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Our Insights Director, Eva Vandenberge recently attended the annual ESOMAR Congress and in this interview, she shares her insights.

This annual event by the global association for market research and insights offers researchers and consultants from all over the world the chance to connect and discover the latest trends and innovations in their industry. More than 1,000 people from 78 countries attended this year’s congress in Athens and boobook’s Insights Director, Eva Vandenberge, was one of them. With over 135 speakers, choosing which sessions to attend was a daunting task, but judging by the insights and reflections she shares with you today, she mastered it gracefully.

It was not the first time you attended the congress. What keeps you coming back?

“Nowhere else do you get such a comprehensive image of the current state of affairs in market research and a glimpse into its future. The presentations from the industry's top minds are inspiring, and the informal conversations with researchers from all over the world are fascinating. While listening to their insights, success stories and challenges I get so many new ideas to help our clients even better with their business questions. The industry has been rapidly evolving since the arrival of AI. It is important to keep track of the developments and discover whether new technologies and tools can help us gain better and more reliable customer insights.”

Before we dive deeper into the AI topic: were there any activities you particularly enjoyed?

“I appreciated that ESOMAR organised the YES Awards: a global competition that allows young research professionals to share their - often refreshing - ideas with the public. From all the pitches submitted, a jury selected ten who got to share their 60-second pitch at the congress. Via live voting, the audience selected three finalists who could give their full presentation. The winning presentations were on cultural bias, regression analysis using AI and data collection via WhatsApp in emerging countries. As a young researcher, it is quite an honour to get the chance to share your findings with such an audience.”

“There were no Belgian candidates this year, unfortunately, but I am also a board member of the Belgian Research Federation CUBE, and at the CUBExEsomar event that will take place on 24 October, several young researchers will share their take on the future of research and insights. Hopefully, this will lead to some interesting pitches by young Belgian research talent next year.”  

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Did you discover ideas that you would like to implement yourself?

“Absolutely. One of the YES award winners talked about cultural response bias, something we also struggle with. Certain cultures have difficulty sharing negative feedback and judge everything so positively that we barely detect differences between groups and brands in our analyses, while in reality, of course, there are. In one of our studies, ran in India, we corrected for this during the data analyses, but we are now inspired to tackle this issue by asking different questions. Questions about behaviour instead of attitudes, for example, or questions with neutral rather than numerical scales, so there is no longer a better or worse option. I had a couple of interesting discussions about the topic and will certainly put the new insights to the test.”

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AI was undoubtedly an important topic in Athens. Is it already reshaping the industry?

“AI was a hot topic indeed. Many AI developments and solutions were showcased. And several speakers discussed the potential, but also possible pitfalls of synthetic data, meaning data that has been artificially created as opposed to collected from humans. This could be used in analysis in the same way human data is used. Filling in missing data is nothing new; statisticians have been maximising samples via imputations for a long time. It might be as simple as filling in the occasional empty response with ‘don't know’ or mean scores based on the rest of the sample. Other times more complex models are used to predict those missing values, based on respondents' other answers, and what the rest of the sample says. Using AI, we can now do much more than fill in the occasional missing value.”

What is already possible with synthetic data today?

“As described, synthetic data might be used to complete missing answers, but you can also generate additional cases. If, for example, your study lacks young men from a specific region, you can generate more of them to boost your sample. You could even generate synthetic respondents; virtual participants that provide answers to a survey just like human participants would, and whose answers you can analyse as you normally would. It sounds futuristic, but companies are already experimenting with it, although there is still a lack of trust. Based on what we have seen and tested ourselves, we believe this distrust is justified and we would not recommend making business decisions based on synthetic data at this point.”

It doesn’t look like human respondents will be obsolete any time soon?

“The future will tell. The presented cases show that synthetic respondents can generate reliable results if closed behavioural questions are used. But virtual respondents can’t tell you how they feel, and they can’t answer open-ended questions well. We are curious to see if and how this will evolve.”

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What are some of your own experiences with using AI?

“At boobook, we have done several test on how AI would handle a segmentation study, for instance. When comparing the segments AI came up with the ones we determined, some overlapped but others didn’t. As long as results remain unreliable, real data, captured from real people, remains a must. But, of course, we keep track of the new developments and will continue running tests.”

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What would you like to put to the test in the short term?

“There is quite some enthusiasm about using synthetic cases to map hard-to-reach groups, such as B2B audiences. Synthetic boosters in under-sampled groups would increase the reliability of the study. We have our doubts because it seems unlikely that this data would indeed be reliable. How can it be, when your research pool is small to begin with? But the proof of the pudding is in the eating, so we plan to run a test and find out for ourselves if it could be useful.”

“What was also discussed at the congress, and I very much agree with, is that you should mainly use AI when a human being can’t add value. If using AI to script and translate questionnaires means resources are freed to generate better insights, we can only encourage it. But in terms of data collection and insight generation, we still need actual respondents and human researchers to bring brands closer to their consumers.”

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Which speaker left a lasting impression?

“It is not easy to choose just one, but I will not soon forget the closing keynote by Vivienne Ming, an incredibly intelligent woman who has travelled an unlikely path and achieved so much. She has founded several start-ups and solving seemingly unsolvable problems is her life's goal. She believes AI can be truly transformational and has developed several AI tools, but she is most passionate about maximising our human potential. To do so we need to create open cultures and safe spaces where people dare to experiment. Most of our initial ideas are wrong she claimed, so it is only through failing that we will stumble upon the great ones that can potentially change the world. It left me inspired and proud to be part of the boobook family, where we are all encouraged to share our ideas and get the chance to pursue them.”

“Especially now that there are so many new developments, companies must give their employees room to try new things. If you want to grow you need to accept that you will also fail sometimes. Sharing experiences and ideas is more important than ever, so I would leave you with a warm invitation to connect with us if you want to explore the possibilities of AI together!”

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How can businesses achieve unparalleled growth and maximise profitability in today's competitive landscape? At boobook, the answer lies in our data-powered strategic consulting, anchored firmly on four interconnected pillars: brand, product, pricing, and customer/consumer. By understanding and leveraging these fundamental elements, we guide businesses toward excellence and sustainable growth.

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In the previous weeks, we discussed the connection between brand, product and pricing strategy. As we close our "Pricing 360" series, we still have one crucial topic to cover: customers/consumers. When it comes to price, product or brand decisions, it's essential to focus on the individuals or companies who buy your products or services – your customers. Your targeted customers are a crucial aspect of your strategy because they influence how you market your product and are excellent guides when determining which products to put on the market and at what price.  

To truly cater to your customers and attract new ones, you need to understand who they are, how they behave, how they feel about certain things, what motivates them, their sensitivity to price (changes), and their purchasing habits.  

There are three approaches to get these insights: customer segmentation, path to purchase analysis (P2P), and price sensitivity measurement.  At boobook, these approaches are not exclusive but rather interconnected, each informing and shaping the other to optimise the product and pricing strategy. In this last article of our 360-pricing series, we'll explore these three methods to help you learn how to listen and understand your customers better so you can craft more effective pricing strategies.  

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Understanding your target audience and navigating the customer journey  

Customer segmentation is a powerful tool for businesses that want to identify and understand customer groups with diverse preferences and purchasing behaviours.    

We all know that the average customer does not exist. There are different types of customers in terms of age/gender, size of business, type of business, purchasing behaviour, as well as what drives their purchase behaviour, i.e. the why behind the behaviour. The latter is key to optimising the product offer and its pricing, as willingness to pay is very much linked to the type of customer you are.  

Tailoring your pricing strategy to segments can boost customer satisfaction, brand loyalty, acquisition, and, hence, business performance. A segmentation analysis also encompasses the evaluation of market segment size, providing a solid foundation for your upcoming business cases when introducing new products or potentially even sub-brands. This approach not only aids in understanding the potential reach of your offerings but also offers valuable insights into the most effective strategies for their promotion.  

There are several methods to identify customer segments. Most of them are related to analysing market research data, which collects data around the who, what and why of the (prospect) customer.  

You might have heard of methodologies such as k-means, hierarchical clustering, latent class and cluster ensemble. Each aims to find segments of people that differ in key characteristics. The most suitable methodology depends on the data as well as on the outcome. At boobook, we use the method that results in segments that are most actionable for the business question.

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Discover how Pernod Ricard decoded the travellers’ buying behaviour  

‍The path to purchase analysis covers the entire customer journey, from the moment a need is recognised to the point of product usage. The way we shop and buy today has become non-linear and is changing into omnichannel buying retail. As a result, throughout the customer journey, we now have various ways of shopping, which significantly impacts our decision-making process.    

Analysing this path is crucial to impact customer decisions and perceptions. By identifying different triggers and touchpoints along the journey, you can pinpoint the "moments of truth" most influential in leading to conversions. It's important to note that different customer segments may have distinct behaviours and are influenced by different touchpoints – and this is precisely where segmentation becomes valuable.  

Conducting a comprehensive study on the path-to-purchase can bring key insights, such as understanding the factors that trigger a customer's need, the specific channels and stores they prefer for buying, the duration of their decision-making process, and how to optimise in-store displays and online activations.    

Additionally, this approach can also shed light on how to create the most suitable pricing strategies. However, pricing may not always be the most critical element. It is crucial to understand what customers prioritise before price. Could it be recommendations from peers, friends and family? Perhaps it's catching the brand's advertisement while surfing online? By exploring these factors, you can tailor your approach, so your brand resonates with customers before they even consider the price.  

Want to learn more about the path to purchase? Learn more about how we helped Pernod Ricard UK understand their customers' path to purchase.  

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Analysing consumer behaviour through willingness to pay and price sensitivity  

Willingness to pay refers to how attractive a product or brand is at a specific price level. In other words, how many customers will consider your product given a certain price?  Price sensitivity goes one step further. It refers to how changes in prices impact consumer buying behaviour. So, how much will a price increase or decrease impact customer choice? The more price-sensitive your brand is, the more careful you have to be with price increases. Conversely, dropping the price of highly price-sensitive brands would be the way to go if you are after boosting volume. Though also realise that increasing again afterwards might not be easy. Temporary promotions could be a solution to this.  

Factors like brand image, brand equity, economic conditions, competition, brand loyalty, and ability to pay can affect consumers' price sensitivity. Researching and tracking price sensitivity, together with understanding brand image, helps businesses understand buying behaviour, adapt to market trends, optimise the price strategy and maintain a competitive edge.    

To measure price sensitivity, businesses can use quantitative consumer-research-based pricing methods like the Van Westendorf Price Sensitivity Meter, the Gabor-Granger Method, or a Conjoint Analysis. These methods provide insights into customer (pricing) preferences and help determine optimal pricing.  

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Discover how Center Parcs Europe optimised revenue management and increased profits

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Closing the loop  

Pricing is more than numbers; it's a language that communicates brand equity, product proposition value, quality, features, key customer groups, exclusivity, etc.  

Setting the right price for your product or service demands a strategic interplay of customer needs, perception and psychology, market dynamics and data analytics. By doing so, you can position yourself as a leader in your industry, resonate with customers, and drive sustainable growth.  

As we move away from traditional price-setting models, such as solely relying on cost-plus methods, embracing the dynamic realm of modern economics is essential. Your pricing strategy should be a continuous conversation with customers, exchanging value and expectations. Learning, adapting, and refining your pricing strategy through data-driven insights will help you stay ahead and build a thriving customer ecosystem.  

If you want to learn more about optimising your pricing, contact us at info@boobook.world!  

Artificial intelligence has pervaded every aspect of our lives, driven in large part by the remarkable popularity of generative AI applications. Among these, ChatGPT stands out as the app that boasts the most rapidly expanding user base in history, sparking extensive discussions around the boundless possibilities and future potential of this cutting-edge technology. Naturally, with such fervour comes a fair share of misconceptions and misinformation circulating.  

In his influential 2004 paper, John McCarthy defines artificial intelligence (AI) as “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable." The goal of AI is to develop a machine that can think like humans and mimic human behaviours. But let's debunk the myth right here - AI isn't a looming menace ready to overthrow the industry (or take over our jobs) - no matter what the media is trying to tell you. Instead, it's a game-changer, a potent tool reshaping our sector in ways we could barely have imagined a few years ago. ChatGPT, or more broadly, Large Language AI Models (LLMs), have become ubiquitous in our lives. LLM (Large Language Modelling) is an AI technology that efficiently processes and condenses vast amounts of information. LLM excels in tasks such as summarising and creating analytical models, but it does not possess the ability to think or discern between good and bad, it doesn't make any judgment, nor can it validate what it creates. Hence, human supervision is necessary to evaluate the accuracy and significance of the AI-generated output.

Imagine AI as an e-bike for your brain. It equips you to go further, quicker, and with less effort. However, just as the cyclist needs to pedal to propel the bike forward, human intervention is indispensable for AI to perform at its best.  

However, AI does come with its share of shortcomings. While AI has groundbreaking potential to overhaul the way we work, it is not designed to replace human intellect but to augment it and help us be better at what we do. While it's great at crunching colossal volumes of data, businesses need clear and actionable strategic advice, not basic data digests. Moreover, AI falls short when grasping sarcasm, irony, and other nuanced linguistic expressions. In such instances, humans still need to peruse the text to decipher the intricate layers of communication. Emotional interpretation, negotiations, imagination, and vision of the future are still strong qualities of humans, underlining the irreplaceable role of human understanding and contextual interpretation in harnessing the full power of AI.

Impact of AI on insights and analytics

Here at boobook, exploring new methodologies and learning about innovative technologies has always been in our DNA. Over the last year, we’ve been exploring working with AI, and we believe it offers great assistance in the process of gathering, creating, and presenting insights.  

One of the biggest advantages of AI is that it revolutionises the data analysis process by eliminating the risk of human error and bias. Additionally, AI automates the time-consuming tasks of data cleaning and preprocessing, freeing up valuable resources.

AI can greatly assist the industry in multiple ways:

1. Efficiency Increase

By harnessing the power of AI-powered algorithms, organisations can analyse vast amounts of data faster and more accurately than ever before. This capability reveals hidden patterns, trends, and correlations, providing invaluable insights and expanding organisations’ research capabilities in several groundbreaking ways. Let’s take a look at its various applications:  

• Predictive analytics leverages AI algorithms to forecast market trends, consumer behaviour, and product demand.  

• Natural language processing (NLP) algorithms can quickly analyse qualitative information from sources such as focus group transcripts, survey responses, social media posts, reviews, etc.  

• Sentiment analysis enables a nuanced understanding of public sentiment toward products or brands by analysing online content, social media, and customer feedback.  

• Price optimisation, on the other hand, uses AI algorithms to analyse pricing dynamics, competitor strategies, and market conditions.  

• Another critical application of AI in data analysis is demand forecasting. By analysing historical data and external factors, AI models facilitate accurate predictions of future demand patterns for products or services.  

• Additionally, AI aids in fraud detection by identifying anomalous patterns in transactions and user behaviour, enhancing risk management, and ensuring the integrity of research-related financial processes.  

• AI also enables 24/7 focus groups and interactive questionnaires, allowing businesses to gather real-time insights and feedback from their target audience.

• Furthermore, chatbots powered by AI technology provide instant and personalised support to customers, enhancing their experience and streamlining communication.

• The capabilities of AI extend beyond text-based data analysis. Image and video analytics have become increasingly sophisticated, enabling businesses to extract insights from visual content such as images and videos.  

2. Inspiration

AI has the potential to inspire innovation and creativity within the industry. It can generate fresh ideas, identify emerging trends, and offer novel perspectives that fuel growth and progress. For example, you can use AI tool to add value for more profound segmentation. By asking questions such as “Who is your ideal customer?” and “How is your ideal customer feeling?” you can get interesting output when consulting on how to connect with that segment.  

3. Summarization

AI-powered systems excel at generating accurate and concise summaries of large volumes of information. This capability saves time and effort by distilling complex content into easily digestible formats. Numerous tools are available to help you efficiently grasp the core message of any text, but it’s crucial to prioritize the use of closed-loop mode apps to guarantee data confidentiality.

The future-proof MI professionals: Embracing opportunities & acquiring relevant skills

​​The adoption of AI is bound to open up new opportunities for professionals in all industries, and experts in the insights industry also have to rethink how they allocate their time and the skills needed for success. With AI taking care of routine and repetitive tasks, professionals can dedicate their time to interpreting data and extracting meaningful insights with confidence.  

Additionally, AI can also be a valuable tool that allows more coaching and enablement. By automating specific processes, professionals can devote more time to mentoring junior team members and nurturing their growth. This shift allows for a more strategic approach, where professionals can focus on driving innovation and strategic initiatives instead of being weighed down by mundane tasks.

Furthermore, the integration of AI reduces the technical benchmark required for entry-level professionals. With AI handling tasks that traditionally demanded hard technical expertise, junior profiles can contribute more effectively to data analysis and insights creation.  

Even though the benefits of AI are straightforward, professionals are concerned that their jobs are at risk of being replaced. However, it's not actually AI itself that poses a threat, but rather those who are adept at utilising it. Let's discuss new strategies and essential skills that will help future-proof MI (Market Intelligence) professionals to ensure their continued relevance and success:

1. Embrace and engage: Rather than fearing the rise of AI, MI professionals should embrace it as a valuable tool in their arsenal. Learning about AI algorithms, machine learning, and data analytics will broaden your skill set and enhance your ability to work effectively with AI tools.

2. Prompt engineering: One of the critical skills for a future-proof insights professional is the ability to ask the right questions to an AI-using machine. AI can provide vast amounts of data, but it's up to humans to determine which questions to ask to extract meaningful and actionable insights.

3. Insights / Consultancy / Storytelling: At Boobook, we understand the power of storytelling, as we know this is an essential tool to bridge the gap between raw data and strategic, meaningful decision-making. By conveying information concisely and engagingly, insights professionals can demonstrate their value and assist organisations in making effective choices.

4. Advising on AI adoption: By understanding the capabilities and limitations of AI, MI professionals can guide, inform and give the needed nuance to decision-makers on how to use AI tools effectively. This includes identifying the right AI solutions, evaluating their potential impact, knowing their strengths and weaknesses, and meeting ethical considerations.

5. Be extra critical of information sources & validation: AI is only as good as the data it is fed, so ensuring accurate and trustworthy information is mandatory. MI professionals should meticulously cross-reference multiple sources, verify the credentials of their data providers, and analyse the validation methods used.

6. Prioritise and validate: Typically, AI splits out way too much information without stressing and prioritising insights. Not all data is equally valuable, and not all insights are similarly relevant. By identifying and prioritising the most critical data points and insights, you can save time and resources while maximising the impact of your analysis.

7. Make insights actionable: MI professionals should be experts in data analysis and possess a strong sense of business acumen. By interpreting data in the context of the organisation's goals and challenges, you can effectively communicate the implications and recommendations to decision-makers.  

8. Specialise: By deepening your knowledge and understanding in a particular niche, you can position yourself as a valuable and sought-after expert, with AI assisting you in that particular niche.

Unlocking the potential of AI in the insights industry

This year, generative AI has quickly gained momentum, leaving many people feeling challenged to keep pace. Ever since ChatGPT burst onto the scene in November 2022, it has been the talk of the town, attracting businesses eager to seize its immense value. The innovation adoption curve of AI may have had a slow start, it’s growing rapidly, and we are seeing new and exciting applications of it every day. This fast change is not easy to grasp, and for some, it might be just a bit overwhelming. However, it’s important we don’t see AI as a threat but as a liberating force for the industry. It's high time we realise that AI is not designed to replace human intellect but to augment it and help us be better at what we do. By dispelling fears, embracing opportunities, and leveraging AI for insights creation, professionals can unlock the full potential of AI and drive innovation in their fields.