About · Advertise

Research Access header image 4

Mike Pritchard

President - 5 Circles Research

Experienced Insightful Market Researcher - Principal at 5 Circles Research Full service research and helping D-I-Y surveyors

@ThatResearchGuy  ·   ·  mikep [at] 5circles [dot] com

My career spans market research, technology and consulting in marketing and high-tech.

Integrating customer and market needs with technology possibilities is a common theme for my work where my technical and marketing skills can really make a difference.

Along the way, my volunteer activities include the MIT Enterprise Forum, Puget Sound Research Forum (board member) and the WSA (Washington Software Alliance). In the past I have served on the board of the Software Association of Oregon, conducted business plan reviews for the Oregon Entrepreneurs Forum, and founded Internet Professionals Northwest.

Mike Pritchard’s Specialties:

Market Research of all kinds including Segmentation, Branding, Positioning, Concept Testing, Customer Satisfaction, Product Introductions. Plus Marketing and Strategy consulting.

Poor question design means questionable results: A tale of a confusing scale

July 9th, 2010 by Mike Pritchard · Research, essay

I saw the oddest question in a survey the other day. The question itself wasn’t that odd, but the options for responses were very strange to me.

* 1 – Not at all Satisfied
* 2 – Not at all Satisfied
* 3 – Not at all Satisfied
* 4 – Not at all Satisfied
* 5 – Not at all Satisfied
* 6 – Not at all Satisfied
* 7 – Somewhat Satisfied
* 8 – Somewhat Satisfied
* 9 – Highly Satisfied
* 10 – Highly Satisfied

What’s this all about?  As a survey taker I’m confused.  The question has a 10 point scale, but why does every numeric point have text (anchors). What’s the difference between 1, 2, 3, 4, 5 and 6 that all have the same anchoring text?   Don’t they care about the difference between 3 and 5?  Oh, I get it, this is really a 3 point scale disguised as a 10 point scale.

With these and other variations on the theme of “what were the survey authors thinking?”  on my mind I talked to a representative from the sponsoring company, AOTMP.  I was told that the question design was well-thought out and appropriate, being modeled on the well-known Net Promoter Score.   Well of course it is  – like an apple is based on an orange (both grow on trees).  But not really:

1. The Net Promoter question is for Recommendation, not Satisfaction.  There were a couple of other similar questions in the short survey, but nothing about Recommendation. Frederick Reichheld’s contention is that recommendation is the important measure and also incorporates satisfaction; you won’t recommend unless you are satisfied.
2. The NPS question uses descriptive text only at the end points (Extremely Unlikely to Recommend and Extremely Likely to Recommend).  It is part of the methodology to avoid text anywhere in the middle in order to give the survey taker the maximum flexibility.  That’s consistent with survey best practices.
3. The original NPS scale is from 0 to 10, not 1 to 10.  Maybe that’s a small point, although the 0 to 10 scale does allow for a midpoint which was part of the the NPS philosophy.

Other than the fact that this survey question isn’t NPS, what’s the big deal?  Well, this pseudo 10 point scale really doesn’t work.  The survey taker is likely to be confused about whether there is any difference between “3, Not at all Satisfied” and “4, Not at all Satisfied”. Perhaps the intention was to make it easier for survey takers, but either they’ll take more time worrying about the meaning, or just give an unthinking answer, and the survey administrator has no way of knowing.  Why not just use the 3 point scale instead?  I suppose you could, but then it would be even less like NPS. Personally, I like the longer scale for NPS.  I don’t use NPS on its own very much, but the ability to combine with other satisfaction measures with longer scales (Overall Satisfaction and Likelihood to Reuse) means that I’ve got the option of doing more powerful analysis as well as the simple NPS.  More importantly, I don’t have to try to persuade a client to stop using NPS as long as I include other questions using the same scale.  Ideally, I’d prefer to use a 7 or 5 point scale instead, but 10 or 11 points works fine – as long as only the end-points are anchored. For more on combining Net Promoter with other questions for more powerful analysis, check out “Profiting from customer satisfaction and loyalty research”

There’s no justification for this type of scale in my opinion.  If you disagree, please make a comment or send me a note.   If you want to use a scale with every point textually anchored, use the Likert scale with every point identified (but no numbers). Including both numbers and too many anchors will make the survey takers scratch their heads – not the goal for a good survey.

Perhaps the people who created this survey had read economist J.K. Galbraith’s comment without realizing it was sarcastic.- “It is a far, far better thing to have a firm anchor in nonsense than to put out on the troubled seas of thought.”

About Mike Pritchard - Experienced Insightful Market Researcher - Principal at 5 Circles Research Full service research and helping D-I-Y surveyors

→ 7 CommentsTags:

Statistical testing. It’s a good thing

July 6th, 2010 by Mike Pritchard · Research, essay

A recent article in the Seattle Times covering a poll by Elway Research gives me an opportunity to discuss statistical testing. The description of the methodology indicates, as I’d expect, that the poll was conducted properly to achieve a representative sample:

About the poll: Telephone interviews were conducted by live, professional interviewers with 405 voters selected at random from registered voters in Washington state June 9-13. Margin of sampling error is ±5% at the 95% level of confidence.

That’s a solid statement. But what struck me was that the commentary, based on the chart I’m reproducing here, might seem inconsistent with the reliability statement above.

Chart of Elway Research Poll Results from Seattle Times

The accompanying text reads “More Washingtonians claim allegiance to Democrats than to Republicans, but independents are tilting more towards the GOP.” How can this be, when the difference is only 4% (6% more Democrats, 10% more Republicans)? The answer lies in how statistical testing works and the fact that statistical tests take into account the differences arising from different event probabilities.

First, let’s dissect the reliability statement. It means that results from this survey will be within ±5% of the true population, registered voters in this case, 19 out of 20 times if samples of this size were drawn from the registered voter list and surveyed. (One time in 20 the results could be outside of that ±5% range; that’s the result of sampling.) This ±5% range is actually the worst case and is only this high at for 50% event probabilities – meaning the situation where responses are likely to be equally split. Researchers use the worst case figure to ensure that they sample enough people for the desired reliability whatever the results are. In this case, the range for Independents leaning towards Democrats is ±2.3% (i.e. 3.7% to 8.3%) while the range for Independents leaning towards the GOP is ±2.9% (i.e. 7.9% to 12.9%). But these ranges overlap so how can the statement about tilting more to the Republicans be made with confidence?

We need to run statistical tests to apply more rigor to the reporting. In this case t-tests or z-tests will show the answer we need. The t-test is perhaps more commonly used because if works with smaller sample sizes, although we have a large enough sample here for either. Applying a t-test to the 6% and 10% results we find that the t-score is 2.02 which is greater than the 1.96 needed for 95% confidence. The differences in proportions are NOT likely due to random chance, and the statement is correct.

Chart of t-scores for small proportion differences

To illustrate the impact of event probability on statistical testing, this diagram shows how smaller differences in proportions are more able to discriminate differences as the event probability gets further away from the midpoint. Note that even at 6% difference results between about 20% and 70% (for the lower proportion) won’t generate a statistically significant difference, while at 8% difference the event probability doesn’t matter. Actually, 7% is sufficient – just.

Without using statistical testing, you won’t be sure that the survey results you see for small differences really mean that the groups in the surveyed population differ. How can you prioritize your efforts for feature A versus feature B if you don’t know what’s really important? Do your prospects differ in how they find information or make decisions to buy? You can create more solid insights and recommendations if you test.

Tools for statistical testing

The diagram above shows how things work, and is a rule of thumb for one type of testing. But it is generally best to use one or more tools to do significance testing.
Online survey tools don’t generally offer significance testing. The vendors tell me that users can get into trouble, and they don’t want to provide support. So you are need to find your own solutions. If you are doing analysis in Excel you can use t-tests and z-tests that are included in the Data Analysis Toolpak. But these only work on the individual results so if you are trying to look at aggregate proportions (as might be needed when using secondary research as I did above) you need a different tool. Online calculators are available from a number of websites, or you might want to download a spreadsheet tool (or build your own from the formulae). These tools are great for a quick check for a few data points without having to enter a full data set.

SPSS has plenty of tests available, so if you are planning on doing more sophisticated analysis yourself, or if you have a resource you use for advanced analysis then you’ll have the capability available. But SPSS, besides being expensive, isn’t all that efficient for large numbers of tests. I use SPSS for regressions, cluster analysis and the like, but I prefer having a set of crosstabs to be able to quickly spot differences between groups in the target population. We still outsource some of this work to specialists, but have found that most of full-service engagements include so we recently added WinCross to our toolbag. We are also making the capability available for our clients who subcontract to 5 Circles Research.

WinCross is a desktop package from The Analytical Group offering easy import from SPSS or other data formats. Output is available in Excel format, or as an RTF file for those who like a printed document (like me). With the printed output you can get up to about 25 columns in a single set (usually enough, but sometimes two sets are needed), with statistical testing across multiple combinations of columns. Excel output can handle up to 255 columns. There are all sorts of features for changing the analysis base, subtotals and more, all accessible from the GUI or by editing the job file to speed things up.

Conclusion

I hope I’ve convinced you of the power of statistical testing, and given you a glimpse of some of the tools available. Contact me if you are interested in having us produce crosstabs for your data.

Idiosyncratically,
Mike Pritchard

About Mike Pritchard - Experienced Insightful Market Researcher - Principal at 5 Circles Research Full service research and helping D-I-Y surveyors

→ No CommentsTags:

Market research for startups

May 19th, 2010 by Mike Pritchard · essay

So, you’ve come up with a cool idea, but how do you test the market for your new product or service?

First, you need to figure out what is essential and what might be helpful. Before we get into the detailed recommendations, do you need research? Some entrepreneurs discount the idea of market research for various reasons:

  • You may think that your new idea is so special that research won’t tell them anything useful. With rare exceptions, this is simply not true.  Even if your new idea is so different from what’s currently on the market that people won’t understand it immediately, you can gather information about current pain points, needs, wants, and motivations.

Perhaps you believe that people will see the product and rush to buy it (the better mousetrap fallacy.)  Maybe, but usually not unless you’ve done your homework on how to reach prospects and their influences and decision making.  Are you sure they want a better mousetrap?  Perhaps they just acquired a cat and don’t need a mousetrap.

  • Maybe you are convinced that you only need a fraction of a big market in order to succeed.  Get in front of an angel investor or a V.C. and you’ll learn that they aren’t impressed by that idea.  But even if you are bootstrapping without outside investment, you should understand that the riches are in the niches.  As has been well demonstrated in theory and practice, a niche is important so that product development, marketing, and sales costs are minimized; greater market share in a niche should lead to more profitability and better defense against competition.  Even if your product really could address a broad market, it is important to identify your starting niche.

  • And of course, some people have the perception that market research is too expensive.  Don’t forget that making a mistake is even more expensive; it is much better to find out that your idea isn’t really going to succeed, or needs some changes, before launching.  Out of pocket costs can still be a concern, but hopefully you’ll get some ideas from this article that will help save money.

With that out of the way, the most important market research for startups is generally market sizing, concept validation, and pricing.  These aren’t necessarily separate projects, but the research plan should be written to give valuable information in these areas.  We tend to use both qualitative and quantitative techniques, and often secondary research before the primary custom work.

Secondary research is likely to give some general information about the broad market, such as the overall size and large subgroups (for example classes of hotels, or types of users for a software product).  Hopefully you’ll also learn something about terminology used by your prospects, which may well be different from your own jargon. Start by looking for free sources of information such as the Census Bureau (for both business and consumer), or the Pew Research Center.  The best free sources are also well documented, with published methodologies that give you confidence that the results will be applicable to your situation. Other secondary sources include investment analysts and industry groups. But the information isn’t likely to give you all the answers you need to identify and size your niche, much less to give you much to go on as you create the product that solves a problem, which is one reason to think hard before you spend money on a published report.  My ex-colleagues building high-technology startup products tell me that they rarely trust any of the secondary sources for the industry because much of the data about projections is simply gathered by asking a bunch of insiders who exaggerate so the overall numbers will be higher.  Maybe that is a thing of the past given the current state of the economy; you be the judge.

Sizing the niche for a new product is usually a matter of triangulation with inputs coming from a few secondary research sources, and also from primary research that you commission or conduct yourself.  How much effort (and cost) is needed for the sizing exercise is somewhat dependent on your need for outside investment.  An angel or a VC is likely to want to know that the market niche is large enough to justify their investment, as well as whether competition makes the niche less attractive.  Don’t forget that competition includes the status quo.  Your potential customers may just choose to live with an inferior approach if they don’t have sufficient incentive to change.

The level of reliability needed for accurate sizing requires a sample that is representative of the overall target population.  For example, if you are building an iPhone application that you think might appeal to a wide range of types of people you should sample all iPhone owners.  Research can tell you whether your seemingly general app should be targeted at, say, college students rather than business users and also the percentage of students.  To find out the percentage of college students you can turn back to secondary research, combining iPhone general demographics with census data for college enrollment.  But you might also want to know some other things about the target that could only come from surveying them; perhaps your product would be particularly useful to students whose grades have declined in the past semester.

Your representative sample could most likely come from a panel company.  Assuming you do an online survey, there are a range of online panel providers to choose from.  The most reputable suppliers follow industry standard protocols for recruiting and maintaining their panels, and the results aren’t likely to come from people who cheat on surveys.  In addition to vendors who recruit people specifically for surveys, sample can be obtained from companies like Peanut Labs who have relationships with social media sites or LinkedIn who can send invitations to members.  Obtaining sample like this should generate representative results, but sending invitations to lists you are on or posting in social media sites without control will not.  Panel companies charge by the complete, based on incidence.  The cost may seem high to a bootstrapping startup, but it should be money well-spent, and you’ll get results quickly.  Self-service survey tool companies, including QuestionPro and SurveyGizmo, generally have easy integration with panels.

For a representative sample for a consumer product you’ll want to gather at least 400 completes to give reliable results (±5% at 95% confidence). Larger samples allow reliable analysis of subgroups, and also support more advanced analysis such as segmentation.  For B2B research, sample costs tend to be higher and the target more limited so a smaller sample is often acceptable; 200 completes will give ±7% at 95% confidence.  Not only do these confidence intervals give the ability to project to the entire market, they also allow you to know that one result is statistically different from another.

It may be OK to use a non-representative sample if you don’t need overall market sizing but you still need to validate the concept.  For example, if you have a fairly good idea of the target and don’t need to convince an investor, you can refine your understanding by just tapping the right people.  In this case, posting your survey to one or more email lists, or just posting yourself in social media sites can be effective.

Another possibility for sample is a customer list.  This is a great way to invite people to take surveys, but startups don’t usually have existing customers.  If your company already has customers and you are producing a new product, maybe the customer list will work, but the results won’t tell you much about market size, and may give you false impressions if the products don’t appeal to the same people.  You might also think about using pop-under invitations on your website, but this is unlikely to be representative, to say nothing of the chicken and egg problem from the likely small traffic.

Before I describe some of the important content of the survey, let’s take a look at qualitative research for startups.  Qualitative research can give you a rich understanding of pain points, needs and motivations and other intangibles.  It is exploratory, so you don’t need to know all the questions before you start, but doing early research can help create a more effective survey. Just remember that qualitative means not projectable. One of the cardinal sins is to take results from a few people and assume that they reflect the overall market.  Useful qualitative techniques include in-depth interviews, focus groups, and idea generation and evaluation.

You’ve probably already heard the maxim that startup founders should talk to prospects.  If you make those in-depth interviews structured, you’ll be able to get useful information that is comparable from one discussion to the next.  A structured discussion guide makes this work.  Use mostly open-ended questions, and allow for probing to dig deeper than the initial response. You may need help to write the guide, and you may prefer to have someone else do some of the interviews, or to sit in on the conversations.

Focus groups have been used for many years, generally conducted at custom designed facilities allowing audio and video recording and two-way mirrors for real-time observation.  Focus groups are also held online, using real-time or bulletin board approaches.  Although online might seem to be a natural progression and a way to cut costs, most groups are still face-to-face.  Much like in-depth interviews, focus groups use structured discussion guides to ensure that topics are covered in the right order and with opportunity for open-ended discussion and probing.  The group setting creates opportunities for more active discussion where what one person says can stimulate new ideas from others.  Some worry that the setting creates a potential for groupthink or domination by a few participants, but skilled moderation avoids the issue and instead uses group dynamics for positive outcomes.   The moderator is the most important element in successful focus groups.  Most startups benefit from using an experienced independent moderator because entrepreneurs are generally too close to the topic and too passionate.   As with in-depth interviews, it is important to remember that this is research; it is more important to hear from participants rather than try to sell the concept.  While focus groups are ideally held at a dedicated facility, it is possible to conduct the groups in other settings that may be less expensive.

Ideation techniques are often used in focus groups, but similar techniques can be used in an online setting too.  Private discussion areas are set up, perhaps as part of a broader community environment, or perhaps just to discuss ideas about a product or idea.  Ideas may be posted by the organizer for comments and voting by participants, and in some cases discussion threads may be started by regular users. These tools are great for gathering freeform input, but don’t treat them as quantitative.

Armed with information from the qualitative research, and/or your own knowledge of the issues and what you are building to solve the problem, you can create or commission the survey with questions about the situation, pain points (problems), products currently being used and what’s missing to validate the concept. If the new product is easily described, you might be able to ask about features in a concrete fashion, but in any case you should be able to ask about importance of specific issues, as well as how well current products solve the issues.  Include questions about information sources (both online and offline), and key influences for new product choice. You should include demographics relevant to the topic, but also attitudinal questions. These days, psychographics (attitudes, interests, opinions) tend to be more significant because people aren’t defined simply by demographics. In addition (and particularly relevant to startups), it is important to understand where prospects fit on the adoption life-cycle for that product.  You’d naturally expect early adopters to be more interested in a radically new solution, but if you can’t distinguish between them and mainstream buyers you might underestimate the market size or make the mistake of assuming that messaging for everyone should look the same.

Finally, what about pricing?  Research can generate some guidance for pricing decisions, but it will rarely provide definitive answers, especially in startup situations.  Pricing decisions incorporate many factors including competition, cost, purchase authority, share of wallet, etc., with research providing some of the inputs.  Pricing research works better when the product is easily understood, and may not work at all when the new product is a big innovation.  In this case you could try asking about the value of fixing various problems, but results are likely to be inconclusive at best. I generally steer startups to Van Westendorp analysis because it is easy to implement, analyze and explain, and of course useful! My article (http://www.5circles.com/wordpress/blog/2009/05/van-westendorp-pricing-the-price-sensitivity-meter/mike-pritchard/) includes a comparison of other pricing approaches including why you shouldn’t just ask “what would you be prepared to pay?”  Using Van Westdendorp’s 4 questions, you can generate a range of acceptable prices which is a useful input to the pricing decision.  An additional Likert scale question(Very unlikely, Unlikely, Unsure, Likely, Very Likely) is often used to assess willingness to purchase – with the results modeled to generate purchase likelihood at each price point.  Further tweaking can be used to generate simulated demand curves, but at this point using conjoint analysis techniques may be more appropriate.  Conjoint is powerful, but tends to be more expensive because of the complexity of setup and analysis, as well as increased survey length (meaning higher sample costs).  Because so many factors are often moving targets in a startup situation, a simpler and cheaper method for pricing research is generally preferred.

As you can see, there are a number of types of research that benefit the startup.  Fortunately, you don’t have to worry about customer satisfaction (although you might want to assess satisfaction with competitors), brand awareness, advertising effectiveness or any of the other applications for market research.  The most important thing is to do some research, whether you do it yourself or work with professionals.

Idiosyncratically,

Mike Pritchard

About Mike Pritchard - Experienced Insightful Market Researcher - Principal at 5 Circles Research Full service research and helping D-I-Y surveyors

→ No CommentsTags:

Partner Feedback Network :