Marketers’ awareness of the importance of brands in purchasing decisions is growing. A strong brand allows us to avoid price wars and convince buyers through aspects that go beyond the usual product/service purchase criteria. With a strong brand, we can also retain our existing customers and build deeper business relationships. Therefore, the brand is one of the most important assets of a company, supporting sales and renewals and providing further financial benefits through brand licensing, franchising, etc.

For this reason, many companies are working to create a stronger brand. Our global study with B2B decision-makers, the Superpowers Index, shows that in an increasingly intense competition for customers, the brand is more important than ever, and brand building has become the highest priority for B2B marketers.

The brand ladder helps illustrate where brands are in their journey. While most B2B brands are in the recognition stage, we observe that stronger brands manage to position themselves clearly in the market and differentiate from the competition, at least in terms of a clearly differentiated offering. However, a large majority fails in the development of a brand personality.

A strong, engaging, and relevant personality offers many advantages for a company. The personality can help:

• Distinguish the brand from the competition
• Build an emotional connection beyond the primary decision maker
• Make the brand more memorable
• Attract new customers

Before showing the brand’s personality externally, a strategic decision must be made on how a brand wants to be perceived. The personality must:

• Align with the existing company and employee culture
• Consider how the brand may want to evolve in the future
• Be engaging and relevant to current and potential customers
• Be relevant to society, as brands nowadays seek to have significance and influence outside their industry

Further Reading

How Market Research Can Help Enhance Brand Communication in B2B

Read Now

Carl Jung’s framework with twelve ‘Brand Personality Archetypes’ serves as a very good template. The archetypes are derived from four categories defined by their primary motivations:

• Provide structure: Stability, control, and discipline
• Pursue connection: Belonging, enjoyment, and engagement
• Leave legacy: Risk, mastery, reward
• Explore spirituality: Independence, fulfilment, values

While all personality types can be found among B2C brands, B2B brands are more often characterized by the following personality types: Creator, Ruler, Caregiver (all personalities from the ‘Provide structure’ category), as well as Sage (Explore spirituality), Everyman (Pursue connection), and Hero (Leave legacy). The latter is especially visible in industries or product categories related to climate protection.

However, it strongly depends on the product category whether the company dares to choose more ‘courageous’ personality types, such as ‘Lover’. What might be difficult to imagine for medical devices could potentially work well for craftsmen, etc.

It is also important that the framework provides guidelines but does not limit, and it’s entirely possible to claim a mix of two personalities.

Market research can support companies in the process of personality development at different stages:

1. Firstly, in developing a clear personality, in-depth conversations with stakeholders (management, employees, investors) as well as customers or potential customers can help identify which personality types might be considered.

2. If certain brand personalities are considered, they can be tested or evaluated qualitatively (for optimization) or quantitatively (for final selection) at a later stage.

3. Regularly conducted brand studies help measure whether the brand personality strategy has successfully reached and engaged customers, potential customers, employees, and other important target groups. They also help determine where our competition stands, whether we differentiate ourselves sufficiently from them, and whether market expectations of brands have changed.

A strong personality that appeals to the emotional needs of the market not only benefits a company financially but also strategically within its industry and broader society, becoming a highly valuable asset worth protecting and building upon.

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 Why B2B Companies Should Consider Their Brand Personality appeared first on B2B International.

In the B2B sector, improving brand communication is a common goal of market research. Unlike B2C, where emotional appeal often drives consumer behavior, B2B marketing requires a more nuanced approach that links rational decision-making with emotions. This article highlights the importance of brand communication in B2B marketing and how companies can use market research insights to enhance their brand communication strategy.

Why Brand Communication is Important in B2B

Brand communication in the B2B context includes activities aimed at positioning a brand and communicating with business customers. This encompasses advertising, public relations, social media, and content marketing. Effective communication helps build a strong brand image, increase awareness, and create trust.

Further Reading

How to Determine the Strength of Your B2B Brand

View Playbook

How Market Research Helps to Develop Effective Brand Communication Strategies

Market research is essential as it provides the data and insights needed to make smarter, more informed decisions when developing brand communication strategies. By researching industry trends, the competition, and customer needs, companies can tailor their communication strategies to better resonate with their target audience.

Insights-Driven Strategies for Effective Brand Communication in B2B

1. Identify Your Target Audience and Competitors: Understand who your customers are, their pain points, and their needs. This allows you to create tailored communications that set you apart from the competition. Studying competitors provides valuable insights into the competitive landscape and helps identify what makes your brand stand out. Creating personas is a useful method to understand your target audience.

2. Maintain Consistent Messaging Across All Channels: Ensure your company’s communication is consistent across all key channels. This strengthens the brand image and reduces confusion for potential buyers.

3. Combine Emotional and Rational Appeal: While rational factors such as cost and timelines play a big part in B2B decision-making, building emotional appeal can show your target audience why your business makes a unique and positive impact on their day-to-day work life and on the wider industry.

4. Implement a Multichannel Strategy: Use a mix of different channels to reach your target audience. This can include social media, industry publications and events, third party partnerships, and email marketing. Analyze what combination works best for your company.

A Brand Communication Research Case Study

A great example of using market research to enhance brand communication in the B2B sector is GEALAN, a leading supplier of window profiles in Europe. By conducting market research with B2B International, GEALAN was able to better understand its brand position and create more differentiated customer communications and services tailored to their customers.

Case Study

Helping GEALAN Understand and Strengthen Its Brand Position

View Case Study

Summary

Effective brand communication is essential for a successful B2B brand strategy. By combining market research insights with creative and unique communication strategies, companies can deliver their brand messages effectively and build strong relationships with their customers. Understanding the needs and preferences of your target audience and incorporating these insights into your brand communication is key to achieving long-lasting success.

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 Market Research Can Help Enhance Brand Communication in B2B appeared first on B2B International.

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

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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.

A deep dive into the latest Superpowers Index reveals what’s important to professional services buyers, the top priorities for professional services marketers, and the key trends impacting the professional services sector in 2025.

The landscape of professional services marketing is evolving at an unprecedented pace. The Superpowers Index provides a comprehensive analysis of the critical factors influencing professional services buyers, highlighting their priorities and preferences.

This report delves into the essential insights from the research, offering marketers a strategic roadmap to navigate the complexities of the sector. By understanding the top priorities for professional services marketers and identifying the key trends shaping the industry, this report equips you with the knowledge and tools needed to excel in 2025.

The post What Marketers in the Professional Services Sector Need to Know to Win in 2025 appeared first on B2B International.

A deep dive into the latest Superpowers Index reveals what’s important to tech buyers, the top priorities for tech marketers, and the key trends impacting the tech sector in 2025.

In the rapidly evolving landscape of the technology sector, staying ahead of the curve is crucial for marketers aiming to make a significant impact.

This report delves into the findings of the Superpowers Index, offering invaluable insights into the preferences and priorities of tech buyers. This comprehensive analysis highlights the top priorities for tech marketers and identifies the key trends that will shape the industry in 2025.

The post What Marketers in the Technology Sector Need to Know to Win in 2025 appeared first on B2B International.

A deep dive into the latest Superpowers Index reveals what’s important to financial services buyers, the top priorities for financial services marketers, and the key trends impacting the financial services sector in 2025.

In the rapidly evolving landscape of financial services, staying ahead of the curve is crucial for marketers aiming to capture and retain customer interest.

This report offers a comprehensive analysis based on insights from the Superpowers Index. It delves into the core values and priorities of financial services buyers, highlights the top strategies for marketers, and identifies the key trends that will shape the sector in the coming year.

The post What Marketers in the Financial Services Sector Need to Know to Win in 2025 appeared first on B2B International.

A deep dive into the latest Superpowers Index reveals what’s important to manufacturing buyers, the top priorities for manufacturing marketers, and the key trends impacting the manufacturing and industrial sector in 2025.

As the manufacturing and industrial landscape continues to evolve, marketers within this sector must stay ahead of emerging trends and shifting buyer priorities to maintain a competitive edge.

This report delves into the critical insights from the manufacturing cut of the Superpowers Index, highlighting what matters most to buyers, the top priorities for marketers, and the key trends shaping the sector.

The post What Marketers in the Manufacturing & Industrial Sector Need to Know to Win in 2025 appeared first on B2B International.

B2B International’s Superpowers Index research, an annual study into the dynamics of B2B buying behavior, has revealed a significant trend: the growing importance of active thought leadership.

Each year, the study examines 30 decision drivers, covering both professional and personal factors, to uncover what truly differentiates winning brands from their competitors. In 2024, “being an active thought leader in the category/sector” jumped from 20th to 3rd place globally, a clear sign of its growing relevance as a key tool in the B2B marketer’s toolbox.

For Gen Z and Millennial B2B buyers, thought leadership holds even greater weight, ranking as the #2 decision driver. This signals a generational shift in expectations and priorities for B2B brands.

Top Decision Drivers in 2024

The top factors that set winning brands apart:

1. I feel safe signing a contract with them
2. Is known as being a good employer
3. Active thought leaders in their category/sector
4. Comply with regulations, law, industry standards & ethical practices
5. Takes care of its suppliers, business partners, and communities

A Gap in Thought Leadership Performance

Despite its growing importance, few B2B brands are excelling at thought leadership. Only 25% of B2B buyers believe the brands they engage with are doing it well—a statistic showing no signs of improvement from 2023.

Encouragingly, B2B marketers recognize this gap, with just 30% rating their brand’s content creation efforts as strong. This presents an opportunity for B2B brands to meet the demand for compelling thought leadership. However, to create compelling content, brands must understand what makes effective thought leadership.

Further Reading

The 2024 Superpowers Index: Full Report

Download now

What Makes Effective B2B Thought Leadership?

Our research identified areas where B2B brands are succeeding and falling short when it comes to thought leadership:

The Basics – Doing Well

These are the foundations, where brands are performing effectively:

• Identifying topics that your target audience needs to know in response to trends
• Keeping your audience up to date on trending topics

Neglected Areas – Room for Improvement

These are the areas critical to buyers but where brands are underperforming:

• Communicating on industry-specific topics and trends
• Exploring unique angles on topics others overlook
• Conducting original research on trending issues
• Offering an interesting point of view on trends

Low-Impact Areas – Deprioritize

These are less important to buyers and therefore may not be worth the focus:

• Being the first to talk about topics later adopted by others
• Communicating on trends specific to individual job roles

Further reading

4 Reasons to Use Thought Leadership Research to Inform Your Content Marketing Strategy

Read now

Summary

Interestingly, the focus is on the quality of content that adds value to the buyer, rather than being the first to discuss a topic. As thought leadership grows in importance, B2B marketers need to ask:

• Are we focusing on what really matters to our audience?
• Are we offering unique insights and perspectives?

By addressing neglected areas and elevating the quality of their content, B2B brands can position themselves as trusted thought leaders, ultimately building greater trust in their brand overall.

At B2B International, we conduct primary research that empowers brands to create impactful, evidence-based content. As part of dentsu B2B, our team of copywriters, creatives, and strategists is here to help you make the most of your unique insights. Learn more about our thought leadership 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 B2B Thought Leadership: Why It’s Important & How to Do It Well appeared first on B2B International.

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In this episode of the B2B Insights Podcast, B2B International’s Conor Wilcock and Simi Dhawan discuss the challenges marketers face when targeting B2B buyers and how they can use segmentation to overcome these challenges.

Key discussion points:

• The complexity of customer needs and the competitive landscape
• The importance of differentiation in B2B
• Personalization of buyer experiences
• Challenges with data privacy
• How to conduct and use customer segmentation
• Benefits, risks, and pitfalls of segmentation
• How to make segmentation actionable through creative outputs

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Read the full transcript:

Jump to section:

• Main challenges facing marketers
• How marketers can effectively market to their audiences
• Benefits of implementing a segmentation strategy
• Risks and pitfalls with segmentation
• Key trends and considerations for successful segmentation projects

Conor: Hello and welcome to the B2B Insights Podcast. I’m Conor Wilcock, Managing Director at B2B International, and I’m joined by Simi Dhawan, Senior Research Director, for an important discussion on the challenges marketers face when targeting B2B buyers and some of the things they can think about to overcome these challenges.

This is particularly important for B2B companies who need to engage a varied customer base with complex needs, whilst at the same time balancing tighter marketing budgets.

In this episode, we’ll explore this challenge alongside delving into segmentation as an important opportunity to effectively target B2B customers.

Main challenges facing marketers

Conor: To start things off, Simi, what would you say are the main challenges marketeers need to keep in mind, based on your experience?

Simi: You mentioned it earlier but marketers have been experiencing tighter budgets since the pandemic and we expect this trend to continue. However, there’s a dichotomy here between tighter budgets, alongside more complex customer needs, expectations and buying decisions, as well as more competition. Brands therefore have to work harder to stand out.

Conor: That’s my experience too and our recent Superpowers data certainly backs this up. Would you agree?

Simi: Definitely…. there’s a number of trends we’ve observed within this wave of Superpowers that suggest that marketers have an increased challenge in making their brand stand out. Our latest wave of Superpowers speaking to over 14,000 B2B buyers globally since 2021 has shown there’s more decision-makers involved in B2B purchasing decisions than ever before – over 7 on average, where this is growing by roughly 6% every year. B2B organizations can’t rest on their laurels since there’s also more brands being considered.

Conor: I’d agree, it’s been quite telling to see that even a third of incumbents have been losing out to other brands which is higher than we observed last year! So, marketers certainly have to work harder than ever to win against the competition.

Simi: I’d go even further and say that they probably think they are doing a better job to stand out from the crowd than they are since we found that 7 in 10 felt they were communicating a distinct USP and brand position whilst 7 in 10 buyers actually felt that B2B brands ‘..have very similar messages’ and ‘all sound and act the same’.

Further reading

Market Segmentation in B2B Markets

Read more

How marketers can effectively market to their audiences

Simi: I suppose the key question then is what is it that marketers can do to more effectively market to their audiences and stand out from the crowd?

Conor: That’s the key question! I suppose what we’ve seen is the power of personalizing experiences and marketing efforts to the needs of buyers.

Whilst on the one hand, it makes sense for marketers to place focus on the aggregate needs of the organization, increasingly, we are finding that the needs of the individual are key and as you mentioned earlier, there are more buyers that are involved in decision-making, so it’s being able to talk to the needs of buyers at different levels – from the senior decision-makers right through to those in more junior or influencer roles.

Marketers should really be demonstrating they understand the day-to-day working world and challenges of these individual buyers. They need to show that they are willing to tailor their offering to fit their needs.

Simi: I think that’s really at the heart of everything we’ve seen and when B2B brands do deliver more personalized brand experiences, they are able to reap the benefits.

On this topic, I’d read something interesting* which said that traditionally, buyers would be happy to share their personal details in exchange for more personalized marketer’s content. However, with increasing concerns about data privacy, fewer buyers are willing to part with this information before they are doing business with a company. What would you say to marketers facing this challenge?

*https://business.linkedin.com/marketing-solutions/technology-marketing/tech-insights/the-rise-of-self-personalised-buyer-journeys

Conor: I think brands can have a human face and offer more tailored experiences without expecting customers to part with personal information upfront.

One effective means is through creating a customer segmentation. With this approach, marketers can seek to better understand their diverse range of customers and use this insight to replace a ‘one size fits all’ marketing strategy into one which is more tailored. Segmentation allows B2B marketers to group customers into ‘like’ segments, for example, because they have similar needs, behaviors, attitudes, firmographics or indeed a combination of these.

This means they can create more targeted value propositions and messaging for each segment they wish to serve, whilst also identifying which segments might be less attractive or aligned to their offer. All of this helps to drive efficiencies in their marketing efforts and resources, which in view of marketing budgets being squeezed, can only be a good thing!

Simi: I think it’s interesting because whilst we are discussing business-to-business, we’re also focused on a more personalized approach for individuals – all of which have their expectations set at what they might experience elsewhere in their consumer lives too.

For example, if we think about subscriptions such as Netflix, we can see personalization at play with personalized content recommendations based on analyzing user behavior. Similarly, B2B brands are adopting a similar approach and an example we’ve seen from our recent Superpowers research is HP, who have been successfully running account-based marketing campaigns that help their sales and marketing teams to understand their target customers better, communicate the right messages to them and connect with them better on both a functional and emotional level.

Further reading

Best Practices For Implementing Segmentations

Read more

Benefits of implementing a segmentation strategy

Simi: I think risk and reward are two sides of the same coin, so it would be helpful to explore both for a more balanced discussion for our listeners. Focusing on the reward first, how would you summarize the benefits of implementing a segmentation strategy?

Conor: Well, you could treat all customers the same as a segment of one and try to market in the same way to everyone, but this could result in a business being too generalist in its offerings and marcomms and could be detrimental because it doesn’t demonstrate the tailored approach that B2B business buyers value.

On the flip side, in a perfect world, we could argue that every customer has unique needs and so we should be treating each customer as its own segment, however, this is impractical and inefficient to deliver in practice so we need an approach that is effective and practical. Segmentation is that compromise. It’s a tailored approach to marketing rather than a one size fits all and as we know a more personalized experience can drive commercial benefits.

Simi: What sorts of commercial benefits can be achieved by tailoring a marketing approach in this way?

Conor: We’ve observed that B2B businesses that adopt more personalized experiences can enjoy winning in contracts of higher value, shorter sales cycles and obtaining higher loyalty levels from their customers. However, as our recent Superpowers research demonstrated, only 5% of B2B experiences are delivering fully on this, so there’s an opportunity here.

Risks and pitfalls with segmentation

Conor: You mentioned risk versus reward earlier, so whilst there’s clear benefits to segmentation, what would you say are some of the possible pitfalls to be aware of?

Simi: First and foremost, I think it’s important to identify your business goal for running a segmentation. If your goal is to better understand your existing customer base, which segments are most attractive to focus attention on or increase your sales or loyalty levels amongst these, then it makes sense to run a segmentation project focused on speaking to your existing customer base.

Beyond this, if your goal is to build market share amongst prospective customers or build your brand then you may need to cast the net wider and conduct a market segmentation to include existing and prospective customers. There’s different approaches to designing these studies and so it’s important to understand the main business focus.

Once you identify the type of segmentation project that’s right for you, there’s some common pitfalls to be mindful of.

It might sound obvious, but the first is making sure you have a manageable number of segments – too many of these and you might find that some of your segments are too small or niche to serve and that there’s too many to action. If you have too few, you might find that segment needs or characteristics are too broad and they aren’t tailored enough, so it’s important to get the balance right.

Going back to a point mentioned earlier, it’s also important to determine upfront what you want to achieve from the segmentation. If the main point is to create suitable value propositions and marketing messages to different customer groups, then you’re likely going to benefit from a needs-based segmentation which is led by grouping customers based on their needs and challenges. However, if you’re wanting to use the segmentation to allocate customers within your CRM, then you may well need to place more emphasis on customer firmographics such as customer type, sector, size and so on, so they are easier to characterize.

Conor: I agree, I think the project design is key and led by the outputs needed. There’s lots of factors to consider as mentioned but also even thinking about whether we are after quantitative measurement to allocate segments or some qualitative understanding of the segments too.

Further reading

Buyer Personas: How to Research, Develop and Activate Buyer Personas in B2B

Read more

Key trends and considerations for successful segmentation projects

Simi: We have seen a growing rise in projects which combine both quantitative and qualitative elements, for example, with persona development. If you had to summarize, what would you say are the key trends marketers should watch out for when it comes to successful segmentation projects?

Conor: Firstly, I think segmentation is changing in terms of how B2B businesses are using this. I think traditionally, we’d place more emphasis on segmentation to group different customers, but now, as you mentioned, there is an added layer in terms of persona development which takes things further by bringing the segmentation to life so that marketers can target individual fictional buyers based on similar characteristics and behaviors. Persona development also helps B2B teams to more clearly identify the segments so these are less abstract and more relatable in a way.

Secondly, I’d say that there does appear to be a growing trend and interest in our clients wanting to integrate their internal data, such as on their CRM with any segmentation or persona data from the research. Again, this is brilliant because it talks to a desire to action the findings by having the data to empower you to personalize your marketing and messaging which as we discussed earlier, is a key means that businesses can stand out from the crowd.

Simi: Thanks Conor, on the last point, I think this is key and ultimately for a segmentation to be successful, it needs to be actionable. It’s a big reason behind why there’s so much value to our clients that we have an in-house statistics team, as to help our clients, we’ve been able to create customized tools that help them bridge the gap between their data and ours, such as by having a segment allocation tool which helps to easily identify which customer or future customer falls into which segment.

I’d also add that the internal cascade and understanding of the segments is key since our clients should be able to easily understand the segments or personas in order to action them. So further to having a statistics team, having a wider creative offering through our in-house teams within B2B International and dentsu means we can really bring the segments to life through visual representations, videos and so on, which can be more engaging than a PowerPoint deck!

Conor: Thanks Simi….I completely agree and I must admit, some of the outputs we’ve seen on segmentation studies are definitely amongst the most creative, so watch this space on upcoming projects!

For those listening, if you’ve enjoyed today’s episode, make sure to subscribe so you don’t miss future episodes.

To explore more of our content on how segmentation research is used to inform successful business outcomes, or to subscribe to our B2B Insights Newsletter, please visit B2B International’s Insights Hub.

The post B2B Insights Podcast #66: How To Effectively Target B2B Buyers in 2025 appeared first on B2B International.