MarketTools, Inc. blogs

Simple Market Research Analytics That Extend Report Value

Market Research Team — Thu, 23 Feb 2012 22:32:00 +0000

Hank Khost

For marketing research projects that collect product awareness and usage data, there are some ratios that are easy to calculate and which lead to deeper insights. These simple analytics are often called “conversion ratios”.

One such ratio is Awareness-to-Trial, which tells the percentage of trial received from consumers for each point of awareness generated by a product. “Trial” is the proportion surveyed who have ever used/purchased the product, and this can be gauged against top-of-mind awareness, total unaided awareness, and total awareness levels overall. Here’s a simple example:

Goliath Snack Cakes have an unaided awareness level of 32%. The level of people who have “ever tried” Goliath’s cakes is 8%. The Awareness-to-Trial Conversion ratio is 4 to 1, or 25%. For every dozen people Goliath’s ad and promotion plan makes aware of the brand, they get three people to actually try it. (Note that you could do an Awareness-to-Purchase ratio instead, asking consumers if they have “ever purchased” Goliath Snack Cakes.)

Is that Awareness-to-Trial Conversion ratio good? There are two ways you could know:

1) Assess the results agains norms for the snack cake category (MarketTools can provide you with norms for various categories); or 2) In the absence of norms, run the same exercise for a competitor (or multiple competitors) measured in your A&U survey and compare your results. Let’s say category leader Little David’s Snack Cakes has a trial level of 16%, double that of Goliath’s. Their unaided awareness level is the same (32%), resulting in an Awareness-to-Trial Conversion ratio of 2 to 1, or 50%. That means that for every dozen people that know about Little David’s cakes, 6 become triers, twice Goliath’s rate.

So what does that mean? Knowing this degree of discrepancy vs. the prime competitor, the brand manager at Goliath is now aware that her ad and promotion efforts are not as productive as Little David’s in driving trials of her product.

That information opens up many possibilities to help guide her strategies: review the ad copy to try new appeals; do a price comparison to understand the cost difference between the two brands; hire some market researchers to do a depth study comparing the two brands to get shopper insights about motivations behind the purchase; do a review of Little David’s marketing strategy and tactics; conduct a retail assessment for issues (does Little David get more SKUs on the store shelf, better shelf position, end aisle displays, etc.?). The Awareness-to-Trial Conversion ratio comparison helps identify direction for further thinking that may not be immediately apparent. Further, using the ratio the brand manager can calculate the potential dollar payoff of investing in actions that would close the gap between Goliath and Little David.

A similar analysis can be conducted with a Trial-to-Regular-Use ratio (“brand you use most often,” “favorite brand,” etc.). In this case, a sub-standard or non-competitive ratio may indicate a product shortcoming. For example, let’s say that Goliath converts 10% of its triers to regular users while Little David’s gets 25% trial-to-use. Maybe Goliath promises great chocolaty taste in its ads but doesn’t deliver the expected flavor; maybe it has a displeasing texture, has too many calories, or some other issue that turns off consumers. The Trial-to-Regular-Use conversion ratio guides the marketer’s thoughts toward additional product testing (if it hasn’t been done already). It could also indicate a retail problem, such as poor placement in the grocery store.

These conversion ratios are useful in helping to eliminate possibilities, narrow the scope of where efforts to improve should be concentrated, increase efficiency, and provide a quantitative benchmark for tracking progress of development efforts – ultimately enhancing your product’s chances for success in the marketplace.

Shopper Insights: The Path to Purchase (Part 2 of 3)

Market Research Team — Fri, 17 Feb 2012 19:35:42 +0000

Dan Bot

This is Part 2 of a series of Market Research blog posts on Shopper Research; check out Part 1 of the Path to Purchase series.

In a previous post I discussed the ways that constructing the Path to Purchase for your customers allows you to build a deeper understanding of their motivations and behaviors as they shop for your products. By understanding the components of the path, you can better strategize ways to maximize a product’s impact upon the consumer at each phase, and ultimately win the sale.

A well-designed survey program can ensure that you successfully collect all the data you need to detail your customers’ Path to Purchase – and here are some tips to help you develop your questionnaire.

Get consumers in their purchasing frame of mind.
Before just diving into your survey questions, it’s important to encourage the respondent to recall all the nuances that go into a shopping experience. Consider asking a shopper to recall a specific recent shopping occasion in which they bought a particular product. That way, you can uncover all details of the trip – instead of a watered-down recollection of a more general shopping experience that might miss some subtle, but important, steps. It can be useful to ask the respondent to describe the shopping trip in a detailed paragraph at the beginning of the survey, in order to jog their memory and make the most of the subsequent questions.
Understand that your consumer base may not be as interested in the category as you are!
In designing the survey experience, remember that not all products are as exciting to talk about as new cars, plasma TVs, or vacation packages. In fact, some consumers may tell you something like, “It’s just kitty litter (or another category)…don’t overanalyze it.” But therein lies the challenge. We need to extract as much quality path-to-purchase information as possible while being reasonable to the respondent (especially those that may be lighter category users). Explain the need for in-depth detail to respondents and they’ll be better inclined to give more thoughtful answers. Mix some interactive elements into the survey experience to make it more engaging – such as “virtual shelf” technologies that provide an online retail environment so respondents can shop for products like they would in the real world. And don’t forget basic survey principles such as keeping the questionnaire length manageable and providing a fair incentive in return for the consumer’s time.
Create an exhaustive series of questions to uncover as many of elements along the Path to Purchase as possible.
The path likely includes marketing touchpoints such as advertising, “triggers” that create the need for the purchase, steps such as gathering information or collecting coupons, and the actual physical route through the store to the product and to checkout. Be sure to ask about the dynamics behind the shopping trip – was it a quick in-and-out trip or a trip to stock up on supplies; an impulse buy or a planned purchase? You’ll be able to refine or supplement your question set as you learn more about your customers.
Include an open-ended question asking for anything else consumers would like to tell you about their personal path.
Even the most air-tight survey might leave out certain elements that the respondent was not able to relate during the survey. Give the consumer the opportunity to let you know if they have some great ideas on how to improve the path.
Consider recruiting for a follow-up study.
When you analyze the data you’ve collected, you’ll likely uncover some interesting findings worthy of a follow-up survey. There won’t be a better time than the current survey to recruit respondents interested in being re-contacted for further research. Provide your respondent with the heads-up, and you’ll increase the likelihood that they’ll participate again.

Coming up: I’ll discuss tips on analyzing and interpreting your path-to-purchase data for maximum impact in part 3 of this series. Check back soon!

Using NPS to Measure Your Customer Loyalty

CustomerSat Team — Fri, 10 Feb 2012 21:42:46 +0000

Joe Camirand

This blog post revisits a examination of best practices for using Net Promoter Score* in your customer satisfaction program by Joe Camirand, Vice President of Research & Consulting Services for MarketTools CustomerSat.

One of the questions clients focused on customer satisfaction and loyalty often ask me is “how do my NPS scores compare to others?” NPS, or Net Promoter Score, is a metric for measuring customer loyalty, based on the question: “Please rate your willingness to recommend us to a friend or colleague." The Three Segments of Net Promoter Score (NPS) are Promoters, Passively Satisfied, and Detractors, and they are typically determined using a 0-10 or 1-10 scale. The NPS score is determined by subtracting the percentage of responses that rated a 0 through 6 (Detractors) from the percentage of responses that rated a 9 or 10 (Promoters). The end calculation of this is your NPS score. Note that the Passively Satisfied individuals, those that rated a 7 or 8, are not included in the calculation.

While NPS benchmarks by industry and best-in-class scores exist, there are really no better scores to benchmark yourself against than your own. Really, the most important thing to remember is that you are looking for improvement. When thinking about comparing your scores with other companies, please remember that there are many things that can affect your NPS score – including the industry you’re in, the type of products you sell, where you sell your products, who you sell your products to, the economy, etc.

Also, remember that NPS is only a single metric, one of many that should be tracked and monitored to measure performance. NPS should not be relied upon alone, any more than a pilot would rely on a single gauge in the cockpit to monitor his flight. Use NPS in conjunction with overall customer satisfaction scores and other actionable measures (like detailed performance metrics, individual customers’ scores, etc.) to garner the greatest insights into your performance.

Based upon conversations with clients using NPS, I would suggest the following strategy for benchmarking NPS:

1) Establish your own benchmark

The only benchmark that is applicable for you is your own. Measure for a time period (a quarter, a year) and then set the average as your baseline.
Set a goal for increasing your NPS score year over year. The important thing here is to improve. Do not get caught up in determining by how much. How much depends on several factors that are usually out of your control – budget, ability to make changes based on data, resources to affect change, internal company culture, economy, etc.
Year over year, or rolling 6-12 months is the best way to trend, as you will most likely see dramatic changes in scores from month to month or quarter to quarter. See point #2 below.

2) Watch your mix

Develop a sample plan that allows you to keep your survey population mix as comparable as possible from time period to time period. For example, if 75% of the people you survey in Q1 are in North America and in only 40% of the people you survey in Q2 are in North America, you will see dramatic differences in your trending.
Typical things to think about when developing your NPS survey sample plan include:
What type of people are you surveying? Titles, positions, and other factors affect survey scores.

Where are the people you’re surveying located? Certain countries will typically not give high scores of 9 or 10 out of 10 when responding to surveys, so is NPS the right metric when measuring that region? Whatever you decide, the mix of countries should be comparable.

How long have your survey respondents been clients? If your Q1 sample is 75% customers that have been with you 3 years or more and your Q2 sample contains only 40% of customers that have been with you 3 years or more, this could dramatically affect your trending.

Where in the survey are you asking your key satisfaction question? If you ask it at the beginning of the survey, you will get a higher number of people responding to that question, but only a “top of mind” answer. If you ask at the end, you may get fewer responses, but the responses you do get will be more thought out after the respondent gets more context as they answer all the other survey questions.

What languages did you use for the survey? Survey respondents may not understand the questions if they’re not written in their primary language. If you translate a survey written in English to other languages, it is imperative that the translation is done in a way that explains the key question properly.

These are just some of the many things to think about when using NPS as a metric, but perhaps most importantly, don’t lose sight of the fact that there are many other metrics out there that may be more useful to your organization – such as the Secure Customer Index and those mentioned by SimplexGrinnell’s Karl Sharicz in his blog post on key customer loyalty metrics.

^{* Net Promoter, Net Promoter Score, and NPS are trademarks of Satmetrix Systems, Inc., Bain & Company, Inc., and Fred Reichheld.}

Shopper Insights: The Path to Purchase (Part 1 of 3)

Market Research Team — Sat, 04 Feb 2012 00:06:57 +0000

Dan Bot

There’s been a lot of buzz lately surrounding Path to Purchase – the course of events or activities that consumers follow from the first glimmer of interest in a product up through the point where they actually make the payment to complete the buy. By understanding the components of the Path to Purchase, marketers are able to strategize ways to maximize a product’s impact upon the consumer at each phase to ultimately win the sale.

Like many aspects of market research, there’s a lot more to this process than meets the eye – it can be much more complex than you’d expect. While investigating your product’s Path to Purchase, you’ll uncover some interesting shopper insights along the way, such as:

Much of the Path to Purchase may be purely mental. The actual physical path through a store from the shelf to the cash register can be an important aspect of your customer’s Path to Purchase, and it’s worthwhile to understand ways to overcome barriers and facilitate their way. However, the most valuable insights often lie in the decision-making steps that precede the retail transaction.

Paths may begin long before the consumer ever reaches the store. Depending on the category, the consumer may actively seek out information beforehand. In fact, this research may dictate the specific channel or retailer they visit to shop for and ultimately buy the product.

Paths often vary wildly across categories. For large purchases such as cars, the path may start with a consumer’s dissatisfaction with their current car, leading to extensive time spent researching models online. For a pack of gum, the path may start with the sight of a flashy package near the cash register and end just seconds later.

Some of the insights you could expect to uncover by mapping out your customers’ path to purchase include:

Is your product a “destination buy” that gives shoppers a reason to leave the house and go to a store, or is it an impulse buy they throw in the cart when they’re already there? If the latter is true, perhaps you should think about ways to make your product more of a trip driver.
Which other products tend to be purchased in the same basket as your product? This could lead to some great cross-promotion ideas.
What do consumers like/dislike about shopping for your product? Perhaps there is a way to improve your customers’ shopping experience. Can you break down any barriers?
What other insights can you uncover to assist retailers in selling your product in store? Providing insights to the retailer might win you some more distribution and shelf space.

Over the next couple of weeks I’ll be following up this discussion of Path to Purchase with questionnaire tips to help you effectively survey your consumer audience so you can better understand each phase of the path (Part 2 of the series), and tips on analyzing and interpreting your path-to-purchase data for maximum impact (Part 3) Be sure to check back!

Designing a Customer Survey: Best Practices

CustomerSat Team — Fri, 27 Jan 2012 19:09:54 +0000

the CustomerSat Team

This blog post revisits some practical tips from from Jolinda Decad, one of our MarketTools CustomerSat Research Consultants, who offered up great advice on survey design best practices for Voice of the Customer programs in a 10-minute interview that you can listen to here.

Here are some of Jolinda's key considerations to keep in mind when you develop your customer feedback surveys:

Focus on a specific purpose: As you begin development, resist the urge to pull together a cross-functional team to start brainstorming survey questions. The best surveys focus on a specific purpose and ask only questions that are relevant and actionable. The more people involved, the more likely the purpose will be clouded and the more difficult it will be to get the information you need from the survey.
Clarify survey objectives: What problems are you trying to solve by asking customers about their experience? Often companies are looking to understand how to meet their customers’ evolving needs within a particular touchpoint, and it’s helpful to think through the end-to-end customer experience within that touchpoint. For example, if you’re evaluating customer satisfaction in your contact center, you’ll want to think through the typical contact center experience and determine questions you could ask about each aspect of the experience, such as how long they are on hold, how they are greeted, how their issue was handled, if there was any follow-up, etc.
Anticipate action: Think through how you might incorporate the survey results into decisions and actions. This will help you in two ways:

First, it helps you determine the demographic variables you need for decision-making. If you want to make decisions based on customer segment, region, product lines, etc., you can ensure you include these demographic variables in the survey to more easily segment the data for analysis.

Secondly, you can ensure that questions are asked in a way that drives clear action without setting false expectations. For example, if you sense that some customers want longer support hours but need a better idea of the number of customers that actually require this, you will want to ask whether the current support hours are meeting their needs without asking a question like “would you use support after-hours if it were available?” Not only is this question leading the respondent, it also sets the expectation that you’re considering extending support hours, which you may determine isn’t necessary if only a small percentage of customers feel their needs are not being met.

Check out the interview with Jolinda to learn more about how to design a great customer feedback survey from one of our EFM best practice consultants.

And as always, MarketTools offers ready access to experts in the fields of survey design, customer loyalty, and market research who can provide advice about best practices. Let us know how we can help you!

Profiling Segments: The Most Dangerous Game

Market Research Team — Wed, 18 Jan 2012 01:05:00 +0000

Michael Conklin, Chief Methodologist,

I’ve been reading Daniel Kahneman’s new book Thinking, Fast and Slow, and I highly recommend it to any market researcher. Be warned, though: the implications can be kind of scary. One of the topics the book covers is the “conjunction errors” that people commonly make – as demonstrated by the classic experiment known as the “Linda Experiment” (see page 156 of Kahneman's book).

Linda is described (keep in mind that the experiment is very old) as “31 years old, single, outspoken and very bright. As a student, she was deeply concerned with issues of discrimination and social justice and also participated in antinuclear demonstrations.” Participants in the experiment are asked to indicate which is more probable – that Linda is a bank teller, or that Linda is a bank teller AND active in the feminist movement. Even experimental subjects who were highly trained in statistics tended to choose the conjunctive description (bank teller AND feminist) as more probable than the simpler, more general description, even though it is impossible for a conjunctive to be more probable than any of the components of the conjunction.

This can be easily seen by the Venn diagram, above. The probability of being a bank teller AND a feminist can be at most equal to the probability of being a bank teller and that only occurs if all bank tellers are also feminists.

So, what does this have to do with Segmentation Profiling?

The issue to explore is why people consistently make the conjunctive error in assessing the likely probability of events. The answer, as Kahneman describes in experiment after experiment, is that once a plausible scenario has been constructed, all information about underlying base rates and probabilities is routinely ignored by the human mind. By putting together various “facts” describing Linda, it is easier for the mind to find examples of people who fit those facts and attribute all of the other characteristics of those people to the Linda description. An internal system that makes judgments by matching patterns replaces a system that is capable of more complex reasoning.

Now think about your last segmentation study. Did the research company supply you with a handy “description” of each segment? Of course they did. Descriptions help make the segment “come alive” and make it seem more “accessible”. We find it easier to connect with a segment if we “know” that it is “more likely to be female, age 45-55, high income, and works for a large company”. As demonstrated in the conjunctive error statements, this description is likely to be representative of a vanishingly small proportion of the actual people in the segment. (“More likely to be female” is often concluded when the proportion of females in a segment is 55% instead of the 50% in the overall population). If we write ad copy targeted at this description, chances are good it will not be relevant to a large proportion of our target.

What can we do? The tendency to simplify a description and ignore the actual results is hardwired into our brains and our clients’ brains. To minimize the risk of losing this dangerous game we need to be aware AND make clients aware of the basic problem. We need to constantly test our conclusions and recommendations against the knowledge that we are likely to assume a lot more about our segment targets than is supported by the data.

Do We Really Need Behavioral Economics? YES INDEED.

Market Research Team — Thu, 12 Jan 2012 16:00:00 +0000

Michael Conklin, Chief Methodologist,

A recent blog post by Brandon Ellse on Research World’s RW Connect blog argues :

"How can behavioural economics practically change the market research landscape? This remains less than clear to me. We’ve all heard about the experiments, their control and treatment groups beautifully poised in anticipation of the 'ta-da' moment where 'real' human behaviour is revealed like a rabbit pulled from a hat. These behavioural economics-inspired tales seem to waft over our intellectual senses, leaving us with a strange feeling of certainty that only the perception of knowledge exclusivity can bring. I won’t lie, I’m impressed. Getting at causality is no mean feat and I am often left in awe of the ingenuity of these researchers, but I want practical application and I get the feeling that you do too."

The benefit of behavioral economics is the insight it gives into the proper specification of the models we use every day in marketing research. Behavioral economic theories about how and why people overstate their estimates of frequency of purchase enable us to construct models that better predict actual behavior from the imperfect survey answers we collect. Understanding that losses loom larger than gains enables us to construct appropriate non-linear price functions in discrete choice models.

For example, behavioral economics tells us that threshold prices exist, that is, prices where exceeding them causes an unexpectedly steep decline in the price-demand function. Knowing this has enabled MarketTools to specify pricing DCM models with threshold points identified. I gave a presentation (Wildner R., and Conklin M., “Price Thresholds and Prospect Theory”, The 14th Annual Advanced Research Techniques Forum of the American Marketing Association) showing that pricing models with thresholds always predicted better than models without. Using behavioral economics theories allowed the specification of a better, more predictive model.

The linear models of the past have worked the best when the true form of the model was close to linear. This provides a bias towards small incremental changes because small changes are going to be approximately linear. To understand the potential for large gains or losses we need to have the right underlying functional form and behavioral economic theories help us get there.

Using Real-Time Feedback to Understand Our Distribution Partners

CustomerSat Team — Tue, 10 Jan 2012 21:11:52 +0000

Candy Michael, American General Life Companies,

Guest blogger Candy Michael is Vice President of Customer Feedback at American General Life Companies, one of the top insurance providers in North America – and a longtime MarketTools CustomerSat customer.

This past year, American General Life Companies launched a Voice of Producer (VOP) program series, to ensure that we deliver an exceptional customer experience for our distribution partners. We leveraged our EFM system with MarketTools CustomerSat to maximize the success of our partner feedback program.

Our distribution partners are independent marketing organizations, insurance agencies and producers offering our products and services, plus those of our competitors. Prior to our Voice of Producer program series, their opinions and satisfaction levels were filtered or anecdotal. At times we heard that we were not easy to do business with in areas such as marketing tools, technology, and new business processing.

In today’s highly competitive world, we must know our distribution partners by understanding their needs, finding out what works for them and what doesn’t so we can respond. The VOP program series is designed to survey American General’s distribution partners to:

Determine overall satisfaction with us, so we can identify areas of dissatisfaction, pursue initiatives to increase satisfaction in various business areas and objectively measure results
Gather input on specific distribution areas of focus to better differentiate our products and services from our competition
Seek feedback on select products, services and transactions, which is usable for more finite measurements of service delivery and quickly identifying areas requiring service recovery activities.

The VOP program series design and development resulted from a collaboration between our Customer Feedback Office and key operational and internal marketing groups who served as a steering committee for soliciting feedback from distributors. At one point we collected 11 topics and 200 questions from our committee! Knowing that our distribution partners receive emails and surveys from many carriers, we did not want to frustrate respondents with long surveys covering multiple topics. The outcome is that our VOP program series offers distinct surveys on specialized topics, covering Marketing Software and Advanced Sales tools, Underwriting and Issue, and Technology.

Averaging an 11% response rate, we leveraged MarketTools CustomerSat to conduct email-based surveys and send post-communication emails to 40,000+ respondents outlining their feedback and our action plan. As a result, our business groups made modifications to business processes, including significant performance gains on software load times and an encrypted email attachment process. All three VOP program series utilized segmentation within distribution upload records and branching, and skip patterns within CustomerSat to ensure that only relevant questions were displayed to appropriate respondents. This same segmentation and question logic provided deep and relevant analytics, and comprehensive action management capabilities for each VOP program series.

Our VOP Program series will be enhanced and expanded next year. This year’s launch has given us a baseline measure of our distribution partners’ satisfaction and loyalty, in addition to establishing a service benchmark for key operational services. Our EFM system, powered by MarketTools CustomerSat, has enabled us to provide powerful data collection and analytics to our organization to help us gain new insights about our partners in a cost-effective manner.

QR Codes in the U.S.: Wave of the Future, or "The Next Metric System”?

Market Research Team — Thu, 05 Jan 2012 00:28:58 +0000

Dan Bot

This is part of an ongoing series of blog posts on Mobile Market Research technologies from the MarketTools Market Research team.

Back in September I blogged about 5 things that every researcher must know about QR (Quick-Response) codes – those digital barcodes that allow users with a camera phone equipped with the correct reader application to scan the code and launch a web page, a survey, or otherwise connect to information. Since then, the hype around the technology has continued, countered by a lot of nay-sayers pointing out the limitations of QR codes.

Let’s look at some of these arguments against the technology and reevaluate if QR codes can still be an effective market research tool.

1. QR code adoption in the US continues to lag Europe and Asia. Some even say that the QR code “fad” has already come and gone. Maybe it’s true that the technology will never catch on in the US as much as it has in other markets. There is plenty of evidence out there that Americans are less likely to adopt technology that doesn’t benefit us immediately (see: metric system). However, that’s not to say QR codes can’t still be a useful tool. Thankfully, the cost to implement QR codes as a facet of your research plan can be done with absolutely minimal cost. And the key word is facet. Obviously you wouldn’t make it the focus of your research plan. We’re not going to dissolve online survey panels or eliminate phone or mail research and only use QR codes. Printing a QR code on a product or displaying one in a captive area where you’re looking for specific feedback is a low cost way to collect on-demand, in the moment customer data. At the very least, for many of us, it certainly trumps typing in a long url on a mobile device.

2. College students (a group that by all accounts should embrace QR codes) are still confounded by them. A recent study reported that the majority of college students with smartphones have no idea how to scan a QR code. This is a troubling statistic, but has been completely corroborated by MarketTools’ experience with this demographic. The key is to leverage QR technology in the right way so you can use the numbers to your advantage. Let’s assume that QR code access rate is only 3% among college students. If you’re trying to use QR codes to survey a particular college classroom, this obviously spells disaster! But what if you’re using a QR code to launch a survey of the readership of a nationwide publication targeting college students, or for a CPG product targeted toward college students – with an attractive incentive? All of a sudden, that 3% access rate yields you more completes than you know what to do with.

3. Newer technologies will surpass QR code technology. In fact, Google has recently started phasing out QR codes to focus on a developing technology called Near Field Communication (NFC). NFC works much like a QR code, but is activated by proximity to a touch point rather than by scanning. In other words, you only have to hold your phone up to something rather than sift through your apps to open up a reader, hold your hand steady, and scan a code. What’s not to love?

First and foremost, it will be several years before NFC technology is included in US phones. Second, as I rub my research crystal ball, I predict NFC will suffer from at least several of these issues as adoption builds:

Some mobile device manufacturers will drag their feet on including the technology while initial consumer demand is low
Many consumers with the technology will struggle to understand how to use it, such as how to toggle it on/off
The technology will come under heavy scrutiny by Congress as consumer privacy continues to come to the fore
Some industry experts (like QR code “traditionalists”) will mock NFC for its low adoption rate and argue the technology will never work as a research tool

Did you notice that some of these barriers are the same faced by QR code technology? The point is that no new research medium is perfect – it’s a matter of carefully selecting the right mix of media that best meets your needs.

As fun as it is to make blanket statements about how a new technology is either the best thing since sliced bread or completely useless, the best approach is to acknowledge its strengths and weaknesses and think carefully about how to benefit from them. QR codes are not going to completely revolutionize the market research industry, but they will be around for a while. They will be used successfully by those who understand how to take best advantage of the strengths of the technology, and make it worthwhile for the consumer to engage with them.

Thoughts on Market Research for 2012

Market Research Team — Tue, 27 Dec 2011 18:00:00 +0000

April Turner

Every year ends with dozens of prediction pieces looking back on the past year or ahead to the next. Heading into 2012, some of the “Top 10” lists are expanding to 12 to celebrate, with topics ranging from beer to mobile infrastructure. While there are many year-end lists for the market research world, I’ll offer a single theme instead – Convergence.

Mathematically, convergence may signal the arrival of computational limits or even the irrelevance of sequence order. However, in market research “convergence” is more like what happened in the convergence of the telecommunications industry – where several services are available from the “same pipe”. Here are some examples of areas in market research where we might see convergence in the year ahead:

Mobile and Shopper research will collide. A National Retail Federation poll recently found 41% of members were increasing investment in mobile retail and marketing. It’s crucial to continue to develop and test new mobile research programs to help us continue to progress.
Mobile is no longer the “3rd Screen”. Many households are moving to digital entertainment and away from traditional TV, and the use of mobile streaming apps is on the rise. For brands, the ability to track advertising, image and sentiment will get more complex and the sheer volume of data will require creative new models to make sense of it. This is an area where research analysts can bring expertise to the conversation.
Gamification of research will be extended to mobile devices. While there is still much investigation into the best implementations for game-based research, the movement to mobile devices will create pressure on game-like and game-based research. This will affect the validation of both gamification and mobile research.
Economic issues in Europe will force brands to more closely manage pricing. The interconnected economies of North America and Europe create the need to have a global aspect for domestic price plans.
Pricing and Packaging will converge into a single study architecture. The tradeoffs between changing or holding price points will drive new studies that collect attitudes on simultaneous changes to price, package size and quantities that are all variables for pricing strategy.
Social Media drives statistical research, and vice versa. Social media monitoring is being used to inform sentiment analysis across brands – however the elements needed to make predictive assessments are still missing. Research analysts will increasingly experiment with adding social media metrics into time-tested models.
Brands will begin using traditional methods to test and quantify social media trends. As the flexibility and speed of traditional studies increases, it’s harder to ignore the value of social media data in helping us better understand consumers. But what we need is the discipline of traditional MR methods to deliver predictive data necessary for business change. This will be especially useful in proving ROI for event sponsorships as well as understanding “cause marketing” endeavors.
The definition of “actionable” data will change to include visualization for the non-research stakeholders. Insights teams will partner with other organizations to deliver results to a wider audience. The need for storytelling calls for us to leverage research, business intelligence and analytics to create directive results that can be interpreted by a wide variety of audiences.
Research analysts will increasingly utilize data from non-traditional sources to answer market problems, including customer databases, POS data and web analytics. This may or may not be part of the “Big Data” evolution, but multiple data sources will increasingly be found in study research.
Traditional research methods will not die. Tried and true methods like surveys, ethnographies and the like will be combined in new ways, with yet-to-be-discovered methods to deliver actionable insights for business. There is no end to the opportunity for change.

I guess we ended up with a “Top 10” list after all!

2012 will be a transformative year for Market Research. We at MarketTools are excited about some of the new technologies and methods we are working on, and look forward to finding new tools and partnerships in the months ahead. Happy New Year!