It’s no secret that qualitative data is harder to analyze than quantitative. The replacement of ticked boxes for a plethora of data from verbatim, videos, group conversations and observations means that participant answers are vastly more time consuming to categorize. Not only this, but the context and motivations of the individuals need to be considered in addition to their responses and behaviors. But the effort is worth it; you can work to really understand consumer behavior and get to the ‘why’ behind the ‘what’.

Of the many techniques used to analyze qualitative information, sentiment analysis and semantic analysis are two we often come across. These provide insight into the data by looking at attitudes and themes. And as new technology continues to make its way into qualitative analysis, we need to understand how it can impact these two methods of analysis, and how it can help develop more meaningful insight.

As a reminder, sentiment analysis categorizes language based on opinions, often checking for a positive or negative viewpoint. This can include, for example, the views of consumers expressed on social media or online communities toward a specific product, idea or service. Although this deep analysis can be done by humans, it is quicker using technology – most often machine learning. Machine learning is helpful in analyzing large quantities of information and making judgements based on language that would be far too time consuming for researchers.

However, it’s important that sentiment analysis is not entirely reliant on technology. We are often asked to analyze data in a binary way, but the expressed sentiment is rarely that black and white. Technology often makes assumptions about the sentiment of a response to make it fit a binary format, while humans would be more cautious to force content into a category. Moreover, while humans can contextualize written language and understand colloquialisms and contradictions, as well as turns of phrase and humor such as sarcasm, machines struggle with this. Machines simply cannot understand all the nuances of language and its grammatical patterns to the same accuracy that a human can. In spite of the claims of some of the technology providers, we are far off being in a place where text can be analyzed by a machine as well as by a human. It is therefore important to use a joint approach to sentiment analysis; machines may make things quicker, but they’re also more likely to create inaccurate data through generalization, lack of contextual knowledge and a lack of natural language skills.

Semantic analysis, on the other hand, generates meaning by grouping together the themes in qualitative data. It is often referred to as topic or keyword analysis. This is an important approach when trying to understand the important subjects in the field of study, regardless of how a participant feels about them. This is often useful in the early stages of a research project, when exploring a broad idea and looking into other relevant and more specific ideas that need to be examined further. As with sentiment analysis, this can easily be done by a computer, and indeed, it is far more efficient to do so, leaving the researcher with more time for deeper analytical thinking. Furthermore, a machine learning process can take place with no prior knowledge of the documents – again saving time. However, there are limitations, as the grouping of themes needs to go beyond basic classification to give a meaningful representation of the qualitative information.

So machine learning speeds up sentiment and semantic analysis; for text based analyses this can open up a whole new world of information about consumers from social media, communities, and other online resources. The tech guys are working hard to solve the nuances of language such as sarcasm – machine learning’s biggest barrier in sentiment analysis. And then with semantic analysis comes the challenge of context – such as the ability to distinguish Mars the chocolate bar from Mars the planet. Technology is certainly getting closer to solving these problems, but in the meantime, it’s nice to keep the humans around.

For more on qualitative analysis, download our white paper here.

For the deepest, most comprehensive insights and personal opinions, it is difficult to beat the one-on-one interview.

However, there are other options.

Many people assume that B2B qualitative research cannot use the same variety of methods as B2C qual, or even B2B quant for that matter.

While it’s true that the in-depth interview is a great way of gaining deep insights into a participant’s thoughts, the vast potential of the internet means there are more convenient ways to reach professionals with tight schedules across varying locations.

Moreover, observational methods such as ethnography provide objective viewpoints of businesses that personal interviews may miss.

Online Focus Groups For B2B Qualitative Research

Admittedly, focus groups are less frequently used in B2B qualitative research than they are in B2C because of the time and geographical restrictions of participants.

B2B projects often require researchers to reach senior staff members to provide insights on specific issues and these are, of course, the people with the busiest schedules who have little spare time to provide feedback.

Moreover, geographical issues mean that even if spare time could be aligned between participants, there would be no convenient place for everyone to meet.

This issue can be solved by holding online focus groups so participants can log in to a virtual research space wherever they are.

And, to make sure participants join, remember to send a meeting request to participants’ work calendars so the event is firmly scheduled.

Flexible Online Discussion Groups For B2B Qualitative Research

Even better, for those with extremely tight schedules, is holding an online discussion group, allowing participants from any location to add to the conversation at a time that suits them.

This flexibility is important when respondents have little time to spare.

It will allow them to give more thoughtful, meaningful responses without the pressures of time and what is more it means they can talk with each other in a secure and controlled environment.

In an increasingly technological world, respondents often feel more comfortable giving their opinions to a faceless researcher online than they would to a real person who they may feel judged by.

Another benefit to researchers is that participants who do not engage satisfactorily in the discussion can be replaced, ensuring that productive discussion takes place between the moderators and participants (and the participants and participants) so authentic insight can be gathered.

This is also a great method for categorizing participants, as researchers can target specific group members with different questions to develop insight further.

B2B Ethnography: An Objective Approach To Qualitative Research

Depending on your research problem, B2B ethnography is also a great method to consider.

Although there can be a process involved in recruiting participants, a research technique that simply requires researchers to watch and learn is a great solution for participants with limited time as no changes to their normal habits are required.

This technique is excellent for research problems that look to assess behavior in the workplace and to observe the intricate happenings within a business, without asking employees for their own personal opinions which may be wound up in internal politics.

Ethnography is therefore a good way to get an objective B2B viewpoint.

Choosing The Right B2B Qualitative Research Methods

So, although interviews are important in B2B qualitative research, there are other, sometimes overlooked, methods which are a great fit for the B2B sector.

We all know that working life is busy, especially if you’re the CEO, so resources like online tools are useful for bringing people together regardless of location.

Online focus groups provide a universal research location, while online discussion groups allow research on demand for those whose unpredictable schedules mean they can’t commit to a specific time slot.

And for objective, unopinionated research, why not try ethnography, to see the background of a business without working around the time restraints of employees.

To access more information on qualitative data collection tools, download our white paper on fieldwork and data collection in qualitative research here.

Consumers have a lot of opinions and valuable advice to offer, but how can we ensure they’re heard?

Video puts data collection tools right into the hands of consumers. It does a great job in engaging research participants so that they know they are really being listened to. It puts them center stage, making them feel their worth and their value. With video open ends, participants aren’t constrained by text box responses, or even worse, a pre-written answer key. They are able to elaborate on their thoughts and opinions, while conveying their true emotions and feelings. And this is all done much quicker and easier, and on a more personal level, than typing in a box.

For researchers, video analysis means accessing deep insight from responses that are much richer than reading numbers or text alone. In fact, text box open-ends on average only get three or four word responses. While video gives researchers access to six times more content.

But although video brings a lot to the table, there’s still some confusion around how researchers can fully harness the value of video open ends.

Open-ended video questions can seem out of reach, especially for independent research consultants. But as a powerful source of insight, they’re well worth it. After all, it’s our jobs as researchers to take advantage of every technology and every resource available so as to access the raw emotions and the complex opinions of our participants, to elicit deeper insight to share with our clients.

So, how can you make video work for your research project?

Add video to current methodologies. Video works very well with in the world of qual, but it also applies to quant projects. Video helps to marry the two for richer insight, to let consumers tell the story behind the data. Video can be added to online surveys, NPS studies, online communities, brand trackers, ad testing, focus groups, ethnographies and more.

Take advantage of mobile. Mobile phones and laptops can easily capture video from anywhere in the world, at any time. Mobile is giving consumers 24/7 access to sharing their views with brands. Researchers and brands can reach out to consumers at any time with ‘push’ content to solicit video feedback in the moment.

Show and tell. Video is a great way to put products in front of participants. It can help the participants understand how a new product is used in a certain setting, and how it will be positioned, in order to find out how the messaging and language will resonate with target consumers.

Put it into context. Video should be used to go into greater depth, to get the fuller story, and to put everything into context for the researcher. A video can show the researcher how the participant uses the product in their home, or what products fill the shelves at the store.

Feelings. The facial expressions and the pure emotion that is picked up in a video within ten seconds can say much more than the typical text box response of 50 characters. Make sure the questions are designed to explore the participant’s feelings and emotions, and to probe their opinions.

Don’t settle on quality. The quality of the video software used is important as it can affect both the participant’s experience and the insight collected. Poor quality video can negatively impact the quality of the insight, so if you can control the video quality, so if you are using the software in a survey for example, the video must be of a standard that doesn’t distract or negatively influence the participants.

It’s easy to dismiss the video trend in research as a tool that is out of reach and too complex to integrate into current methodologies, but there are a lot of partners and tools out there to help. If you’re looking for more on video and other data collection tools, download our white paper on fieldwork and data collection in qualitative research here.

6 Steps to Perfect Qual

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Step 3: Fieldwork & Data Collection

When you back your fieldwork and data collection with the latest technology, you can’t go wrong.

Download the latest L&E trust guide to access new (and go-to) qualitative research techniques, along with the in-the-moment tools, video analytics, and other technology they run on. In this paper we’ll also share practical tips in choosing the right facility and creating an optimal research environment for participants.

This paper includes:

• Emerging fieldwork techniques (and the technology they run on)
• Practical tips on making your participants and clients comfortable on site
• How to deliver an environment that will optimize data collection
• Strategy for choosing the right facility

To continue reading, download our latest white paper by clicking here (no form to fill out).