan her next adventure.

Topics: big data, travel and hospitality research, predictive analytics