Market research plays a vital role in every company's growth and success. It offers information on potential competitors, target audience, and market tendencies. It is the most effective way to keep up with the market trends and maintain your business competitiveness. So, it is no wonder that spending on market research is rising.  

According to the latest IPA Bellwether report, the number of businesses increasing their investment in market research has grown rapidly, from a net balance of 0.7% in the third quarter of 2021 to 7% in the fourth. As a result, researchers need to make sure they are doing everything they can to maximize the ROI (Return on Investment) of their market research.  

However, conducting market research in today's ever-changing landscape is quite challenging. Researchers are under tremendous pressure to deliver fast, reliable, and accurate insights in these uncertain times, which means there is no room for mistakes and missed opportunities.  

With that in mind - here is a look at five of the biggest market research mistakes and what researchers can do to avoid them.

Mistake #1. Not Focusing on Qualitative Research

Qualitative research has the power to transform your business into a results-driven, successful brand. Still, most companies focus on the quantitative side. In fact, quantitative research and analysis are so common that it is rare that you will find a brand that is not conducting quantitative research.

While quantitative research is valuable, it is a huge mistake to only focus on quantitative data and forget about qualitative data. After all, your customers are humans, and numbers cannot tell you everything about humans.

Now every brand knows this, but very few conduct qualitative research. Why? Because traditional qualitative data collection is expensive and time-consuming, as it is predominantly dominated by in-person and focus group discussions.  

But with the growth of online research platforms, it has become super easy and accessible to conduct qualitative research and analyze qualitative data to get qualitative insights considerably faster. So do not forget to collect qualitative data, as it is equally important to get the complete picture.

Mistake #2. Not Conducting the Pilot Tests

Market research can help you improve your marketing strategy, product, digital experience, or something else. But only if you get the data you need; otherwise, it can be a total waste of time. How do you ensure that you get the data you need? By conducting pilot tests, which means doing a few test runs of your interviews ahead of time. It is critical to run a smooth qualitative research study, yet most researchers do not do it. This is among one of the biggest market research mistakes.  

Pilot testing is crucial for every interviewer to know the discussion guide. The interviewer can work out the kinks in question order by conducting it. Can do time management and know which questions they will be able to shorten or cut if one part goes longer than anticipated.  

Basically, it serves as a double-check for researchers to ensure their research goals are met with the questions and did not miss anything important. It also helps them figure out if the participants understand the questions or if they need to be rephrased. This often comes into play because of organizational bias where we do not even realize we are using internal vocabulary that's not common knowledge to someone outside our company. And all it takes is one practice session to work out the kinks.

Do not forget to conduct the pilot test if you want to get the most out of your research. Test your questions, get comfortable with your guide, and you will be able to get the data you need every time confidently.

Mistake #3. Not Creating a Single Source of Truth

We all know to succeed in today's competitive world, it is crucial to sell products and services that customers want. And to learn what customers want and how to present it attractively drives the need for market research.

In fact, doing business without market research is akin to flying an airplane blind, and that is why every company is conducting market research. But there are very few who organize this data in a centralized location.  

Usually, what happens after data collection is that you wind up with a ton of data all over the place, totally disorganized, and it becomes tough to leverage this data if you do not have a single repository or a single source of truth. So, every researcher should create a centralized repository for all their user research and customer conversation data to make the data easily discoverable.

Also, this will ensure that you do not lose any data or do not have to waste time when collecting relevant information because you have to go to multiple sources. Once this is done, you can group your data into categories to make it super easy to leverage this data.

Mistake #4. Not Using Emotion AI for Deeper Market Research

If you are in business, you must be aware that market research is now a well-developed discipline. Though people have been doing market research for more than a few decades now, systematic market research began in the 1920s. And since then, it has changed over the last hundred years. But over the previous seven years, it has changed a lot faster.  

In fact, we can say we have experienced more change in the last seven years than in the last 100 years combined as market research entered the stage of an AI-powered revolution.  

Market research practitioners are reexamining every aspect of how AI can help them know their customers deeply. And the most recent and noticeable development in this regard is Emotion AI.  

Brands can understand their consumer emotions at a granular level using emotion AI and other technologies like voice tonality and text-based sentiment analysis. And this in-depth understanding of customers will directly impact your business's bottom line. How?

Well, the more you can uncover when it comes to people's feelings, the more empathetic you can be, the better action you can take, and the better the message, product, and service you can deliver.  

In short, understanding what your customers are feeling can help you empathize with your customer and create better experiences for them.

Mistake #5. Not Analyzing Qualitative Data

One of the biggest mistakes most researchers make when conducting qualitative research is not analyzing data. There is no point in doing research just for the sake of it - it has to matter. It has to shift something in your business; otherwise, investing time and money into it will waste resources. That's why synthesizing and analyzing your data is so critical. This is where the magic really happens. But since qualitative research creates a lot of data, it's tough to analyze this data manually. So, what can you do?

Well, to get value from these conversations, you need a platform that lets you analyze these conversations. One that enables you to translate your qualitative research interviews into tangible data so you can gain valuable insights into these conversations.  

One that lets you conduct, collate, collaborate, and comprehend these conversations. Wondering how you can comprehend or extract insights from them? With conversation analytics technology, you can do that. With it, you can extract usable data from human speech and conversation.

In other words, conversation analytics can transform your conversations into data by evaluating these conversations and deriving insights from them. This technology uses artificial intelligence (AI), machine learning (ML), natural language processing, and (NLP) to analyze virtual conversations and find insights from them.  

If you want to leverage conversation analytics to get actionable data from your qualitative research interviews, you can use Decode. With our conversation analytics platform, you can bring all your consumer conversations across platforms under one roof and get detailed transcripts and analytics on them.  

Also, with the help of Emotion AI, voice tonality, and text-based sentiment analysis, it lets you measure consumer attention, engagement, and emotional responses and turn that data into scaled, structured data.

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