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how-to-green-marketingDo people buy green products? Yes, of course. The real question for green marketers is whether they buy enough. In other words, are green sales in line with pro-green attitudes? Not really, as huge majorities of consumers show at least some green tendencies while purchases lag far behind. Why is that? Economics tells us that consumers buy based on value (trading off cost and benefits). Since eco-friendly products are seen as being more expensive, higher prices can lower the value of a green product enough to make a conventional alternative more attractive.

While the cost trade-off is clear, it is not the only one. The benefit side has at least two major components. One is the environmental benefit, which may or may not seem tangible enough to make a difference. For instance, a dozen eggs at Acme goes for less than a dollar, while some cage-free varieties can run north of $4 at Whole Foods. So, an environmentally conscious consumer has to make a trade-off at the time of purchase – is the product worth the additional cost? For items like food, the benefits may seem small enough, and far enough out, that many may decide the value proposition does not work for them. In other product categories (say, green laundry detergent), the benefits may seem both long term and impersonal, making the trade-off even harder.

The second major component is the effectiveness of the product in performing its basic function. If consumers perceive green products as inherently inferior (in terms of conventional attributes like performance), they are less likely to buy them. So a green laundry detergent (that uses less harsh chemicals) could be seen as more expensive and less effective in cleaning clothes, further dropping its overall value. (A complicating issue is that the lack of effectiveness itself could be a perceptual rather than real problem). Unless the company is able to offset these disadvantages, the product is unlikely to succeed.

A direct way to increase demand is to offer higher performance on a compensatory attribute. In the case of LED TVs, for example, newer technology consumes less power and provides better picture quality. (Paradoxically, this can sometimes lead to the Rebound Effect, whereby greener technologies encourage higher use, thus clawing back some of the benefits). But in reality, most products are not in a position where green attributes offer performance boosts.

And of course, as it is with every other market, there are segments in this market as well. Consumers who are highly committed (dark green) are willing to buy, as the value they place on the longer term environmental benefits is high enough. And, often they are affluent enough to afford the price. But a product looking for mainstream success cannot succeed only with dark green consumers (who rarely account for more than 20% of the market). Other shades of green will also need to buy. Short of government subsidies and mandates, green marketers have to find ways to balance out the components of the value proposition for the bulk of the market.

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3 mistakes-conjoint-in-new-product-research-pricing
Discrete Choice Conjoint is a powerful tool for among other things conducting pricing and product development research. It is flexible and can handle even the most complex of products. With that said, it requires thoughtful design with an understanding of how design will impact results. Here are three mistakes that often lead to flawed design:

 

Making the exercise too complex

The flexibility of conjoint means you can include large numbers of features and levels. The argument for doing so is a strong one…including everything will ensure the choices being made are as accurate as possible. In reality, however, respondents are consumers and consumers don’t like complexity. Walk down the isle of any store and note that the front of the package doesn’t tell you everything about a product…just the most important things.   Retailers know that too much complexity actually lowers sales. Our own research shows that as you add complexity, the importance of the easiest to evaluate feature (normally price) rises…in other words, respondents ignore the wealth of information and focus more on price. 
 

What to do:

Limit the conjoint to the most critical features needed to meet the objectives of the research. If you can’t predict those in advance, then do research to figure it out. A custom Max-Diff to prioritize features or a product like our Idea Magnet (which uses Bracket) will tell you what to include. Other features can be asked about outside the conjoint.  

 

Having unbalanced numbers of levels

Some features only have two levels (for example, on a car conjoint we might have a feature for “Cruise Control” that is either present or not present). Others, however have many levels (again on a car conjoint we might offer 15 different color choices). Not only can including too many levels increase complexity (see point 1), but it can actually skew results. If one feature has many more levels than the rest, the importance of that feature will almost certainly be overstated.  
 

What to do:

As with point one, try to limit the levels to those most critical to the research.  For example, if you are using conjoint to determine brand value you don’t need to include 15 colors…five or six will do the job.  If you can’t limit things, then at least understand that the importance of the feature is being overstated and consider that as you make decisions.  
 

Not focusing on what the respondent sees

Conjoint requires a level of engagement that most questions do not. The respondent has to consider multiple products, each with multiple features and make a reasoned choice. Ultimately they will make choices, but without engagement we can’t be sure those choices represent anything more than random button pushing. Limiting complexity (point 1 again) helps, but it isn’t always enough.   
 

What to do:

Bring out your creative side…make the exercise look attractive. Include graphics (logos for example). If you can make the choice exercise look more like the real world then do so. For example, if the conjoint is about apparel, present the choices on simulated “hang tags”, so consumers see something like they would see in a store. As long as your presentation is not biasing results (for example, making one product look nicer than another) then anything goes. 
 
These are three of the most common design errors, but there are of course many more. I’m tempted to offer a fourth, “Not working with an experienced conjoint firm”, but that of course would be too self-serving!
 
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3-tips-for-30-in-new-product-research

TRC is celebrating 30 years in business…a milestone to be sure.  

Being a numbers guy, I did a quick search to see how likely it is for a business to survive 30 years. Only about 1 in 5 make it to 15 years, but there isn’t much data beyond that. Extrapolation beyond the available data range is dangerous, but it seems likely that less than 10% of businesses ever get to where we are. To what do I owe this success then?  

It goes without saying that building strong client relationships and having great employees are critical. But I think there are three things that are key to having both those things:

Remaining Curious

I’ve always felt that researchers need to be curious and I’d say the same for Entrepreneurs. Obviously being curious about your industry will bring value, but even curiosity about subjects that have no obvious tie in can lead to innovation. For example, by learning more about telemarketing I discovered digital recording technology and applied it to our business to improve quality.

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new-product-research-and-conjoint

So much has been written about conducting research for new product development. Not surprisingly, as this is an area of research almost every organization, new or old, has to face day in and day out. As market research consultants, we deal with it all the time and thought it would be beneficial to provide our audience with our own recommendations for some useful sources that explain conjoint analysis – a method most often used when researching new products and conducting pricing research.

Recommendation #1: In 15 Minutes

Understanding Conjoint Analysis in 15 Minutes

This is a relatively brief article from Sawtooth Software, the makers of software used for conjoint, that provides an explanation of the basics of conjoint. The paper uses a specific example of golf balls to make it easy to understand.

Recommendation #2: For Managers

Managerial Overview of Conjoint Analysis 

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new-product-resesarch-development-inventorA few times a week I get the privilege of talking to an inventor/entrepreneur. The products they call about range from pet toys to sophisticated electronic devices, but they all have one thing in common…they want a proof of concept for their invention. In most cases they want it in order to attract investors or to sell their invention to corporate entities.   
 
Of course, unlike our fortune 500 clients, they also have limited budgets. They’ve often tapped their savings testing prototypes and trying get a patent so they are weary of spending a lot to do consumer research. Even though only about a third of these conversations end up in our doing work for them, I enjoy them all.
 
First off, it is fun educating people on the various tools available for studying concepts. I typically start off telling them about the range of techniques from simple concept evaluations (like our Idea Audit) to more complex conjoint studies. I succinctly outline the additional learning you get as the budget increases. These little five to ten minute symposiums help me become better at talking about what we do.
 
Second, talking to someone as committed to a product as an inventor is infectious. They can articulate exactly how they intend to use the results in a way that some corporate researchers can’t (because they are not always told). While some of their needs are pretty typical (pricing research for example), others are very unique. I enjoy trying to find a range of solutions for them (from various new product research methods) that will answer the question at a budget they can afford. 
 
In many cases, I even steer them away from research. For many inventions something like Kickstarter is all they need.  In essence the market decides if the concept has merit. If that is all they need then why waste money on primary research? My hope is that they succeed and return to us when they have more sophisticated needs down the road.
 
Of course, I particularly enjoy it when the inventor engages us for research. Often the product is different than anything else we’ve researched and there is just something special about helping out a budding entrepreneur. The fact that these engagements make us better researchers for our corporate research clients is just a bonus.   
 
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new-product-research-floating-grilleI recently heard an old John Oliver comedy routine in which he talked about a product he'd stumbled upon...a floating barbeque grille. He hilariously makes the case that it is nearly impossible to find a rationale for such a product and I have to agree with him. Things like that can make one wonder if in fact we've pretty well invented everything that can be invented.

A famous quote attributed to Charles Holland Duell makes the same case: "Everything that can be invented has been invented". He headed up the Patent Office from 1898 to 1901 so it's not hard to see why he might have felt that way. It was an era of incredible invention which took the world that was largely driven by human and animal power into one in which engines and motors completely changed everything.

It is easy for us to laugh at such stupidity, but I suspect marketers of the future might laugh at the notion that we live in a particularly hard era for new product innovation. In fact, we have many advantages over our ancestors 100+ years ago. First, the range of possibilities is far broader. Not only do we have fields that didn't exist then (such as information technology), but we also have new challenges that they couldn't anticipate. For example, coming up with greener ways to deliver the same or better standard of living.

Second, we have tools at our disposal that they didn't have. Vast data streams provide insight into the consumer mind that Edison couldn't dream of. Of course I'd selfishly point out that tools like conjoint analysis or consumer driven innovation (using tools like our own Idea Mill) further make innovation easier.

The key is to use these tools to drive true innovation. Don't just settle for slight improvements to what already exists....great ideas are out there.

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In today’s fast paced, high stakes business environment, where budgets are tighter than ever, finding the time and dedicating the resources needed for generating breakthrough product ideas can be very challenging. Many companies we’ve worked with either have no process in place or rely on internal brainstorming to come up with their next product ideas.

As we know, brainstorming sessions often include a variety of stakeholders. Our academic colleague, Jacob Goldenberg, points out in his book “Inside the Box,” that a better approach to coming up with new ideas is to brainstorm on your own independently, and then bring ideas to the drawing board anonymously for further discussion and feedback. 
 
In addition, while ideation brainstorming sessions often draw upon a wealth of information and trends accumulated via various types of past research, it is difficult to come up with product ideas that are truly new. Thus, most efforts result in close-in modifications or adaptations to existing offers.
 
However, there are some key advantages to such a process as well. Likely, the most important of those is early stakeholder engagement. Having the team onboard early and throughout the process certainly increases your odds of success, and those odds increase even more when consumers are also included early in the process.
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  • Kevin Dona
    Kevin Dona says #
    I am glad that you liked the webinar! The most efficient way somewhat depends on how you define “efficient” and the end purpose o

resolutions market ResearchWe are now officially two months into 2017, which means it’s time to keep up with those New Year’s resolution goals. Resolutions can be difficult to attain in both personal and professional life settings. Recently, I stumbled upon an article by Crawford Hollingworth, an interesting read about behavioral science and its effect on New Year’s resolution goal attainment. As I was reading the article, I realized the suggestions for preparing resolution goals provided in the article also relate to the process of preparing a market research study. The four steps for developing a New Year’s resolution recommended in the article are: Make a plan, Substitute old behavior for new behavior, Make it easy, and Make only one New Year’s resolution. My view on how these strategies relate to market research is as follows:


1. Make a plan

The first step of the market research journey is to make an action plan. Figure out what the objective of your research is going to be – what do you want to know and from who do you want insight? Next, consider the methods through which you will obtain the most meaningful and useful results for your research objective. Finally, put together a schedule that includes every aspect of the research, including questionnaire design, fielding the survey, data delivery and reporting the research findings.


2. Substitute old behavior for new behavior

In the grand scheme of market research methodologies, there are plenty of approaches to choose from that will provide the results needed to make powerful decisions about your product or service. Of course, it is normal human behavior to have the desire to stick to what you know, and market research isn’t much different. However, methodologies are continuing to evolve and can provide findings in various ways. For example, TRC has developed methodologies such as Message Test Express™Idea Mill™ and Bracket™, along with other solutions that are increasingly popular among the research we conduct. This is an opportunity to be creative and try methodologies that have been tested and offer proven results, which will allow you to view research findings from an alternative perspective.


3. Make it easy

In order to get reliable results from your research, it is best to start with consideration of the questionnaire design. Plan the design with the end in mind first, then work your way to the front; if you consider what you want to know first, the questions themselves will come together easily. This will allow you to easily interpret and analyze data during the final reporting stages. On the other hand, in terms of the actual survey, you want to avoid developing questions that are overly complicated or time consuming for respondents. Make sure the questions asked make sense and the instructions are clear and concise so that respondents can quickly grasp the idea of what you are asking of them.


4. Make only one New Year’s resolution

A colleague of mine, Rajan Sambandam, provided insight during a recent meeting about the scope of market research studies being “Broad and Shallow” versus “Narrow and Deep” that I found to be interesting. A take-away from his statement is that you should either have a broad and shallow scope through which you will have less informative findings about a larger group of topics, or a narrow and deep scope through which you will have an abundance of detailed findings about one topic. Instead of striving to accomplish both “broad and shallow” and “narrow and deep” research in one initiative, focusing on one or the other will provide the most meaningful and useful information to be applied to your product or service.

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economistOver the years our clients have increasingly looked to us to condense results. Their internal stakeholders often only read the executive summary and even then they might only focus on headlines and bold print. Where in the past they might have had time to review hundreds of splits of Max-Diff data or simulations in a conjoint, they now want us to focus our market research reporting on their business question and to answer it as concisely as possible. All of that makes perfect sense. For example, wouldn’t you rather read a headline like “the Eight Richest People in the World Have More Wealth than Half the World’s Population” than endless data tables that lay out all the ways that wealth is unfairly distributed? I know I would…if it were true.

The Economist Magazine did an analysis of the analysis that went into that headline-grabbing statement from Oxfam (a charity). The results indicate a number of flaws that are well worth understanding.

•    They included negative wealth. Some 400 million people have negative wealth (they owe more than they own). So it requires lots of people with very low positive net worth to match the negative wealth of these 400 million people…thus making the overall group much larger than it might have been.    

•    For example, there are 21 million Americans with a net worth of over $350 Billion. Most of them would not be people you might associate with being very poor…rather they have borrowed money to make their lives better now with the plan to pay it off later.

•    They were looking at only material wealth…meaning hard assets like property and cash. Even ignoring wealth like that of George Baily (“The richest man in town!”), each of us possesses wealth in terms of future earning potential. Bill Gates will still have more wealth than a farmer in sub-Saharan Africa, but collectively half the world’s population has a lot of earnings potential.

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recycle market research pricingIn my two previous blogs about recycling, I reported on gender gaps in recycling behavior and general knowledge about what is curbside-recyclable and what isn't.

Now we turn to the real question: why aren't consumers recycling on a more consistent basis? Again we turned to our online consumer research panel and asked those with curbside recycling access who don't recycle regularly a simple question: Why not? What behaviors and attitudes can Recyclers act upon to educate their customers and encourage more recycling?

Well, like any complex problem, there's no one single answer. Lack of knowledge of what's recyclable and being unsure how to get questions answered play a big part (28%). Recyclers can raise awareness through careful and consistent messaging.

But just as significant as knowledge is overcoming basic laziness (29%). Sorting your recycling from your trash takes effort, and not everyone is willing to expend energy to do so. Recyclers may not be able to motivate them, but another concern is addressable, and that's scheduling – having trash and recycling pick-up on different days can de-motivate consumers to recycle (15%).

Another challenge is forgetfulness. Some folks are willing to recycle, but it slips their mind to do so (25%).
Education could help promote a feeling of responsibility and elevate recycling's importance:


•  I don't feel that whether or not I recycle makes a difference (14%)
•  Recycling isn't important to me (10%)
•  I'm not convinced recycling helps the environment (8%)

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pets pricing-researchIn a recent survey we conducted among pet owners, we asked about microchip identification. We found that cat owners and dog owners are equally likely to say that having their pet microchipped is a necessary component of pet ownership. That’s the good news.

The bad news is that when it comes time to doing it, the majority haven’t taken that precaution. 69% of the cat owners and 64% of the dog owners we surveyed say they haven’t microchipped their companion.

Why is microchipping so important?  Petfinder reports that The American Humane Association estimates over 10 million dogs and cats are lost or stolen in the US every year, and that 1 in 3 pets will become lost at some point during their lifetime. ID tags and collars can get lost or removed, which makes microchip identification the best tool shelters and vets use to reunite pets with their owners.

One barrier to microchipping is cost – it runs in the $25 to $50 dollar range for dogs and cats. Not a staggering amount, but pet ownership can get expensive – with all the “stuff” you need for your new friend, this can be a cost some people aren’t willing to bear. Vets, shelters and rescue groups sometimes discount their pricing when the animal is receiving other services, such as vaccines. Which begs the question, if vets want their patients to be microchipped, what’s the best way for them to price their services to make this important service more likely to be included?

It seems that pet microchipping would benefit from some pricing research. Beyond simply lowering the price, bundle offers may hold more appeal than a la carte. Then again, a single package price may be so high that it dissuades action altogether. Perhaps financing or staggered payments would help. And of course, discounts on other services, or on the service itself, may influence their decision. All of these possibilities could be addressed in a comprehensive pricing survey. We could use one of our pricing research tools, such as conjoint, to achieve a solid answer.

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Brand PerceptionsIs the Mini Cooper seen as an environmentally friendly car? What about Tesla as a luxury car? The traditional approach to understanding these questions is to conduct a survey among Mini and Tesla buyers (and perhaps non-buyers too, if budget allows). Such studies have been conducted for decades and often involve ratings of multiple attributes and brands. While certainly feasible, they can be expensive, time consuming and can get outdated over time. Is there a better way to get at attribute perceptions of brands that can be fast, economical and automated?

Aron Culotta and Jennifer Cutler describe such an approach in a recent issue of the INFORMS journal Marketing Science, and it involves the use of social media data – Twitter, in this case. Their method is novel because it does not use conventional (if one can use that term here) approaches to mining textual data, such as sentiment analysis or associative analysis. Sentiment analysis (social media monitoring) provides reports on positive and negative sentiments expressed online about a brand. In associative analysis, clustering and semantic networks are used to discover how product features or brands are perceptually clustered by consumers, often using data from online forums.

Breaking away from these approaches the authors use an innovative method to understand brand perceptions from online data. The key insight (drawn from well-established social science findings) is that proximity in a social network can be indicative of similarity. That is, understanding how closely brands are connected to exemplar organizations of certain attributes, it is possible to devise an affinity score that shows how highly a brand scores on a specific attribute. For example, when a Twitter user follows both Smart Car and Greenpeace, it likely indicates that Smart Car is seen as eco-friendly by that person. This does not have to be true for every such user, but at “big data” levels there is likely to be a strong enough association to extract signal from the noise.   

What is unique about this approach to using social media data, is that it does not really depend on what people say online (as other approaches do). It only relies on who is following a brand while also following another (exemplar) organization. The strength of the social connection becomes a signal of the brand’s strength on a specific attribute. “Using social connections rather than text allows marketers to capture information from the silent majority of brand fans, who consume rather than create content,” says Jennifer Cutler, who teaches marketing at the Kellogg School of Management in Northwestern University.

Sounds great in theory, right? But how can we be sure that it produces meaningful results? By validating it with the trusted survey data that has been used for decades. When tested across 200+ brands in four sectors (Apparel, Cars, Food & Beverage, Personal Care) and three perceptual attributes (Eco-friendliness, Luxury, Nutrition), an average correlation of 0.72 shows that social connections can provide very good information on how brands are perceived. Unlike with survey data, this approach can be run continuously, at low cost with results being spit out in real time. And there is another advantage. “The use of social networks rather than text opens the door to measuring dimensions of brand image that are rarely discussed by consumers in online spaces,” says Professor Cutler.

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pollsters-went-wrongThe surprising result of the election has lots of people questioning the validity of polls…how could they have so consistently predicted a Clinton victory? Further, if the polls were wrong, how can we trust survey research to answer business questions? Ultimately even sophisticated techniques like discrete choice conjoint or max-diff rely upon these data so this is not an insignificant question. 

 
As someone whose firm conducts thousands and thousands of surveys annually, I thought it made sense to offer my perspective. So here are five reasons that I think the polls were “wrong” and how I think that problem could impact our work.

 

 

5 Reasons Why the Polls Went 'Wrong'


1) People Don’t Know How to Read Results
Most polls had the race in the 2-5% range and the final tally had it nearly dead even (Secretary Clinton winning the popular vote by a slight margin). At the low end, this range is within the margin of error. At the high end, it is not far outside of it. Thus, even if everything else were perfect, we would expect that the election might well have been very close.  

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2016 election sample representativenessI always dread the inevitable "What do you do?" question. When you tell someone you are in market research you can typically expect a blank stare or a polite nod; so you must be prepared to offer further explanation. Oh, to be a doctor, lawyer or auto mechanic – no explanation necessary!

Of course, as researchers, we grapple with this issue daily, but it is not often we get to hear it played out on major news networks. After one of the debates, I heard Wolf Blitzer on CNN arguing (yes arguing) with one of the campaign strategists about why the online polls being quoted were not "real" scientific polls. Wolf's point was that because the Internet polls being referenced were from a self-selected sample their results were not representative of the population in question (likely voters). Of course, Wolf was correct, and it made me smile to hear this debated on national TV.

A week or so later I heard an even more, in-depth consideration of the same issue. The story was about how the race was breaking down in key swing states. The poll representative went through the results for key states one-by-one. When she discussed Nevada she raised a red flag as to interpreting the poll (which has one candidate ahead by 2 - % points). She further explained it is difficult to obtain a representative sample in Nevada due to a number of factors (odd work hours, transient population, large Spanish speaking population). Her point was that they try to mitigate these issues, but any results must be viewed with a caveat.

Aside from my personal delight that my day-to-day market research concerns are newsworthy, what is the take-away here? For me, it reinforces how important it is to do everything in our power to ensure that for each study our sample is representative. The advent of online data collection, the proliferation of cell phone use and do-it-yourself survey tools may have made the task more difficult, but no less important. When doing sophisticated conjoint, segmentation or max-diff studies, we need to keep in mind that they are only as good as the sample that feeds them.

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Recycling market researchIn my previous blog, we determined that people with access to recycling services don’t necessarily recycle. And men were far less likely to recycle regularly than women.

One problem potential recyclers face is there is no federal standard for what is collected and how. Services vary from one contractor to the next. Items deemed recyclable in one municipality may not be the next town over. As a general rule, bottles, cans, and newspapers are curbside-recyclable. Also as a general rule, prescription drugs, electronic devices, CFL bulbs and batteries are not – they shouldn’t go in the trash either - they require special handling.  But does the average consumer know this? We asked our online panelists who have access to recycling services how they believe their trash/recycling haulers would like them to handle certain items. And here’s what we learned:

  • Knowledge of recycling the Big-3 (glass bottles – aluminum cans – newspapers) is quite high. At least 80% of our panelists with access to recycling services know each of these should be recycled as opposed to trashed. And men and women are equally knowledgeable.
  • Word has spread that electronics do not belong in the trash. But our consumers are divided as to where they should go – 35% believe their contractor wants them in their recycling bin while just 46% believe electronics require special arrangements.
  • When we get to other items, things get a bit murky:
    1. Our panelists are as likely to believe that batteries can go out in the trash or recycling (45%) as believe batteries require special arrangements (41%). The rest aren’t sure.
    2. 19% aren’t sure what to do with compact fluorescent light bulbs.
    3. 22% believe that prescription drugs can be put out in the trash. 17% aren’t sure.
  • Meanwhile, some items that are traditionally “trashed” make consumers take pause – 26% of our consumers believe their hauler wants them to recycle linens and towels.

Focusing solely on those who say they recycle, women are more likely than men to know what goes where…

Recycling Market Research part2

Ladies, you may want to re-think having your gents handle the trash and recycling - or give them a quick lesson on what you've learned!

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  • Michele Sims
    Michele Sims says #
    Thanks for your question! We surveyed 507 adults in the US.
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Do Americans Recycle Enough? - PART I

Posted by on in Consumer Behavior

access to recycling utilityAccording to Economist.com, Americans aren’t doing a good job of recycling.  There is actually a shortage of materials from recycling facilities that could be used to produce new products. The author posits that there are a variety of reasons for this, including simple access: “a quarter of Americans lack access to proper bins for collecting recyclable material, and another quarter go without any curbside recycling at all.”  But I think it goes beyond access, and I surveyed our intrepid online panel of adult consumers to find out.


A little over a quarter (28%) of TRC’s panelists say they do not have residential recycling service. Increasing awareness and access for rental properties would certainly make a dent: renters are more likely to say they don’t have it (44%) than homeowners (19%).


But what if you are aware and have access?  Does that mean you’re recycling?  Not necessarily.  Only 75% of those who could be recycling are doing so on a regular basis (usually or always). There’s no difference between renters and homeowners with recycling access as far as how often they recycle. But there is one key difference between those who regularly do and those who don’t: gender. Women are far more likely to say that they recycle regularly (84%) than men (62%). I’m not sure why there is a gender disparity, but we’ll explore how knowledgeable men and women are about recycling in my next blog.

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new product pricing research ebayI’ve become a huge fan of podcasts, downloading dozens every week and listening to them on the drive to and from work. The quantity and quality of material available is incredible. This week another podcast turned me on to eBay’s podcast “Open for Business”. Specifically the title of episode three “Price is Right” caught my ear.   
While the episode was of more use to someone selling a consumer product than to someone selling professional services, I got a lot out of it.
First off, they highlighted their “Terapeak” product which offers free information culled from the massive data set of eBay buyers and sellers. For this episode they featured how you can use this to figure out how the market values products like yours. They used this to demonstrate the idea that you should not be pricing on a “cost plus” basis but rather on a “value” basis.
From there they talked about how positioning matters and gave a glimpse of a couple market research techniques for pricing. In one case, it seemed like they were using the Van Westendorp. The results indicated a range of prices that was far below where they wanted to price things. This led to a discussion of positioning (in this case, the product was an electronic picture frame which they hoped to be positioned not as a consumer electronic product but as home décor). The researchers here didn’t do anything to position the product and so consumers compared it to an iPad which led to the unfavorable view of pricing.  
Finally, they talked to another researcher who indicated that she uses a simple “yes/no” technique…essentially “would you buy it for $XYZ?” She said that this matched the marketplace better than asking people to “name their price”.  
Of the two methods cited I tend to go with the latter. Any reader of this blog knows that I favor questions that mimic the market place vs. asking strange questions that you wouldn’t consider in real life (what’s the most you would pay for this?”). Of course, there are a ton of choices that were not covered including conjoint analysis which I think is often the most effective means to set prices (see our White Paper - How to Conduct Pricing Research for more).
Still there was much that we as researchers can take from this. As noted, it is important to frame things properly. If the product will be sold in the home décor department, it is important to set the table along those lines and not allow the respondent to see it as something else. I have little doubt if the Van Westendorp questions were preceded by proper framing and messaging the results would have been different.
I also think the use of big data tools like Terapeak and Google analytics are something we should make more use of.  Secondary research has never been easier!  In the case of pricing research, knowing the range of prices being paid now can provide a good guide on what range of prices to include in, say, a Discrete Choice exercise. This is true even if the product has a new feature not currently available. Terapeak allows you to view prices over time so you can see the impact of the last big innovation, for example.
Overall, I commend eBay for their podcast. It is quite entertaining and provides a lot of useful information…especially for someone starting a new business.

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storytelling market researchMany researchers are by nature math geeks. We are comfortable with numbers and statistical methods like regression or max-diff. Some find the inclusion of fancy graphics as just being a distraction...just wasted space on the page that could be used to show more numbers! I've even heard infographics defined as "information lite". Surely top academics think differently!
No doubt if you asked top academics they might well tell you that they prefer to see the formulas and the numbers and not graphics. This is no different than respondents who tend to tell us that things like celebrity endorsements don't matter until we use an advanced method like discrete choice conjoint to prove otherwise.
Bill Howe and his colleagues at the University of Washington in Seattle, figured out a way to test the power of graphics without asking. They built an algorithm that could distinguish, with a high degree of success, between diagrams, equations, photographs and plots (bar charts for example) and tables. They then exposed the algorithm to 650,000 papers with over 10 Million figures in them.
For each paper they also calculated an Eigenfactor score (similar to what Google uses for search) to rate the importance of each paper (by looking at how often the paper is cited).
On average papers had 1 diagram for every three pages and 1.67 citations. Papers with more diagrams per page tended to get 2 extra citations for every additional diagram per page. So clearly, even among academics, diagrams seemed to increase the chances that the papers were read and the information was used.
Now we can of course say that this is "correlation" and not "causation" and that would be correct. It will take further research to truly validate the notion that graphics increase interest AND comprehension.
I'm not waiting for more research. These findings validate where the industry has been going. Clients are busy and their stakeholders are not as engaged as they might have been in the past. They don't care about the numbers or the formulas (by the way, formulas in academic papers reduced the frequency with which they were cited)...they care about what the data are telling them. If we can deliver those results in a clear graphical manner it saves them time, helps them internalize the results and because of that increases the likelihood that the results will be used.

So while graphics might not make us feel smart...they actually should.

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conjoint-vs-configuratorWe at TRC conduct a lot of marketing research projects using Conjoint Analysis. Conjoint is a very powerful tool for determining preferences for the various components that make up a product or service. The power of Conjoint comes from having consumers make mental trade-offs in evaluating products against each other. Do they prefer a lower cost product that contains few features, or a higher priced product that provides many benefits? How willing are they to choose a product that meets 2 or 3 of their criteria, but not all? Conjoint forces consumers to make these decisions, and the results can then be simulated to determine purchase preferences in a variety of scenarios.
But not all product development problems can be solved with Conjoint. Conjoint requires certain steps in the development cycle to have already been taken (defined features, some idea of pricing – see my previous blog on the topic.) In some cases, though, you may be at a stage in which Conjoint is feasible, but a different approach may be more appropriate, such as a Configurator. In a Configurator, otherwise known as a "Build-Your-Own" approach, you would use the same product features as in a Conjoint, but instead of pitting potential products against one another, the consumer "builds their own" ideal product.
So why choose one technique over the other? There are many reasons, but here are a few:
1. If determining overall product price sensitivity is the goal – Choose Conjoint. Conjoint will produce scores that assess both the importance of price overall as well as price tolerance for the product as features are included or excluded.
2. If you just want to know which features are the most popular, or which ones are selected when choosing or not choosing other features – Choose Configurator. In an a la carte scenario, respondents can choose which items to throw in their shopping cart and which ones to leave on the shelf. Getting simple counts on which features are popular and which ones are not – and in what combinations – can be very useful information, and it's an easier task for respondents. Keep in mind though that the Configurator works best if each feature is pre-assigned a price (to keep respondents from piling on).
3. If understanding competitive advantage/disadvantage is paramount – Choose Conjoint. Conjoint allows you to include "Brand" as a feature, and the results will link brand to the product price to see if respondents are willing to pay more (or less) for your product vs. that of a key competitor. You can also simulate competitive market scenarios. While you can include Brand in a Configurator, modeling the trade-off between brand and product price is far less robust.
4. If you have a lot of features, or complex relationships between the features - Choose Configurator. It's much easier for a respondent to sift through a long list of features and build their ideal product just once than to choose between products with a gigantic feature list multiple times. Conjoint works best when the features are not dependent on one another; a long list of restrictions on the features can disqualify Conjoint as a viable solution from a design perspective.
There are plenty of times when a technique may present itself as an obvious choice, and other times when the choice may be more subtle. And in those cases, we turn to our senior analysts who use their expertise and understanding of the research objectives to make sound recommendations.

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when not to use conjointAt the beginning of my research career I grew accustomed to clients asking us for proposals using a methodology that they had pre-selected. In many cases, the client would send us the specs of the entire job, (this many completes, that length of survey) and just ask us for pricing. While this is certainly an efficient way for a client to compare bids across vendors, it didn’t allow for any discussion as to the appropriateness of the method being proposed.  
Today most research clients are looking for their research suppliers to be more actively involved in formulating the research plan. That said, we are often asked to bid on a “conjoint study.”  Our clients who’ve commissioned conjoint work in the past are usually knowledgeable about when a conjoint is appropriate, but sometimes there is a better method out there. And sometimes the product simply isn’t at the right place in the development “chain” to warrant conjoint.
Conjoint, for the uninitiated, is a useful research tool in product development. It is a choice-based method that allows participants to make choices between different products based on the product’s make-up. Each product comprises various features and levels within those features. What keeps respondents from choosing only products made up of the “best” features and levels is some type of constraint – usually price.   
We look to conjoint to help determine an optimal or ideal product scenario, to help price a product given its features, or to suggest whether a client could charge a premium or require a discount.  It has a wide range of uses, but it isn’t always a good fit:  

  1.  When the features haven’t been defined yet. One problem product developers face is having to “operationalize” something that the market hasn’t seen yet. You need to be able to describe a feature, what its benefits are, and its associated levels in layman’s terms. We can’t recommend conjoint if the features are still amorphous.   
  2. When there are a multitude of features with many levels or complex relationships between the features. The respondent needs to be able to absorb and understand the make-up of the products in order to choose between them. If the product is so complex that it requires varying levels of a lot of different features, it’s probably too taxing for the respondents (and may tax the design and resulting analysis as well). Conjoint could be the answer – but the task may need to be broken up into pieces.   
  3. When there are a limited number of features with few levels. In this case, Conjoint may be overkill. A simple monadic concept test or price laddering exercise may suffice.   
  4. When pricing is important, but you have absolutely no idea what the price will be. Conjoint works best when the product’s price levels range from slightly below how you want to price it to slightly above how you want to price it.  If your range is huge, respondents will gravitate toward the lower priced product scenarios and you won’t get much data on the higher end. It may also confuse respondents that similar products would be available at such large price differences.
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