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Remesh’s pioneering AI technology revolutionizes research using the most common conversational medium today—text. Refined over the last decade, our approach slashes time-to-insights from weeks to hours. The world's most respected organizations trust Remesh to tackle everyday research problems and stay at the forefront of innovation.

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Editor’s Note: Every year, participants in the <a href="https://www.greenbook.org/grit">GreenBook Research Industry Trends (GRIT) Report</a> survey vote for the most innovative companies in market research. In <a href="https://www.greenbook.org/grit">Insights That Work</a>, these top companies share the real-life challenges and solutions of their biggest clients.
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<h4>Challenge</h4>
Loneliness and social isolation can have a major impact on an individual’s health, and many rural communities have seen increasing rates of loneliness amongst its residents. Loneliness is known to be a major risk factor for depression, which accelerates functional decline and increases mortality rate. A Council in the UK had to answer the question: how do you bring a vast rural community together to tackle the issue of loneliness and isolation?
With support from a world-renowned consulting firm, the Council was working on an initiative to reduce the number of days people are delayed in leaving the hospital. As part of this project, the Council realized there were opportunities to do more to support community-led projects that prevent people from needing to be admitted to hospital. The Council thought that perhaps by identifying ways to better support the community to tackle loneliness, they might be able to keep people happy and healthier for longer and reduce admissions to the hospital.
The client and the consulting firm believed that in order to produce a solution that met the needs of this unique group they’d needed to foster a true dialogue between the council and its citizens. However, with such a diverse group, many of whom were living in quite isolated areas, engaging with them directly posed a significant challenge.
<h4>Solution</h4>
Traditional town halls, focus groups, or home visits can be prohibitively time-consuming and expensive, and instead, the Council needed an approach that could scale across the county, drive a productive conversation, and capture high-quality, actionable data to allow the Council to make a clear decision about how to address the situation.
To help gather or ‘crowdsource’ information, ideas and insights from people who want to help solve the challenges of loneliness, the Council partnered with the consulting firm to host a live conversation.
The consulting firm selected Remesh, an online insights platform with a chat interface, to facilitate an in-depth, real-time conversation with the Council’s resident community. The Remesh platform allowed residents to provide feedback from wherever they were, enabling the firm and the Council to set up and run a session quickly. The Remesh conversation was conducted with nearly 200 residents of the county, 100% of whom participated the whole time, signifying an extremely high amount of engagement.
<h4>Outcome</h4>
The insights gleaned from the Remesh conversation supported the Council in completely revamping their Adult Social Care Prevention Services to provide more targeted support to community-led projects. The Council was able to answer critical questions surrounding how local communities can best intervene early and how the community can come together to bring about positive change. Using Remesh, the Council found that 85% of people agreed that alleviating loneliness and isolation is something the Council should spend money on. The crowd also offered helpful insights suggesting that more budget should be allocated to the promotion and marketing of current community services, improved transport, and a need for activities that bring the residents together, such as “Coffee Mornings”.

Using the Power of the Crowd to Combat Loneliness and Isolation

<h3>CHALLENGE</h3>
A CPG design team was tasked with creating a label for an energy drink which was aesthetically pleasing and communicated the drink’s primary differentiators – healthy, made of natural ingredients, and makes the drinker feel good. They came up with three different designs which they believed accomplished this goal, but it was unclear what each design communicated to their target consumers.
The challenge was to understand how consumers reacted to the designs, what specific design elements were successful in communicating the drink’s differentiators, and what design improvements could be made. Then use all of this insight to deliver a final design in less than 24 hours.
<h3>SOLUTION</h3>
They used Remesh to engage 100 people from their target market in a 30-minute live conversation. Before it began, demographic &amp; behavioral data were gathered on the participants. During that conversation, participants were exposed to each of the designs and asked a mix of open and closed-ended questions which dug into their perceptions of each design.
By 13 minutes into the conversation, one design stood out as resonating &amp; communicating differentiation better than the others. However, Remesh’s Live Analysis surfaced that even for the best design there was a major concern that the drink’s label did not make its ingredients clear. They spent the next 5 minutes probing into the specifics of how participants expected the ingredients to be communicated.
At 18 minutes into conversation, the moderator explained to participants what they were attempting to communicate and asked what elements of the various designs best achieved those communication goals. During the last 4 minutes, they had participants brainstorm 
slogans &amp; messaging.
When it was over, the designers had learned: Which individual design best accomplished their goals; What design elements were driving positive and negative perceptions; What design elements successfully communicated the drink’s differentiators; That they failed to realize the importance of communicating the ingredients on the label; How they could best communicate ingredients on the label; and what messaging / slogans best resonated with consumers.
<h3>OUTCOME</h3>
From the results of the 30 minute Remesh session, the designers identified which of their three designs best accomplished their goals of communicating the drinks differentiators.
They used their qualitative learnings about how various design elements impacted consumers’ perceptions to further improve the design. They added ingredient information to the label based on their new understanding of what customers expected. Finally, they made a small change to the messaging on the label based on specific language they learned resonated with their target consumers.
The result was an aesthetically pleasing design which they were confident successfully communicated the drink’s differentiators to their target market – and they had the data to back it. From the time they had completed their initial design concepts until they delivered their research findings and final design concept was less than 12 hours.
Their boss was impressed with their turnaround speed and attention to consumer input – the product plan moved forward ahead of schedule &amp; the design team was able to go home early.
Visit Remesh at <a href="https://remesh.ai/">remesh.ai</a>.
Read more innovative case studies in the <a href="https://www.greenbook.org/grit">Insights That Work eBook</a>.

Drink Label Design: So Fast You’ll Freak

<blockquote>Editor’s Note: This post is part of our Big Ideas Series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Andrew Konya will be speaking at <a href="http://iiex-na.insightinnovation.org/">IIeX North America</a> (June 12-14 in Atlanta). If you liked this article, you’ll LOVE IIeX NA. <a href="http://iiex-na.insightinnovation.org/">Click here to learn more</a>.</blockquote>
Nearly all market research aims to illuminate the nature of an experience. We conduct research to understand the experience of using a product, watching a live sports broadcast, or working at a particular company. The understanding is then used to inform decisions and carry out actions which impact that experience the next time. This iterative cycle of &#8216;research, understand, decide, &amp; act’ serves as the bedrock of change and innovation. 
 
However, all research is not created equal. Most research is conducted after the experience being researched has taken place. This means that what is really being researched is the memory of the experience &#8211; often a distorted, low-resolution proxy of the experience itself. Sometimes, this memory may be exactly what we wish to understand; for example, researching how a person remembers an ad. Other times the experience being researched may be simple enough that a low-resolution understanding gets the job done. 
 
In many cases, though, the experience being researched is complex &amp; the nuggets of insight decision makers crave are found in the multi-dimensional nuances of a complete experience. For example, a sports fan’s experience with the broadcast of their favorite team may involve traditions with friends &amp; family, cooking &amp; food, social media interaction, consumption of advertisements, and even co-occurrent purchasing habits. Each of these components combines to create a cohesive experience. Thus, truly understanding the experience requires understanding the complex web of experiential components. 
 
In cases like these, where an undistorted, high-resolution understanding of the experience is crucial, real-time research can be key. So what is real-time market research and how do you get the most out of it? 
 
Real-time research: 
&#8211; Is conducted throughout the experience being researched. 
&#8211; Informs the researcher about each stage of the experience at the time that stage is taking place. 
&#8211; Adapts to deviations from the expected experience as those deviations take place. 
 
Tips to getting the most out of real-time market research: 
1. Develop hypothesis ahead of time but keep an open mind. 
2. Adapt and adjust to new findings quickly. 
3. Focus on remaining “in the moment” — stay relevant to the experience as it happens. 
4. Maintain interactions with research participants which do not substantially change the experience you are aiming to understand. 
 
To learn more about in-experience research, please attend IIeX North America to <a href="https://events.bizzabo.com/202532/agenda/session/184640">see how NASCAR conducted research of its fans during this year&#8217;s Daytona 500</a>. 
 
For additional perspectives on the topic, check out these related IIeX talks: 
<ul>
<li style="font-weight: 400;"><a href="https://events.bizzabo.com/202532/agenda/session/176107">Rapid Response Research: Delivering In-The-Moment Insights to Drive Agile Business Development</a></li>
<li style="font-weight: 400;"><a href="https://events.bizzabo.com/202532/agenda/session/185954">Breathing Life into Real-time Brand Tracking</a></li>
</ul>

Real-Time Market Research

The GRIT Top 50 list signals our industry&rsquo;s direction: increased supplier diversity, tech-led growth, and an emphasis on real innovation.
<h2 dir="ltr">The dawn of a tech-accelerated qualitative era</h2>
Qualitative research, often overshadowed, is primed to deliver exponential growth in value, so it&rsquo;s exciting to see more suppliers focusing on qualitative research. It only underscores that qual should be included in more workflows, and this is growth that the industry needs.
Historically, its adoption has been hindered by complexity. At Remesh, we've been shouting from the rooftops how valuable qualitative data is, and have been on a quest to make it more accessible. Artificial intelligence has been instrumental in our pursuit. The current technological advances are shattering previous limitations&mdash;capabilities improbable for qual research only a year ago are already becoming an established part of workflows.
<h2 dir="ltr">Moving forward, there's room for everyone</h2>
The research industry is huge, and there's a multitude of providers, from DIY enthusiasts to those seeking full-service solutions. The GRIT Top 50 reflects this varying need in its rankings. What stands out to me on this year's list is how diverse it is &mdash; suppliers of every size and offering can be found.
While currently nobody owns more than 2% of the market, it's probable that a few will carve out a significant market share (20%+). Recent acquisitions, take-privates, and roll-ups suggests as much. These could be providers who can do it all and serve a wide variety of research customers. And still, there will be plenty of room for us all at the table. In fact, there's opportunity for services-leaning organizations to leverage the innovative capabilities of smaller tech solutions, creating a harmonious ecosystem where collaboration trumps competition.
<h2 dir="ltr">Fraud has no place in research</h2>
Fraud has no place in research. Ironically, while technological innovations are making fraud more and more prevalent, they&rsquo;re also making it less necessary. There is a place for synthetic sample&mdash;where exactly is still unknown. What is clear is that it must be abundantly transparent that they are not real, human responses. The path forward demands high integrity and accountability from vendors; representing simulated populations as though they were real takes us all down the road to ruin. There&rsquo;s ample opportunity to leverage technology to provide true innovative value (with a healthy dose of competition).
<h2 dir="ltr">Where the GRIT Top 50 could go from here</h2>
The GRIT team has done a phenomenal job of evolving with the industry, which is why they&rsquo;ve maintained such a high level of respect. At Remesh, we&rsquo;ve experienced first-hand the benefits of being recognized on this list, and consider it a high honor.
I hope to see the GRIT Top 50 ascend to the next level of industry insight. Instead of just highlighting marketed innovation, the GRIT Top 50 could elevate true industry changemakers. Let&rsquo;s use the data to reveal who is delivering on their promise to identify who to lean on in the future.
<h2 dir="ltr">Elevating research and researchers</h2>
At the heart of all of this, we are enabling researchers to do their jobs easier and with greater efficiency. Technology enables us to achieve faster, cheaper, and higher quality work, so let's do more, and let's do it right.

The GRIT 50 list of most innovative suppliers shows the breadth of services that benefit from innovation, as well as the industry's ability to support innovators of all sizes.

The GRIT Top 50 List Signals Our Industry’s Direction

Editor&#8217;s Note: This post is part of our Big Ideas series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Remesh&#8217;s Andrew Konya will be speaking at IIeX North America 2019 this week in Austin, TX. If you liked this article, click <a href="https://iiex-na.insightinnovation.org/">here</a> to see what we&#8217;re up to in Austin.  
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The ways that people discover products, services, and information is changing. A sharp rise in the number of “what’s-near-me” searches on Google maps – <a href="https://searchengineland.com/survey-82-percent-of-smartphone-shoppers-conduct-near-me-searches-304512">82% of smartphone shoppers</a> engage in ‘near me’ searches, according to Search Engine Land – represents consumers’ increased expectation of receiving on-demand, personalized results. In this era of hyperlocalization, understanding consumers’ locations and habits possess a significant potential to provide relevant and accurate results whether you are a brick and mortar store or a candidate running for Congress. Personalization and hyperlocalization presents not only an in-depth way to understand people in a well-defined area but is also a powerful way to drive traffic to physical locations and market to consumers in a more personalized, relevant manner.
<h3>Hyperlocalized Marketing: Boosting Business</h3>
Over the last few years, hyperlocal marketing has taken on an increased importance for both small and large scale businesses. Businesses are beginning to realize the tangible benefits of local and time-sensitive marketing, while today’s consumer expects to see relevant messages based on their locations &#8211;  whether at home, at work or in their local neighborhoods.
Increasingly, established retailers are adopting technologies that give them precise consumer information. This is where analytics comes into play; done right, it helps both small and large-scale retailers to tailor messaging, gather product feedback and diversify merchandise to meet local demands. 
Several years ago, one of the best campaigns we know is still <a href="https://www.adweek.com/creativity/spotify-crunches-user-data-fun-ways-new-global-outdoor-ad-campaign-174826/">Spotify’s “Thanks 2016, it’s been weird” ad</a> &#8212; creative use of customer data and personalization to deliver a vibrant, interactive and targeted message. Launched in the United States, United Kingdom, Germany and France, and 10 other countries, Spotify’s brilliant hyperlocalization saw that each version of its ad was inspired by data from listeners in different geographical areas. Not only does the data lend perceptive insights to Spotify’s consumers, but it also presented an interesting take on how people reacted to socio-political developments of 2016.
<h3>Hyperlocalization in Politics: Understanding Voters, in-depth</h3>
The continuing advancements in the technology we use to understand our audience mean that campaigns can now understand citizens in profound depth. In particular, understanding residents based on their geographic location helps politicians understand the area’s culture, interests, and priorities so that they can resonate with voters and serve them better. 
Director of campaigns at Forward Majority, Ethan Roeder focuses on state legislatures and districts where Democrats can gain elected positions before redistricting happens in 2020. The mission entailed embarking on a campaign in the 2018 election cycle that focused on six states. Incorporating hyperlocalization into his campaign, Roeder and his team sought to identify target localities by focusing on Republican states with candidates that had limited monetary resources. Through their AI-driven virtual focus group research, Roeder discovered deal-breaker issues to voters, helping him to shift focus from Foxconn in Wisconsin to healthcare and public school funding, for example. With the flexibility provided by virtual focus group research, Roeder and his team could correctly identify issues that mattered to respective political parties; enabling politicians to resonate with voters at a deeper level.    
When what a political campaign team really wants is to create relevant, personal conversations with their voter audiences from across the country, understanding their voter districts and what’s important to them is paramount. Ranging from personalized emails to tailored local messages in social media about ongoing parliament debates, hyperlocalization plays a significant role in politicians winning the elections of tomorrow.

Hyperlocalization: Changing the Face of Business and Politics

The rate of progress is ever increasing, and with it, so is our need to understand information quickly to make data-driven decisions.  To better illustrate this, let’s step back and take a quick look at the history of what it takes to understand a group of people.
The year was 53 BC. Julius Caesar was ruling the vast Roman Empire and wanted to better understand the sentiment of his people to better deploy his limited resources- what were their needs or fears, was revolution brewing, were there pending food shortages? He called together his legions and tasked them to speak to 1,000 various inhabitants of the empire and return with an understanding of how the empire felt. Off they went on horseback, traveling several days or weeks, interviewing townspeople for a few days, and then riding on until they reached the next town. The whole process of questioning, recording, traveling, and then reviewing the thoughts of 1,000 people took approximately 4 months. The information he learned was instrumental in managing his Empire.
Flash forward a great deal to 1775 when Benjamin Franklin was appointed the first postmaster general and US mail becomes the fastest way of delivering communications over long distances.  With mail, 1,000 people could now be questioned and understood in about 1.5 months.
Take a small hop forward to 1849, and the telephone is the hottest new innovation- it now becomes possible to interview, record, and understand 1,000 people in under 1 month. A little over a century later in 1999, the internet emerges- reducing the time to interview and gain insight of 1,000 people to under a week.  Then, a decade and a half later, we see automation emerge in market research with companies such as ZappiStore.  It now becomes possible to interview, record, analyze, and understand 1,000 people in a matter of days.  However, these leaps in the time needed to understand people are almost universally quantitative in nature not qualitative, ultimately sacrificing a richer understanding of each individual to meet the increasing demands for faster results.  As we move forward, we’re taking smaller and smaller leaps of time, with larger and larger impacts than we’ve ever seen before. Technological and social transformations would happen over centuries; now we see rapid changes in just years. The rate of progress is increasing and we are progressing forward with it &#8211; the way we communicate, interact, and understand.
What’s the fastest way you can understand someone? You have a conversation with them. Since 2012, the next stage in understanding groups of people has emerged and this time, not only is it fast, it’s also rich, qualitative data. So now what’s the fastest way you can understand a group?  Have a conversation with them &#8211; all at once. While speaking with 1,000 people all at once sounds like pure chaos, artificial intelligence is actually making this a reality, a reality that can be achieved through Representative Intelligence.
Representative Intelligence is an intelligence- something you interact with and it has the ability to interact with you &#8211; that effectively represents the thoughts, beliefs, and opinions of a specific group of people.  Representative Intelligence is not the act of AI replacing humans, but AI facilitating humans to speak “as one”.  The insights that are gleaned from a conversation with Representative Intelligence lets you understand the opinions and beliefs that best represent the group as a whole.  In a sense, you separate the idea from the individual, and it’s the ideas that resonate the most with all individuals in the group that rise to the top, ultimately creating the shared “voice”, or Representative Intelligence.
The way you can use Representative Intelligence today is through Remesh which allows you to communicate with a large group of people all online and engaged at once. It works by the individuals in the group entering a response to a single question simultaneously, with machine learning and natural language processing running in the background to understand, analyze, and segment participants responses as they come in.  In addition, the participants engage in a basic voting exercise, selecting the responses they agree with most,  allowing the algorithm to better understand how each individual in the group thinks. In a matter of minutes, the responses are ranked by how much individuals agreed with them, ultimately leaving you with the response that best represents the group.
With Representative Intelligence, the time to insight is a matter of minutes.  Basically, the time it takes to understand a group is the same as the time it would take to understand one person.  The speed at which this takes place is crucial for understanding your audience &#8211; the longer it takes to understand people, the less information you truly gather. Everything changes quickly &#8211; technology, opinions, social norms- to be able to understand a group with the ease of speaking to a single person means you understand the sentiment of a group now- not what they thought a week, month, or year ago. In addition, when carrying out a live conversation, surprising answers may arise, and the fluidity of Representative Intelligence would allow you to dig deeper, and change the path of a conversation- something you can easily do in a one-on-one conversation but would be impossible through traditional mass communication and surveys.  In this fast-paced, interconnected world, the ability to be in-tune with your audience &#8211; customers, employees, constituents, citizens- is vital to a healthy, more understanding world.
Today, we see that much of the data collected on groups of people for marketing purposes is passive.  This is the data that is based on the actions people take, such as clicks, purchases, and visits, rather than data gleaned from actually engaging with them.  Representative Intelligence is a form of active data- each individual is an engaged participant, contributing their ideas and opinions.  Technology has given us the opportunity to “understand” our customers and audiences without ever engaging them.  But is this the way to build an interconnected, understanding future?  Perhaps we cannot base the aspirations and motivations of a person off of the actions we’ve measured.  People are not made of a series of clicks, purchases and website visits.  We’re missing their experiences, their relationships, their passions, their story.  And we’ll never know if we don’t ask.  Maybe it’s time we have a conversation with them. All of them.

Representative Intelligence

Coding transforms raw data into meaningful insights, the cornerstone of any research endeavor. As the research field progresses, we find ourselves at the cusp of a transformative era, where the integration of Generative AI into research practices is changing the game. How exactly does this technology work? Does it change current research practices? And what does it mean for researchers and insights professionals? Let&rsquo;s dive in.
<h2 class="p5">The importance of coding in qualitative research</h2>
When it comes to valuable data, we know that qualitative data has a wealth of insights. But when you ask open-ended questions, you&rsquo;re left with hundreds or even thousands of responses that are all over the place. How do you make sense of it all? With coding.
Coding is the essential link connecting researchers and the insights buried within mountains of qualitative data. It's the process of categorizing, organizing, and extracting meaning from open-ended responses. Through coding, researchers can identify patterns, themes, and trends, ultimately providing a coherent narrative for the data.
Traditionally, coding has been a manual task, demanding researchers to meticulously sift through large volumes of text to identify and categorize relevant information. While effective, this manual approach has drawbacks&mdash;it's time-consuming, susceptible to human bias, and lacks scalability. This article explores how machine learning technology can be fitted to traditional coding methodologies in order to drive the efficiency, objectivity, and scalability of qualitative research.
<h2 class="p5">Two principal manual coding methodologies</h2>
Before we look into how AI enters the picture, it&rsquo;s important to understand traditional methodologies. AI doesn&rsquo;t create new ideas; it works based on how we train it.
There are two principal methodologies for coding: inductive and deductive coding.
<h3 class="p1">Inductive Coding</h3>
In this approach, researchers derive codes and themes directly from the data itself. Grounded theory, a popular framework in <a href="https://www.greenbook.org/insights/qualitative-market-research" target="_blank" rel="noopener">qualitative research</a>, embodies this approach. It starts with open coding, applying initial codes, then axial coding to identify the connections between codes, and then selective coding, where researchers create clusters of similarly themed codes. This method offers a comprehensive framework to code large datasets and allows for the emergence of unexpected insights.&nbsp;
<h3 class="p8">Deductive Coding</h3>
In contrast, deductive coding starts with a predefined codebook. Researchers apply pre-established codes to the data based on a priori theories or hypotheses. While it offers a more structured approach, it may limit the discovery of novel insights.
<h2 class="p5">Emergence of Generative AI in coding</h2>
There&rsquo;s been some automation in coding for a long time, but it&rsquo;s mostly relied on term frequency. This process involves pulling specific words that appear often and then coding based on those words. While this approach can help code a large amount of data much quicker than manual methods, it relies on keywords alone, neglecting nuance and meaning.
In contrast, Generative AI can evaluate the meaning of the entire text to derive comprehensive and nuanced codes. It is trained on vast amounts of text data - hundreds of billions of words - enabling it to predict relevant words and phrases related to a text input. In an application like ChatGPT, a user submits a prompt, and the model can generate a human-like response that feels relevant. In the context of coding, the predictive nature of Generative AI makes it well-suited to identifying potential themes in a body of text.&nbsp;
<h2 class="p5">Looking under the hood of Remesh AI</h2>
As we start <a href="https://www.greenbook.org/insights/insights-technology/why-ai-will-kill-the-researcher-and-how-to-ensure-your-survival" target="_blank" rel="noopener">embracing AI in our research practices</a>, it&rsquo;s essential to have complete transparency about how AI is used and what&rsquo;s happening under the hood. This is how we can maintain the integrity of our research while utilizing innovation to improve our processes and insights.
At Remesh, we&rsquo;re proud to be a pioneer in integrating Generative AI into qualitative research coding through our new feature, Auto Code. By blending the strengths of AI algorithms with proprietary models, our platform is shaving hours off of the researcher&rsquo;s plate. Our process is loosely inspired by grounded theory for inductive coding, which allows for the emergence of unexpected insights. It also requires several handoffs between Remesh&rsquo;s proprietary algorithm and the Generative AI models in order to achieve the right level of nuance and accuracy in the codes.
In the first step, Remesh prompts the Generative AI models to consider both the question and the response and asks it to output many descriptive codes for each response. Meta parameters like temperature are set to ensure nuanced responses.
The Generative AI models are powerful, but they are not always consistent. They often provide slightly different codes with essentially the same meaning to two different responses. To handle this, Remesh runs all of the codes that it gets back from Generative AI through its own proprietary algorithm that can map all of the codes by semantic similarity, which outputs groupings of the codes.
Some of those groupings contain just one code, and some are very large. Remesh has designed a process to assign all the singletons to an existing cluster and break large, multi-theme clusters into unique groups. Once the system has the right set and size of clusters, it asks Generative AI to create a unique name for each. Those names become the codes that are shown to the researcher on Remesh.
All of the codes are then assigned to a larger category grouping, using a similar process to the one used to assign codes to responses. The insights professional can review all of the codes and categories - editing, adding, and organizing as needed to ensure accuracy. This keeps the researcher in the driver&rsquo;s seat while still utilizing AI&rsquo;s powerful capabilities to make their existing processes faster and better.
While Remesh's platform assists in coding, human researchers remain integral to the process. The AI serves as a powerful tool, accelerating coding tasks and reducing the risk of errors, but the researcher's expertise and context are invaluable in refining and interpreting the results.
<h2 class="p5">The future potential of AI-assisted coding</h2>
As we look ahead, the potential of <a href="https://www.greenbook.org/insights/insights/leverage-ai-for-open-end-analysis-choosing-the-right-partner" target="_blank" rel="noopener">AI-assisted coding</a> is promising. The technology can drastically expedite coding tasks, making them accessible to a broader audience. With the assistance of AI, researchers can focus more on analyzing nuanced data cuts, deepening insights, and providing real-time business context. However, AI is not a cure-all; the symbiotic relationship between human researchers and AI tools is paramount for accurate and meaningful results.
<h2 class="p5">The evolving role of the researcher</h2>
In this AI-driven era, the role of the researchers must evolve. Researchers are the stewards of data, leveraging AI tools to extract more profound insights efficiently. They are responsible for interpreting AI-generated codes and ensuring accuracy. They may need to validate and refine the codes to better tell the story of the data, especially given their familiarity with the questions and nuances of the dataset. Additionally, researchers have the invaluable context that AI lacks. The researcher understands the stakeholder needs and historical context that is needed to make recommendations for next steps. AI is a powerful ally, but the human touch turns data into actionable knowledge.

Discover the significance of coding in qualitative research, explore traditional methods, and witness the transformative impact of AI.

AI is Transforming Qualitative Research Coding

Remesh is an AI–powered insights platform that helps uncover the honest audience
insights you need now to determine what’s next. Cleveland, Ohio, United States of America.

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