Recently, we talked about the vital importance of being a thought leader within your industry. Today, we’d like to share one of our recent thought leadership pieces developed last year for dentsu on the topic of Diversity, Equity, and Inclusion (DEI) – what does it mean to organizations, how do they develop it, and how can it transform brands?​

In our interconnected world, DEI is no longer just a set of ideals, they are essential for driving innovation and sustainable growth. Organizations that truly embrace DEI see far-reaching benefits, from driving employee engagement and loyalty to boosting customer advocacy and market growth.​

Whether you’re striving to attract top talent, connect with diverse communities, or outpace competitors, DEI is your gateway to innovation, relevance, and impact. Discover in our summary below how prioritizing DEI isn’t just the right thing to do—it’s the smart thing to do for your people, your business, and society.

Background information

Research Aims​

• Gain insights to advise our clients on their DEI journey​
• Guide and effectively shape clients’ communications and strategy​
• Guide them both on internal and external aspects of communication and strategy

Research Scope​

The primary research was conducted across the UK, Germany, France, Spain, Italy, and Poland.​

Both consumers and b2b decision-makers expect brands to promote diversity and inclusion across a range of categories​

Some of these are currently rarely embraced by organizations, such as physical and mental disabilities. There is therefore an opportunity for brands to lead and differentiate.​

Both amongst consumers and b2b decision makers, brands performing well on DEI enjoy a significant advantage in terms of consumer advocacy (see below), loyalty, and emotional connection. The NPS advantage is:​

And yet, movement is slow. Only 17% of business decision makers self-report that their organizations are DEI leaders, while 30% say their organizations have not yet taken any action on DEI.​

So, what makes a leader?​

We found that a leading organization shows long-term commitment to continuous change management with a focus on its holistic organizational culture.

For example, leading organizations – as reported by their employees – are more likely to consider less obvious and visible layers of DEI including sexual orientation, socioeconomic and marital status, and physical disabilities.​

To become a leader, one should especially think about creating transparent, equal pay structures, a safe employee complaints system, and inclusive hiring practices.​

Externally, to be an authentic leader you should:

1. Show what you stand for​

More than half of customers struggle to form an opinion about a brand’s commitment to DEI. By weaving an authentic narrative into your marketing from the get-go, you’re not just building trust but also laying the groundwork for lasting loyalty.​ Primary thought leadership is one great way of building those levels of trust.

2. Be real​

Be genuine, be transparent. When it comes to DEI, honesty is the best policy. Being upfront about your progress and challenges deepens customer attachment, which can drive tangible business success. It’s about being real with your audience​ and reaping the rewards. The best way to this is through transparent data, trackable KPIs, talent pipeline analysis, and industry-wide development programs.​

3. Don’t fall on your values​

Stay true to your values because these can be your superpower and give you a competitive advantage. Create messaging that resonates with customers, employees, and society as a whole. But remember – actions speak louder than words.

​

Further Reading

To dive deeper into our findings, read the full report.

View full report

​

The post The DEI Advantage: Driving Business, People, and Society 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

Listen to the full episode:

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Jump to section:

• 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.

When considering the growth strategy of your business, there are several circumstances in which it becomes necessary to evaluate and compare market opportunities. You might be planning the launch of a new product or service – either in a new market, or one that you are already familiar with through your existing business activity. Alternatively, you might be seeking expansion through the introduction of an existing product or service into a new market.

Whatever your goals, there is much to consider when evaluating market opportunities – and several ways in which market research can help you to develop a winning strategy. In this article, we will outline some of the key considerations when it comes to market evaluation, and our top tips for making informed, strategic decisions.

Further Reading

How Market Research Can Help Guide Your Market Entry Strategy

Read more

The Size of the Market

One of the first questions you might be asking yourself is: ‘how large is this market for the product / service we are offering?’. Knowing the size of the market can help you to understand whether the market is large enough and profitable enough to be worth exploring further. Additionally, it is important to consider growth rates, i.e. whether the market is moving in the right direction and at a pace that is appealing to you.

Market research – a combination of primary research and desk research – can help to answer this question. Market size is typically understood as the total value or volume of a given market, in a given year, at a given point in the supply chain – so, in order to understand the market size, this work needs to begin with defining what you’re looking for:

• What market size are you interested in? The Serviceable Available Market (based on your current competencies and channels), or the Total Available Market (considering also your underlying potential?)

• Which stage of the supply chain are you sizing? For example, which types of company will you be selling to?

• What specific product(s) / service(s) are you sizing the market for?

With definitions set, there are several ways in which the market size can be calculated. The best market size estimates triangulate from several of these calculations to obtain the most robust possible estimate. You can consider the following as part of this:

• ‘Demand side’: Conducting interviews with a representative sample of end users of the product / service to find out what they spend, then aggregating this data up for the whole market by using data such as the total number of companies that exist

• ‘Supply side’: Assessing the revenue of competitors, and adding it up to obtain an estimate of the existing market value

• ‘Top down’: Identifying the size of a bigger market within which your market is contained, and using this to estimate your own market size by defining your relative position

• It is also important to consider how your own internal data may add to this picture, e.g. in cases where the product / solution you are investigating is already launched in another market

Further Reading

Using the Push-Pull Model to Successfully Bring New Products to Market

Read more

The Structure of the Market

Having established the size of the market, the next task is to understand the structure of the market. Two elements that are essential to consider when evaluating market opportunities are the existing competitive landscape, the context within which the market operates, and routes to market.

Where possible, it is desirable to understand the competitive landscape in three elements:

• Market share – what proportion of the market is currently held by each competitor? How has this changed over time – which competitors appear to be growing?

• Market context – a situation analysis to understand the opportunities and threats in the markets you are interested in

• Market perceptions – how are the different brands perceived? What are their strengths and weaknesses? What qualities or attributes do each of your competitors stand for?

While desk research is a valuable resource in understanding market share, it cannot take you as far in understanding how brands are truly perceived in the market. To do this, we recommend conducting a market survey with end users and / or potential buyers, to ask for their unbiased view of the key brands they are aware of in this market – including, where applicable, your own.

There are several tools and frameworks we can use to help you understand the existing competitive landscape: how brands are perceived and, crucially, what this means for your own brand strategy upon entering the market:

• Brand mapping: Identifying which brands in the market ‘own’ certain characteristics, and which qualities lie ‘unclaimed’

• Three circles model: Displaying the intersection of customer needs, your own strengths, and competitor strengths to help understand where your organization would sit among competitors

• Porter’s five forces: Helps an organization to assess whether the market is attractive to enter, and where the key challenges might lie

Tools like SWOT and PESTLE analysis can help you to understand the context within which the market operates:

• SWOT: An analysis of Strengths and Weaknesses of your own organization, alongside Opportunities and Threats arising from the wider market

• PESTLE: Analyzing the Political, Economic, Societal, Technological, Legal and Environmental context and trends within the market to help further understand opportunities and threats that your organization might face

Finally, when evaluating a market, it is also important to understand how the market operates, including the typical route to market. For example:

• Is the product you are considering typically sold through distributors, or is it purchased directly?

• Would this be considered a strategic purchase or more of a routine / transactional purchase? In either case – who would be responsible for making the purchase decision? We have conducted numerous ‘path to purchase’ research projects which answer this exact question across a range of industries and geographies.

Further Reading

Assessing Market Attractiveness with the Directional Policy Matrix

Read more

Buyer Needs and Preferences

Having developed an idea of the size and structure of the market, the third requirement is in understanding how to appeal to buyers in that market. We can already gain a sense of this through research into brand perceptions, but a dedicated piece of research can give you valuable insight into the challenges faced by your target audience, as well as their needs and expectations. These projects might include:

• Customer needs research – to identify ‘beneath the surface’ customer insights

• Buyer persona research and development – to bring your target audiences to life and inform your strategy

• Path-to-purchase research – to understand the most influential factors driving your buyers’ decision making and identify which touchpoints are most critical to success

How to Compare Between Potential Markets of Interest

It is evident that when evaluating a market opportunity – the size of the market, the structure of the market, and buyer needs – you will be faced with a wealth of data! A key skill of any researcher lies in making sense of such data and applying it to business strategy. In our own research projects, we are often faced with the challenge of distilling such data into a clear story of key learnings or recommendations. To get to this point, you can consider:

• Which market is most appealing?

• Why is this the case? Is it, for example, to do with size, suitability, current brand position?

• How should your organization approach this market, whether with an existing product / service or a new one?

Once we’ve compiled all of our data for market assessment projects, we frequently use the Directional Policy Matrix framework to assess overall market attractiveness for our clients. This tool can direct strategy based on the attractiveness of a market and the capability of the organization to fulfil the opportunity. It is most useful when an organization has several countries, products, or segments to investigate – allowing you to compare the relative appeal of multiple markets you may be considering.

Further Reading

Competitive Landscape Analysis with Porter’s Five Forces Framework

Read more

In Summary:

When evaluating market opportunities, there are several key research objectives that your organization will need to fulfil in order to make an informed, strategic decision. Market research is an invaluable tool at each stage of this process, combined with your existing knowledge and experience. Whatever you feel the ‘gaps’ in your knowledge to be, we have conducted research projects throughout this entire process and would be delighted to help you complete that final piece of the jigsaw puzzle and help towards your eventual winning strategy.

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 What to Consider When Evaluating Market Opportunities appeared first on B2B International.

Four years ago, we started a journey to understand the B2B buyer better, asking B2B buyers about their recent purchase experiences. We wanted to understand what was making the difference between winning and losing. And we wanted to go into detail we didn’t see in other studies.

Covering over 25,000 brand experiences, The Superpowers Index, conducted by B2B International in collaboration with dentsu B2B, is the largest ever systematic study of B2B buying behavior globally.

The 2024 report provides a new perspective on many of the big trends in B2B: the importance of trust and brand building; the ever-narrowing gap between winning and losing; the growing influence of thought leadership; and the challenges of scaling ABM.

To read some of the big headlines from the 2024 Superpowers Index, download the report below.

Listen to the podcast episode:

Listen on Spotify >
Listen on Apple Podcasts >
Watch the Full Recording >

Want to learn more about The Superpowers Index? Check out the Superpowers Hub on the dentsu site.

The post The 2024 Superpowers Index appeared first on B2B International.

New tools and technologies that speed up processes and reduce the time spent on repetitive tasks are a welcome addition to the market research world. The days of relying solely on manual data checks, Excel pivot tables, and transcription from scratch are behind us. Automation has streamlined many aspects of research projects, boosting efficiency. However, how much further can this go without compromising the quality and impact of the insights gathered?

The business landscape is becoming increasingly complex, with B2B decision makers’ behaviors and preferences evolving rapidly. Yet, recent research by B2B International shows that brands are more likely than ever before to sound and act the same. This made me consider the implications of relying solely on self-serve tools and dashboards to gather and interpret data. Is poor insight adding to the challenge that brands are facing to stand out?

Adoption of standardized approaches may be contributing to the challenge of distinctiveness, with insight projects failing to capture the nuanced understanding needed for strategic decision making. This is where custom market research should shine, providing tailored insights that reflect the unique context of your business.

With new tools enabling efficiencies, where should researchers and decision makers ensure that skill and expertise are focused?

Questionnaire design

The phrase “good research is asking the right questions of the right people” has guided me throughout my career. This principle remains at the heart of effective research. If individuals who are not decision-makers for the product or service in question are interviewed, the results will clearly lack reliability. Whilst leading questions or questions asked in an illogical order will fail to deliver an objective view.

If you have ever embarked on the task of designing a questionnaire to be used to address a unique business challenge such as understanding the dynamics of a complex new market or understanding customer perceptions, you will know that this is not an easy task. There is a long list of considerations, including coverage of the research objectives, ensuring key stakeholders will obtain the information they need, questions phrased in an easy-to-understand format using customer friendly language (i.e., not sales or marketing jargon).

Standardized questions are crucial for tracking key metrics over time, such as the Net Promoter Score or brand funnel metrics. Yet, there is no standard list of attributes suitable for every voice of the customer or brand survey. The art of questionnaire design is one of the most crucial skills a researcher possesses, ensuring that objectives are addressed effectively and efficiently.

AI tools can assist in this process providing templates and inspiration for answer lists, but skilled researchers bring irreplaceable value. Researchers will ensure that the right types of questions are included to cover the research objectives and they are asked in a logical order and that screeners are designed to avoid inadvertently disclosing the subject of research before confirming the correct decision maker is being interviewed. In addition, we have tested hundreds of questionnaire variations, we know what can cause distortion in the data, for example introducing the use of color in scales in a repeat piece of research.

Further Reading

Questionnaire Design: Best and Worst Practices

Read more

Tailored methods

Susan Cain’s book “Quiet: The Power of Introverts in a World That Can’t Stop Talking” provides a clear overview of how different people process information and contribute. This concept also applies to research participation preferences. A one-size-fits-all approach, especially in qualitative research, is unlikely to cater to everyone.

A simple example being how different people respond to the invitation to participate in an interview. Some people are happy to participate and require no further information until the interview begins, often several days after initially agreeing to participate. Often, these people are also comfortable sharing their unprompted and initial thoughts to questions. Others may only feel comfortable if they have a sense of the topic and an opportunity to consider some key thoughts beforehand. The outcome of interviews where people are comfortable sharing their views lead to better conversations and insights. It should be noted that sharing information about the topic of the interview is not always possible, especially when the objective is to gather initial, top-of-mind reactions to a concept.

Respondent preference aside, mixed method approaches are common in B2B research for the simple reason that finding B2B decision makers on reputable online panels in sufficient numbers is increasingly challenging. Relying solely on one recruitment method e.g., recruitment via online panels alone will limit the size of the audiences that can be reached.

One final thought around standardization in qualitative research. This approach is well suited to exploring and understanding new topics through open questions that guide a conversation between the interviewer and respondent. A skilled interviewer knows when it is appropriate to go off-script and explore in more detail, and how to get the most out of each interview. Good qualitative research takes time, allowing for review and adjustments of the questioning strategy if needed. This flexibility would be impossible if the same topic guide was used throughout the fieldwork period, as gaps would only be identified during analysis, when it’s too late to adjust.

Further Reading

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

Read more

Getting to insights in reporting

There are a lot of definitions of what an insight is, for example ‘a fresh perspective that reveals new paths to business success.’ An alternative could be described by a question that we often ask ourselves before sharing a first draft of a report with a client: ‘What have we found that the client didn’t already know.’ We measure our success by whether we uncover anything new or different – if not we’ve failed.

Getting to the core insight or insights from a project takes time – the type of time that can’t be sped up. This includes discussing findings with the research team, building on thoughts, and getting feedback from clients.

Automated analysis brings time efficiencies to report writing, especially when a clear structure is needed, such as for multiple market or business unit reports. Various tools can help researchers update and populate charts, but there is a risk that nuances or differences and therefore the insight within specific data cuts may be missed. You may recall the mantra that ‘data does not lie’ but it can hide key insights. For example, analyzing a data set at an overall level may miss significant differences in perceptions between markets or customer groups. In addition, automated tools alone do not have the expertise that statisticians can bring to the table.

Automated tools (today) struggle with of tailored reports, where a story-led narrative is required to capture the attention of senior decision makers. These reports are built from scratch and often visualized by experts in data visualization. A strong and compelling narrative is built around frameworks using inputs from multiple questions asked in the survey as well as secondary information on the business and broader market. This approach is far from reporting question by question and commenting on observations; instead, it involves identifying patterns, causes and connections.

Further Listening

B2B Insights Podcast #61: How to Ensure B2B Market Research is Strategic and Actionable

Listen now

Custom research brings value

I am an advocate of technology, but its application must ensure that market research delivers a competitive advantage. Good custom market research, tailored to your business, can uncover unique insights that drive strategic decision-making and offer a competitive edge in a complex market.

By leveraging both technology and human expertise, custom agencies ensure high-quality, relevant, and actionable data. This approach not only enhances the usefulness of market research but also helps businesses navigate market complexities and achieve their strategic objectives.

The post The Case for Custom Research: The Pitfalls of Standardization and the Quest for Uniqueness appeared first on B2B International.

The Directional Policy Matrix (DPM) is a business tool that can be used to highlight products, services or segments which are attractive to a company (market attractiveness), where the company has a strong competitive position, and also the market size. The purpose of the model is hence to direct strategy based on the attractiveness of a market and the capability of the organization to fulfil the opportunity. It is most useful when an organization has several countries, products, or segments to investigate.

Market Attractiveness

We can use the DPM to assess the attractiveness of markets, products, or segments for an organization. Market attractiveness can be assessed through a variety of factors, depending on the use case of the DPM. We can use attractiveness indicators such as growth prospects, profitability or even suitability to the organization (which may be driven by factors such as previous profitability, for example). Adapting the ‘attractiveness’ indicator to the organization’s strategic priorities means that we can develop a DPM that gives the organization the most relevant measure to drive action.

Ability To Serve the Market

When using the DPM, it is important to understand the capability of the organization to serve each segment, country, or product. The competitive position is usually calculated based on the accessibility of the market or on competitive advantage relative to competitors.

Accessibility Of the Market

The accessibility of the market may be determined based on factors such as existing routes to market, infrastructure, or knowledge of the market. Each of these factors could mean that the considered market is more accessible, and hence may provide ‘low hanging fruit’ for the organization to take advantage of.

Competitive Advantage

We would want to consider how well competitors perform in the market in order to assess the best strategic direction or opportunity for an organization. If there are several very strong existing competitors in a market, it may make the opportunity less attractive for the organization, as it may be more difficult to establish in a saturated market.

Further Reading

Competitive Landscape Analysis with Porter’s Five Forces Framework

Read Blog

Market Size

We can include the market size as a measure in the DPM. This gives the organization a steer on which segments, products or countries provide the biggest market opportunity. Market size can be based on a few different variables, such as the financial value of the market or number of products sold, for example.

Driving Strategy

Based on the findings of the DPM, we can place markets, segments, or products into four different categories, in order to drive strategy and action:

• Areas to invest / ‘go all in’: Markets, segments or product categories which are highly attractive and a very strong fit with the organization.

• Cash generation / ‘cash cow’: Markets, segments or product categories which are a very strong fit with the organization, but which may be less attractive (e.g., in terms of market size). These, however, provide a good opportunity for cash generation for the organization.

• ‘Double or quits’: Markets, segments, or product categories where the organization’s current ability to serve is low, but market attractiveness is very high. These markets, segments or product categories will require a lot of investment for the organization to serve but could provide a big opportunity.

• Evaluate / disinvest: Markets, segments or product categories which are a low fit for the organization and are also low in attractiveness.

Categorizing markets, segments, or product categories into these four segments can indicate the best way for organizations to move forward with their strategy, highlighting key opportunities, but also where they may need to withdraw or disinvest.

Can I Use The DPM In Qualitative Research?

Whilst the traditional DPM is very much suited to quantitative research, its principles can also be translated for qualitative research. It is especially useful to highlight company strengths and weaknesses, whilst also looking at market attractiveness. We may look at comments relating to future opportunities to establish market attractiveness. Plotting these factors on the same chart allows us to draw clear conclusions on where the opportunity lies for the organization, and which products or services are less attractive. This can help to direct strategy and focus for organizations, even without conducting quantitative research.

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 Assessing Market Attractiveness with the Directional Policy Matrix appeared first on B2B International.

Porter’s Five Forces is a framework that we often use to assess the competitive landscape in which our clients currently operate or intend to enter. This framework was designed by Michael Porter of Harvard University, who felt that a more traditional SWOT analysis was not always sufficient in assessing the competitive landscape, and how attractive (or otherwise) it might be.

Why is competitive landscape analysis important?

One of the key applications for this framework is when one of our clients is interested in entering a new market. It is essential to conduct some ‘due diligence’ to help better understand the market and develop a strategy. Using the Five Forces framework can help an organization to assess whether the market is attractive to enter, and where the key challenges might lie.

This framework is also a useful complement to a brand health assessment. It provides valuable context to the market landscape, when assessing awareness and perceptions of different brands in the market.

What is the Five Forces framework?

Porter identified five forces that are present – to varying extents – in all operating environments. Where all of the forces are very strong, this indicates a competitive landscape that is highly challenging – and perhaps unattractive for a new business to enter. Where all are very weak, this represents significant opportunity for a new business.

In most cases, the picture is more nuanced – some forces in a market might be strong, while others are relatively weak. In these instances, we can use the Five Forces framework to understand particular areas of challenge and opportunity, to help our clients develop strategies for market entry or penetration.

What are the ‘Five Forces’?

There are two vertical forces: bargaining power of suppliers, and bargaining power of buyers…

↓ Bargaining power of suppliers

This force indicates the level of power suppliers hold over businesses in the market. Where a supplier holds more power, businesses rely more heavily on them.

An example of suppliers holding strong bargaining power might be due to their holding a monopolistic position in the market. In this instance, they can charge higher prices, which might threaten the stability of any businesses using them as a supplier.

Other considerations to assess the bargaining power of suppliers might be:

• Do suppliers hold patents, or use special, proprietary technology?
• Are suppliers vertically integrated with any of your competitors?
• How many suppliers does your business have; how large are they?

↑ Bargaining power of buyers

This force indicates the power that buyers, or customers, hold over businesses. Some considerations to help assess the bargaining power of buyers might be:

• How price-sensitive is the market?
• How easy is it for customers to switch to new suppliers?
• How many customers does your business have; how large are they?

There are two horizontal forces: threat of new entrants, and threat of substitutes…

→ Threat of new entrants

This force looks at how easy or difficult it is for new competitors to enter the market. If there are high barriers to entry, this force is weaker. If it is easy for new competitors to enter the market, this force becomes stronger. Some key questions to consider might include:

• How strong is your business’ existing reputation in this space; how important is this to customers, and how difficult would it be for a new entrant to build a comparable reputation?
• How difficult and/or expensive is it for a new business to enter this market?
• Are there any unique skills or technologies that a business would require to be successful in this market?
• How cost-sensitive are customers; is it likely that a new competitor could enter the market with lower prices?

← Threat of substitutes

This force relates to the availability of an alternative product or service. If a business is a producer of orange juice, a new entrant would be another company that makes orange juice – whereas a substitute might be a company that makes apple juice. If more customers are switching to apple juice instead of orange juice, this would be a significant competitive force. Some additional considerations:

• How easy or difficult is it for customers to switch to an alternative product or service?
• How cost-sensitive are customers; is it likely that a substitute might be available at lower prices?
• Is it a possibility that customers might choose to undertake the service you offer, internally?

Finally, in the middle of the framework, we look at the current level of competitive rivalry. This is depicted in the middle of the framework, because it is based upon activity within the competitive set – rather than being imposed from other directions.

• Competitive rivalry

There are several elements to consider here, while bearing in mind that all competitors will be exposed to the four forces we have already outlined:

• How many competitors are there?
• How does the market share split between competitors?
• How loyal are customers?
• How big are the profit margins?

Further Reading

How to Apply the Three Circles Framework in Strategic B2B Research

View Blog

What other frameworks might be used during competitive landscape analysis?

• SWOT analysis offers a broader assessment of the Strengths, Weaknesses, Opportunities and Threats that might impact a business. Strengths and Weaknesses are internal factors, while Opportunities and Threats are external. Many of the topics we have discussed within the Five Forces might fall into Opportunities or Threats – for example, a high level of competitive rivalry would be considered a threat.
• PESTLE analysis can help us to assess the more macro factors (Political, Economic, Social, Technological, Legal, Environmental) that might impact the ease of operating in a market and the resultant business strategy.
• It is also important to remember that competitive markets do evolve over time. Therefore, it is wise to re-visit the Five Forces framework over time, to ensure that the business strategy is as strong as possible.

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 Competitive Landscape Analysis with Porter’s Five Forces Framework appeared first on B2B International.

Developing a winning value proposition and brand positioning strategy are two of the biggest challenges that companies face and they can often end in failure because companies may talk about things the market doesn’t care about, they might talk about things their competitors can beat them on, or they might talk about things that simply aren’t strengths of the company.

At B2B International, we use The Three Circles Framework in our strategic B2B research studies to overcome this challenge by ensuring our clients understand customer needs and their own strengths and weaknesses relative to the competition and build their value propositions and positioning strategies accordingly.

The Three Circles Framework consists of customer needs, company strengths, and competitor strengths, and each of these areas interlink to reveal the points of parity in the market and the points of differentiation our clients or their competitor’s may have. These insights help to understand which customer needs are currently being met and by which companies and understand where a company currently sits in the market space against competitors.

The Three Circles Framework Process

1. Assess market needs – companies should not be developing value propositions or building a positioning strategy that the market doesn’t desire, care about, or resonate with. Customer needs and segmentation research is used to identify the needs, wants, and desires of the market and establish which are currently being met by the client and the wider market and where there are gaps for opportunities to grow.

2. Assess your company’s strengths – companies should also be looking inwards and focusing their value proposition and positioning efforts around maximizing their own strengths. The Three Circles Framework helps our clients to identify and understand their unique points, i.e. where they uniquely and effectively meet customer needs, and their irrelevant points, i.e. parts of their offer which may be strong but are not desired by the market.

3. Assess competitor strengths – finally, understanding the competitive landscape is crucial to developing profitable value propositions and building a differentiated positioning. The third step is therefore an analysis of competitor strengths and the extent to which they uniquely meet customer needs or provide a stronger offer compared to our client’s.

How the Three Circles Framework Helps Companies Win

• The ‘points of parity’: If a client’s strengths equal or better the competitor’s strengths, they can choose to play in what we call the ‘brand battleground’ which is the intersection of all three circles. These ‘points of parity’ are the products, services, and capabilities that the customer wants and that everyone provides and so it can be very challenging to win in this space.

• The ‘points of differentiation’: What is often more successful is if a client chooses to focus on their ‘points of differentiation’ which is the intersection of customer needs and their own strengths. These are the existing products, services, and capabilities that successfully meet customer needs and that no-one else provides.

• The ‘white space’: Alternatively, a client can decide to go after the ‘white space’ by developing a set of products, services, and capabilities that answer the market’s unmet needs. If executed correctly, this strategy can help a company capture additional market share and grow quickly.

Further Reading

Competitive Landscape Analysis with Porter’s Five Forces Framework

View Blog

Use Cases for the Three Circles Framework in B2B Research

• Customer needs research: The Three Circles Framework can be applied to customer needs research to help identify unique areas for differentiation by exploring what customers value and how a company can align their offerings to meet customer requirements.

• Customer segmentation research: The Three Circles Framework can also be used to help segment the B2B market based on customer needs and perceptions by identifying key distinct customer groups with varying preferences and tailor the strategies and offering accordingly.

• Value proposition research: As already discussed, the Three Circles Framework can be useful in providing insight for value proposition development. The three distinct areas can clearly highlight what customers value, how customers perceive a company’s offering, and how customers perceive competitor’s offering. The overlap of the different areas can provide valuable insights for crafting effective value propositions.

• Product development research: When introducing new B2B products or services, the Three Circles Framework can be used to understand customer needs, evaluate how the product meets those needs, and compare this to competitors’ offerings. These insights help to inform product features, pricing, and positioning.

• Competitor analysis and understanding research: Finally, the Three Circles Framework can be used for competitor analysis by assessing competitor strengths and weaknesses to understand where an organization can differentiate itself and gain a competitive advantage.

In summary, the Three Circles Framework can be incredibly beneficial to inform a customer-centric strategy, provide white space exploration, delve deeper into competitor awareness, and provide positioning insight, all by taking a holistic view of the business 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 How to Apply the Three Circles Framework in Strategic B2B Research appeared first on B2B International.

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The bar for success in today’s dynamic business environment is higher than it’s ever been before. Business leaders need to keep pace with rising buyer expectations, shifts in purchasing behavior, and rapid advances in technology in order to steer their brand to the top. Navigating difficult macro-economic conditions only adds to the challenge.

To help business leaders stay at the top of their game and build a long-lasting competitive advantage in their own market, we wanted to invite the world’s greatest business thinkers onto the podcast to provide insights, strategies and advice on how to succeed in business today.

Our latest series features an amazing lineup of guests with expertise across multiple disciplines including leadership, problem solving, and strategic decision-making, to give listeners a roadmap for business success.

#1: Celine Schillinger

In the first episode of the series, Nick Hague is joined by Celine Schillinger, an award-winning entrepreneur, change agent, and consultant, for a deep-dive into effective leadership. This is essential listening for anyone who is currently or aspires to be a people manager, whether that’s in a multi-national corporate or a small to mid-sized company and the conversation is based around the insights taken from her book, ‘Dare To Un-Lead’.

#2: Wendy Smith & Marianne Lewis

In the second episode of the series, Nick Hague is joined by Wendy Smith and Marianne Lewis, co-authors of ‘Both/And Thinking’, a game-changing book on how to make more creative, flexible, and impactful decisions in a world of competing demands. If you want to develop a paradox mindset for problem solving, both at work and in your personal life, this is the episode for you.

#3: Amy Edmondson

In the third episode of the series, Nick Hague is joined by Amy Edmondson, Novartis Professor of Leadership & Management at Harvard Business School, and recently ranked #1 in a list of the world’s most influential management thinkers by Thinkers50. Amy discusses insights from her new book, ‘Right Kind of Wrong: Why Learning to Fail Can Teach Us to Thrive’, which provides readers with a valuable framework to think, discuss, and practice failure wisely. If you want to transform your relationship with failure, this episode makes for essential listening.

#4: Kirstin Ferguson

In the fourth episode of the series, Nick Hague is joined by Kirstin Ferguson, an award-winning author, columnist, and company director. Kirstin’s latest book, ‘Head & Heart: The Art of Modern Leadership’, has been named by Thinkers50 as one of the top 10 best new management books in the world in 2023, and the insights and practical advice from this book are what Nick and Kirstin dive into in this episode. If you want to learn what it takes to become an effective leader in today’s world, this episode is for you.

#5: Rita McGrath

In the fifth episode of the series, Nick Hague is joined by Rita McGrath, a best-selling author, sought-after speaker and strategic advisor, and a longtime professor at Columbia Business School. Rita’s 2019 book, ‘Seeing Around Corners’, is an invaluable guide to anticipating and capitalizing on disruptive inflection points in the market, and this episode dives deep into insights and practical advice on how to spot inflection points and use them to gain a competitive advantage. If you want to learn what it takes to strategically plan for future change within your business or sector, this episode is for you.

#6: Herminia Ibarra

In the sixth and final episode of the series, Nick Hague is joined by Herminia Ibarra, a Professor of Organizational Behavior at London Business School and world-renowned authority on leadership and career development. Herminia has authored two bestselling books, ‘Act Like a Leader, Think Like a Leader’ and ‘Working Identity’, but its insights and advice from the latter that Nick and Herminia dive into in this episode. If you want to learn what it takes to successfully reinvent your career, this episode is for you.

The post World’s Greatest Business Thinkers Podcast Series: Catch up on Episodes 1-6 appeared first on B2B International.

Our 2023 Superpowers Index research shows that B2B buyers are placing increased importance on ESG topics when choosing a brand to do business with. A significant opportunity exists for B2B brands willing to demonstrate a true commitment to social and environmental responsibility.

But how many B2B brands are embracing this challenge?

As in any market, there exists a diverse array of companies with varying behaviors, attitudes, and needs. In this infographic, we explore the 5 clear segments categorizing businesses in terms of their attitudes towards sustainability.

View infographic >

To dive deeper into why sustainability is an important growth driver in the B2B sector and the specific ESG-related purchase decision drivers that matter most to buyers, view the full report.

Further Reading

Learn how sustainability can lead to profitability in the B2B sector

Read the full report

Ready to take action? Get in touch to learn how B2B International can help you understand what your target audience values and measure the effectiveness of your sustainability messaging.

The post [Infographic] Exploring B2B Attitudes Towards Sustainability appeared first on B2B International.