Given the number of sources from which data can be collected, making informed decisions is becoming an increasingly demanding task. 

The decisions in question relate to many aspects of the business, including possible approaches to product development, different pricing strategies, marketing campaigns, market entry strategies, and customer experience improvements.

There are plenty of data sources available when it comes to market research strategy. You can collect data for market research from digital sources, such as social media platforms, online consumer reviews, eCommerce transaction data, website traffic statistics, mobile app usage, and email engagement metrics.

Then, there are tactics aimed directly at collecting information from consumers, like surveys, interviews, feedback, and observational studies.

However, one thing is certain: If you want to stay competitive in today's market, you cannot ignore the data, and there is a lot of data around. Plus, there is a long way to go between collecting data and making that data useful and actionable.

This is the reason why businesses are increasingly adopting tools that utilize AI to help with market research automation, pattern recognition, and the generation of new ideas. The arduous tasks of collecting and processing data can be largely automatized, and with the help of AI, employees can now utilize these tools to go beyond simple tasks and add additional creative value to market research strategy and pattern recognition.

What is Market Research?

Before we go on about market research automation, it would be good to clearly define what market research is.

Market research is a multidisciplinary undertaking that involves gathering and analyzing data from multiple sources in order to gain an understanding of current market trends and potential future changes. 

Market research is often aimed at answering questions like:

• What is our target audience?
• What are common consumer behavior patterns in our niche
• What are current consumer preferences
• What are current market trends, and where are they heading?

The type of market research you conduct and the questions you ask should be directly influenced by what is relevant to your business. This is the first step, which requires critical and creative thinking to understand your business's broader circumstances and ask the right questions.

It's also important to mention that even the right questions are useless without choosing the appropriate data to answer them. This often involves gathering data from multiple sources and looking at critical intersections to derive valid conclusions. It's not always the case, but it mostly holds true that the more data is collected, the better and more precise the results will be.

Once the questions are asked and data is collected, the tedious task of data preparation begins.

Data preparation, particularly in large-scale market research, often involves meticulous procedures that require a lot of manual effort.

Some of the steps that take part in data preparation and that are notorious for their time-consuming and labor-intensive nature include:

Data cleaning: Fixing mistakes in the data. This might include filling in missing information or deleting duplicates. It's important to get this right for good results.

Data integration: Putting together information from different places. This can be tricky because the data might look different depending on where it came from.

Data formatting: Getting data organized the same way. This means using standard ways of writing things like dates, as well as giving helpful labels to data.

Data reduction: Making big bunches of information smaller but keeping what's important. This could mean removing data you don't need or putting detailed information into groups.

Each of these steps contributes to the relevance and accuracy of the final result by making data accurate and analyzable. However, those who have been working on sorting those out manually know how incredibly monotonous and time-intensive these procedures can be.

The good news is that each of these steps can now be automated. Automation and AI in market research open the world of possibilities when it comes to playing with data and getting new and creative insights rather than spending time on data preparation.

What is Market Research Automation?

Market research automation is essentially about using specialized software solutions to automate and optimize the collection, analysis, and interpretation of data throughout the market research process.

In the last few years, we have all witnessed what AI is capable of doing. Breakthroughs in natural language processing allow AI to understand what you want to do with your data and machine learning is used to apply those instructions to clean and sort the data based on patterns learned from similar examples.

This gives the data workers a great deal of freedom to apply AI analysis to different unstructured and raw data and prompt it appropriately to prepare that data for specific research.

For example, you can feed the AI a bunch of surveys and then specify what is considered valid and what is considered invalid answer, as well as what percentage of answered questions constitutes the survey as complete or incomplete. 

While this is just one example, there are different types of raw and unstructured data that all require some degree of customization in the process of data preparation, and with AI and automation, you can set initial parameters that require critical thinking and let the automation finish the rest of the task so that in the end you can get the answers to questions like “what % of people rated both orange juice and beef burger as highly desirable additions to the menu” without sorting out the answers manually.

AI and automation can assist you at practically every step of the way of data preparation. With AI-powered error detection, finding anomalies, inconsistencies, and missing data is many times faster and more accurate than doing those manually. 

AI also understands relationships between data from different sources, even if they don’t have the same format, and enforces constant formatting across large datasets. It can also identify the most important data points and patterns to reduce the volume of data while preserving the essence of what it communicates.

Essentially, it is now possible to creatively apply AI and automate a lot of the work in market research, especially the process of data preparation, including data integration, data cleaning, and data formatting. 

You can also ask any additional questions and get immediate answers to questions that give you valuable insight into market research, like “How many of our users that purchased product A also purchased product B?” and similar. With AI and automation applied to your data, knowledge, and insights become widely available, and only your imagination limits how you can use them.

Whether you want to prompt the AI to find duplicates and errors or to find patterns and insights, you are free to manipulate your data and get the answer to a set of different questions regarding your data that each used to require hours to get answered and can be completed within minutes with the use of AI and automation tools.

Making AI and Automation Available Across the Enterprise

As we have illustrated, market research automation is often not a straightforward process that happens simply by inputting data and getting the results. Many different types of research require different types of prompting to set up the right circumstances for AI to do its job, and this requires a degree of human input and clarifying the way in which you want to set up AI automation.

The best way to make this happen is to bring AI directly to where you work with the data that is relevant to your research. While many of the available market research automation tools offer certain aspects of AI automation, none of them can cover the wide range of scenarios and applications that can be achieved with a combination of human input and Large Language Models like ChatGPT or Gemini.

Currently, it's very hard to match contextual understanding and NLP capabilities seen in LLMs, so the best way in which you can bring data relevant to your research to LLMs is through the enterprise search that integrates with LLMs.

Enterprise search creates a unified library of data scattered across the enterprise databases and SaaS tools employees use in their workflow. Once the enterprise search gets connected with large LLMs through the API, it becomes possible to utilize generative AI in a setting that is highly relevant to researchers' needs and where they have access to all the relevant company data.

This means that you can prompt AI to perform an analysis of a congregation of data from different sources and derive conclusions.

For example, imagine a retail company looking to understand recent market trends to adjust its marketing strategies. The company has data spread across various platforms, including Salesforce for customer relationship management, Asana for project management, and internal databases for sales and inventory data.

The enterprise search system is configured to access data from Salesforce, Asana, and the company's internal databases, and when researchers use a simple query in the enterprise search portal like "Gather recent customer interactions, project statuses, and sales data for the past quarter," enterprise search can feed the relevant company data into an LLM like ChatGPT or Gemini through an API that integrates the LLM with the enterprise search tool.

The AI is then prompted with a specific question and additional instructions from the researcher about how to approach data cleaning and standardization. This process may involve a few steps depending on the needs of the research and the ways data needs to be treated, which is another reason why the freedom provided by this method often yields the best results in market research. For simplicity's sake, let's say you prompted the AI with a simple question like "Analyze the collected data to identify trends in customer preferences and product demand over the past quarter."

The LLM processes the provided data and identifies patterns such as an increase in online purchases, a shift towards eco-friendly products, and peak sales periods.

After all is said and done, you have a comprehensive report based on careful research and hard data that is completed within minutes.

AI Market Research Automation with Akooda

Conducting market research and deriving conclusions involves multiple data sources and conducting research through all of them, and that is exactly what Akooda enables.

Market research is never the same; researchers can use different tools for various purposes. There are tools like Asana or Competely for project management and competition analysis. Then, there are tools that track website traffic and user behavior, such as Google Analytics and Adobe Analytics. Survey tools such as SurveyMonkey or Typeform are used to collect direct feedback from customers.

You also have different business databases that contain saved documents and precious information about past quarters' sales rates or customer behavior.

At the intersections of these tools, you can find interesting insights, and you can decide whether to derive a conclusion by examining the relationship between website traffic and sales or customer satisfaction and yearly revenue. 

All of this is now possible without the tedious task of sorting out data for each question and each new relation. Akooda integrates with Google LLM, which offers capabilities to automate data preparation and analysis and allows users to ask any questions related to analyzed data.

With Akooda, all of the insights are within reach, and your imagination is free to find them because when all the company data is integrated into a unified and searchable database, you can use AI and set up automation to ask any question and get any answer.