One of the workshop sessions I attended on the first day of the 2011 Marketing Research Association (MRA) Annual Conference, in Washington DC., was about mixed data collection modes. One of the hottest topic in our industry today.

ROUND I

Annie Petit from Conversitions/Research Now showed examples of how online surveys, text messaging and social media research can be used in combination to get the best out of each. I totally agreed with Petit when she said there are not perfect methods. We have to use all methods that are available and get the best out of each.

For example, surveys are great for frequencies, and representative demographics; text messaging is good for on-spot live behavior, to check what people are seeing, eating, drinking right now; and “social mediaresearch is like doing a 10-hour survey,” which can give us tons of data, from thousands and millions of people on any one topic. We can use them all together. Examples:

• Use social media research to identify the best options to put on a grid question (e.g. list of the most popular restaurants)
• Use sharing of media research with respondents as an incentive
• Combine social media and survey with diaries via text messaging (SMS) to understand behavior
• Invite people to a SMS survey from an online survey
• Use social media research to do a deep-dive into the results from an online survey or a SMS survey

ROUND II

In the same spirit, Stephen Murrill, from Meta Research presented  a case study of the mixed mode approach used for the California State Fair, which was interested in understanding:

• Satisfaction drivers
• Barriers to attendance
• How to motivate families to maintain the tradition of coming to the Fair
• What activities may attract teenagers

The Fair usually used exit surveys to measure satisfaction with the event, but now they needed to talk to teenagers and Non-Fair-goers for whom exit surveys were not an option. The solution was to combine data collection on-site using iPads to get feedback from Fair-goers, SMS surveys to reach teenagers that came to the Fair, and phone and online surveys to reach Non-Fair-goers.

ROUND III

Sima Vasa from Paradigm Sample and Leslie Townsend, from Kinesis Survey Technologies, joined forces to present a case study where the primary data collection mode was mobile surveys. The problem was presented by a CPG client wanting to gain insights about consumers of convenience stores (C-stores), particularly Millennials (18 – 34). The goal was to create a consumer panel of C-store shoppers and keep them engage to learn more about these consumers over time.

Since this age group often doesn’t participate in online surveys, the solution was to develop a customized mobile panel application, using the Kinesis platform, that users would download to their cell phones. This application allowed to send surveys and capture the C-store experience of this age group. The application was optimized for many different devices and detected which device were used, which allowed to target the surveys appropriately, so if a longer survey was sent, they could advise users to take the survey on a PC instead of on their cell phones.

ROUND IV

Finally, Rick Kelly from Opinionology/Survey Sampling, talked about a study done for the Tour of Utah cycling race to understand the impact of advertising during the race on purchase intent. Participants were given the chance to participate in the study  using one of three modes: a mobile survey (SMS), an online surveys, or an IVR survey. The mobile survey had 12 questions, while the other two modes had 20 questions. The survey was open during a whole month, achieving 10% response rate.  Among the results presented, we learned that the SMS survey was the most common mode used buy those answering the survey at the event, which were also younger respondents. Most of those who answered the survey online did so within 24 hrs, and participants over 55 were overrepresented among IVR survey respondents. The results also showed an increased in purchase intent over the time the survey was open, for which there was no clear explanation.  Of all the four presentations, this one left me wanting for more in order to understand how the different modes actually compared.

KEY TAKEAWAYS

Although I didn’t learned anything totally new from this workshop, the speakers made a good case in favor of using mixed data collection modes. However, we should first define what we need to know and who we need to reach and then decide which data collection modes are more appropriate for the research objectives.

Petit showed good examples of how we can incorporate social media research to support more traditional research methods. Use it to refine survey questions, dig deeper about survey results, recruit respondents or provide incentives.

Murrill showed a great example of how different modes can be used to reach different target markets, while Vasa and Townsend showed how new nechnologies can help ups gain insights about hard-to-reach market segments.

What I missed was a discussion about the major issue that combining different data collection mode has, namely,  the measurement error introduced by different data collection modes as they have an impact on how people answer questions. Maybe, next year.

Attitudinal questions are common in surveys. They are often asked using an agree-disagree rating question format. The challenge is always to create statements that capture important elements of the attitudes we are trying to measure. Ideally, if the budget allows it, we should do qualitative research to gather insights into such elements and how people think and talk about them.

Even with qualitative data available, writing good attitudinal statements is not an easy task. Here are some guidelines to facilitate the process:

• Include only relevant items for the attitudes that are being measured: Adding irrelevant items or missing important ones is detrimental to the quality of the analysis.
• Write statements in present tense and avoid referencing to the past: Past tense relies on memory plus we will never know if the respondent is referring to yesterday, a week ago of ten years ago.
• Avoid giving to much information about facts or elements that can be interpreted as tales.
• Avoid ambiguity.
• Create statements that express in-favor or against opinions related to what it is being measured. Think, “I go to work every day” vs. “I love to work every day.” Do not use items that would describe different points in a continuum (“Sometimes I like to work”), as this can be confusing. If you need to do that, it means you would be better off converting the item to a separate rating question with a specific scale.
• Use direct and simple language.
•  Avoid technical jargon and use words respondents can understand.
• Make the statements short to facilitate comprehension and minimize fatigue. In long sentences, people tend to skip parts to get to the end faster, which can lead to misunderstandings. Avoid using more than one sentence.
• Use only one logical phrase per statement. Having more than one creates confusion as to what it is being evaluated.
• Avoid words like “always,” “everybody,” “nobody”. Gross generalizations are not credible. Respondents are likely to skip answering such statements or assume an artificial extreme position.
• Avoid negations and double-negations. They can lead to misinterpretations.
• Balance the number of positive and negative statements. Don’t make them all positive or negatives, which can mislead respondents in one direction or the other.

Writing short surveys is an uphill battle with many clients. Whenever the word is out that a survey will be conducted, everybody close to the subject, being the product team, senior management or operations, wants to add questions. The thought is, “since we are doing a survey let’s get as much as possible out of it.”

Unfortunately, the only thing you get out with very long surveys is bad quality data.  Why?

NON-RESPONSE & ABANDONMENT

As the survey length increases, so does the non-response bias and abandonment rate. Simply said, respondents won’t stay too long answering questions. Many won’t even start if they know the survey length (It is a best practice to announce the length of the survey in the invitation).

For those who think they can get away with it by not announcing how long the survey will be, think again. Respondents can always figure out the length from the progress bar and will drop in the middle of the survey if they perceive it as too long (even if no progress bar is shown).  High abandonment and non-response rates affect sample representativeness negatively.

In an experiment conducted by Galesic and Bosnjac (2003) to prove this point, 3,472 respondents were divided in 3 groups based on an online survey with different lengths (10, 20 and 30 minutes).  The chart above shows how the number of respondent who started and completed the survey declined as the survey length increased.

DATA QUALITY

Respondents, who are willing to endure a long survey, are at high risk of experiencing high burden and becoming “satisficers.”

Satisfacing occurs when the respondents select the answer options without giving them too much thought. They go for the most effortless mental activity trying to satisfy the question requirement, rather than work on finding the optimal answers that best represent their opinion.  Respondents may start selecting the first choice in every question, straight-lining in grid questions (selecting the same across all options) or simply selecting random choices without much consideration.  This type of behavior renders the data worthless.

The same experiment by Galesic and Bosnjac was set to test the impact of survey length on data quality, which was measured with a variety of indicators including response times, item response rate, length of answers to open-ended questions, and variability of answers to questions in grids.

Of all the indicators, item response rate (defined as the percentage completed from all questions presented in a block) was the only one that seemed unaffected by survey length, however it is unclear if the survey was programmed to force respondents to answer before going forward in the survey.  For the other indicators, the results strongly suggest that survey length affects quality.

There are powerful reasons that push clients and force research vendors to launch long surveys. Budget, time constraints, and different agendas from internal groups are some of them. However, when surveys start getting too long, clients and research vendors should take a minute to think about the implications. After all if we get bad data, we have wasted the little time and money we started with.

Focus Groups are probably the research method with the highest top-of-mind awareness. For many clients I encounter the first thing that comes to mind when they need to conduct market research is Focus Groups.  Focus groups are not right for every research purpose. They should be used for exploration and in-depth understanding, but never to make final decisions.

Focus Groups are more than mere discussions and need a lot of planning if you want to extract any insights out of them. Below are some of the most common mistakes you should avoid when doing Focus Groups (Greenbaun, 1998).

PLANNING

• The research objectives are not clearly defined: Sometimes, due to tight deadlines, clients want to rush Focus Groups without putting time into thinking through what they want to accomplish with the research. Lack of clear objectives often leads to useless results and wasted money and time.

• Disruptive method of communication between the moderator and the clients in the back room: Clients behind the one-way mirror often send notes asking the moderator to probe on a particular question or issue. Sending notes to the moderator creates a set of problems:
  
  
  • The wrong participants are recruited: The quality of Focus Groups depends greatly on the quality of the participants, so clear screening criteria need to be established to avoid:
  • Participants who are not familiar with an issue, product, brand and organization and have little to contribute to the discussion
  • Participants who only have positive feelings about a brand, product or organization. Although  sometimes we want to learn what heavy users and loyal customers want to say, if we don’ have groups we divergent opinions, we won’t  have a point of reference or learn about sources of dissatisfaction and areas in need of improvement

• Groups that are not homogeneous enough in certain variables relevant to the issue which may disrupt the dynamic and course of the discussion (e.g. different educational levels, different socioeconomic levels, gender, age, etc.)

• Not enough time is dedicated to the development of the discussion guide: This should be developed between the client and the moderator and put in writing. It seems obvious, but there are moderators that come to Focus Groups with general ideas of what should be discussed, without any formal discussion guide. Although discussion guides often are modified on the spot depending on the course of the discussion, the moderator has to make sure that the discussion doesn’t take a long detour from the main objectives and that key questions serving those objectives are posed to the group.  It helps to have a formal discussion guide.

• Not enough time is dedicated to the development of adequate stimuli: Often Focus Groups are used to explore reactions to product prototypes, packaging, positioning statements, or ad-like objects (TV commercial, print ad, etc.). Planning is needed to make sure that the stimuli are easily understood and appropriate for the research objectives and different enough to allow us capture different reactions.  The mistake is often to come with not well developed stimuli or with too many of them, resulting in sessions that are not as productive as they could be otherwise.

• Disruptive method of communication between the moderator and the clients in the back room. Clients behind the one-way mirror often send notes asking the moderator to probe on a particular question or issue. Sending notes to the moderator creates a set of problems:
  
  
  • The discussion stops and may make participants lose their train of thought
  • The moderator gets distracted trying to figure out how to incorporate the request in the discussion flow
  • Diminish the moderator’s perceived authority

• Inexperienced moderator: With the increase of DIY research teams at client organizations and smaller budgets, experience sometimes tends to have less weight on the decision about who moderates Focus Groups. This can be disastrous. Experienced moderators use a set of techniques to leverage group dynamics to maximize the positive benefits of interaction among participants and avoid the discussion being dominated by a few voices that can influence others’ reactions to specific questions. Experience in Focus Group moderation can make a difference between successful groups that provide great insights and groups that provide misleading information.

ANALYSIS

• Observers are biased: More often than not, clients come to Focus Groups with pre-conceived ideas and tend to focus on opinions given by participants that confirm what the client already believed to be “true.” There are cases, where the clients totally dismiss what they hear and suddenly become research experts blaming the results on the methodology or the moderator. Clients should be aware of the advantages and disadvantages of Focus Groups and come with an open mind to listen to what ALL participants have to say, not just a few which happen to agree with the client’s point of view. Both the client and the moderator should be as objective as possible if any real insights are to be gained from Focus Groups.

• Results are quantified: More than once I have met clients that want to do several groups and count how many participants express a particular opinion, hoping they can project the results. Results from Focus Groups should not be quantified. It is useless and misleading to quantify results from Focus Groups since they are not projectable. Focus groups are about the big picture and overall feelings (about an issue, brand, ad, etc.) and not individual comments.

The best way to avoid these problems is to plan visits from the moderator to the backroom during the discussion flow, so he or she can discussed with client the intentions of any probing requests. Clients should come to Focus Groups with an open mind and listen to what ALL participants have to say, not just a few which happen to agree with the client’s point of view. Both the client and the moderator should be as objective as possible if any real insights are to be gained from Focus Groups

Focus Groups can provide a lot of insights if done right. Put time into planning, pay for experienced moderators and make sure you use Focus Groups for the right purpose.

As of late I have been receiving many requests to do focus groups. When I ask what the objectives of the research are, and how the information is going to be used, in 99% of the time, doing focus groups is the wrong methodology for what the client wants to accomplish.

In one of the cases, the client wanted to measure advertising effectiveness of a campaign. In another, the client wanted to see how potential customers use some electronic devices with the goal of writing instruction manuals. But the most worrisome case was that of a client wanting to understand the size of the market and who his potential customers were.

Focus groups make sense when the primary goals of the research are to:

• Explore feelings, perceptions and motivations
• Understand why consumers react to a product or advertising message in a certain way
• Provide guidance to a development process (e.g. advertising, packaging, product development)
• Explore issues to form hypotheses when none exist
• Understand the story and why behind the numbers from quantitative studies or key performance metrics (e.g. sales)
• Provide input about issues that should be measured using quantitative research

Focus groups are about exploration and guidance, but don’t give definitive answers. In a recent article about focus groups by Freya Gaertner, she quotes Karen Sandberg who in a Harvard Management Communication Letter writes, “use focus groups not to draw conclusions, but to understand the conclusions drawn.”

Focus groups are not appropriate for:

• Making go/no-go decisions on product, advertising, or promotion launches
• Profiling and sizing target markets
• Measuring marketing effectiveness, awareness and usage

Focus groups have their place in our research toolbox and like any other research method they have advantages and disadvantages, which means they are not a good fit for every research need.

COMMON & APPROPRIATE USES OF FOCUS GROUPS (Greenbaum, 1998)

• New Product Development Studies:  To obtain initial reactions to a new concept or prototype in order to identify strengths and weaknesses and provide guidance on improvements and input for further quantitative and qualitative research.

• Positioning Studies: To explore effective ways to communicate and talk to target consumers about a product, brand or service. However, the final positioning solution should be tested quantitatively.

• Habits and Usage Studies: To collect preliminary data to help understand how consumers utilize products and services before developing a quantitative data collection instrument.

• Packaging Assessments: To identify strengths and weaknesses of various packaging elements during the design stage; to help copywriters to develop the package copy that is most effective, memorable and visible. In more advanced stages of the packaging development, focus groups can be used as a “disaster check” to make sure it is consistent with the brand. Final decision on packaging should then be tested with the help of quantitative research.

• Attitude Studies: To understand how consumers feel about different products and services before or after a quantitative study

• Advertising/Copy Evaluation: To provide input about the potential effectiveness of the advertising based on exposure to rough ideas using storyboards during the creative development stage, and help copywriters understand attitudes towards the advertising during the copy development stage

• Promotion Evaluations: To obtain consumer reactions to promotion concepts so that ideas can be later refined and made more appealing and easier to understand. After a promotional campaign is launched, focus groups can be used to understand why consumers did or did not participate in the promotional program.

• Idea Generation: To identify specific areas where new products – or modifications of existing ones – might offer benefits. As Greenbaum points out, “participants can’t be expected to create new ideas and products. They can talk about the problems they are having and the wishes they would like to fulfilled, but they will not normally be the source of new ideas. These have to come from the client’s or moderator’s interpretation of their comments.”

• Employee Attitude and Motivation Studies: To assess corporate employee’s attitudes towards their organization and identify any problems that should be addressed.

In all the types of research mentioned above, focus groups should be used for exploration and guidance for further research, often quantitative. Never, ever make final decisions on whether to launch a product, select a packaging, go with a positioning concept, or get married to a creative solution for an advertisement or promotion, solely based on focus groups.

as published on April 1, 2011 by the Dallas Business Journal

For entrepreneurs who are considering investing part of their marketing budget in market research, I have a piece of advice: Think very carefully about how you plan to use the research results in your business decision making.

Although this should be obvious, I meet many business owners who are very interested in doing market research, but have a vague idea of how they will use the resulting data. Then, they get disappointed when they don’t get the data they ultimately need. The solution is to spend time upfront aligning research objectives and business goals. Unfortunately, when market research is a last-minute thought before a big decision, not enough time is spent on clarifying goals and desired outcomes because of very tight deadlines.

In the great scheme of things, businesses are either working to acquire or retain customers or both, for sustainability and profitability purposes. This means that any market research should contribute with insights that support decisions related to customer acquisition and retention strategies. The choice of research methodology is often guided by these two strategies.

Depending on whether customer acquisition or customer retention is the main priority, we have to determine

• The research design and analytical approach we should use to analyze the data.
• The questions that go in a survey or discussion guide in qualitative research techniques (e.g., focus groups).
• The type of people we need to include in the sample.

In a recent conversation with a client wanting to implement a brand tracking study, he asked me who we should include in the study sample: customers or non-customers? I got the same question from another client interested in conducting pricing research before making a decision to change prices.

In both cases my answer was: What is your priority at the moment: acquire or retain customers? If the main goal is customer acquisition, we need to include non-customers in the sample to uncover how receptive they are to our brand and prices and how likely they are to join our customer base. If, on the other hand, our focus is on customer retention, we need to target our customers to take a pulse on our brand and understand their likelihood to defect our brand or buy more of it when faced with price changes.

By aligning business goals with the outcome that can be expected from different research methodologies, entrepreneurs would be able to maximize the return on the investment made in market research and make the research insights actionable.

 Multiple-choice questions (check all that apply) are one of the most common question formats found in online surveys. However, there are a couple of problems with this type of question:

• It often makes it easy for respondents to engage in satisficing behavior, which occurs when respondents select the answer options without giving them too much thought. They go for the most effortless mental activity trying to satisfy the question requirement, rather than work on finding the optimal answers that best represent their opinion.

• We really don’t know what it means when an item from the list is not chosen. This could happen (Sudman and Bradburn, 1982) because:

1. The option didn’t apply to the respondent
2. The respondent is neutral or undecided
3. The respondent overlooked the item

 WHAT CAN WE DO ABOUT IT?

A solution to this problem is to ask multiple-choice questions as a series of forced yes/no answers for each of the question items. This format requires that respondents report a judgment on each of the items. Research has shown that forced yes/no questions encourage deeper processing time and discourage satisficing response strategies as measured by the time spent on answering forced yes/no vs. check-all questions and the number of items marked affirmatively in each question format. Research by Smyth et al. (2003), comparing results from both types of formats in online surveys has found that:

• Respondents who answered forced yes/no questions spent significantly more time responding than did respondents to the check-all formatted questions.

• The forced yes/no format yielded more options marked affirmatively than the check-all format.

We can argue that the longer time spent answering the forced yes/no questions is a mechanical function of the fact that respondents are forced to give an answer for each item and spend extra time marking “no,” which is not required in the check-all question.

However, the positive correlation between time spent on answering the question and the number of options selected has also been shown to be an indicator of deeper processing and more thoughtful answers for the check-all formatted questions as well. Respondents who spend more time answering check-all questions mark significantly more answers than those who answer check-all questions in less time.

Another research result supporting the hypothesis of deep processing is that no significant differences have been found in the number of options marked affirmatively between respondents that take longer time answering yes/no questions and check-all questions.

The yes/no format for multiple-choice question are not a 100% foolproof, as some respondent may still show satisficing behavior by marking yes or no for all options or marking them randomly. In this case we need to put quality checks in place during programming that take into account the time spent on the question and any straightlining patterns.

An issue that we also need to manage is the fact that sometimes respondents are undecided or think an option doesn’t apply to them. In this case it would be wrong to force them to give a yes or no answer. The best remedies against this problem are respondent screening and survey skips that would avoid showing options that don’t apply. In cases in which there is still room for this problem, I recommend adding a third “Don’t Know/Not Applicable” option.

IMPLICATIONS FOR MIXED DATA COLLECTION MODES

 The forced yes/no format for multiple-choice questions is commonly used in phone surveys since it is impractical to read all the options to respondents and expect them to remember them all to answer the question. Often, when mixed data collection modes are used, (phone/online, phone/paper), the yes/no and check-all question formats are treated as equivalent, assuming they are answered the same way. Research suggests that this would be a mistake.

Experiments carried out by Smyth et al. (2008) with phone and online survey using both question formats have shown that the forced yes/no format yields consistently more options marked affirmatively than check-all formatted questions in online self-administrated and phone-administrated surveys. This supports the idea that results from both question formats are not comparable and shouldn’t be treated interchangeably.

KEY TAKEAWAYS

• You are better off using forced yes/no format for multiple-choice questions in order to elicit deeper processing and minimize satisficing behaviors.

• Do not mix the yes/no and check-all formats across data collection modes, as results are not comparable.

Concept testing is one of the most widely used research techniques used in new product development. I often meet clients wanting to test new product concepts, who get surprised when we discuss all the issues we need to consider in concept testing.

In a recent article by Jerry Thomas, from Decision Analyst, discusses what he has seen to work best in concept testing, which has also been my experience. Thomas calls for creating a research system supported by standards and standardized processes to make sure all new product concepts are tested the same way inside a company, which allows for comparisons of concept tests over time and across concepts. According to Thomas, concept standards should address at least the following:

• Format: Print or video
• Presence of illustrations
• Content of illustrations: Type and size of illustrations (include retail package or not?)
• Copy: Style, tone, complexity and length
• Font types and sizes
• Degree of finish (rough or magazine-ready?)
• Presence of price (priced or unpriced?)
• Presence of brand (branded or unbranded?)

Unfortunately, in my experience, most companies don’t have such a system and treat concept tests as isolated ad-hoc projects. This is also reflected in the quality of the concept descriptions, which differ in style, content richness and format across concepts. Concepts are often poorly written by internal research of marketing staff and have little resemble to how the product will actually be positioned and presented to consumers.

Thomas also discussed some of the decisions we need to make, when designing concept tests. These decisions include: which approach to use (monadic vs. multiple, decision often driven by cost), whether to show prices and brands, which sampling strategy to follow, which normative data to use as a reference (if available), and what analytical approach to adopt.

As Thomas indicates about “one-in-10 concepts will be good enough to warrant investments in product development to create the product that fulfills the promise of the concept. And roughly one-in-five of this group will eventually be deemed worthy of taking to market. It’s a numbers game, and the odds are against you.” Companies that establish research systems to support consistent concept testing and chose to follow best practices in the issues discussed above, are more likely to develop successful new products.

To learn more about our Product Concept Testing service visit Concept Testing and Product Optimization.

 We, as consumers, don’t always do what we say. This is a fact that market researchers have to wrestle with in the design phase of any research project. As Jeffrey Henning pointed out in his recent article, Respondents as Economic Actors: Behavioral Economics & MR, some schools of economic thought have been drawing attention to the inadequacy of the rational choice theory, which assumes that consumers have perfect information about all the alternatives and weigh in pros and cons before making a purchase decision.

Although sometimes this may be true for certain purchase decisions in product categories a consumer values and is engaged with for a variety of reasons, many times consumers make decisions with incomplete information or simply skip the evaluation of options and make impulse purchases or go with what is available.

Henning summarizes the situations where, as behavioral economists have pointed out, actual behavior doesn’t match the rational approach to decision making. These are:

• Rules of thumb: Here consumers rely on heuristics or rules of thumb as shortcuts to decision making. They don’t take into account all possible options and may not make optimal decisions, but their decisions are “good enough.” This may be due to information overload, too many options, time constraints, financial situation, and lack of category involvement, among other factors.

• Emotional arousal: Consumers sometimes make purchase decisions influenced by their emotional state. If the person is calm she is more likely to think through her purchase decision. However, if the person is experiencing strong emotions (positive or negative), they are more likely to succumb to impulse purchases without much thought of the long term consequences.

• Framing: The context often influences purchase decisions in ways consumers may not be aware of. We often compare products to others that are present, particularly on price. The same product may look like a good value at one store or terribly expensive at another depending on competing alternatives and our expectations. Store atmosphere, layout, music, scents, in-store advertising can invite or discourage consumers to buy. For online retailers, the website design, layout, navigation path, graphics, type and amount of information, and trustworthiness indicators, among others, provide a context that influence our decision to buy from a particular online retailer.

• Cognitive biases: As Henning puts it, “individuals overvalue items they own (the endowment effect) or have invested in (the sunk-cost fallacy),” and tend to feel losses more intensely than gains (this may explain why for some, paying for shipping feels worse even if the cost of shipping may be compensated by a price discount). We often assume that others think like us, but are also influenced by the decisions of others (e.g. recommendations by word-of-mouth). We also seem to be wired to think short-term and have a hard time resisting instant gratification, which may interfere with rational decisions that would be more beneficial to us in the long run.

We can probably find these situations in many categories and some are likely to be more prevalent than others. In my opinion, in order to tackle this problem from a research perspective, we first need to understand how consumers make purchase decisions in a particular product category and identify potential segments with different decision making approaches.

For instance, a consumer may consider clothing detergent a commodity and buys whatever brand is on sale at the time of purchase, while another browsers the detergent aisle, opening bottles to check for fragrance, and reading packaging labels searching for harmful ingredients for herself or the environment. The key is segmentation within product categories based on purchase decision approaches.

To capture the nuances and situations influencing purchase decisions, we can’t rely only on traditional concept tests or focus groups. These need to be combined with methods that go deeper and allow us to understand consumer emotions, purchase context, cognitive biases and rules of thumbs. Some of the research techniques that are useful for these purposes are:

• Adaptive Choice-Based Conjoint analysis: Consumers are asked to build their own product based on a set of criteria, information that is used to understand the rules they use to choose products (must-haves and unacceptables) and to present relevant alternatives they would actually purchase.
• Shop-alongs: We go along with consumers in their shopping trip and observe how they make purchase decisions, what the motivators are, how the context influence their decision, the role of emotions, etc.
• Mystery shopping: Consumers get immersed in a shopping occasion and report back their personal experience with different aspects of the purchase occasion.
• Journaling about experiences: Consumers report about their experience with products and services in a journal format using text, video or pictures as the experience progresses.
• Ethnographic interviews: Consumers are interviewed as they carry on different tasks or use products in real time and environment.
• Mobile surveys in real time: Consumers are asked about their immediate and current experience, feelings and opinions via text messages.
• On-site observation: Acting like a fly on the wall, we can watch how consumers buy and use the products and integrate them in their daily life.
• In-Depth interviews: We delve deeper into purchase drivers, cognitive biases, situational factors, etc. Projective techniques can be used to uncover motivators not consciously recognized.
• Neuromarketing research: We use neuroscience, psychology and other cognitive science techniques to study consumer responses to marketing stimuli and products. Some of the responses measured include eye tracking, heart rate, electroencephalography – EEG, functional magnetic resonance imaging – fMRI, galvonic skin responses, etc.

If you want to understand the gap between what consumers do and say, don’t rely only on one research methodology, as each research method provides data that reflect only a few facets of the consumer.

The market research field is going through changes driven in part by the advent of new data collection technologies, increased competition in almost any product category, and research clients’ limited resources in terms of budget and staff.

In my opinion, many of these changes are here to stay and will continue to be “hot” trends to consider in years to come.

TOP 5 HOT, LIVING MARKET RESEARCH TRENDS

• Democratization of research:  Thanks to cheap online data collection tools for both quantitative and qualitative research more companies are doing surveys and using online tools for qualitative research. This has also accelerated the proliferation of DIY research. DIY is here to stay, but vendors will still be needed to provide expertise and objective insights.

• Commoditization of research: In an effort to save money, many companies have laid off many experienced (and more expensive) market researchers, and replace them with DIY research teams with less experienced staff.  It seems many companies are resigned to get “good enough” research as long as it is fast and cheap. Unfortunately, this attitude combined with access to inexpensive data collection tools has often a negative impact on the quality of research. Many companies that still use research vendors, are creating smaller preferred research vendor lists (with the risk of overpaying for services due to lack of competition) and are handing vendor selection to Procurement departments, which have a hard time realizing research services are not widgets. On the agency side, off-shoring of many tasks of the research process that have become commodities will continue to grow for cost saving purposes.

• Use of market segmentation research: More and more companies are realizing that the “one-size” fits all approach doesn’t work in today’s market place where customers have so many options available, so market segmentation research will continue to be of great interest to marketers and research clients to increase marketing strategy effectiveness.

• Use of branding research : Like market segmentation, branding is top of mind for companies looking for differentiation. Companies need to understand sources of brand equity in order to find a brand positioning that resonate with target audiences.

• Increased use of new online and mobile qualitative research techniques: Qualitative research has seen an explosion of online data collection tools that allows capturing data faster, cheaper and in large quantities, breaking geographic barriers and giving access to hard-to-get groups and real-time consumption occasions. These techniques have revitalized the qualitative research field and their use will continue to grow.

On the other hand, some practices are bound to die with time as they don’t add values to research users or new affordable research alternatives become available.

TOP 5 NOT-SO-HOT, DYING MARKET RESEARCH TRENDS

• Rely only on one data collection methodology: Almost all sample sources suffer from coverage bias nowadays. We know we can’t reach all groups online and more and more households are dropping their LAN lines and becoming cell-phone-only households. The use of hybrid data collection methods will become a requirement for many research studies.

• Provide data without insights and marketing implication: The days of the big “data-dump” are numbered.  More and more clients are requiring insights and strategic recommendations that can be derived from the data.

• Long research reports: In the current culture of sound bites and fast Twitter-like messages, few decision makers have the will and time to sit and read thick research reports full of charts and comments. Top-line reports with only a few slides highlighting key findings and insights are becoming the norm.

• Wait for big “in-depth” research projects to gather insights: The highly competitive environment is pushing companies to increase the pace at which they make business decisions. Nobody has time to wait until large research projects are completed, and many small projects are deployed to answer tactical questions.

• Reliance on “gut feeling”: More and more companies are realizing the importance of using key performance metrics, which often include customer feedback. Often, there is too much at stake to rely only on “gut feeling” in decision making. Many companies are harnessing the power of their customer databases to gather insights on what course of action to take. Database analytics combined with primary research will be used more and more to understand customer behavior and its motivators and thus provide support for smart business decision making.