The Voxco Answers Anything Blog

Ready for a fresh take on participant engagement? We thought so! That's why we invited Annie Pettit, an industry expert in data quality and participant engagement, to share her insights. Whether you're here for practical tips or thought-provoking ideas, this post will get you thinking. Enjoy!

Creating engaging customer experiences is so important that nearly every retail and customer group has prepared extensive guidelines on how to do so. Among thousands of other guidebooks, manuals, and compendiums, the AMA has a Customer Engagement Playbook and Workbook, Hubspot has its “Ultimate Guide to Customer Engagement in 2024,” and Forbes has its “Customer Engagement in 2024: The Ultimate Guide.”  

Retailers, marketers, and stakeholders put a lot of effort into creating engaging experiences for their consumers, constituents, and employees for good reason. According to Gallup, increasing customer engagement can lead to a 10% increase in profits, 66% higher sales growth, and 25% higher customer loyalty.

Because they spend so much time researching it, market researchers have deep insights into what exceptional customer experiences really are and how important it is. They also realize that participating in social and marketing research has the potential to be an intensely engaging and personally satisfying experience as well.  

Why, then, does the research experience seem to be such a transactional exchange? Researchers write surveys. Participants give answers. Participant experiences decline. Response rates decline. Repeat.  

It’s time for research and marketing leaders to apply what they’ve learned about the customer experience to the survey experience. Let’s consider a few ways of creating intensely engaging research experiences for participants that will ultimately benefit stakeholders and elevate the ROI research.

Desirable incentives and fun questions are table stakes

When we think about creating an engaging research experience, most of us turn to creating a more fun and entertaining experience. In addition to creating simply better quality questions, we do this by:

• Offering incentives such as cash rewards, loyalty points, and exciting prizes. Research participants are human, after all and something is often better than nothing to convince someone to “Click to start” a survey. That’s one step forward for completion rates and representativity.

• Incorporating fun question types that help keep people motivated. For example, rather than asking people what they like best about ten different insurance companies, they can be asked what the superpower of each company is. Or, what animal or comic character or celebrity best reflects each company.

However, incentives and fun questions are table stakes. Participants look for and expect to see these things in every research study. If your research doesn’t already incorporate these things, it’s time to demand better.

Take the next step to ignite curiosity and encourage personal growth

Perhaps more importantly, though, are intrinsically engaging experiences. Many people like participating in the research experience because they value being heard and keeping informed about new products and services. There are, however, much more significant opportunities for personal growth. For example:

• Questionnaires that incorporate personality, descriptive, or preference statements can encourage self-reflection and highlight areas for personal growth and development.  

• Health, fitness, food, beverage, financial, and environmental research can cause people to reflect on their personal behaviors and consider whether they are interested in changing any components of their lifestyle.  

• Many studies are simply a good way to stimulate thinking, enhance concentration, and test out new ways of thinking, particularly for people who have fewer opportunities to do so in their daily lives.

Let’s return for a moment to the customer experience. When marketers present new products or services to customers, they explain the benefits clearly. People expect to learn what is new or fun or intriguing about a product they are considering purchasing.

The research experience should be no different. Researchers need to help participants understand how they will benefit from participating. Among many others, here are a few ways we can do this.  

• At the beginning of a questionnaire, invite people to consider their participation as a small journey in self-discovery. Invite them to use their curiosity to its fullest and try out new ways of thinking.

• Add a question at the end of the study inviting people to share with other participants what they’ve learned about themselves as a result of their participation. Most participants are curious to learn about the outcomes of the research projects they participate in and, with consent, this question is perfect for sharing when others cannot.

• At the end of a questionnaire, conclude with an offer to share links to trustworthy third-party websites so interested participants can learn more about the topic. If someone selects the “Yes, please share” box, offer links to free college courses or trusted, neutral websites with information about finances, the environment, healthcare, or child development.

Remember, offering these benefits must always be offered upon consent.

Help people be the change they want to see

It’s fun to joke about online algorithms that serve us weeks of advertisements for vacuum cleaners after we’ve just bought one that should last twenty years. But in the research space, it’s a different story.

After we’ve bought that vacuum cleaner (or soap or beer), we do want to talk about it for weeks. We want to ensure that other people benefit from our experience. We want to share our opinions, offer advice, and shape new innovations. It feels good to help other people make decisions that are right for them.  

By participating in research, people don’t simply help others buy a better vacuum cleaner. Sharing experiences with new products and services helps brands build products that enable people to eat healthier, have more fun, become more self-sufficient, access essential social services, and improve life itself. Research improves lives and can even save lives.

As before, we can’t simply assume that people will know the benefits of participating in research. Just as marketers tell people that this vacuum cleaner has the best suction, researchers should tell people how research helps the broader community. How do we action this?

• At the beginning of a study, remind people of the good that will come out of it. You already know the business objectives and the research objectives. You simply have to translate those into consumer facing language. Tell people that their participation will help many people in the future by creating more beneficial products and services.

• At the end of a study, offer more specific outcomes. Explain that their contributions will help people who have skin problems find personal cleaning products that are less irritating. Or, that everyone deserves a little joy in their lives even if that means determining which flavor of potato chips they’re going to make next. Tell people that their contributions make it easier for people to stay healthy, enjoy meals with their family, or give them more free time.

Naturally, it’s important not to jeopardize the research goals so ensure any specifics are left to the end of the research.

Summary

It’s so easy to pull out a survey template, change the brand names, add a couple new questions, and launch it. We’ve got decades of experience doing just that. But it’s time to say no to the templates we’ve relied on for years and built a new, and better template. One that prioritizes the survey experience just as marketers, companies, and organization have prioritized the customer and employee experience.

With a more engaging and personally fulfilling survey at hand, research participants will find it far easier to truly engage in the content, think deeply about their answers, and provide richer, more accurate data. Ultimately, investing in the survey experience translates to unlocking better quality insights, more informed decisions, and happier customers.

If happy customers are important to you, please get in touch with our survey experts. They’d love to help you collect more valid and reliable data. Talk to a survey expert.

Linguistic Analysis Explained

Editor’s note: This post was originally published on Ascribe and has been updated to reflect the latest data

Figuring out what humans are saying in written language is a difficult task.  There is a huge amount of literature, and many great software attempts to achieve this goal.  The bottom line is that we are a long way off from having computers truly understand real-world human language.  Still, computers can do a pretty good job at what we are after. Gathering concepts and sentiment from text.

The term linguistic analysis covers a lot of territory.  Branches of linguistic analysis correspond to phenomena found in human linguistic systems, such as discourse analysis, syntax, semantics, stylistics, semiotics, morphology, phonetics, phonology, and pragmatics. We will use it in the narrow sense of a computer’s attempt to extract meaning from text – or computational linguistics.

Linguistic analysis is the theory behind what the computer is doing.  We say that the computer is performing Natural Language Processing (NLP) when it is doing an analysis based on the theory.  Linguistic analysis is the basis for Text Analytics.

There are steps in linguistic analysis that are used in nearly all attempts for computers to understand text.  It’s good to know some of these terms.

Here are some common steps, often performed in this order:

1. Sentence detection

Here, the computer tries to find the sentences in the text. Many linguistic analysis tools confine themselves to an analysis of one sentence at a time, independent of the other sentences in the text.  This makes the problem more tractable for the computer but introduces problems.

“John was my service technician.  He did a super job.“

Considering the second sentence on its own, the computer may determine that there is a strong, positive sentiment around the job.  But if the computer considers only one sentence and individual word at a time, it will not figure out that it was John who did the super job.

2. Tokenization

Here the computer breaks the sentence into words. Again, there are many ways to do this, each with its strengths and weaknesses.  The quality of the text matters a lot here.

“I really gotmad when the tech told me *your tires are flat*heck I knew that."

Lots of problems arise here for the computer. Humans see “gotmad" and know instantly that there should have been a space. Computers are not very good at this.  Simple tokenizers simply take successive “word" characters and throw away everything else.  Here that would do an OK job with flat*heck → flat heck, but it would remove the information that your tires are flat is a quote and not really part of the surrounding sentence.  When the quality of text, syntax, or sentence structure is poor, the computer can get very confused.

This can also pose a problem when new words are introduced, or there are multiple meanings of words in one response or group of responses.

3. Lemmatization and cleaning

Most languages allow for multiple forms of the same word, particularly with verbs. The lemma is the base form of a word. So, in English, was, is, are, and were are all forms of the verb to be. The lemma for all these words is be.

There is a related technique called stemming, which tries to find the base part of a word, for example, ponies → poni.  Lemmatization normally uses lookup tables, whereas stemming normally uses some algorithm to do things like discard possessives and plurals.  Lemmatization is usually preferred over stemming.

Some linguistic analysis attempt to “clean up" the tokens.  The computer might try to correct common misspellings or convert emoticons to their corresponding words.

4. Part of speech tagging

Once we have the tokens (words) we can try to figure out the part of speech for each of them, such as noun, verb, or adjective. Simple lookup tables let the computer get a start at this, but it is really a much more difficult job than that.  Many words in the English language can be both nouns and verbs (and other parts of speech).  To get this right, the words cannot simply be considered one at a time.  The use of language can vary, and mistakes in part of speech tagging often lead to embarrassing mistakes by the computer.

Common Linguistic Analysis Techniques Explained

Most linguistic analysis tools perform the above steps before tackling the job of figuring out what the tokenized sentences mean.  At this point, the various approaches to linguistic analysis diverge.  We will describe in brief the three most common techniques.

Approach #1: Sentence parsing

Noam Chomsky is a key figure in linguistic theory.  He conceived the idea of “universal grammar", a way of constructing speech that is somehow understood by all humans and used in all cultures.  This leads to the idea that if you can figure out the rules, a computer could do it, and thereby can understand human speech and text.  The sentence parsing approach to linguistic analysis has its roots in this idea.

A parser takes a sentence and turns it into something akin to the sentence diagrams you probably did in elementary school:

At the bottom, we have the tokens, and above them classifications that group the tokens.  V = verb, PP = prepositional phrase, S = sentence, and so on.

Once the sentence is parsed the computer can do things like give us all the noun phrases.  Sentence parsing does a good job of finding concepts in this way.  But parsers expect well-formed sentences to work on.  They do a poor job when the quality of the text is low.  They are also poor at sentiment analysis.

Bitext is an example of a commercial tool that uses sentence parsing.  More low-level tools include Apache OpenNLP, Stanford CoreNLP, and GATE.

Approach #2: Rules-Based Analysis

Rules-based linguistic analysis takes a more pragmatic approach.  In a rule-based approach, the focus is simply on getting the desired results without attempting to really understand the semantics of the human language.  Rules-based analysis always focuses on a single objective, say concept extraction.  We write a set of rules that perform concept extraction and nothing else.  Contrast this with a parsing approach, where the parsed sentence may yield concepts (nouns and noun phrases) or entities (proper nouns) equally well.

Rules-based linguistic analysis usually has an accompanying computer language used to write the rules.  This may be augmented with the ability to use a general-purpose programming language for certain parts of the analysis.  The GATE platform provides the ability to use custom rules using a tool it calls ANNIE, along with the Java programming language.

Rules-based analysis also uses lists of words called gazetteers.  These are lists of nouns, verbs, pronouns, and so on.  A gazetteer also provides something akin to lemmatization.  Hence the verbs gazetteer may group all forms of the verb to be under the verb be.  But the gazetteer can take a more direct approach.  For sentiment analysis the gazetteer may have an entry for awful, with sub-entries horrible, terrible, nasty.  Therefore, the gazetteer can do both lemmatization and synonym grouping.

The text analytics engines offered by SAP are rules-based.  They make use of a rule language called CGUL (Custom Grouper User Language).  Working with CGUL can be very challenging.

Here is an example of what a rule in the CGUL language looks like:

#subgroup VerbClause: {

 (

   [CC]

   ( %(Nouns)*%(NonBeVerbs)+)

   |([OD VB]%(NonBeVerbs)+|%(BeVerbs) [/OD])

   |([OD VB]%(BeVerbs)+|%(NonBeVerbs)+ [/OD])

   [/CC]

 )

 | ( [OD VB]%(NonBeVerbs)[/OD]   )

}

At its heart, CGUL uses regular expressions and gazetteers to form increasingly complex groupings of words.  The final output of the rules is the finished groups, for example, concepts.

Many rules-based tools expect the user to become fluent in the rule language.  Giving the user access to the rule language empowers the user to create highly customized analyses, at the expense of training and rule authoring.

Approach #3: Deep learning and neural networks

The third approach we will discuss is machine learning.  The basic idea of machine learning is to give the computer a bunch of examples of what you want it to do, and let it figure out the rules for how to do it.  This basic idea has been around for a long time and has gone through several evolutions.  The current hot topic is neural networks.  This approach to natural language machine learning is based loosely on the way our brains work.  IBM has been giving this a lot of publicity with its Watson technology.  You will recall that Watson beat the best human players of the game of Jeopardy.  We can get insight into machine learning techniques from this example.

The idea of deep learning is to build neural networks in layers, each working on progressively broader sections of the problem.  Deep learning is another buzzword that is often applied outside of the area intended by linguistic researchers.

We won’t try to dig into the details of these techniques, but instead, focus on the fundamental requirement they have.  To work, machine learning and artificial intelligence need examples.  Lots of examples.  One area in which machine learning has excelled is image recognition.  You may have used a camera that can find the faces in the picture you are taking.  It’s not hard to see how machine learning could do this.  Give the computer many thousands of pictures and tell it where the faces are.  It can then figure out the rules to find faces.  This works really well.

Back to Watson.  It did a great job at Jeopardy.  Can you see why?  The game is set up perfectly for machine learning.  First, the computer is given an answer.  The computer’s job is to give back the correct question (in Jeopardy you are given the answer and must respond with the correct question).  Since Jeopardy has been played for many years, the computer has just what it needs to work with: a ton of examples, all set up just the way needed by the computer.

Now, what if we want to use deep learning to perform sentiment and language analysis?  Where are we going to get the examples?  It’s not so easy.  People have tried to build data sets to help machines learn things like sentiment, but the results to date have been disappointing.  The Stanford CoreNLP project has a sentiment analysis tool that uses machine learning, but it is not well regarded.  Machine learning today can deliver great results for concept extraction, but less impressive results for sentiment analysis.

BERT

Recent advances in machine learning language models have added exciting new tools for text analysis.  At the forefront of these is BERT, which can be used to determine whether two phrases have similar meanings.

BERT stands for Bidirectional Encoder Representations from Transformers.  This technique has been used to create language models from several very large data sets, including the text from all of Wikipedia.  To train a BERT model a percentage of the words in the training data set are masked, and BERT is trained to predict the masked words from the surrounding text.  Once the BERT model has been trained we can present two phrases to it and ask how similar in meaning they are.  Given the phrases, BERT gives us a decimal number between 0 and 1, where 0 means very dissimilar and 1 means very similar.

Given the phrase “I love cats", BERT will tell us the phrase “felines make great pets" is similar, but “it is raining today" is very dissimilar.  This is very useful when the computer is trying to tell us the main themes in a body of text.  We can use tools such as sentence parsing to partition the text into phrases, determine the similarity between phrases using BERT, and then construct clusters of phrases with similar meanings.  The largest clusters give us hints as to what the main themes are in the text.  Word frequencies in the clusters and the parse trees for the phrases in the clusters allow us to extract meaningful names for each cluster.  We can then categorize the sentences in the text by tagging them with the names of the clusters to which they belong.

Summary

Linguistic analysis is a complex and rapidly developing science.  Several approaches to linguistic analysis have been developed, each with its own strengths and weaknesses.  To obtain the best results you should choose the approach that gives superior performance for the type of analysis you need.  For example, you may choose a machine learning approach to identify topics, a rules-based approach for sentiment analysis, and a sentence parsing approach to identify parts of speech and their interrelationships.

If you’re not sure where to start on your linguistic and semantic analysis endeavors, the Ascribe team is here to help. With CXI, you can analyze open-ended responses quickly with the visualization tool – helping to uncover key topics, sentiments, and insights to assist you in making more informed business decisions. By utilizing textual comments to analyze customer experience measurement, CXI brings unparalleled sentiment analysis to your customer experience feedback database.

By Rick Kieser, Ascribe CEO

The customer satisfaction survey has become an epidemic. Whether you are buying a product, eating at a restaurant, or enjoying some other experience, it won't be long before you receive an email asking you to complete a survey about the experience. As sociologist Anne Karpf writes in The Guardian, "So many organizations now want our feedback that if we acceded to them all, it would turn into a full-time job – unpaid, of course. … The result is that I'm suffering from feedback fatigue and have decided to go on a feedback strike." She is certainly not the only consumer who feels this way!

Quality of Feedback: A Tale of Two Surveys

With consumers having such negative perceptions and experiences with customer satisfaction surveys, you have to wonder about the quality of the feedback going back to the business. I recently took my family to Disneyland. As usual for Disney, most of the experience was stellar. After our visit, I had two ideas I wanted to share. 1) The staff was outstanding, knowledgeable, and helpful, and 2) They should not upcharge Genie+ customers for lightning lanes on select rides. As I expected, less than 24 hours after leaving, I received the usual invitation to give my feedback about our experience at Disneyland. Given my profession, I was looking forward to this! I clicked on the link and started the survey.

Ten minutes later, I had completed less than 20% of the questionnaire, it was a compilation of closed and open end questions with no end in sight. I was done. I aborted the survey. Even worse, in my ten minutes invested, I did not find an opportunity to provide the two pieces of feedback I wanted to share!

Now, compare that to the survey sent by a hotel I visited. It was only two questions long. The first question asked me to rate my experience on a 10-point scale. The second was an open-ended question: "Please tell us about your experience." Again, I wanted to share two thoughts: 1) The hotel restaurant was spectacular, with a beach view and great food. 2) We had to wait over 20 minutes before a server came to help us. As you can imagine, I was happy to complete that survey! Three minutes, DONE.

Which survey do you think gave better information about my thoughts and feelings? The hotel survey, of course, because it let guests tell them what they wanted to share about their visit in their own words.

Customer Satisfaction Surveys that Customers Like

Now, there may be internal or political reasons that make it difficult to change from a rating scale-based survey to one that is primarily open-ended. However, if we want more insightful feedback and customers who are happy to give it, we need to respect the customer’s time and move beyond lengthy surveys with many frustrating questions. We need short and sweet surveys that allow the respondent to express their thoughts clearly and quickly their way.

One of the traditional complaints about using open-ended responses over scaled responses is that open-ended responses are too wordy, too complicated, and too expensive to code and analyze quickly. That is no longer true, as we have the technology today to interpret these results efficiently and cost-effectively. Because of this, we need to get our surveys aligned with what is possible in data analysis solutions now, or we risk alienating our survey respondents to the point where they will no longer volunteer to answer questionnaires and we risk eroding their view of the brand or service.

The best solution is to create questionnaires with a few closed-end questions and one open-ended question: "Tell us about your experience." Yes, just one open-ended question. The technology can separate and analyze the responses. A few closed-end questions are needed to filter for data analysis, such as satisfaction rating, demographics, and so forth. But you can replace all the open-ended questions (e.g., What did you like? What did you dislike? Why did you give that rating?) with just one question.

Open-End Analysis in Just Minutes

The latest and best technologies can take even the most wordy, rambling, and detailed responses and analyze them in minutes. When you are thinking about collected customer opinions, social reviews are the epitome of vehicles through which customers express how they are really feeling in their own words. Here's an example of over 1,500 reviews scraped from the internet from recent London Eye visitors, all unstructured, open-ended comments. As you may know, the London Eye or Millennium Wheel, on the South bank of the Thames, is the most popular paid tourist attraction in the U.K., with over 3 million visitors annually. Here is an example of one person's review.

In spite of some rather lengthy reviews, within a matter of minutes we were able to identify and quantify the dominant themes from these 1,500 reviews using Ascribe's CX Inspector with Theme Extractor. We also created a cross tab identifying differences in responses based on who else was along for the experience: family, couples, friends, or solo. If coded manually, this data set would have taken a market research firm two days to analyze, at significant cost. With CX Inspector the results were ready within 30 minutes.

Here is another example of what is possible with today's technology. We analyzed 1,500 customer reviews with 145,000 words on a local ice cream shop in just over 20 minutes using Ascribe's CX Inspector. Again, the key themes were immediately identified, and using sentiment analysis, we could quickly understand customer likes and dislikes. It looks like the ice cream is delicious, and some staff are friendly and provide a positive experience, but some people indicate the experience is marred by poor service and expensive prices! This store owner would be able to quickly understand what they need to address to improve customer satisfaction.

As a final example, here are results of 2,500 customer surveys for a sports arena. In addition to a seven-point rating question, the survey included a follow-up open-ended question: "Why did you rate your experience 1 to 7?" The responses, which included a total of 58,000 words, were analyzed in 20 minutes with CX Inspector to reveal that while the arena delivers a great experience with terrific staff, concession lines and parking are key drivers of dissatisfaction. Again, the arena management can quickly understand what they need to work on to improve the visitor experience.

Find Out What Customers Really Think

Customer satisfaction surveys are ubiquitous, but the traditional approach of lengthy questionnaires may not be the best way to understand what customers are truly thinking if they get impatient answering the questions or are not willing to finish the survey. With new technology capable of coding and analyzing open-ends so easily, quickly, and cost-effectively, there is no need to have burdensome customer satisfaction surveys with a battery of close-ended and open-ended questions. By allowing customers to express themselves in their own words quickly, brands can better understand the customer experience and what matters most to them, while building customer loyalty through an improved survey experience. You will get better and richer customer feedback. And the best and only open-end question you need to ask is, "Tell us about your experience."

Embracing open-ended questions in your customer satisfaction surveys lets you alleviate feedback fatigue and invite genuine insights. The advent of generative AI-driven text analytics tools like Ascribe's CX Inspector with Theme Extractor allows brands to delve deeper into open-ended feedback quickly and easily. Customers will reward brands willing to ditch the traditional satisfaction survey in favor of an open-ended approach with more meaningful and actionable customer feedback. 

Increase your customers' satisfaction by simplifying your surveys! Contact Ascribe today to discuss your needs, and we will find the best solution for you!  

By Rick Kieser, Ascribe CEO

In a dynamic market research landscape dominated by Gen AI, Ascribe unveils a new release of its groundbreaking innovation - Theme Extractor. This cutting-edge technology integrated into Ascribe's CX Inspector and AI Coder addresses the challenges presented by ever-evolving Gen AI and delivers effective, faster, and cost-efficient open-end analysis.

Theme Extractor with Gen AI initially delivered deeper understanding of customer thoughts and ideas by transforming single-word topic results into descriptive, meaningful themes, for example "Checkouts" to "Have more checkouts open," and by quantifying emotions factoring in sentiment strength and frequency.

The latest release takes a giant leap forward and overcomes issues created by the latest Gen AI, improving result accuracy, simplifying complex themes and reducing overlap. For example, it separates complex codes like "Salty and spicy" into "Salty" and "Spicy".

Perhaps Theme Extractor's most innovative and important feature is enabling human control, allowing users to guide and oversee the analysis. Manual changes such as toggling Gen AI, providing context, editing results, training codebooks and setting the number of codes enable flexibility that empowers market researchers to ensure that the text analysis results align with business needs.

In a landscape where Gen AI is no longer a question but a necessity, Ascribe's Theme Extractor remains a game-changer for analyzing open ends quickly and accurately. Connect with us today or request a demo to learn more.

Contact Us Request a Demo

By Rick Kieser, Ascribe CEO

Market Research has been around for about 100 years. Some might say we are set in our ways, but in reality the industry has been rapidly innovating in response to marketing changes, technology, and other challenges. We've come far from door-to-door interviewers and Mad Men-esque focus groups! And we are now experiencing one of the most fundamental shifts in our industry and specifically in text analysis in decades.

An Erupting Volcano

In the market research landscape, our traditional data analysis approach resembles a long-dormant volcano getting ready to erupt. For years, many have focused on the top of the volcano, the visible summit—the structured data that is easy to access and straightforward to analyze from surveys. However, a repository of unstructured data remains largely untouched beneath the surface. These text comments hold valuable insights that have been challenging to access and analyze. While difficult to explore, this unstructured data contains incredible potential if it could be unleased.

As we all know, the landscape is changing rapidly. Loaded with open-end responses and unstructured data from surveys, social media, and the internet, the volcano is exploding. According to Gartner, unstructured data constitutes 80% to 90% of all new enterprise data and is growing rapidly. Just as molten lava transforms the surrounding landscape, this eruption of unstructured data, which is now overwhelming for many, will similarly reshape our approach to understanding consumers. Harnessing the insights from this eruption has been one of the most significant disruptions in the evolution of market research since we began.

Many businesses are unprepared for this shift. Only about 10% of unstructured data is currently stored, and even less is analyzed, according to the International Data Corporation. Historically, analyzing unstructured data was a time-consuming process, akin to decoding hieroglyphs. Few marketing people had the time, resources, or motivation for such an effort.

Is your company experiencing this volcanic eruption of open-end comments coming from customers, partners, and employees? Are you ready to dig below the easy-to-analyze closed-end data to get beneath the surface to find true insights, thoughts, desires, and intentions? A fast, cost-effective, and proven analysis approach is the key to unlocking the potential of unstructured data.

Enter Generative AI, heralding the next evolution of Market Research. Leveraging Gen AI with text analytics to analyze this massive amount of unstructured data provides access to the insights hiding underneath the volcano. It's a transformation from the elementary knowledge of the known structured data landscape to a new universe of depth and clarity of consumer understanding. While much of the uncovered insights will be used to answer everyday business questions, if analyzed properly, the data has the reliability and strength to guide even the most critical strategic decisions for your business.

Gen AI's Rapid Evolution Creates Challenges

Gen AI is advancing at warp speed, generating constant evolution across the MR industry. And with that evolution comes challenges. Remember that less than a year ago, the Market Research industry was vilifying Gen AI as the industry's demise! The MR Industry is now embracing Gen AI to unleash the value of the huge mass of previously-neglected verbatims and unstructured feedback. Many new, young firms are jumping in to sell their Gen AI solutions to marketers, and to the less experienced, their solutions appear magical at first glance. As new versions of Gen AI are being rapidly released, those platform developers are having difficulties keeping up with providing human-like theme-based insights that are quantifiable and verifiable to the unstructured data analysis. Some of the challenges created by the more recent Gen AI releases include:

• Gen AI analysis creates a plethora of results – too many ideas and codes, making it challenging for brand marketers to sort out the most important ideas.
• Gen AI is now creating complex codes, combining two ideas into one. For example, "too salty and spicy," combines two ideas about how a food tastes. However, this result makes it difficult to determine how big an issue "salty" is and how big an issue "spicy" is. For ideas to be actionable, we need concise, singular ideas, each of which is quantified.
• Summaries created by Gen-AI are not precise enough for use in decision-making. Now, of course, marketers are enamored with the summarizing capabilities of Gen AI. If you have purchased anything online lately, you can quickly find a consumer ratings summary created by Gen AI. However, these summaries are often too broad to be meaningful, and it is always challenging to identify the underlying data the summary was sourced from. The summaries miss important insights and may not represent information in the right proportions. Finally, it is well recognized that Gen AI may also make things up (now referred to as hallucinations or mirages) depending on how the tool was trained.

Ascribe Leverages Gen AI to Develop Innovative Solutions

Ascribe has been innovating to solve these Gen-AI-created issues with the latest release of one of our most impactful techonology developments – Theme Extractor. Helping Ascribe stay one step ahead of the industry, Theme Extractor is included in all of Ascribe's solutions, including CX Inspector for text analytics and Coder, a verbatim analysis platform for market research companies,. Ascribe has a 25-year history as the original verbatim analytics platform for the MR Industry, building state-of-the-art open-end analysis solutions. We have processed more than 4 billion unstructured comments, exponentially more than all other providers' experience in processing customer feedback combined. Our developers have deep experience with Gen AI and are uniquely equipped to build solutions that meet the needs of our partners.

Theme Extractor Extracts Superior Well-Developed Ideas from Open-End Comments

The initial version of Theme Extractor leveraged Generative AI to transform the results of open-end analysis from single-word codes to descriptive, meaningful codes that articulate the essence of the idea, a huge advancement for the market research industry. Note the example below from customer satisfaction results for a retailer; whereas before an idea might be a single-word topic such as "items", with Theme Extractor, the idea becomes "have popular items in stock." Similarly, a code of "employees" becomes "more employees would be helpful." As you can see, Theme Extractor extracts much more detailed information from the customer responses, giving you a deeper understanding of the consumers' thoughts and feelings.

Old Technology Creates Single Word Topics

Theme Extractor Creates
Superior Descriptive Ideas

Percentage of Mentions

Checkouts

Have more checkouts open

13%

Store

Great store

12%

Items

Have popular items in stock

5%

Employees

More employees would be helpful

5%

Prices

More competitive prices

4%

The most recent release of Theme Extractor has improved the accuracy of the results and addresses the issues of too many codes and complex codes being created by the latest versions of Gen AI. Theme Extractor creates concise ideas focused on one theme, correcting the tendency of Gen AI to combine ideas into complex topics. As such, in a mascara product study, the complex code "Lengthens and separates lashes" Theme Extractor separates into two themes, "Lengthens lashes" and "Separates lashes." Separating ideas is important in the analysis to make the results useful for decision-making. This is a vital detail that can easily be overlooked in a sales demonstration but is critical to the experienced brand marketer or market researcher in real life.

Also, Theme Extractor reduces the overlap between codes, thereby reducing the number of themes. In the same mascara study, the "High price" and "Too expensive" codes are more likely to be one combined idea, resulting in less overlap and more effective analysis. Finally, it is important to note that during analysis with Theme Extractor, the user can suggest the number of codes to classify the results into, which further puts the power of AI under the user's control.

Another important Ascribe innovation is the ability to quantify the emotions and empathy Gen AI identifies around a topic. Emotion and empathy can be insightful, but if they are unquantified, they are insufficient to be helpful to brand marketers. The magnitude of those emotions (or topics) and how they link to satisfaction, dissatisfaction, loyalty, and other customer states must be quantified to use the insights identified. For example, an unquantified analysis of emotion might yield two ideas, "Love the service" and "Wish the front desk staff were friendlier", from which you would conclude that "Love the Service" is more important as it is a stronger emotion. However, if the results indicated that only one person said, "I love the service," and 100 people said, "I wish the front desk staff were friendlier," the latter becomes more important. The ability to understand the emotion must be combined with frequency and quantification to get a useful insight, giving brand marketers the understanding they need for actionability and decision-making.

Enabling Human Guidance of Gen AI Produces Powerful Customized Results

Other innovations in the most recent Theme Extractor upgrade enable human control to guide and oversee the analysis and output of the results. Many users, especially experienced market researchers, want a deliverable similar to what they have produced in the past, for example, to enable tracking against previous studies or to use language relevant to the business. Ascribe's platforms enable manual input, giving you control over the automation in real-time and letting you adjust the results to meet your business needs. Some of the manual changes available in the latest Theme Extractor:

• Turn Gen AI on or off.
• Provide context for the data for more accurate results.
• Easily edit your results by renaming, combining, drag and drop manipulation, etc.
• Train a codebook and save it for future use; great for trackers.
• Set the number of codes and levels of nets appropriate for your needs.
• Connect the theme to the original responses (drill down.)
  

Ascribe's innovative Theme Extractor enables market researchers to analyze a dataset with open-end comments in minutes or hours, adjusting the amount of manual editing to control costs, timing, and end results.

Ascribe Innovations for 2024

Being the original verbatim analytics platform, we at Ascribe are continually exploring ways to harness advanced technologies, including Gen AI, to develop superior open-end analysis solutions that are easy to manage and maximize efficiencies in meeting your business insight needs. We are building APIs to enable direct access to our system for real-time integration with other platforms. We are developing a Gen AI tool to summarize results from the analysis accurately and will also provide updated charting and visualization capabilities to meet your analysis and reporting needs.

Choosing the Right Gen AI Approach

Using Gen AI will be critical to tap into the value delivered through successful analysis of unstructured data. It is essential that you choose the right Gen AI approach for you. If you seek a partner to help you get value from your text analysis, choose that partner carefully. Look for a trusted partner with years of experience in MR, extensive experience with Gen AI, and live training and support services to help you when needed. When evaluating the platform, request a live demo using your data to see firsthand if the results meet your business needs. Finally, ensure the interface is user-friendly and integrates easily with your current operating processes and management tools.

Recently, we have had discussions with companies attempting to build their own analysis platforms. While their initial solution might work for one specific use case, it becomes difficult and expensive when they need to scale it and make it repeatable, and even more so as they will need to continually absorb and respond to the rapid advancements in Gen AI. AI expertise is scarce and expensive, so building your own platform quickly becomes a costly and time-consuming strategic commitment for your company. We welcome the opportunity to discuss your business needs and share how Ascribe can meet those needs with speed and cost efficiencies.

Gen AI Is No Longer A Question - It's a Necessity

As you work to harness the explosive insight power of open-ends flooding your company from disparate sources, you must decide how to implement Gen AI in text analysis so that it becomes more effective, faster, and less costly. Your decision about the partner you choose to help you with this could pay huge dividends in the future. After all, it's no longer a question of if you should implement Gen AI; it's only a question of how.

Remember to look beyond the demo to your partner's expertise in Gen AI, text analysis, and the MR industry. Their experience in the insights industry will ensure that they are not only keeping up with the rapidly evolving status of Gen AI but also how to translate that evolution into products and innovations that can best serve you and your business.

Contact Us Request a Demo

In today's digital age, understanding your audience and gathering valuable feedback is paramount for any business or organization. Video survey tools have emerged as a powerful solution, revolutionizing how we collect insights and opinions. By integrating audio-visual elements into surveys, these tools provide a deeper and more comprehensive understanding of respondents' perspectives. In this complete guide, we delve into the world of video survey tools, exploring their benefits, and use cases, and presenting a curated list of the top options to aid informed decision-making.

What is a Video Survey Software?

Video survey software is a technological advancement that combines traditional survey methods with multimedia elements. It allows you to create surveys enriched with videos, audio clips, or images, enhancing user engagement and providing a holistic view of respondents' opinions. This dynamic approach to data collection leads to more actionable insights, enabling businesses to make informed decisions.

Key Features of Video Survey Tools

Video survey tools have become essential for businesses and organizations looking to gather insights and feedback in a dynamic and engaging manner. Here are the top 5 key features of video survey tools:

1. Multimedia Integration

Video survey tools allow users to seamlessly integrate multimedia elements into their surveys. This includes the ability to add videos, audio clips, images, and even interactive media elements. This feature enhances the survey experience for respondents, making it more engaging and informative.

2. Real-Time Analytics

Many video survey tools provide real-time analytics and reporting. This means that as respondents complete surveys, the data is immediately available for analysis. Real-time analytics enable businesses to make timely decisions and adjustments based on incoming feedback.

3. Customization Options

Video survey tools often offer a high degree of customization. Users can tailor surveys to match their brand's aesthetics, ensuring a consistent and professional look. Customization also extends to question types, allowing for flexibility in creating questions that suit the specific research objectives. To enhance the accessibility and engagement of your video surveys, consider incorporating a subtitle creator tool, which can automatically generate accurate captions for your multimedia content.

4. Cross-Platform Compatibility

To reach a wider audience, video survey tools ensure cross-platform compatibility. Surveys can be accessed and completed on various devices, including smartphones, tablets, and desktop computers. This flexibility ensures that respondents can participate regardless of their preferred device.

5. Ease of Use

User-friendly interfaces are a hallmark of video survey tools. Both survey creators and respondents benefit from intuitive and easy-to-navigate platforms. This ease of use streamlines the survey creation process and encourages higher response rates as respondents find it straightforward to participate.

These key features collectively contribute to the effectiveness and popularity of video survey tools in gathering valuable insights and feedback from respondents. Businesses and organizations can leverage these features to enhance their decision-making processes and improve user engagement in surveys.

How Video Survey Tools Can Be Used?

Video survey tools offer a versatile approach to collecting feedback and insights by incorporating audio-visual elements into surveys. Here are some examples of how video survey tools can be used across different domains:

1. Product Feedback and Testing

Video survey tools revolutionize product feedback and testing by integrating multimedia elements into the feedback collection process. For product managers and developers, these tools offer a dynamic platform to showcase new features, prototypes, or application walkthroughs through engaging videos. With unlimited video editing capabilities, teams can refine their presentations and highlight specific features to elicit targeted feedback. Beta testers and users can provide detailed feedback by recording video responses, elucidating their experiences and preferences effectively. This rich and visual feedback aids in identifying areas for improvement, validating product concepts, and ensuring that the final product meets users' expectations. Video survey tools, therefore, significantly enhance the product development cycle, enabling iterative refinements and ultimately leading to a more user-centric and successful product launch.

2.Customer Satisfaction Surveys

By seamlessly integrating videos and multimedia elements into surveys, video survey tools offer businesses a potent means to gauge customer satisfaction in a more engaging and insightful manner. Customers can now provide feedback not just through words but through recorded video responses, enabling a deeper expression of their experiences and sentiments. This approach not only enhances response rates but also provides a comprehensive understanding of customer perceptions, ultimately assisting businesses in fine-tuning their services and strategies to elevate overall customer satisfaction levels.

3. Training and Onboarding Evaluation

Video survey tools prove indispensable in the realm of training and onboarding evaluation. When integrating these tools into training processes, organizations can capture crucial insights from employees regarding their training experience. This includes their perception of training material effectiveness, clarity of instructions, and engagement levels. Employees can record video responses, enabling a more comprehensive understanding of their learning journey. For onboarding, video surveys can serve to gather feedback on the onboarding process, ensuring it aligns with organizational goals and addresses new hires' needs. The real-time nature of video survey analytics allows training and HR teams to promptly assess feedback and make adjustments, ultimately fine-tuning training programs and onboarding processes for optimal efficiency and employee satisfaction.

4. Event Feedback and Planning

Event management and organizers can distribute video surveys showcasing event highlights and key moments. Attendees can provide their feedback and suggestions through video responses, offering a more engaging and detailed perspective. This feedback becomes instrumental in understanding attendees' experiences, identifying successful aspects, and pinpointing areas for improvement. Event organizers can then utilize these insights to plan future events that align better with attendee preferences, ensuring a more enjoyable and tailored experience for participants. The incorporation of video survey tools in event feedback and planning significantly contributes to the enhancement of events and the overall event strategy.

5. Market Research and Product Development

In market research, these tools provide a dynamic platform to showcase product prototypes, advertisement concepts, or new features to a targeted audience. By collecting video feedback, businesses gain critical insights into consumer preferences, opinions, and potential buying behavior. This data becomes a compass guiding marketing strategies, product enhancements, and market positioning. Similarly, within the sphere of product development, video survey tools allow for in-depth testing of new features and functionalities. Developers can showcase prototypes and ideas through engaging videos, and the subsequent video responses from users provide invaluable feedback for refining and perfecting the product before launch. In both market research and product development, the integration of multimedia elements through video survey tools enriches data collection, resulting in more informed and successful business decisions.

6. Educational Assessments

Video survey tools are increasingly gaining traction in the realm of educational assessments. They offer a dynamic approach to evaluating students' understanding and engagement with course material. Teachers can utilize these tools to present educational videos or interactive content, followed by survey questions, enabling students to provide detailed video responses. This method offers a more comprehensive view of students' comprehension levels, allowing educators to tailor their teaching methods for better learning outcomes. It also promotes student involvement and provides a platform for self-expression, enhancing the assessment process in educational settings.

These examples illustrate the versatility and effectiveness of video survey tools across various scenarios, from product development to educational assessments. The use of multimedia elements in gathering feedback enriches the data collected and allows for a more comprehensive understanding of respondents' opinions and experiences. Incorporating a VR player like DeoVR into video survey tools can elevate engagement further, offering immersive feedback collection that enhances the quality of insights gathered across diverse applications.

‍

An example of Video questions in a survey

The screenshot above showcases a snippet of a survey designed to gather insights on preferred music for a vacation. The brilliance lies in the integration of a video question within the survey. Instead of asking respondents to search for a song and listen to it independently, the video survey presents the song within the survey itself. This thoughtful design significantly reduces the possibility of respondents abandoning the survey due to inconvenience.

Top 5 market research analysis tools

1. Voxco:

Voxco offers a robust video survey feature, enhancing survey experiences with multimedia elements. It provides powerful survey management tools and a user-friendly interface for effective feedback collection.

Key Features:

• Multimedia Integration: Integrate videos and images to enhance engagement.
• Real-Time Analytics: Access immediate feedback and analyze responses instantly.
• Customization: Customize surveys to match the organization's branding.
• Cross-Platform Compatibility: Surveys are accessible on multiple devices for wider participation.
• Advanced Survey Management: Streamlined tools for efficient survey creation and management.

2. Qwary

Qwary is a versatile survey platform that empowers businesses to gather feedback and enhance satisfaction. It supports omnichannel surveys, allowing customers to express their opinions freely. The platform offers CSAT, CES, and Net Promotion Score surveys, enabling businesses to measure client satisfaction and improve loyalty. Qwary also facilitates gathering feedback from internal staff, promoting a positive work environment.

3. Testimonial

Testimonial software has truly transformed the way we showcase our product's value. Creating a unique link and effortlessly sharing it via various channels is incredibly convenient. Our customers find it user-friendly, allowing them to record video testimonials using any device they prefer. The ability to download testimonials in MP4 format adds to its versatility. Whether you're an individual or a Small and Medium enterprise, this software is a game-changer for promoting your product effectively.

4. Discuss

Discuss.io is a transformative platform that empowers leading organizations, esteemed brands, and top-tier agencies worldwide to translate people's experiences into actionable insights. Trusted by Market Insights, CX, and UX professionals globally, Discuss.io transcends mere data points, breathing life into comprehensive insights in real-time, thereby revolutionizing customer relationships. The platform facilitates structured conversations, unmoderated feedback at scale, seamless scheduling and consent management, maximized respondent engagement, and auto-generation of unprecedented insights.

5. Videoask

VideoAsk by Typeform redefines the way businesses engage with their audience and gather valuable insights. This platform lets you effortlessly capture or upload video questions and share a public link, encouraging video and voice responses from your audience. The standout feature is the ease of embedding these interactive forms into websites using a floating widget—just a simple copy-paste, and you're good to go. Responses, whether text, audio, or video, are automatically transcribed and organized for convenience. Managing interactions in the inbox is a breeze, and the option for email or push notifications keeps you connected.

These video survey tools offer varying features and capabilities, catering to different needs and preferences. Whether you're focusing on user engagement, real-time analytics, or usability testing, there's a tool on this list to suit your specific requirements.

Conclusion:

Video survey tools have revolutionized the feedback collection process, enabling businesses to gather deeper insights and make informed decisions. By incorporating multimedia elements, these tools enhance user engagement and provide a more comprehensive understanding of respondents' views. As you embark on the journey of collecting feedback through video surveys, consider the features and benefits offered by the top tools mentioned in this guide.

Artificial intelligence (AI) has gone mainstream and there is simply no escaping reading, hearing and talking about it. No matter the industry, AI will impact it. Indeed, AI is already impacting such disparate industries as finance, health care, travel, national security and farming!

Marketing research has traditionally been hesitant and slow to adopt change as an industry. However, in the case of AI, we’ve already rapidly embraced many AI-based tools. To increase the value of these tools, researchers will benefit from understanding them and what they can do to help now and in the future to satisfy the ever-increasing demand for lower costs, greater efficiency and faster and better insights.

Why marketing research needs AI

Marketing research is the foundation of successful marketing, product development and customer service. But traditional research can be expensive and time-consuming. Simple surveys can cost between $15,000 and $45,000 and often much more, and B2B one-on-one interviews cost $300 to $500 each and more. Focus groups, on average, cost $8,000 each. And a simple project takes weeks to complete.

Further, traditional research is not designed to handle the mass of data now available in some businesses (e.g., retail, CPG and others). As a result, due to the cost and time investment needed, many companies are priced-out of using research completely. And many companies opt to use market research only for the most critical and risky projects, such as product launches.

As an industry, we must find ways to reduce the cost and time barriers to marketing research and make insights more available for businesses inundated with omnipresent feedback. AI is an essential piece of that solution.

AI 101: Moving to advanced generative AI

AI is a wide range of technologies that apply to many different situations and refers to the ability of machines to perform tasks that typically require human intelligence, such as learning, problem-solving and decision-making. Generative AI is a specific type of AI that uses algorithms to generate new content, such as text, images and audio, based on the data on which it has been trained. The main difference between AI and generative AI is that AI is a broad term encompassing many different algorithms. In contrast, generative AI is a specific type of AI that focuses on generating new content.

The earliest implementations of AI used a rules-based approach limited by a lack of context, a limited vocabulary and overreliance on patterns. Natural language processing (NLP) enabled machines to understand and work with human language. Other language models, such as BERT, provided pretrained models that can be fine-tuned for specific NLP tasks. With the introduction of machine learning algorithms such as generative adversarial networks (GANs), AI has become more powerful and capable of creating convincingly authentic content. More recently, rapid advances in generative AI and in large language models (LLMs) have provided the ability to produce even more creative results. As a result, generative AI has become a game-changer in many fields, including art, biology, programming, writing, translation and more.

While there is currently some experimentation with using AI to develop surveys and analyze survey datasets for comprehensive insights, most AI usage in the insights industry has historically been in the analysis of open-end comments. Generative AI already positively impacts the industry, especially in analyzing open-ends from surveys, social media, reviews and other sources. It has been used in the industry for nearly a decade. It is always important to remember that AI's value is to increase the speed of delivery and reduce expense, not to eliminate critical human oversight, validation and interpretation.

Where are we at currently?

In November 2022, generative AI was launched globally. Because the developers understand that researchers must always use results to deliver meaningful insights, the companies developing these tools are trying to understand how researchers want to use generative AI and what they want the tools to accomplish. As a result, there has been a flurry of applications using generative AI within the open-end analysis space and the tools are evolving rapidly to be not only easy to use but also effective in providing better analytics and insights.

One important evolution was to advance the results produced from topics (simple, one-word facts) to themes (more descriptive ideas with more human-like insights). Because important insights are often found in open-end responses, analysis that results in themes allows the researcher to find those insights faster without all the manipulation necessary with topics. For example, a topic-based analysis for a clothing manufacturer might give the result “zipper,” and you would have to look deeper to find the issue. Themes, however, would return “the zipper is broken.” Similarly, for a restaurant, a topic results might be “food,” where themes would give you "the food is delicious" or “the food is cold,” telling you immediately what action you need to take. Themes are all-encompassing, while topics are narrow, and themes, as logical ideas, give you direction for subsequent actions.

Generative AI has also powered the development of several other advances:

• The ability to easily net themes for increased insight gave even more power to generative AI open-end analysis by quickly facilitating human interpretation and combining similar themes.
• Adding sentiment analysis to themes also helps researchers quickly sort and communicate the brand or product’s status and track how it changes over time, significantly reducing analysis time.
• Segmentation analysis allowed researchers to understand better how perceptions differ by segments within the data.
• And finally, multilingual capability eliminated the need to translate responses before analysis.

These advances came from developers understanding what researchers needed and responding by building new and better capabilities with generative AI into their market research solutions.

Case studies

The latest evolution of generative AI is moving beyond delivering topics or themes to providing more comprehensive and illustrative summaries from the analysis. To shed light on this capability, here is a case study based on a one-question customer satisfaction survey about a health club. A total of 2,000 responses were analyzed.

• The first analytic query was, “Are customers generally satisfied with their experience at the clubs?” The generative AI tool responded: "No, customers are not generally satisfied with their experience at the clubs. While some customers have had positive experiences with the local employees, many customers have expressed dissatisfaction with the condition of the facilities, the lack of updates and the lack of amenities."
• The second query was,“What are some actions the owners can take to improve customer satisfaction?” which got this response: "1. Ensure that maintenance issues are addressed promptly, even on weekends. 2. Improve the cleanliness of the facility, especially in areas such as the women's bathroom, shower area, pool, jacuzzi and steam room. 3. Set all clocks correctly. 4. Install more fans to improve air movement in the facility. 5. Allow members to extend their trainer sessions at the rate they signed up for. 6. Reallocate funds from transforming and furnishing offices to improving the club."
• The next query was, “What are some things the fitness clubs are doing well?” Positives for the clubs are: "The fitness club is doing well in having a highly trained and professional personal training staff. They have also done a good job of making the gym look fresher and more inviting by adding plants. They have also been friendly to guests and offered free 10-day memberships to family and friends."
• The next query was limited to the segment of NPS detractors. The query asked what NPS detractors say about the clubs and generated this response: "NPS detractors are saying that the bathrooms are not clean, the personal training area is too small and uncomfortable, there is not enough variety in the extra workout classes, the markings on the plates on most machines are rubbed off, it is very hot and humid in the locker room and the ceiling fans in the basement are growing hair on them."
• Finally, a query about a specific area of the club, the pools, resulted in this response: "The biggest complaints about the pools are that the water is cloudy and too cold, the whirlpool is broken or not hot, the sauna is not very hot and the walls and floors of the pool area are peeling paint and have chipped and broken tiles. Additionally, there is no temperature regulator on the pool shower and no paper towels in the locker room.”

This type of analysis also applies very importantly to non-survey open-end comments. Here is an example analysis of 1,500 social media posts from visitors to the London Eye:

• The first query was, “What is this dataset about?” The response was: "This is about customer reviews of the London Eye experience. It includes information about the customers' experiences and recommendations and responses from the Customer Services Team."
• The second query was, “What are customers happy with?” The response was: "Customers are happy about the knowledgeable and humorous guides, the champagne experience, the great views and the ability to create good memories and take good photos."
• “What are the top three improvements the London Eye could make to improve people’s experience?” was the third question. AI generated the following suggestions: "1. Provide an audio commentary to give visitors more information about the landmarks they are seeing. 2. Make the wheel stop periodically to allow less-mobile or disabled visitors to board. 3. Provide a map or guide to help visitors identify the landmarks they are seeing."
• The next query was, “Looking only at reviews from families who visited the London Eye, what are their key likes and dislikes?” The results were: "Likes: Beautiful views, Spectacular views of London, Our tour guide was wonderful with descriptions and jokes. Dislikes: Overcrowded cabins, Lack of rules for how many people can get in the cabins."
• Another query, asked only of a segment of all social media reviews, was “What are people happy about in reviews posted since April 2022?” This list of the most frequent topics was AI-generated:
  • Iconic, amazing 360° views of London
  • Knowledgeable, approachable, fun tour guides
  • Comfortable way to see the city
  • Great historical facts and pointers for our trip
  • 2-for-1 tickets
  • Running commentary was really entertaining and informative
  • Fun and educational trip

Generative AI is very good at summarizing text information but it is not good at quantification. And as researchers, we typically need to quantify the analysis results. When you need to build a code book, using themes is a good start, but to refine and develop the codes to be applied, you need human oversight, validation and interpretation. Applying AI themes analysis to the London Eye open-end comments successfully identified 138 codes (covering 96% of the total comments). After the themes were analyzed and aggregated by the researcher, the results were quantified as shown in Figure 1.

Further, once the comments are coded and quantified, they can be further analyzed to explore different segments of respondents and to generate additional insights, as in Table 1.

Summaries, which generative AI helps develop, are an essential advancement in open-end analysis solutions because they give researchers enough detail to quickly understand the verbatim responses while saving time in wrangling the data into meaningful and valuable information. However, to turn verbatim comments into quantitative information, you need a human being to massage the results of the generative AI analysis and communicate it to the end client. It is important to remember that the key benefit of generative AI is not to eliminate human review and interpretation but to put human time and talent where it is most valuable: in validating, evaluating and communicating essential insights to clients. Generative AI will continue to evolve and improve but human oversight will always be needed. In any event, generative AI unleashes the power of summaries to quickly comprehend what open-end comments are telling you.

Evolve and change

As our industry continues to adopt and embrace generative AI, the research solutions will evolve and change to meet our needs better. At this point, they deliver sound value to the market research organizations using them, saving time and labor and helping researchers put their effort where they are most valuable. If you haven't tried solutions that analyze open-ends, or if you tried them and rejected them previously, now is a great time to take a look at these platforms. Generative AI for open-end analysis is here to stay. If you need to analyze open-ended comments, you can't afford not to jump on board.

Contact Us Request a Demo

Want to conduct market research? You need a tool to help you create surveys, analyze survey feedback, generate actionable insights, and save you tons of effort & hours of time. We’re talking about market research software.

In this blog, we’ve compiled a list of 15 best software for market research & market research tools with a comparison of their best features, pros & cons, and pricing plans. It will help you choose the one that fits your needs & suits your budget.

But first, let’s take a closer look at some key concepts related to market research.

What is Market Research?

Market research is the process of gathering, analyzing, and interpreting information about a specific market, industry, or target audience to support informed business decisions. Market research results are used to make informed decisions regarding product development, pricing strategies, marketing campaigns, distribution channels, and overall business planning.

It is a crucial marketing and business strategy component, helping organizations understand their customers, competitors, and the overall business environment.

Some key aspects of market research include

Data Collection: This involves collecting data from various sources, such as surveys, interviews, focus groups, observations, and online sources. The data can be quantitative (numbers and statistics) and qualitative (descriptive information).

Market Segmentation: Researchers often divide the market into segments based on demographics, psychographics, behavior, and geography. This segmentation helps businesses understand their target audience better.

Competitor Analysis: Assessing competitors in the market is essential to identify strengths, weaknesses, opportunities, and threats. This analysis helps businesses position themselves effectively and develop competitive strategies.

Consumer Behavior: Understanding how consumers make purchasing decisions, what influences their choices, and their preferences is vital for product development, pricing, and marketing strategies.

Trend Analysis: Monitoring and analyzing market trends, such as technological advancements, cultural shifts, and economic factors, can help businesses adapt and stay competitive.

Feasibility Studies: Market research can also include feasibility studies to determine whether a new product, service, or business venture is viable in the current market.

Risk Assessment: Identifying potential risks and challenges in the market allows businesses to develop risk mitigation strategies and make informed decisions.

What are the key elements of market research?

Market research is composed of several key elements that are fundamental to its success. Here are the key elements of market research:

Research Objectives: Clearly defined research objectives are the foundation of any market research effort. These objectives specify what you intend to achieve through the research, such as understanding customer preferences, assessing market size, or evaluating product feasibility.

Target Audience: Identifying and defining the target audience or market segment is crucial. Knowing who you want to study allows you to tailor your research methods and questions to gather relevant data.

Research Design: This element involves making decisions about the overall research approach, including the research method (e.g., surveys, interviews, focus groups, observations), sampling strategy (how participants are selected), and data collection instruments (questionnaires, interview guides).

Data Collection: Gathering data from the selected sample or data sources is a critical step. Depending on the research design, this may involve conducting surveys, interviews, observations, or using other data collection methods.

Data Analysis: After data collection, the information must be processed and analyzed to identify patterns, trends, and insights. Data analysis can be quantitative (using statistical techniques) or qualitative (coding and interpreting text-based data).

Interpretation: Interpretation involves making sense of the data in the context of the research objectives. Researchers draw conclusions, generate insights, and provide explanations for the findings.

Actionable Insights: Market research aims to provide actionable insights that inform decision-making. Insights should be specific, relevant, and useful for addressing business challenges or opportunities.

Reporting: Research findings are typically documented in a report or presentation. The report should present the research objectives, methodology, key findings, and recommendations in a clear and organized manner. Visual aids may enhance data presentation.

Ethical Considerations: Ethical principles are crucial in market research. Researchers must protect the privacy and confidentiality of participants, obtain informed consent when necessary, and adhere to ethical guidelines throughout the research process.

Quality Assurance: Ensuring the quality and reliability of data is essential. Quality assurance measures may include pre-testing research instruments, validating survey questions, and conducting regular data accuracy and consistency checks.

Budget and Resources Management: Managing the budget and resources allocated for market research is critical. Researchers must plan for data collection, analysis, and reporting costs while effectively allocating human resources and time.

Continuous Learning: Market research is an ongoing process. Organizations should be open to continuous learning, adapting research methods as needed, and staying updated on changes in the market and consumer behavior.

Feedback Loop: Establishing a feedback loop allows organizations to assess the impact of their decisions based on research findings. This feedback helps refine strategies and make necessary adjustments.

These key elements of market research ensure that the process is systematic, rigorous, and capable of providing valuable insights that support informed decision-making in businesses and organizations.

Now that you know what market research is all about, let’s introduce you to market research software.

Featured Resource: Market Research Tool Kit

Kickstart your market research with survey templates, a market research guide, the latest trends, and more.

What is the definition of market research software?

Market research software is a tool used by researchers, businesses, and organizations to conduct the market research process. It is mostly used to automate the most time-consuming tasks in the market research process, such as building & sharing surveys, analyzing data, generating reports, etc.

For startups, leveraging software development for startups can be a game-changer, enabling them to create tailored market research solutions that meet their unique needs. Market research software can encompass a wide range of tools and applications, and its features may vary depending on the specific needs and objectives of the user. However, the best software for market research must have some non-negotiable features we’ve mentioned in the following section. Read on to know what those are.

Market research software can encompass a wide range of tools and applications, and its features may vary depending on the specific needs and objectives of the user. However, the best software for market research must have some non-negotiable features we’ve mentioned in the following section. Read on to know what those are.

What are some of the must-have features of the best market research software?

The features of the best market research software vary depending on the specific needs and objectives of the user. Still, several must-have features are essential for effective market research software. These features include

Survey and Questionnaire Design Tools:

• User-friendly interface for creating and customizing surveys.
• A variety of question types (multiple choice, open-ended, rating scales, etc.).
• Logic and branching options to create dynamic surveys.
• Templates and question libraries for efficiency.

Data Collection and Management:

• Multichannel data collection (online surveys, email, mobile, social media).
• Capabilities for capturing data offline and in real-time.
• Integration with panel management tools.
• Data validation and quality checks.

Data Analysis and Reporting:

• Advanced data analysis tools, including statistical analysis.
• Data visualization features (charts, graphs, heatmaps).
• Customizable dashboards for real-time insights.
• Report generation with export options (PDF, Excel, PowerPoint).

Data Security and Compliance:

• Robust data encryption and secure storage.
• Compliance with data privacy regulations (GDPR, HIPAA, etc.).
• User access controls and permission settings.
• Audit trails for tracking data changes and access.

Sample and Panel Management:

• Panel creation and management tools.
• Sampling and targeting options for specific demographics.
• Automation of sample management tasks.

Integration and Compatibility:

• Integration with other data sources and systems (CRM, analytics tools).
• API support for custom integrations.
• Compatibility with various devices and platforms.

Collaboration and Workflow:

• Collaboration features for team-based research projects.
• Task management and assignment capabilities.
• Version control for survey design and reports.

Survey Distribution and Data Collection:

• Email distribution and tracking.
• Mobile optimization for respondent accessibility.
• Offline data collection for field research.

Customer Support and Training:

• Access to customer support and training resources.
• Online documentation and tutorials.
• User communities for peer support.

Scalability and Performance:

• Scalability to handle large volumes of data and respondents.
• Reliable performance and uptime.

Cost and Pricing Models:

• Transparent pricing structures.
• Flexible pricing options (subscription, pay-as-you-go).
• Competitive pricing relative to features offered.

Usability and User Experience:

• Intuitive interface for users of varying technical expertise.
• Responsive design for mobile devices.
• User-friendly reporting and data visualization tools.

Data Export and Portability:

• Data export options to third-party tools.
• Data portability to ensure users have control over their data.

Feedback and Survey Testing:

• Tools for testing surveys before deployment.
• Collecting feedback from respondents and users for improvements.

Analytics and Trend Monitoring:

• Long-term trend analysis features.
• Predictive analytics for forecasting.

When evaluating market research software, it's essential to consider the specific requirements of your research projects and organization. The best software will align with your goals and provide the necessary features and capabilities for effective market research. If there is a need to develop some custom features, companies may opt to hire developers in Canada, Mexico and other nearshore countries. There are also a bunch of ready-to-go tools available on the market.

Now, look at some of the best market research tools & software.

List of 15 best market research tools & software

1. Voxco survey software

Voxco is a scalable survey and feedback management platform with multilingual capabilities, robust analytics, and data collection options. Here are some features of Voxco survey software:

• NPS dashboard for granular analysis
• Built-in text & sentiment analytics
• Omnichannel survey capabilities
• Sample provider
• Integration with Salesforce

2. Qualtrics

Qualtrics is a leading market research platform that offers a wide range of features, including survey creation, data collection, and analysis. It is a good choice for businesses of all sizes and industries.

3. SurveyMonkey

SurveyMonkey is another popular market research platform that is easy to use and affordable. It offers a variety of survey templates and features, making it a good option for businesses new to market research.

4. Google Forms

Google Forms is a free and easy-to-use survey tool that is perfect for small businesses and startups. It is not as feature-rich as some other options on this list, but it is a great way to get started with market research.

5. Typeform

Typeform is a visually appealing survey tool that captures attention. It offers a variety of templates and features, making it a good choice for businesses that want to create engaging surveys.

6. Alchemer

Alchemer (formerly SurveyGizmo) is a powerful market research platform that offers a wide range of features, including survey creation, data collection, and analysis. It is a good choice for businesses that need a more complex solution.

7. QuestionPro

QuestionPro is a user-friendly market research platform that offers a variety of features, including survey creation, data collection, and analysis. It is a good choice for businesses that want a cost-effective solution.

8. Zoho Survey

Zoho Survey is a cloud-based market research platform that offers a variety of features, including survey creation, data collection, and analysis. It is a good choice for businesses that need a scalable solution.

9. HubSpot Survey

HubSpot Survey is a free market research tool designed for businesses using HubSpot's CRM platform. It offers a variety of features, including survey creation, data collection, and analysis.

10. KeySurvey

KeySurvey is a market research platform offering various features, including survey creation, data collection, and analysis. It is a good choice for businesses that need a customized solution.

11. Kwik Surveys

Kwik Surveys is a quick and easy way to create and distribute surveys. It is a good choice for businesses that need to get feedback quickly.

12. Confirmit

A comprehensive customer experience and research platform with advanced analytics and multichannel feedback management.

13. Think with Google

Think with Google is a free market research platform that offers insights into consumer behavior and trends. It is a good choice for businesses that want to learn more about their target market.

14. ProProfs Survey Maker

ProProfs Survey Maker is a cloud-based survey creation and analysis tool that helps businesses collect feedback from customers, employees, and other stakeholders. It offers a variety of features, including survey creation, data collection, data analysis, etc

15. Qualaroo

Qualaroo is a software platform that collects user feedback and conducts market research. It's designed to help businesses and organizations gather insights from website visitors and customers to improve user experience, conversion rates, and overall satisfaction.

When evaluating market research tools, it is crucial for businesses to consider their specific needs, from simple data collection to comprehensive analytics. Choosing the right tool, whether it’s for capturing quick consumer feedback or conducting in-depth software development outsourcing market analysis, can significantly enhance strategic decision-making. Ultimately, the best tool will provide not only the necessary data but also the insights needed to drive business growth.

Tabular comparison of the 15 best software for market research

How to choose the best software for market research?

When choosing a market research software, it is important to consider your budget, the features you need, and the size of your business. Here are some additional things to consider when choosing market research software:

The size of your business: If you are a small business, you may not need all of the features offered by a large enterprise solution.

Your budget: Market research software can range in price from free to hundreds of dollars per month.

Your needs: What specific features are you looking for in market research software? Do you need to create surveys, collect data, or analyze results?

Your level of experience: If you are new to market research, choose software that is easy to use.

The type of market research you need to conduct: There are many types of market research, such as surveys, focus groups, and interviews. Choose software that is designed for the type of research you need to conduct.

The features offered by the software: The software you choose to conduct market research should have some non-negotiable features that we mentioned earlier in this blog. These features include survey templates, tools for data analysis, visual dashboards, etc.

The customer support offered by the software: Make sure the software provider offers good customer support in case you need help using the software.

Additionally, consider specialized tools like BrewSurvey for mobile-first and offline data collection if your research involves field work or on-the-go customer feedback.

We recommend reading software reviews on credible sites like G2 and comparing options before deciding. Hence, you get an unbiased opinion of the market research tool you’re considering.

Ready to Choose the Best Software for Market Research?

Now that you’ve reviewed the list and compared the 15 best market research tools & software, choosing the one that fits your requirements shouldn’t be difficult.

No matter which market research tool you choose, conducting market research will push you closer to gaining the correct insights for your project. Remember to consider the factors that affect the choice of your tool. Be wise and choose right!