Good quality research, whether it’s user research or market research, takes time to bring together. The average generative research phase can take anywhere from 4-6 weeks in user research, and a general rule of thumb for market research is 6-7 weeks.
From finding interview candidates, to creating the questions, and even just conducting interviews, these processes are, and have typically always been, quite manual and human-driven. However, that human touch is also what makes the insights that they provide for strategy and Product incredibly valuable.
Herein we have the trade-off – spending more time speaking with people can lead to better, more valuable data than quantitative research alone, but to do so we need to swallow the fact that time-wise it’s going to take a whole lot longer and cost more money.
The key to remember when dealing with any element of AI is that it should be used to enhance and not replace – and this is certainly the case when it comes to AI moderated research.
AI Moderated Research is interpreted in many different ways, but in this instance we are using it to describe a process where researchers can be helped in their interviewing tasks with AI.
From all meetings with interviewees being automatically transcribed during the session, to the AI automatically tagging chunks of the conversation using prior knowledge of other sessions and the interviewer’s style, and even the conversations being summarised instantaneously afterwards.
While user research and market research are very different roles, with very different methods, they do share a couple of common themes – building rapport and asking questions to get the answers that will drive a company forward.
Spending more time speaking with people can lead to better, more valuable data than quantitative research alone.
Benefits of Using AI Moderated Research in Research
For the researcher there are a number of different stumbling blocks that can happen when conducting research, either in 1:1s or in panel discussions. While AI Moderated Research won’t solve all of them, there are some things that having that additional support can help to overcome.
- Bias
Bias happens in all areas of research. It can be interviewer bias, respondent bias, and happen in many ways. It can also slip through the view of some human interviewers as well. Being able to bring in a third party with no obvious bias can prevent and alleviate this risk. AI can transcribe, tag and detect based on very clear parameters. These can then also be manually checked following the interview, allowing the interviewer to conduct the interview knowing that any issues or bias will get picked up and flagged by the AI. Not only does this help with the summarisation and the data interpretation, but also allows for additional learning for the researchers as well.
- Accuracy
Even the most talented of shorthand writers will struggle to keep pace with people talking at times, and maintaining accuracy when taking manual notes can be difficult. Even if a call is recorded, there was a time where the meeting would need to be manually transcribed using audio typing. This is, thankfully, a thing of the past and now we have smart AI that can not only transcribe, but do it well and accurately. Again, this allows the researcher to concentrate on the task at hand while the tool can pick up the slack and transcribe it all immediately. This also prevents any further delays from waiting for tens, if not hundreds, of interviews to be typed up.
- Speed
While AI can also improve accuracy of the information and data collected, it can also speed up the entire process. As well as being able to accurately transcribe, tag and record multiple research sessions, AI can be trained to summarise everything in a way that makes it easy to present. While it’s always ideal for a human to check through and make sure that it all makes sense, the time savings can be incredible. For a single interview of 30 minutes, to transcribe and summarise for a report can take hours, and for more complex subjects – perhaps even days. If you have hundreds of these to do as a rearcher then it can take months to get a true idea of the final results.
AI can take that same interview, tag it and summarise it in minutes. This not only saves the researcher time and energy on the whole data management process, but it means insights can be gained faster, products can be brought to market faster, or changes to operations can be implemented sooner. It can give companies an edge over their competitors, and takes what has always been qualitative but gives it the same characteristics as quantitative.
AI can transcribe, tag, and detect based on very clear parameters.
Using Tools For AI Moderated Research
The one key factor that makes AI Moderated Research so valuable is people. No matter how much AI comes on in leaps and bounds, there is no replacement for human interaction when it comes to these two-way conversations.
However, there are tools that can help such as Beings. This AI meeting assistant is specifically designed for research purposes, both user and market. And while many other tools will use the content that speakers say in meetings to train their AI across all of their users, this isn’t the case for Beings.
The beauty is while the AI gets smarter, helping researchers out even more and learning their research and interview style, the data remains in a silo. This means that security and privacy are top notch, and the insights and information shared is not being used to train broader models.
Use Cases For AI Moderated Research
Scenario 1: In Market Research
A water utility company is conducting a comprehensive market research study involving diverse customer groups, including homeowners, renters, landlords, and individuals from various socio-economic backgrounds and age groups. The goal is to gather extensive insights to improve service delivery and customer satisfaction, while also floating the idea of mandatory smart meters
The study requires numerous interviews, making manual data management challenging and time-consuming.
The company implements AI moderated research tools to help streamline the interview and summary process.
- Interview Conducting and Transcription: During interviews, an AI meeting assistant automatically transcribes conversations in real-time. This ensures no detail is missed, allowing researchers to focus on building rapport and asking probing questions.
- Tagging and Summarising: After each session, the AI tags key topics and themes based on prior knowledge of other sessions and the interviewer’s style. It generates a preliminary summary of the conversation, highlighting critical insights and areas of concern.
Benefits
Bias Reduction: The AI mitigates interviewer and respondent biases by providing consistent question delivery and flagging potential biases in real-time, ensuring more objective data collection.
Accuracy: AI transcriptions eliminate manual note-taking errors, ensuring precise records of interviews. This allows researchers to concentrate on the conversation without worrying about missing details.
Speed: AI accelerates the research process by transcribing, tagging and summarising interviews within minutes. This drastically reduces the turnaround time for reports, allowing quicker decision-making and implementation.
Result
As a result, the water utility company gains actionable insights within weeks rather than months. They identify key pain points for different customer groups, enabling them to tailor their services more effectively. For example, they learn that homeowners prioritise water quality and conservation, while renters are more concerned with billing accuracy and customer service responsiveness. This targeted approach enhances customer satisfaction and operational efficiency.
Scenario 2: In User Research for Product Development
A tech startup is developing a new smart home device and needs to conduct extensive user research to ensure the product meets the needs of its target audience. The target users include tech-savvy individuals, families, and elderly users from various backgrounds. The research involves multiple rounds of interviews, usability tests, and feedback sessions, which are time-consuming to manage manually.
The startup implements AI moderated research tools to enhance the interview and summary process.
- Real-Time User Interaction Analysis: During user interviews and usability tests, the AI meeting assistant captures and analyses user interactions in real-time while the researcher speaks to the interviewees. This allows researchers to see immediate feedback on how users engage with the product, providing insights into user behaviour and pain points.
- Dynamic Feedback Integration: The AI tags user feedback and usability issues during the sessions. This tagging includes tracking recurring problems, user suggestions, and emotional reactions, allowing researchers to adjust questions and focus areas in subsequent interviews.
Benefits
- Enhanced User Experience Insight: The AI captures subtle things in user feedback and interactions, providing deeper insights into user experience and behaviour that might be missed during manual note-taking.
- Real-Time Iteration: With rapid transcription and summarisation, the team can quickly iterate on the product design between sessions, incorporating user feedback in near real-time to improve the prototype before the next round of testing. This allows them to get to market even faster as a result.
- Comprehensive Data Analysis: AI can analyse large volumes of qualitative data from multiple sessions to identify overarching themes and trends, helping the team to make data-driven decisions about product features and improvements.
As a result, the tech startup gains actionable insights within days rather than weeks. They identify key usability issues and user preferences early in the development process, enabling them to make necessary adjustments to the product. For example, they learn that tech-savvy users prefer advanced customisation options, while elderly users need a more intuitive interface. This targeted approach ensures that the final product meets the wide-ranging needs of its user base or adjusts their target user base accordingly, enhancing user satisfaction and increasing the likelihood of product success in the market.
AI + Human = Better Research
From making the role of researcher easier in terms of general administration, to the benefits of having more accuracy, less bias and faster results, AI Moderated Research offers a chance for real enhancements to research. Businesses and agencies have a chance to streamline their efforts, allowing their teams to focus on deeper engagement and strategic analysis.
If you are interested in how AI can enhance your research capabilities, allowing your team to work faster and more efficiently in marketing or Product, then get in touch with us for a demonstration and conversation about how Beings may be the solution you need.
