Within less than a year, the popularity of artificial intelligence (AI) has expanded dramatically.
From songs to images to videos, AI can seemingly create anything. But what does this mean for industries like market research?
Believe it or not, AI in market research isn't new - but it's becoming more clear how this tool can be successfully leveraged. That said, there are areas where AI can hinder the research process.
Keep reading to learn more about when it's best to use AI market research and when to avoid it.
History of AI in Market Research
AI began to make a significant impact on the market research industry in the 1980s.
During this time, AI was primarily used for basic market research functions like data analysis. But, throughout the 90s, AI functions became more advanced and allowed researchers to complete more in-depth tasks.
The real boom came in the early 2000s as machine-learning algorithms gained popularity. In addition, data mining became possible, along with the use of text analytics and sentiment analysis.
Starting in the 2010s and into the present, AI tools have become even more advanced in their capabilities.
Such advancements include:
- Natural language processing (NLP)
- Market trend prediction
- Customer behavior and demand measurements
💡 The Key Takeaway: Market research is no stranger to AI. Over the past few decades, these tools have made advancements in the field, gathering important data and creating new information on current trends.
How AI Will Impact Market Research
Clearly, the role of AI in marketing research is not slowing down. If anything it's progressing - and quickly, at that!
AI certainly has many ways it can positively impact the industry. Perhaps one of the largest ways AI can help with market research is the ability to organize and analyze raw data.
In turn, this also takes some of the pressure off researchers and allows them to focus on other tasks.
Additionally, AI has impacted market research purely on a time-saving level. The amount of information it's able to gather, process, and even report in a short amount of time is unparalleled.
However...
There are some major drawbacks to relying solely on AI as market researchers. The main issues are privacy and confidentiality.
As we know, AI content-generating tools gather and process information that's already available online. This can be a big problem when uploading research data to AI marketing tools.
Think of it this way. Any transcript or data set you upload will then be made public due to the nature of the AI tool. This creates an issue if there are NDAs and other confidentiality agreements in place.
💡 The Key Takeaway: AI's impact on market research has both positive and negative consequences. Positives include the ability to quickly process data, while negatives include potential privacy breaches.
When to Use AI in Market Research
Now, we'll dive into the details. Below are multiple ways to properly use AI tools for market research.
Summarizing Transcripts
Remember how we mentioned natural language processing earlier (NLP)? This is what AI tools can do when summarizing transcripts.
AI tools can quickly go through all the transcript information that is essentially unstructured data, and rank it based on importance. It will look for repeated phrases and similarities in the text, generating the information based on importance.
Other key features AI tools will keep an eye out for while transcribing include...
- Sentence splitting
- Sentence position
- Summarization mimicking
- Selecting data based on previous transcripts
Coding Open Ends
This feature has been helping researchers for some time now.
Reviewing open-ended question responses can be immensely time-consuming for researchers.
As AI tools are fed more and more data over time, they're able to swiftly take care of coding themes and details of open-ended responses due to a build-up of knowledge.
Much of this is related to NLP, where AI tools can quickly evaluate and close open-ended questions.
This is largely due to the two systems it uses: convolutional neural networks (CNN) and Recurrent Neural Networks (RNN). These tools allow AI to learn from labeled data to accurately handle close-ended questions.
Time-Intensive Tasks
As we've already covered, AI helps immensely with cutting down on time-consuming research duties.
Researchers are able to input whatever data needs to be processed and, more often than not, the results are acceptable to very good. This saves researchers a significant amount of time, letting them work on other important duties.
This can aid the success of a business as a whole, as research and other related teams can spend time working on other strategies.
Data Cleaning
Cleaning survey data is one of the more laborious market research steps.
It involves:
- Checking for duplicate responses
- Response completion time
- Response straightlining
- Reviewing open-ends
AI tools can catch these errors in a short amount of time, again saving researchers time. Data mining and NLP allow for proper responses to be kept, eliminating the others.
Facial Coding
A useful AI marketing tool, facial coding is essential to understanding consumer purchasing behavior.
These platforms are able to recognize facial movements in real-time, gathering unique insights into the shopping experience.
Ideally, these are used during focus groups or other related instances involving consumer behavior patterns.
As a result, researchers can have accurate data to gauge the level of consumer satisfaction. This aids in the creation of marketing and additional outreach strategies.
List Generation
AI tools can also generate lists of contacts for research purposes.
For instance, say researchers are looking to contact influencers in New York City for a project. Certain AI platforms can quickly put a list together of relevant contacts.
But this is limited, as it depends on the "type" of contact you're reaching out to.
While AI may be able to gather information about a group of influencers, it may not be able to generate lists of other contacts such as B2B professionals.
💡 The Key Takeaway: There are numerous benefits to using AI tools for market research. Advanced data systems provide many helpful services for researchers like closing open-ends, transcript summarization, and more.
When Not to Use AI in Market Research
Just like there are many reasons for researchers to use AI, there are also reasons to stay away from it.
For example, we've covered that AI tools gather all their data and information from what's existing online. This can become an issue when researchers and marketers are trying to create authentic material.
Since AI can only go so far, it may have already fed this information to competitors - which creates an obvious problem.
Below, we'll cover additional reasons why AI isn't always your best bet.
Interpreting Survey Data
This is a big one.
Using AI in market research can be good for effectively reporting or even interpreting survey data, but it can also incorrectly interpret it. It all depends on the type of data researchers are using.
If an AI model has been "trained" with faulty data in the past, this can affect all future data it receives. For example, if this initial data has errors, this can be replicated in other data sets.
Additionally, AI tools may misinterpret data if they don't understand certain human phrasing or the context of the data being analyzed.
Survey Design
This isn’t as good as the experience of someone who has years of experience doing this – don’t expect it to write your survey for you.
In fact, we tested it ourselves. Read our blog post for our full review: We Used AI to Write a Survey - Here's What Happened.
Anyway, let's face it, the best surveys are those designed by humans.
Researchers have years of experience doing this, unlike AI, which simply pulls data to create a survey.
Survey design is critical to quality responses, and AI tools can create so much. Researchers understand what to avoid, the types of questions to include, and so on.
Sure, AI tools can technically create a survey, but they will lack important background information.
This includes...
- Who the survey is targeted for
- Certain phrases/wording
- Variety of question style
Next Steps and Recommendations
Much like our previous section, providing clients with a set of next steps and recommendations is best done by researchers versus AI.
Why? They are able to add the human touch to a final report. And creating a quality report is important. It includes all the key survey findings and ways to move forward with that data.
An AI tool may give some vague recommendations, but nothing that would compare to the advice from a person.
Interviewing Robot V. Actual Persona
Gathering information from participants is one of the most important aspects of market research.
This is why it's essential to use human researchers to gather information from them versus an AI tool. AI can be used to provide question ideas, but it's necessary for the researcher to craft (and ask) the questions.
Creating Summaries
The same strategy goes for creating final summaries for clients.
Because each client's final report will have unique details and characteristics, it's important researchers create the summary from scratch, or at least from informed past experience.
Relying solely on AI to create this feature could result in faulty and/or impersonal results.
Again, AI marketing automation tools could certainly be used as an outline in this case - but it's important for researchers to do the bulk of the work here.
Client Report Readouts
Readouts are also a key part of the debriefing process in market research.
The debriefing meeting is a chance for the client and researchers to get together and review the key findings of a project.
Simply running all the data through an AI tool will only give you so much information.
And it's essential that researchers come prepared with the proper information! Just using what AI has provided simply isn't enough.
Clients need to be aware of each detail in the report, and researchers need to spend extra time reviewing secondary data. What's more, researchers need to be doubly prepared so they can answer client questions.
💡 The Key Takeaway: There are many aspects of AI that researchers need to be wary of when using the tool. The main areas of concern center around the lack of human details AI can pick up on.
Final Thoughts
All in all, AI can be a great tool for market researchers to use.
Using these tools allows researchers to save time on certain tasks, providing a plethora of knowledge in seconds. That said, AI tools may also provide faulty information, tainting projects.
This is why it's important to use AI tools while acknowledging these potential faults. Another key is reviewing the information it provides to ensure the data is accurate.
Additionally, when creating more content-based items, using AI for idea generation versus taking it word-for-word is also important.
💡 The Key Takeaway: AI in market research is great for certain tasks--reviewing data, evaluating responses, and creating content. But it's essential to have all of these reviewed by human eyes before anything is finalized.
Contact Our Market Research Company
Put simply, AI can be embraced by the market research industry but understanding its benefits and shortcomings is an important part! If you are looking to work with an expert team of researchers, contact Drive Research, our New York-based market research company.
Our team of researchers has access to quality market research tools and can partner with your business to deliver crystal-clear data.
If you'd like to learn more about our market research services, get in touch with us today!
- Message us on our website
- Email us at [email protected]
- Call us at 888-725-DATA
- Text us at 315-303-2040
Lark Allen
As a Content Marketing Specialist, Lark has a strong background and passion for creative, professional, and journalistic writing. She is also a self-proclaimed music freak and 90s enthusiast.
Learn more about Lark, here.
