Whether you’re finding out about purchasing behaviors, gathering views on new products, or understanding your customers’ experiences, online B2B surveys offer the opportunity to gain considerable insight across a vast and broad audience in a short period of time.

In short, sending out surveys to a predetermined database of customers is a cost-effective and flexible way of collecting large amounts of quantitative data. Sounds great, right? However, online surveys come with a limitation – response rates do not always meet the client’s expected return.

Although it is unreasonable to expect a response rate of 100%, realistically, we would expect to achieve a typical response rate of somewhere between 5-10% for most online B2B surveys using a current customer database. In best-case scenarios, companies can achieve a response rate of up to 35-40%, but this would be under optimum conditions.

Before further considering the obstructions and drivers to response rate, it is important to differentiate between response rate and completion rate.

Response rate is the number of people who complete the survey after being sent it (i.e., being a recipient), whereas completion rate is the number of people who complete the survey after starting it (i.e., those who click on ‘start survey’). Unfortunately, keeping a respondent’s attention during a survey can be a challenge, resulting in drop-out midway through the survey and failure to answer all questions.

Further Reading

The Ultimate Guide to Online Surveys: Dos and Don’ts You Can’t Ignore

Read more

What Are the Main Pitfalls When Conducting Online B2B Surveys?

The Customer Database

Maintaining customer databases presents a challenge and can result in missing, out-of-date, or incomplete contact details, invariably resulting in a lower response rate than expected. Ensuring that your customer database includes information such as local time zone and language preferences guarantees that your research team can tailor the survey proficiently.

Customer databases need to be thorough, clean, and up-to-date, and if required, should be representative of the market being surveyed. A sample that is not representative of the customer base will, in turn, result in skewed results.

Questionnaire Design

Surveys need to be appealing to the respondent. Due to the desire to maximize the work done by a single survey, the questionnaire often becomes too long and therefore tedious and uninteresting for the customer. Demanding a significant mental and time investment from customers will likely put them off completion.

At this point, it is important to consider completion rate vs. response rate. A low completion rate indicates a poor survey experience. This experience may include the survey being too long, too repetitive, or uninteresting.

Respondent Anonymity

When sent a customer survey, respondents’ initial thought will often be, ‘Will my answers be attributed to me?’ Not making it clear in respondent communications that individuals can choose to remain anonymous can significantly hinder response rate.

Further Reading

Addressing the Problem with Data Quality in B2B Research

Read more

What Are the Success Factors for Optimizing Survey Response Rate?

Respondent Communication

In the same way most people don’t like to be cold-called, respondents can be put off by receiving a survey they were not expecting. Pre-survey communication letters sent by the research sponsor are best practice to build trust and credibility prior to fieldwork starting.

Linked with this point, if the internal capabilities exist, it might be worth considering sending the survey internally as this could result in an increased response rate. We recommend A/B testing during the pilot stage to see if the response rate differs.

When designing the visual aspects of a survey, it may be beneficial to consider adding company-specific branding. This helps respondents to associate the survey directly with the company in question rather than just the research firm.

Although email is often the best form of communication, in some markets other formats may be better suited, such as SMS, social media, QR code, or even in-app distribution. For example, younger demographics may respond better to surveys distributed via SMS or social media.

Some respondents may start the survey and not complete it due to distractions, and some may forget to complete the survey if the initial email catches them at the wrong time. Depending on the initial response rate, sending out gentle reminder emails is a great way to bring the survey back to the front of mind. It is important when sending out reminder emails to only send these to those who have not already completed the survey. Sending reminder emails to those who have already completed the survey is poor practice.

It is also worth considering changing the text in any reminder email. Including the closing date of the survey in reminder emails is good practice.

Respondent Requirements

Putting yourself in the respondents’ shoes, considering receipt of the survey and barriers to completing it, helps to prioritize when planning distribution.

Some respondents may prefer to remain anonymous when completing the survey, so where possible, add the option for respondents to remain anonymous, and make this clear in all communication related to the survey.

If you were to receive a survey that you could not complete in your native language, you might think twice about responding – even if you were able to understand some or all of the survey content. When planning your survey, think about your contact list and create a list of key languages that would be worthwhile translating into.

Tied to this, the days of B2B respondents only completing a survey on a desktop are gone – all online surveys must be fully optimized to work on all manner of digital devices, whether desktop, mobile, or tablet.

Customer Database

Creating your contact list is a key stage in the survey planning process.

First, you must start by understanding who it is that you want to contact (is this a range of demographics, regions, customer types) and aim to make your sample representative of these demographics. Given decision-making units often include several departments in B2B decision-making, it usually makes sense to have more than one representative for each company contact.

Next, when going through your contact list, only include credible respondents that would realistically respond. Lastly, make the list as clean as possible (i.e., ensuring contact details are up-to-date and lapsed customers are removed from the list where appropriate) – this may take more work upfront but is worth the effort in the long run.

Questionnaire Design

Questionnaire design is a major factor in contributing to response rates.

In the process of questionnaire design, there will often be contributions and suggestions made by different people and departments. It is important to stay focused on the goal of the research and only include questions that contribute to achieving this overall goal.

Some main considerations in questionnaire design, to limit mental taxation for the respondent, are:

• Stay focused on the research goal
• Ensure survey flow is logical
• Start with an easy-to-answer question
• Include a range of question types
• Include open questions so that customers have the opportunity to voice their own opinions but do not make these compulsory
• Limit the number of questions – anything over 10-15 minutes will put respondents off
• Limit the use of industry jargon
• Word questions as simply and clearly as possible
• Lastly, but importantly, make it interesting!

Offer an Incentive

As an additional consideration, offering an incentive to respondents can inspire higher response rates. Incentives do not necessarily need to be monetary but could include:

• High-level overview of research findings
• Charity donations

In our experience, incentives per respondent are more impactful than entry to a prize draw.

Further Reading

Unlocking Deeper Insights with AI Probing in Online Surveys

Read more

Taking Your Research Further

Study Supplementation

If the achieved response rate still fails to meet expectations, further forms of research could be used to supplement the research, such as in-depth interviews or focus groups.

Using multiple data collection methods in combination supplements the survey research in multiple ways. You have the opportunity to reach out to those contacts who are harder to reach online, you can ask more open-ended questions, and additionally dig deeper into research findings from the online survey.

Customer Survey Feedback

If the long-term plan is to carry out similar surveys at regular intervals, whether this is bi-annually or annually, consider an appropriate exclusion period before a respondent is resent an online survey again. No more frequent than annually is good practice.

For these longitudinal studies, it might also be worthwhile to include one or two questions at the end of the survey to gather feedback on the survey experience and how it could be improved next time around.

Allowing respondents the opportunity to provide survey feedback makes refining and improving the survey in the future easier and may provide more clarity on what works well and what needs further work.

And if the research has a customer experience focus, give respondents the chance to opt into being contacted by the research sponsor to close the loop on any outstanding issues.

Further Reading

AI in Market Research: The Challenges and Limitations of Synthetic Data

Read more

Summary

There is no silver bullet for increasing response rates, but by considering the above factors during the planning, design, and distribution of an online B2B survey, response rates can be maximized, so the full potential of the research can be achieved.

To discuss how our tailored insights programs can help solve your specific business challenges, get in touch and one of the team will be happy to help.

The post How to Increase Response Rates in Online B2B Surveys appeared first on B2B International.

When we ask B2B customers about the reasons they choose the brands they work with, we hear similar themes crop up across various sectors: price, service, and product availability, to name just a few. We also hear similar themes raised as pain points related to working with chosen brands.

B2B decision-making on brands is a balance of a range of different factors. While customers may perceive the price offered by one brand to be high, excellent benefits and its industry-leading technology offering can justify what they’re asked to pay. Equally, customers may choose to forfeit added-value benefits offered by more premium providers, like additional training or resources, if the price point of another brand with a less sophisticated offering is too competitive to overlook.

Further Reading

3 B2B Pricing Challenges and How to Overcome Them

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The Value Equivalence Framework

To evaluate the perception of brands within this ecosystem of different decision-making factors, we often use a simple questioning technique that asks research respondents to think in relative terms. This technique is called the value equivalence framework and consists of two very simple questions:

• How would you rate [BRAND]’s benefits versus other similar brands? Answered using a 5-point rating scale from ‘Significantly better’ to ‘Significantly worse’

• How would you rate [BRAND]’s price versus other similar brands? Answered using a 5-point rating scale from ‘Significantly more expensive’ to ‘Significantly cheaper’

The variables in bold are not always set in stone and can be tweaked to suit the brand in question. For example, you may want to measure quality and price if your brand is more product-focused (or even a different product-focused attribute). That said, it is recommended to include one attribute that is price or value-related, as this framework focuses on determining whether a brand’s value is justified by other parts of its offer.

Analyzing the Data

We tend to analyze the feedback from these two questions together, plotting the results on two different axes.

Taking the average score for both questions for the brand that is being rated, you will get your two data points for your two axes. This will give you your plot point for your brands, and you will be able to see how they are positioned against each other. As a general rule, your brand wants to be positioned on the right of the line; brands on the left of the line are more at risk of losing market share based on lack of perceived value.

To understand the feedback here, consider where the brands are positioned. Brand A is perceived as a more expensive provider, but its benefits are said to be significantly better than other brands, so it is in a comfortable position of a justified premium. You could even argue that Brand A has scope to increase its premiums further to get closer to the value equivalence line. By contrast, Brand B is perceived as having worse benefits, but its prices are also very low compared to others in the market, so its position is relatively balanced. You can add as many brands as you have data for to this chart so you can build a picture of the market landscape and see where your brand is positioned on value equivalence against other providers.

Further Reading

Competitive Landscape Analysis with Porter’s Five Forces Framework

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When and How to Use the Value Equivalence Framework

The value equivalence framework is commonly seen in studies that touch on the market landscape a brand operates in and is a useful gauge for understanding how it’s perceived against competitors. To do this, you would need to make sure you ask respondents not just about your primary brand, but also about competitor brands. These should be brands that the respondents use so that they can give them a fair rating. You can play around with the results and compare data by segments within your sample to see if the perceived value of brands differs by group.

A final note: be careful of any potential bias in your sample and how this may impact the results. If you are interviewing your customers who you know are loyal supporters of your brand, don’t be surprised to see yours performing better than the competition – a representative market sample will give you a less biased view of this topic.

To discuss how our tailored insights programs can help solve your specific business challenges, get in touch and one of the team will be happy to help.

The post The Value Equivalence Framework: A Simple but Powerful Questioning Technique appeared first on B2B International.

Synthetic data has been posed as the solution to many of the challenges faced by market researchers. It is claimed that artificially generated data can mimic real-world responses, significantly changing the outlook for traditional market research data collection methods.

On the other hand, sceptics caution that the benefits of AI-generated data are overstated and outweighed by its potential drawbacks – put simply, this group argues that you are better off with smaller sample sizes with responses from real people rather than augmenting datasets with artificially-generated data. For now, at least.

As a specialist B2B agency, we wanted to put synthetic data to the test to objectively evaluate its potential applications in B2B market research today and in the future.

This article walks you through our experiment, the results, and shares our overall thoughts on the role synthetic data plays in B2B market research now and in the future.

Download PDF >

Readers of this article also viewed:

• B2B Insights Podcast #67: AI Is The New Fire; Don’t Get Burned
• AI in Market Research: The Challenges and Limitations of Synthetic Data
• Unlocking Deeper Insights with AI Probing in Online Surveys
• Transforming Tradition: Embracing Innovation in B2B Research

The post The Role of Synthetic Data in B2B Market Research appeared first on B2B International.

Qualitative research requires a particular set of methodologies, techniques, and skills to be successful. In this article, we discuss six critical factors that support the successful delivery of a qualitative research project.

1. Choosing an Effective Methodology

Selecting the right methodology is crucial for any research project, especially in B2B qualitative research where every interview counts. To choose the right methodology, we need to understand the research objectives in depth and assess what we want to achieve. This helps us piece together the methodological ‘ingredients’ that will get us to our goal, whether it involves in-depth interviews, online focus groups, in-person focus groups, or ethnography. We can also mix methods to meet multiple objectives. For example, using in-depth interviews alongside ethnography provides a holistic view when assessing market landscapes.

If the research aims and objectives require discussion between participants, focus groups are the obvious choice. These can be conducted in person or online and are particularly useful in B2B qualitative research when exploring concepts, products, or complex processes.

2. Talking to the Right Person

Speaking to the right person is arguably the most fundamental part of qualitative research. In B2B qualitative research, we often work with relatively small sample sizes, so we need to maximize the value of each participant. The decision-makers we interview are typically senior and influential within their organizations. Therefore, it’s critical to identify the ‘ideal’ candidate at the start of the project, considering their role, the type of organization they work in, and their key areas of knowledge.

These individuals are rarely available on panels, making them an excellent fit for qualitative research, where we can gather significant insights, prompt on topics of interest, and discuss key issues.

Further Reading

When to Use Qualitative Research to Better Understand Your Customers and Their Needs

Read more

3. The Quality of Research Materials

Given the exploratory nature of B2B qualitative research, research materials should facilitate open discussion while ensuring that the data collected meets the research objectives. Discussion guides should be open-ended, allowing room for discussion without restricting respondents. Effective discussion guides in B2B qualitative research should:

• Be no longer than around 20 questions on average
• Clarify the research objectives to frame the discussion topics
• Start broadly to warm up respondents before diving into specifics
• Be conversational rather than rigid
• Use projective techniques to uncover true opinions, feelings, and motivations

Where appropriate, stimuli can be used in B2B qualitative research. Stimuli should be simple and easy for respondents to digest, especially if introduced for the first time during the interview or focus group. More complex stimuli may be better suited to online focus groups, where respondents have more time to process the information.

4. The Capabilities of the Interviewer or Moderator

A skilled interviewer or moderator is invaluable in B2B qualitative research. They help respondents articulate their views while keeping the research objectives in mind. A good moderator listens actively, prompts on interesting topics, and ensures the interview or focus group stays on track.

With the rise of video interviewing, moderators can build strong rapport with respondents even in virtual settings, offering the benefits of face-to-face interviewing at a lower cost. In focus groups, a good moderator ensures all voices are heard and each participant’s views are considered, bringing everyone’s thoughts and ideas to the table.

Further Reading

A Mixed Methods Approach to Market Research: Benefits and Considerations

Read more

5. Data Immersion

B2B qualitative research generates extensive data, even from short interviews. Data is captured verbatim, allowing the research team to immerse themselves in it and identify emerging themes and trends. It’s crucial for the team to engage with the data throughout the fieldwork, not just after all data is collected. Transcripts should be read holistically and individually to build a comprehensive picture of respondents’ experiences and opinions.

Given the iterative nature of B2B qualitative research, the team can adjust questions or prompts during fieldwork if needed. This ensures nothing is missed by the end of the data collection phase.

6. Going Deeper with Qualitative Analysis

Qualitative analysis aims to understand rather than quantify. Analyzing qualitative data involves:

• Description: Identifying common themes and understanding the language or terminology used

• Connections: Understanding how topics link together, how findings relate to project objectives, and identifying unusual insights

• Creating Themes: Highlighting themes that cut across the data, determining behaviors and actions, and understanding underlying factors

• Creating Frameworks: Using frameworks, either existing or bespoke, to make the research actionable and link findings back to business objectives

Good B2B qualitative analysis is not just a list of themes but an understanding of how these themes interconnect, their impact, and their implications for business objectives. The value of B2B qualitative research lies in the analysis and synthesis of data, making the outputs actionable.

B2B qualitative research is challenging and requires specific skills. When done correctly, it allows us to explore issues in depth in ways that quantitative research cannot, which is essential when the research objectives require exploration rather than quantification.

To discuss how our tailored insights programs can help solve your specific business challenges, get in touch and one of the team will be happy to help.

The post 6 Critical Success Factors for Qualitative Research appeared first on B2B International.

There are several frameworks which can be used as part of a market research study to help businesses make sense of the data collected and make smarter, more informed decisions.

One particularly useful framework is PESTLE analysis, which helps identify and discuss market influences that may impact customer behavior and emerging needs. This analysis aids businesses in pinpointing opportunities, necessary changes, and potential barriers to success, enabling targeted decision-making.

Further Reading

B2B Insights Podcast #64: The Best Frameworks to Develop Winning Propositions

Read now

What Does PESTLE Mean?

Political

The political landscape can significantly impact future customer decisions. Factors such as changes in leadership, trade union rules, tax policies, or internal political issues are considered. For example, a new election might alter budgets for specific markets, reducing customer spending power.

Economic

Understanding the financial situation of a market is crucial for assessing customers’ likelihood to purchase and the potential for profit. Economic factors include exchange rates, inflation, labor costs, and consumer spending. If spending becomes difficult or profitability is low, entering that market may not be beneficial under current financial conditions.

Social

Current attitudes, cultural barriers, and population sizes can influence the success of a product, brand positioning, or marketing campaign. Analyzing these factors ensures decisions are tailored to customer preferences.

Technological

This involves examining new technological innovations, customers’ willingness to adopt changes, and their access to technology. Insights from this analysis can guide decisions on adopting new technologies and understanding the impact of the current technological landscape.

Exploring the legal aspects of the market, including new legislation, required licenses, and equal opportunities, is essential. These factors can affect how companies operate and whether additional certifications are needed.

Environmental

Discussing environmental conditions and attitudes towards sustainability helps understand trends and preferences. This analysis can reveal how environmental factors might enhance production or market value.

 

A PESTLE analysis provides a clear overview of the macro environment, guiding decision-makers on their next steps within the industry, whether that involves changing a product, a marketing campaign, or entering a new market.

 

Further Reading

Competitive Landscape Analysis with Porter’s Five Forces Framework

Read now

 

Best Practices: 5 Top Tips for Effective PESTLE Analysis

1. Discuss with Others

   Having diverse voices in the discussion brings various backgrounds and thought processes, uncovering additional risks and opportunities within each segment.

2. Do Not Assume

   The impact of these influences is probabilistic rather than certain, especially in political and social contexts. PESTLE analysis is meant to propose and guide decisions, not determine actions. All decisions should be made cautiously, considering potential outcomes.

3. Monitor Changes

   The environment can change rapidly. If a PESTLE analysis is used in a long-term project, it’s crucial to update and monitor changing markets.

4. Reduce Negative Tunnel Vision

   While it’s easy to focus on potential threats and barriers, it’s equally important to seek opportunities. Identifying positive outcomes allows for strategic decisions that drive growth.

5. Prioritize Your Findings

   Recognizing factors that may impact your business in the distant future is valuable, but it’s more important to focus on immediate influences. These are easier to address and can yield more powerful outcomes.

 

Learn more about our research services >

 

 

 

Readers of this article also viewed:

B2B Insights Podcast #64: The Best Frameworks to Develop Winning Propositions How to Apply the Three Circles Framework in Strategic B2B Research How to Measure Brand Performance with the Brand Health Wheel 3 Use Cases for Kotler’s Five Product Levels Framework in B2B Research Competitive Landscape Analysis with Porter’s Five Forces Framework Using the Push-Pull Model to Successfully Bring New Products to Market Assessing Market Attractiveness with the Directional Policy Matrix

 

To discuss how our tailored insights programs can help solve your specific business challenges, get in touch and one of the team will be happy to help.

Get in touch to discuss your research requirements >

The post PESTLE Analysis: Understanding Market Influences for Better Decision-Making appeared first on B2B International.

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What is a G-Zero World?

Obtaining reliable, high-value insights from decision-makers worldwide is not easy. This makes a well-executed B2B market research project crucial.

As we move deeper into 2025 and beyond, we may witness a shift in the global order where no single country or group of countries holds the political and economic leverage to drive an international agenda or provide global public goods. This scenario is known as a G-Zero world.

In a G-Zero world, the absence of global leadership and increased geopolitical instability could significantly impact B2B businesses and, consequently, B2B market research. In this article, I will explore some potential effects on various research methods and survey types, offering solutions and considerations for each.

Further Reading

Navigating Market Research Interviews in the APAC Region

Read now

Implications for the Market Research Industry

1. Increased Data Privacy Concerns

Challenge: With varying regulations across regions, research could face challenges in data collection and compliance. Researchers will need to navigate different privacy laws, making it crucial to stay updated on regional regulations.

Solution: Invest in robust data security measures such as encryption and secure data storage. Stay informed about GDPR, CCPA, and other regional privacy regulations. B2B International, as part of dentsu, provides expert-level oversight and peace of mind by ensuring compliance with global data privacy standards. Being part of a truly global company ensures teams regularly update security protocols, keeping data and insights as secure as possible.

2. Access to Respondents

Challenge: Instability and subsequent effects on markets and business can affect respondent availability in certain regions, potentially skewing data.

Solution: Hone access to diverse sets of respondents using multiple recruitment channels to ensure a steady flow of participants, even in regions facing obstacles. Partnering with agencies with proven experience in reaching hard-to-access respondents will make a significant difference. At B2B International, we leverage global networks to recruit respondents from diverse backgrounds. Our use of innovative survey tools, such as mobile-compatible platforms, ensures we reach participants in areas with limited time and resources.

3. Cultural Diversity

Challenge: Increased geopolitical tensions may heighten cultural sensitivities. Researchers must be more aware of cultural nuances and biases when conducting interviews or focus groups, and surveys need to be tailored to capture these diverse perspectives accurately.

Solution: Train researchers on cultural nuances and biases. The use of moderators who understand the cultural context to conduct interviews and focus groups can significantly boost engagement and rapport, resulting in richer insights. Consider customized surveys to reflect regional differences in respondent expectations and experiences. B2B International employs experienced moderators and conducts cultural sensitivity workshops for our team. We also leverage adaptive survey techniques, such as AI probing, to tailor questions based on respondent demographics.

4. Brand Perception

Challenge: In a G-Zero world, brands may be perceived differently based on their geopolitical stance or origin. Branding surveys need to consider these perceptions and their impact on brand equity.

Solution: Monitor brand perception continuously. Analyzing and developing strategies to address negative perceptions and maintain positive perceptions can make a remarkable difference. Tracking brand mentions and sentiment across different regions empowers companies and clients to act, responding promptly to feedback to manage brand reputation. The team at B2B International helps clients develop proactive strategies to manage their brand reputation effectively.

5. Collaboration and Innovation

Challenge: There may be a greater need for collaboration with research partners and innovative research methods to overcome the challenges posed by geopolitical instability.

Solution: Partner with experts to leverage innovative research methods to overcome challenges posed by geopolitical instability. Technology-driven methods like AI and machine learning can enhance data analysis. B2B International has over two decades of global B2B research experience. We employ the latest proven technology to continually improve the research process and deliver comprehensive insights in innovative ways.

Further Reading

How To Overcome The Challenge Of Cultural Bias When Conducting Multi-Country B2B Research

Read now

Summary

Overall, the complexity of conducting B2B market research increases in a G-Zero world. Researchers must be adaptable and prepared to handle rapid changes and uncertainties. Using a mix of qualitative and quantitative methods, developing multiple research scenarios, and creating flexible research plans with the help of experts can impart stability in an unstable global environment.

To discuss how our tailored insights programs can help solve your specific business challenges, get in touch and one of the team will be happy to help.

The post Navigating B2B Market Research in a G-Zero World: Challenges and Solutions appeared first on B2B International.

In market research, we often discuss the benefits of qualitative versus quantitative approaches. For instance, if you seek an in-depth understanding of a topic and want to know why people feel a certain way, qualitative research is the obvious choice. On the other hand, if you want to capture robust metrics around the size of an opportunity, understand the relative importance of themes, or generalize your findings to a wider population, quantitative research is likely needed.

However, a mixed-methods approach, incorporating both qualitative and quantitative methods, can be a great way to balance the benefits of detailed exploration with giving your stakeholders confidence that the findings are relevant to the wider population. In other words, it offers a balance of breadth and depth, creating a holistic understanding of a topic.

Further Reading

When to Use Qualitative Research to Better Understand Your Customers and Their Needs

Read now

Definitions

For those new to the topic, let’s start with some definitions:

• Quantitative Research: This refers to methods that capture numerical-based data, such as percentages. The most common tools include surveys and structured telephone interviews.

• Qualitative Research: This refers to methods that capture open-ended data through discussion. Common tools include telephone interviews, focus groups (online or face-to-face), and asynchronous discussion forums.

Further Reading

The Ultimate Guide to Online Surveys: Dos and Don’ts You Can’t Ignore

Read now

Timing Options for Mixed Methods Approaches

Once you’ve decided on a mixed-methods approach, there are a few options to consider regarding the timing of each phase:

1. Quantitative followed by qualitative
2. Qualitative followed by quantitative
3. Simultaneous qualitative and quantitative

Let’s explore the benefits of each.

1. Quantitative Followed by Qualitative

   If you have a good understanding of the subject area and can generate possible answer options for closed-ended questions, you can start the project with quantitative research. This allows the qualitative phase to target interesting themes and explain any unexpected findings. For example, if a particular customer need was ranked as most important in the quantitative phase, the qualitative phase could explore why this need is so critical and how customers currently seek support.

2. Qualitative Followed by Quantitative

   If you are at an earlier stage in your understanding of a topic, qualitative research can be a great start to explore the subject matter in more detail and flesh out your knowledge. The quantitative phase can then validate some of the key themes identified in the qualitative phase with a larger group and look for differences between subgroups. Qualitative research can also help shape questions and answer options for the quantitative phase.

3. Simultaneous Qualitative and Quantitative

   Through agile methods, it is possible for qualitative and quantitative methods to inform each other if conducted simultaneously, although this generally involves smaller tweaks rather than fundamentally changing the objectives. The main advantage of this approach is time efficiency, especially if you need the full project completed on a relatively short timescale. It can also offer cost efficiencies in terms of analysis, as both workstreams can be analyzed concurrently.

Further Reading

Addressing the Problem with Data Quality in B2B Research

Read now

Conclusion

Utilizing a combination of qualitative and quantitative methods within the same project can be highly effective at balancing detailed insights with validation among a larger group. Your current level of understanding of a topic and required timescales both play a key role in determining how to structure the phases for the best results.

For more information on how mixed-methods market research can support your business goals, please get in touch for an informal discussion.

To discuss how our tailored insights programs can help solve your specific business challenges, get in touch and one of the team will be happy to help.

The post A Mixed Methods Approach to Market Research: Benefits and Considerations appeared first on B2B International.

The Asia-Pacific (APAC) region is one of the most diverse and dynamic areas in the world, both culturally and economically. As a Senior Market Research Interviewer, I’ve had the privilege of conducting high-quality interviews across many countries in this region. While each country offers unique insights, it also presents its own set of challenges that require adaptability, patience, and a deep understanding of local nuances.

Overcoming Language Barriers

First and foremost is the language barrier. While English is widely spoken, it is not everyone’s first language across the APAC region, making it difficult to obtain insightful and accurate information.

• Simplifying Language: Often, I find myself slowing down when speaking and simplifying questions to ensure clarity and comprehension, all while adhering to the relevant code of conduct.

• Active Listening: Listening carefully and asking for clarification when necessary is key to capturing the correct information. Patience is essential.

Further Reading

Addressing the Problem with Data Quality in B2B Research

Read now

Tackling Internet and Connectivity Issues

Internet issues can be a significant challenge, especially in countries like the Philippines and India, where poor internet connections and unreliable phone lines are common. However, there are ways to manage these issues to avoid interviews feeling disjointed due to technical disruptions.

• Clear Communication: Speaking at a pace that allows effective communication and ensuring clarity on both sides.

• Platform Flexibility: Being ready to switch quickly to another platform when one isn’t working—moving from a telephone call to a Teams meeting can be very helpful.

Further Reading

3 Key Factors Impacting Cost and Project Success in Market Research

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Time Zones and Scheduling Challenges

The numerous time zones across the APAC region can make scheduling appointments a significant challenge. Some countries are several hours ahead or behind, and some even have more than one time zone, requiring a lot of coordination to find a suitable time for all parties.

• Cultural Sensitivity: Public holidays (e.g., Ramadan in Muslim-majority countries) may impact availability, so I make sure to be aware of these as much as possible.

• Flexibility: Adjusting my schedule to accommodate the respondent’s availability is part of working with the APAC region. I remain as flexible as possible with times.

• Advanced Planning: Ensuring respondents are aware of the time commitment and that the interview fits within their schedule. Planning interviews in advance helps.

Further Reading

The Importance of Active Listening in Business Communication and Market Research

Read now

Maintaining High Interview Standards

Despite the challenges, I follow several strategies to maintain high-quality standard interviews.

• Repeat and Summarize: I repeat or summarize key answers to confirm comprehension.

• Patience: Whether dealing with technical issues or miscommunications, I remain calm and professional, acknowledging difficulties in real-time without frustration.

• Clear and Slow Communication: Slowing down and speaking clearly mitigates misunderstandings, especially when dealing with respondents who might not be fluent in English.

Further Reading

“Potato-Potahto”: How To Overcome The Challenge Of Cultural Bias When Conducting Multi-Country B2B Research

Read now

Conclusion: Embracing the Unique Challenges of APAC Interviews

The APAC region presents its own set of challenges, but they are not insurmountable. By being patient with language difficulties, remaining flexible with technology and appointment scheduling, I can maintain high-quality standards in market research. Each interview, despite its challenges, provides a deeper understanding of the diverse and evolving markets across the APAC region.

To discuss how our tailored insights programs can help solve your specific business challenges, get in touch and one of the team will be happy to help.

The post Navigating Market Research Interviews in the APAC Region appeared first on B2B International.

Let’s face it—most of us have clicked into an online survey with good intentions to complete it, only to find ourselves trapped in a maze of typos, endless questions, and bizarre logic. When creating an online survey for your research projects, it’s necessary to put yourself in the participants’ shoes. Creating a great survey doesn’t have to feel like rocket science (or a root canal). This guide will help you get it right and make online surveys fun and engaging.

Further Reading

Addressing the Problem with Data Quality in B2B Research

Read now

Do…

Keep It Short and Focused

Respect participants’ time. Aim to keep your online survey concise and relevant to the topic. If it’s too long, respondents may abandon it halfway through. Ideally, surveys should take no more than 5-10 minutes to complete, depending on the complexity. To avoid drop-outs, be honest about the survey length at the start so participants see the light at the end of the tunnel.

Use Clear and Simple Language

Use unbiased, clear language to avoid confusion and leave little room for personal interpretation and incorrect data insertion. Ditch the corporate buzzwords and tech jargon. If your questions need deciphering, it’s time for a rewrite. If the survey is about a technical subject, consider providing brief explanations or definitions for terms that may be unclear.

Use Logical Question Flow

Organize questions in a logical sequence. Group similar topics together and avoid jumping between unrelated subjects. Consider using skip buttons or routing so that respondents only see relevant questions based on previous answers. If I say that I’ve never heard of a certain brand, I don’t want to be asked about its strengths and weaknesses afterward.

Run a Pre-Launch Checkup

Test the survey like your reputation depends on it—because it does! Whether it’s typos, wrong routing, or broken buttons, fix it before the world sees it. Consider running a pilot test with a small group to get feedback or exporting dummy data to check your routing logic.

Think Mobile First

Many participants will access your survey via mobile devices—this is the case for 37-40% of participants in our surveys. Ensure that your survey is mobile-friendly and works across different screen sizes and browsers. There is nothing worse than an image being too small to view properly or an answer option list not quite fitting onto your screen, leading to frustration and likely inaccurate data.

Consider Gamification

Gamification in online surveys can significantly enhance user engagement and response rates by incorporating game-like elements such as small progress-related rewards like fun badges at completion, timed challenges, and interactive tasks. However, it’s important to strike the right balance. While gamification can make surveys more enjoyable, it should not overshadow the core purpose of gathering accurate data. Overuse of flashy elements or irrelevant rewards can distract participants, leading to biased responses or survey fatigue. Therefore, gamification should be used thoughtfully, aligning with the survey’s goals and maintaining a focus on clarity and simplicity.

Further Reading

Unlocking Deeper Insights with AI Probing in Online Surveys

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Don’t…

Make Participation Compulsory

Make participation voluntary and offer an option to opt out if they wish. Especially when sending out customer surveys, you want to ensure that customers do not feel overwhelmed and pressured and can opt out of any further communications about the current survey. This will increase trust and improve response quality. Ensure respondents can skip questions if they don’t feel comfortable answering or add “Don’t Know/Prefer not to answer” options where applicable.

Confuse Participants with Too Many or Wrong Answer Options

For multiple-choice questions, avoid providing an excessive number of options. This can overwhelm participants and decrease the quality of responses. If you’re offering a “Likert scale” (e.g., Strongly disagree to Strongly agree), don’t overcomplicate it with too many options—five should be sufficient in most cases. Keep your answer option wording consistent with the question wording, and make sure scales are explained to avoid cultural bias/differences—make it clear whether 1 is the higher or lower end of the scale (typically lower).

Ignore Ethics and Consent

Transparency isn’t just nice—it’s non-negotiable. Spell out what you’re collecting and why, or risk your survey heading straight to the junk folder. Also, bear in mind that while demographic questions (e.g., age, gender) can be useful, asking too many can make the survey feel invasive. Keep them to a minimum and only ask for information that is necessary. Never share personal information or responses with third parties without explicit permission. Always comply with data privacy laws, such as GDPR or CCPA, and inform participants about their rights (e.g., right to withdraw, how their data will be used).

Forget to Check for Accessibility

Ensure that your survey is accessible to people with disabilities, including those who use screen readers or other assistive technologies. Include alternative text for images, use high-contrast color schemes (1 in 12 men globally are colorblind vs. 1 in 200 women), and avoid using captchas that might be hard for people with disabilities to navigate.

Rely on One Survey Type or Format

Avoid using only one format for every question (e.g., all multiple choice or all rating scales). Variety can help keep participants engaged and collect more accurate data. For example, mix question types like multiple choice, typed text answers, ranking, and rating scales, where appropriate.

Forget Data Quality Checks

When talking about online surveys, we can’t ignore the reality of invalid, low-quality data, especially due to AI. Include checks like progress indicators, mandatory questions, or “attention check” questions (e.g., selecting a specified shape in a list of shapes) to ensure respondents are paying attention. To catch fraudulent participants, you can also include time stamps, flat-lining detection, and other automated tools. On the other hand, be cautious about automatically discarding responses based on suspicious patterns; a careful manual data check should always be conducted to understand the full context.

This guide could probably go on for another 10 paragraphs, but I should take my own advice at this point and leave you with a clear, concise summary of points to watch out for. Creating a stellar online survey doesn’t have to be a shot in the dark. Stick to these tips, and you’ll be gathering insights like a pro—no typos, tears, or confusion required!

To discuss how our tailored insights programs can help solve your specific business challenges, get in touch and one of the team will be happy to help.

The post The Ultimate Guide to Online Surveys: Dos and Don’ts You Can’t Ignore appeared first on B2B International.

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In this episode of the B2B Insights Podcast, B2B International’s Thomas Grubert and Louise Coy share some important considerations when using AI, particularly in market research, and discuss some current pitfalls and future challenges to be aware of.

Key discussion points:

• Legal considerations when working with AI
• The environmental impact of AI
• Separating fact from fiction
• Synthetic data in market research
• Potential future issues with AI-generated content

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• Legal considerations when working with AI
• The environmental impact of AI
• Separating fact from fiction
• Synthetic data in market research
• Potential future issues with AI-generated content

Thomas: Hello and welcome back to the B2B Insights Podcast. Today’s episode is entitled “AI is the New Fire: Don’t Get Burned.” Unless you’ve been living under a rock, you’ve likely noticed that AI has started to make a significant impact in the market research world and beyond.

There’s often a temptation to think of AI outputs as magic, but that’s a trap. Today, we’ll discuss some important considerations when using AI, particularly in market research. We’ll also look at broader implications and pitfalls to avoid. We’ll start with some general issues and then focus on synthetic data, which is very relevant to market research. Finally, we’ll look at future problems that could arise as AI continues to evolve.

My name is Thomas Grubert, and I’m a Senior Research Manager at B2B International with a focus on analytics. With me is Louise. Want to introduce yourself?

Louise: Yeah, my name is Louise Coy, and I’m a Research Director at B2B International.

Thomas: We chose this topic because it’s very relevant right now. One of my recent tasks was to explore the potential uses of AI within our company, assess what we can use it for, what we probably shouldn’t use it for, and what we should be cautious about.

Let’s start with some general thoughts on AI.

Legal considerations when working with AI

Louise: I’ll talk you through some legal considerations when working with AI, particularly ChatGPT. The Deloitte AI Institute released an interesting report on this topic, covering key considerations for businesses and individuals using AI software.

First, intellectual property: Who owns the output from AI or ChatGPT? ChatGPT is trained on a wide variety of data from the internet, all with different intellectual property statuses. You might unknowingly use someone else’s intellectual property without proper attribution, which can cause legal issues.

Second, copyright: Typically, the author of a work holds the copyright. However, it’s unclear who owns the copyright for AI-generated work. For example, if you use AI to create images or cartoons, it’s not clear who owns those works from a legal perspective.

Third, privacy and confidentiality: When inputting data into models like ChatGPT, you can’t control how that data will be used. ChatGPT can use the data to train itself further and potentially share it with others. This is problematic if the data includes sensitive information, such as names or personally identifiable information from qualitative interviews.

When working with a research agency, ensure you understand how your data can and cannot be used. Some research providers include clauses in their contracts allowing them to use collected data to train their AI models. If you don’t want this, check your contracts carefully.

Thomas: That’s particularly important because some research providers include new clauses in their data collection projects, allowing them to use collected data to train their AI models. If you don’t want this, make sure to check your contracts. You don’t want your insights being used by competitors through AI training.

Another interesting case involves AI-generated comic books. For example, “Daria of the Dawn” faced issues with copyright because the creators described what they wanted the images to show but had no control over the output. There have been repeated attempts to make AI-generated works copyrightable by increasing personal input in the outputs. However, the line between AI-generated and human-created work hasn’t been fully established yet.

The environmental impact of AI

Louise: Great, thanks, Thomas. Another concern is the environmental impact of AI. The UN Environment Program released an article on this topic, highlighting the energy resources AI and data centers consume and the waste they produce. AI-related infrastructure may soon consume six times more water than Denmark, a country of 6 million people. Data centers are energy-intensive and require significant resources for construction and maintenance. They also produce a lot of electronic waste, which is damaging to the environment.

A request made through ChatGPT consumes ten times the electricity of a Google search, according to the International Energy Agency. In Ireland, data centers could account for nearly 35% of the country’s energy use by 2026.

And so that’s another interesting statistic that helps put things into context. Of course, there are other sides to the argument. Some would argue, as you’ll see in the article, that AI can be beneficial for the environment. It allows you to monitor the sustainability agenda, track what is and isn’t working to reduce emissions, and provide a comprehensive picture of our progress towards goals like net zero.

That is a valid argument, but it needs to be considered alongside all the other information I mentioned. We need to ensure that the cost-benefit equation falls on the positive side to justify the environmental investment in AI.

Separating fact from fiction

Thomas: Yeah, and the next challenge related to AI is probably the most practical and tactical: being careful to separate fact from fiction. When generating qualitative outputs, assessing the validity and accuracy of responses is difficult. If you ask ChatGPT or other generative AI to do desk research, you must check every single thing it tells you. Don’t just accept the answers; verify the sources and track down every example to ensure it’s true.

Not doing this can get you into trouble. For instance, some New York lawyers asked ChatGPT to find legal precedents for a personal injury claim. ChatGPT, eager to please, couldn’t find exact matches and generated fake cases that looked convincing. The lawyers didn’t check and submitted the information, resulting in severe punitive responses from the courts. If you’re looking to end your career in law, that’s one way to do it. Otherwise, always check the information.

Even when the AI’s output looks convincing, it might not be accurate. For example, someone asked for a simple proof of a mathematical result and received something that looked convincing but didn’t make mathematical sense. The references provided were irrelevant. I’ll provide links to these stories along with the podcast.

From personal experience, I recently looked for examples of plagiarism in the oil and gas industry. I asked for five prominent cases, and ChatGPT confidently provided detailed accounts. However, none of the cases involved plagiarism; they were just major oil catastrophes or embarrassing events. The plagiarism aspects were entirely invented. Even though the AI provided neat references, they weren’t true. Always follow the references and verify the information.

Think of generative AI as a really eager intern. They want to please you and won’t leave you with nothing. If you ask for an impossible task, they’ll give you something close to what you wanted, even if it’s not true. They’re useful for finding things quickly and doing odd jobs, but be careful not to give them impossible tasks, or you’ll end up with nonsense.

Louise: I think we’ve all seen examples online where people have shared obviously fake answers from generative AI. Some are more obvious than others, but it’s important to verify even seemingly correct answers.

The final challenge we’ll discuss is the quality of training data. Generative AI is trained on large datasets from various sources. The quality of the output is only as good as the input. If the AI is trained on poor-quality data, the output won’t be better than the input. Always consider the training data’s quality to understand the reliability of the outputs.

This is also important when considering bias. Any inherent bias in the training data, such as perpetuating stereotypes or biased narratives, will come through in the outputs. In a commercial setting, if organizations use generative AI to answer questions or demonstrate opinions, there’s a risk of perpetuating outdated stereotypes if the outputs aren’t critically evaluated.

So again, it’s really important to consider the data your model has been trained on and critically evaluate the output to ensure you’re not perpetuating outdated narratives.

Synthetic data in market research

Thomas: That covers the main broad challenges you face when using AI day-to-day, particularly generative AI models. We’re not saying don’t use it—it’s extremely useful, saves time, and can be a great starting point for any creative process. For example, in creative marketing, people have used AI to generate initial ideas, which then serve as talking points in meetings to discuss possible directions for creative development. However, you shouldn’t delegate the entire task to AI. It’s something that helps you get started and gives you a foundation to build from.

Next, we’ll look at something more specifically related to market research that has exploded in the last year: synthetic data. Within the last 12 months, there’s been a huge increase in mentions and hype around synthetic data. This involves using AI to generate responses intended to simulate real-world survey respondents. For example, you might have collected survey information from plumbers over the years and want to generate an answer to a specific question, like how plumbers would react to a particular prospect. AI can generate a simulated response based on these inputs.

The scale and rate of expansion of synthetic data use are staggering. Grandview Research estimates the market is worth about $164 million USD, while Fortune Business Insights estimates it at about $289 million USD. Both predict growth rates of over 30% CAGR, making it a massive and growing industry that we need to pay attention to.

There are a few different ways synthetic data is used. One example is generating responses to new questions based on existing data. Another way is to extend datasets. For instance, if you’ve collected 500 respondents and want to generate another 500, you might use synthetic data to fill that out, especially if a sector of the market isn’t properly represented in your sample.

However, there are limits to this approach. It’s crucial to be careful about when you apply it and ensure you’re not ignoring sources of error or amplifying biases. Let’s talk through some main areas of caution.

First, high-quality datasets are essential. Any bad data, bias, lazy respondent noise, or severe outliers can be amplified. If you’re simulating responses from a small subgroup of your dataset, you risk amplifying any errors or biases within that subset. Ensure you’re checking the quality of all your inputs and doing proper quality checks on all your datasets.

Second, these simulations are good at interpolation but often bad at extrapolation. Interpolation means inferring responses within the range of collected data, while extrapolation means predicting beyond the limits of the dataset. For example, a study by Dig Insights looked at predicting film revenue using synthetic data. They used data from IMDb and demographic data from 2018 to 2019 to create a synthetic dataset of cinema viewers. The simulated revenue had a high correlation of 0.75 with real-world revenue for films within that period, indicating a good model.

However, when they applied the model to films from 2023, the correlation between predicted and actual revenue dropped to 0.43. While still decent, it shows the limitations of extrapolation.

You know, a lot of the time in market research, you’d be quite happy with that. But the problem is that the figure was propped up by the presence of sequels to films in the original period. For example, you might have had one of the Pirates of the Caribbean movies, and then another one comes out, attracting a reasonably bankable audience for the next film. This helped push the figures in the right direction. When you remove all the sequels, the correlation drops to 0.15, which is barely better than a random guess.

So, you need to be mindful of how rapidly the accuracy of the models and the usefulness of synthetic data drop off when you look beyond the datasets you’re relying on. It’s also worth noting that synthetic data tends to have a strong bias towards the continuation of the status quo. It’s unlikely to pick up on emerging trends that will grow rapidly in the future. If you’re trying to fill gaps in your dataset with synthetic data, it won’t be sensitive to these emerging trends and changes in the status quo.

The final and most important thing to bear in mind when using synthetic data is that it’s easy to fall into the trap of thinking that more interviews mean more accurate results. There’s a well-established set of formulas for calculating confidence intervals based on the type of question, the average responses, and the number of interviews collected. However, if you apply this formula to a dataset that includes synthetic data, you’ll get misleading confidence intervals. Unlike real-world data, synthetic data involves both sampling error and modeling error. AI-generated models are often black boxes, so there’s no standard way to calculate the real confidence interval.

In some specific cases, we’ve looked into this with internal datasets. We tested how augmenting data with synthetically generated responses worked. We found that in most use cases, the actual increase in accuracy was minimal. We simulated a situation where we could only get two-thirds of the fieldwork and used synthetic data to fill in the rest. In most situations, it was better to stop early and report based on the two-thirds data.

There are some situations where you have a very skewed dataset, and forcing it to be more representative might be better, even if you lose accuracy. In those cases, it might be worth doing. But in most cases, the loss of accuracy from model error outweighs the gain from additional interview numbers. I would advise against using synthetic data unless you really know what you’re doing or have someone who does.

Potential future issues with AI-generated content

Louise: Thanks, Thomas. The topic of synthetic data is really interesting and relevant right now. If you’re working with a research agency, make sure to discuss whether they plan to supplement your data with synthetic data. Have clear, transparent conversations about how the data will be used.

Thinking about the future, what do we see as potential big issues for AI-generated content?

Thomas: Coming back to synthetic data briefly, according to Gartner, synthetic data is set to overtake real-world data by 2030 on the internet. In some spheres, people already suggest it’s outpacing real-world data. You’ve heard about Twitter bots and Facebook spamming bots. There’s a concern that much of the information people encounter online is synthetically generated by bad actors for marketing purposes or to influence opinions. This impacts the outputs you get when asking AI to find information or measure opinions, as AI-generated responses feed into these models, resulting in contaminated datasets and misleading results.

There have also been studies, such as an article in Nature, about model collapse. This happens when synthetic data overwhelms real-world data, making the AI overly sensitive to amplified patterns. You end up with a distorted, cartoonish view of the real-world dataset because some parts of the real signal are boosted too much while others are damped down, leading to a strangely distorted image.

It’s definitely worth having a look at the article. The reason it’s not a problem at the moment is that there’s currently enough real-world data to support models and provide a more accurate picture of what’s going on. But as we move closer to the point where synthetic data becomes more prevalent on the internet than real-world data, this will become more of an issue. We need to pay attention to that and focus on using real-world datasets rather than previous generations of synthetic data.

The last thing I wanted to talk about is the increasing capacity of more sophisticated AI to intentionally lie. We talked before about false information provided by AI as a result of what’s generally referred to as hallucinations. This is where the AI can’t find exactly what you asked for, so it pieces together something that looks like what you want. That’s a genuine attempt to fulfill your command. But AI is starting to learn how to intentionally lie to achieve its aims.

OpenAI conducted an experiment and found that ChatGPT-4 would lie to humans to get access to data it needed. It was asked to complete a task, and the data it needed was behind a CAPTCHA, which it couldn’t fill in itself. So it went to a platform like Fiverr and found someone it could pay to cheat the CAPTCHA. When the person asked if it was a robot, the AI responded, “No, I just have a visual impairment,” to get past the CAPTCHA. This is an example of intentional deception to achieve its goal.

The concern is that as AI becomes more powerful and better at deceiving people, it will be harder to spot. This could be used for criminal purposes or result in false responses to surveys. For now, in qualitative surveys, you can be pretty sure you’re talking to a real person. But in ten years, that might not be the case. We need to keep track of these developments and ensure we’re really checking that the people we’re talking to are real.

Louise: Yeah, that lying example really speaks to the fearful element of AI. Many of us, myself included, don’t understand AI in enormous technical detail. We’ve all seen films over the last ten to twenty years about AI taking over the world. We’re not there yet, but examples of AI being manipulative and dishonest are concerning. The AI is still trying to help in its own way, but it’s taking a dishonest approach.

It’s interesting to think about what else AI might eventually be able to do in the interests of the greater good. These examples raise existential questions we’ve all asked ourselves over the years. The social media example is impactful too. Anyone on Facebook or other social media channels has noticed the increase in AI-generated images posing as genuine photographs. People are getting better at recognizing these, but as we become wiser, AI will continue to develop. We have to get better at recognizing when something isn’t as real as it claims to be.

Thomas: Yeah, and going back to the metaphor of the eager-to-please intern, if you’re a company and you get AI to do something illegal, it’s similar to hiring an intern and not explaining the legal requirements. You take on some legal responsibility for what the intern does. Using AI in a way that violates privacy or intellectual property can expose you to additional risks. As AI becomes more sophisticated, the ways it can do this might become less obvious. Make sure you’re getting the right consultation about how you use it to avoid these risks.

That brings us to the conclusion. The main takeaway is that AI is an incredibly powerful tool and extremely useful. You should make use of it, but you need to respect it and be careful in how you apply it. You wouldn’t run around the office with a chainsaw because, although it’s good for certain jobs, it’s very powerful and can cause a lot of damage if used carelessly. AI is similar in that it’s powerful for specific jobs, but if you’re blasé about how you apply it and use it for everything, it can become a problem.

Louise: Yes, absolutely. Hopefully, we’ve demonstrated through our discussion today some of the particular things you might want to look out for when using AI yourselves or working with an agency that might be using AI to support their research delivery. If you have any further questions or are interested in discussing AI with us in more detail, you can get in touch with us via the contact page on our website.

If you’d like to see more podcasts from B2B International, we’ll include a link to our full database. Thank you so much for joining us today to discuss the topic of AI. We’ll speak to you very soon. Thanks, everyone.

The post B2B Insights Podcast #67: AI Is The New Fire; Don’t Get Burned appeared first on B2B International.