By: Adam Dince, VP of Owned & Earned Media, CvE
When it comes to market research, the phrase “artificial intelligence” might not be the first thing that comes to mind. But the truth is, AI is rapidly becoming an essential tool for companies looking to gain valuable insights about their customers and the marketplace.
Gone are the days of manually sifting through mountains of data to find patterns and trends. With AI, market researchers can now analyze vast amounts of information in a fraction of the time, allowing them to make better-informed decisions about their products and strategies.
But it’s not just about speed and efficiency. AI-powered market research also allows for more accurate and precise results. By using machine learning algorithms, researchers can identify patterns and connections that would be impossible for a human to spot.
AI is transforming many industries, including market research, by automating tasks and providing insights based on large amounts of data that would be impractical for humans to analyse. AI algorithms can help market researchers identify patterns in consumer behaviour, predict future trends and understand consumer preferences. However, it is important to note that AI should be used as a tool to augment human decision-making, rather than replace it, as AI systems can have limitations and biases based on the data they are trained on and market conditions. With the help of AI, market research can provide a real-time, holistic view of the market that can give companies the edge they need to stay ahead of the competition.
What Is Artificial Intelligence?
Artificial Intelligence refers to the ability of machines to perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI systems are designed to analyze, understand and make decisions based on data inputs, which they use to perform specific functions and solve problems. However, it is important to note that AI should be used to augment human decision-making, rather than replace it, as AI systems can have limitations and biases based on the data they are trained on.
ChatGPT & Market Research
One of the most powerful AI tools that can be leveraged for market research is ChatGPT. Over the past few weeks, I’ve had the opportunity to kick the tires on some of the existing market research capabilities that ChatGPT provides, and it absolutely delivers.
For those just getting acquainted with this new tool, ChatGPT is a powerful language model that can be used for various natural language processing tasks including market research. And ChatGPT can be used to assist in this process by providing insights into consumer behavior, preferences, and opinions. That said, ChatGPT allows researchers to do high-level and in-depth marketing research. In other words, you can go as broad or as deep as you’d like.
Essentially, ChatGPT works when you provide it with a specific prompt (request). From there, ChatGPT generates a relevant response. The prompt can be a question, a statement, or a combination of both. The model uses the prompt as context to generate a response that is relevant and coherent with the input. Additionally, the response is generated based on the model’s training on a large dataset of text, which allows it to understand the context and intent of the prompt and generate an appropriate response.
Getting Started With ChatGPT’s Market Research Capabilities
In the prior section, I mentioned writing about ChatGPT’s model being trained on a large dataset of text. To clarify, only some models have been trained, however, there are more complex operations that require us to train the AI. With that said, based on the ChatGPT’s sort of turnkey training, there are opportunities to leverage high-level prompts to quickly get information for market research purposes; all without having to get too technical. Below are 10 examples of prompts you can get started with:
- Can you tell me about the history and background of [company name]?
- Can you provide an overview of [company name]’s products and services?
- How does [company name] differentiate itself from its competitors?
- Can you share any recent news or developments for [company name]?
- Can you provide any financial information such as revenue or profits for [company name]?
- Who are the key leaders and decision-makers at [company name]?
- What is the company’s mission or vision statement?
- What are the current challenges or opportunities facing the company?
- Can you give me a SWOT analysis of the company?
- Can you provide any customer reviews or testimonials for the company?
See it in action below:
Furthermore, you can simply click on the “Continue” button underneath ChatGPT’s response which will prompt ChatGPT to get more information related to your ask.
And you can keep extrapolating until you get the level of detail needed to move forward.
Now, the aforementioned market research prompts, while powerful, are fairly basic. But, as mentioned earlier, ChatGPT can go far deeper than the basics.
Going Deeper with ChatGPT’s In-Depth Market Research Capabilities
Now that we’ve reviewed a few examples of basic market research marketing prompts that ChatGPT offers out-of-the-box, let’s talk a bit about how we can train ChatGPT to go deeper with market research needs.
One of the most powerful ways that ChatGPT can be used for market research is by analyzing customer feedback and reviews. By training ChatGPT on a dataset of customer reviews, it can be used to identify common themes and patterns in customer feedback. This information can be used to identify areas of improvement for products or services, as well as to gain insight into what customers value most.
Another way ChatGPT can be used for market research is by conducting virtual surveys. By training ChatGPT on a dataset of survey questions and responses, it can be used to generate survey questions and conduct virtual surveys in a conversational format. This can make the survey-taking experience more engaging and natural for participants, leading to higher response rates and more accurate data.
ChatGPT can also be used to generate summaries and insights from large datasets. By training ChatGPT on a dataset of market research data, it can be used to generate summaries and insights that highlight key trends and patterns in the data. This can make it easier for marketers to understand and use the data to inform their decision-making.
Another advantage of using ChatGPT for market research is its ability to understand and respond to natural language inputs. This means that it can be used to conduct open-ended research, such as focus groups or in-depth interviews, in a more natural and conversational way. By using ChatGPT to conduct these types of research, marketers can gain deeper insights into customer attitudes and behaviours, and gain a better understanding of their needs and preferences.
Furthermore, ChatGPT can be integrated with other technologies, such as virtual assistants and chatbots, to create more engaging and interactive research experiences. For example, ChatGPT can be used to generate responses for a chatbot that is conducting a survey, making the experience more engaging and natural for participants. It can also be used to generate personalized responses for virtual assistants, which can be used to conduct in-depth interviews or focus groups.
In addition, ChatGPT’s ability to understand and respond to multiple languages can also be an advantage for international market research. By training ChatGPT on different languages, it can be used to conduct market research in various regions and cultures, providing marketers with a more comprehensive understanding of global markets.
Please note that ChatGPT’s current limitation (and it’s an important one) is that its dataset only goes through the end of 2021. And as of now, ChatGPT does not appear to have plans to expand outward to future years.
However, with Bing including ChatGPT in its engine and for its viability long-term, it’s hard to imagine that ChatGPT won’t refresh its dataset over time.
Training ChatGPT (similar to other AI) on Datasets takes several steps
- Data Collection: The first step is to collect the data that will be used to train the model. This can include customer feedback, survey responses, market research data, and other forms of text data relevant to the research objective.
- Data Preprocessing: The next step is to preprocess the data. This includes tasks such as cleaning the data, removing any irrelevant information, and formatting the data in a way that the model can understand. This can also involve tokenizing the text data, which means breaking the text into individual words or phrases, and encoding the data into a numerical format.
- Training the Model: After preprocessing the data, the model can be trained. This is done by feeding the preprocessed data into the model and adjusting the model’s parameters to improve its performance. The model is trained on large dataset, and the training process can take several hours or even days depending on the size of the data and the computational resources available.
- Fine-Tuning: Once the model is trained, it can be fine-tuned to improve its performance on specific tasks. This can be done by training the model on a smaller dataset that is relevant to the specific task, such as customer feedback or survey responses. The fine-tuning process can be iterative, with the model being fine-tuned multiple times until the desired level of performance is achieved.
- Evaluation: The final step is to evaluate the model’s performance. This can be done by comparing the model’s output to the expected output on a set of test data. The evaluation process can help to identify any areas where the model’s performance can be improved, and the model can be fine-tuned further as needed.
Other AI Platforms that Support Market Research
We are in such a fun period to be in this industry. Technological advances are happening at warp speed, and it’s exciting to watch the birth of many powerful AI tools. And while we focused on ChatGPT for the purposes of this article, there are a variety of other AI tools that can help you with your market research efforts, which I’ve listed below:
- Text analysis and sentiment analysis tools: Tools such as IBM Watson Natural Language Understanding, Lexalytics, and Sentiment140 allow you to analyze large amounts of customer feedback and social media data, identify key themes and sentiment and extract insights.
- Predictive analytics tools: Tools such as RapidMiner, KNIME, and Alteryx allow you to identify patterns and trends in customer data, make predictions about future customer behavior, and optimize business processes.
- Natural Language Processing (NLP) tools: Tools such as NLTK, spaCy, and OpenNLP allow you to extract insights from unstructured data, such as transcribed customer interviews or focus group transcripts.
- Automated Survey Tools: Tools such as SurveyMonkey, Qualtrics, and SurveyGizmo allow you to collect and analyze customer feedback in real-time.
The Bottom-Line
Overall, AI has the potential to revolutionize market research by providing businesses with the ability to analyze vast amounts of data, uncover patterns, and make predictions more efficiently. Businesses that embrace AI in their market research efforts will be better equipped to stay ahead of the competition and make data-driven decisions.
So, whether you’re a market researcher or just a businessperson looking to stay ahead of the game, don’t be afraid to embrace the power of AI in your market research efforts. It’s a match made in tech heaven!
But don’t worry, AI isn’t here to replace human market researchers. In fact, the best results are achieved when AI is used in conjunction with human expertise. By combining the speed and accuracy of AI with the creativity and critical thinking of human researchers, companies can make the most of their market research efforts.
I hope you enjoyed this article. We intimately understand the pain points of the modern marketer and have designed CvE to help solve your most complex challenges. Contact us to discover how we can help you upgrade your marketing sophistication.