Launch successful products with go-to-market strategies that sell

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 userbase 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.  

Are you curious about what customers want and what they're willing to pay? So are we. Actually, that is the most frequently asked question here at boobook so it’s crucial we keep learning to stay on top of all the pricing techniques and methodologies.

This January, I participated in the ESOMAR Training on Survey-Based Techniques for Optimising Prices and Products. Organised in collaboration with MRII, a non-profit educational institute linked with the University of Georgia, teaching professionals of the industry conducting effective and robust market research, the training was the first of the 2023 series. Two speakers, Ed Keller, executive director of MRII (Market Research Institute International) and Brian Orme, CEO of Sawtooth Software, an expert on Conjoint Analysis, shared their insights on how to optimise the pricing of your product or service.

Conjoint analysis

Conjoint analysis is the gold standard survey-based technique for uncovering valuable insights. While Sawtooth Software is a celebrated industry leader in Conjoint & Max Diff, there are also other techniques we'll explore in this overview. Qualitative research plays a key role in laying the foundation for successful quantitative surveys. And with Sawtooth Software's consulting division and survey business, you can trust you're in good hands for all your research needs. It's no wonder they've thrived for 40 years!

If you use a five-point or a ten-point scale, you'll get a lot of high-level numbers, making it hard to distinguish what is essential and what is not. You'll get a lot of straight-lining. There's another problem: cultural scale use bias. Different people use scales differently. This bias is very different across other countries. For example, respondents in Germany score lower on average than respondents from India.

Let's imagine that there are three different groups of people. One group wants to use ratings 5-10, the second want to use 3-8 and the third group uses 1-6. When I plot them between one item on my rating grid, then you'll get an artificial correlation of 46.

This makes it look like everything is related to everything else or drives everything else. Suppose you try to drive multi-variant statistical techniques to tease out the separate effects of things, like through regression; this artificial correlation will give you a lot of problems. This is why in conjoint, we don't use a typical 5 or 10-point scale.

With Conjoint, we are not just testing one thing at a time, e.g., A/B testing. We can vary the features and prices and test hundreds or thousands of realistic-looking products or pricing combinations. It's not A/B testing; it's A to a gazillion testing.

Conjoint is a survey-based technique to optimise features or pricing. We will show people realistic buying scenarios and ask them what they would choose each time.

See the example below: 

We are studying different attributes and features, and we are varying them across each choice task. Each choice task has the same format (see 1 & 2 below), but the features and prices vary each time. When we vary the features independently such that each feature is shown an equal number of times and each feature is shown with another feature an equal number of times, we have a fair and balanced experiment which allows us to tease out what is driving people's preferences. No need to ask them how important is colour or price to them. We are replicating the real world to them and understanding what is going on in their heads. We don't let the respondent get lazy; they can't just respond 5 – 5 – 5, like on a 5-point scale. There is also a "no" option, so they can choose to walk away from all possibilities.

Another advantage is that conjoint analysis works really well on mobile phones. 

• We can statistically tease out what is driving peoples’ choices. We use a model to calculate the scores of the preference utilities that the respondent chooses. You will get high utility scores for features that you pick again and again, e.g. the colour red or low prices.
• For each respondent, you get a full set of utility scores for each attribute, each feature that you are trying to optimise and each price. Based on this, you can predict how each respondent would choose in thousands or millions of different combinations. This is a market simulator that you can build in Excel.

You can change the fields in this simulator, choosing if the car is blue or red and updating the prices. Every time you change a cell, the simulator updates, and you will see the share of respondents who would choose that product with those features. 

It’s a very powerful tool to optimise the products you are offering in a realistic competition with your competitors in order to understand how to capture the biggest share based on the ideal features and portfolio you need to offer. 

Another technique that was developed at the same time as the Conjoint is the Van Westerndorp Price Sensitivity Meter, which is a survey-based technique used to establish an acceptable price range for one single product.

• You show your respondents one single product concept and educate them about the concept
• Then you ask the respondents 4 questions:  

1. At what price is it so expensive that you would not want to buy the product  

2. At what price is it so cheap that you would not buy it because you would doubt the quality  

3. At what price is this product acceptably expensive 

4. At what price is this product acceptably cheap 

See the graph below: 

At 100 dollars, 10% of respondents thought that the product was too expensive for them. The intersection has meaning, according to the author of this method.  

• The intersection of the YELLOW & BLUE = point of marginal cheapness 
• The intersection of the RED & GREY line = point of marginal expensiveness. 

The gap between these two points is thought to be the acceptable price range. 

• The optimal price point is the intersection of ORANGE & YELLOW 
• The indifference price point = intersection between BLUE & GREY  

Weaknesses: 

• Focuses only on 1 product concept.
• If you want to test thousand or hundreds of modifications of your product, it will be very difficult
• Doesn’t put the respondent in a realistic context as the conjoint does

Strength: 

• A quick methodology that you can use in surveys is good when the product is truly new to the market and you have no idea how to price it.
• Open-ended questions mean less chance of bias  

Gabor Granger Stated Willingness To Pay  

Finally, Gabor Granger is another survey-based technique that is used for establishing the price sensitivity curve & optimum price point for one product. You create a series of prices in a list and ask if they would buy your product at each of the prices in the list.

Start out by randomising the prices and ask them if they would pay that price. If no, then they are shown a lower price until you get to the price they would pay. 

Let’s say for respondent 1, we randomly select 25 dollars. If they say yes, they would pay that price, we randomly show them a higher price, eg 40. Then they say no, so we randomly show them a price between the two price points. We do this until we get to the highest price they would be willing to pay. 

• We then can create a price sensitivity curve 

Weaknesses: 

• Only focuses on one product at a time in a vacuum, with no competitors, which is not a realistic market scenario for conjoint 
• It’s obviously a price game which may lead respondents to adopt a bargaining behaviour 

Strengths: 

• It’s a quick and dirty quantitative method
• Overall recommendation for pricing experiments
• Asks people realistic questions that mimic the real world
• Doesn’t ask people what they would pay
• Has them choose among different products or different prices like they would in real life

Best practices for conjoint studies

1. Getting your attributes right is crucial. This is why Qual is so useful. 
2. Recruit people who want to buy your product. 
3. There is a lot of bad data from respondents who do not answer realistically or carefully. Luckily conjoint analysis and max diff offer a fit score. We can compare the questions to see if respondents are answering consistently, and you can throw them away if they have a low fit score.  
4. Hire someone to show you how to do it and use good software. 
5. It’s typical to use samples of 200-800 but you can do more or less, this is just the norm. Sometimes we run models with 40-50-60 respondents because that’s all we can get. The models still work, it’s just that they are not as precise. But it’s still very worth it rather than not doing it at all. 
6. It’s typical to show 8-15 conjoint questions for each respondent. Each question takes about 10 to 15 seconds to answer, it’s a 2–4-minute survey once you have educated your respondents on your subject matter. 
7. In your educational feed up to your conjoint, make sure you aren’t overselling it and biasing your respondents to be positively predisposed to it 

Conclusion

To sum up, the ESOMAR Training on Survey-Based Techniques for Optimizing Prices and Products was an invaluable opportunity to deepen my understanding of pricing strategies. With expert advice from Ed Keller and Brian Orme, I was able to further develop my knowledge base in this area and come away with a better understanding of what it takes to set up an effective pricing study. Additionally, talking with other professionals at the event gave me insight into how they approach pricing, allowing me to adopt new strategies that are suitable for my market research goals.

If you find yourself in need of help setting up your own pricing strategy, get in touch. Together we can work towards creating a pricing structure that will optimize your company’s products or services for maximum profitability.

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In today's digitalised world, every business can access an incredible amount of data. However, the real value of data comes from interpreting it through data analytics and using its insights to make strategic decisions.

Boobook’s unique approach to data and business insights starts with its competent and skilful analytics team but also relies immensely on business savviness. Numbers on their own, nor fancy analytics mean much. Knowing what to look for and connecting it back to the business issue is as important as data science. One of our senior data science consultants, Frederik De Boeck, talked about this in our previous article.

Meet another team member: Eva Vandenberge, one of our consultants making the "bridge" between data analytics and business, translating complex data language into clear business insights.

With her 18 years of experience in marketing, communication, market research, and data-driven business consultancy, Eva is a perfect combination of a marketer with an analytical brain. She specialises in data insights and business consultancy with a high level of storytelling and data visualisation expertise.

Eva is in direct communication with clients, but she also works on conveying the research, getting the insights, and being fully involved with the projects.

"We have a powerful bond with our clients because they trust our collaborative approach and professional consultancy," says Eva.

She joined boobook three years ago as an Insights and Client services director, but her career as a business consultant and data wizard didn't come overnight. With her Master's Degree in Communication Sciences degree, she worked as a communication specialist at De Lijn for seven years. Later on, at Ipsos (Synovate), she was a research manager focused on loyalty research and conveying customer experience surveys. After nine years at Ipsos, she was ready for a new chapter. Luckily, an ex-colleague meddled in.

"Frederik (De Boeck) was my ex-colleague at Ipsos who started working at boobook, and he was very enthusiastic about me joining the team. I scheduled a meeting with Nicole, and she offered me the job after a few days. I didn't have any second thoughts because I liked Nicole's attitude and philosophy. I could easily relate to the values that boobook stands for."

Challenges are the best ways to grow and learn

As a natural problem-solver, Eva looks at challenges as a regular part of work and life.

"If I face any issue, I say, okay, this is a problem, how can I solve it? I don't stress because I understand challenges are the best ways to grow and learn." 

This mindset and determined attitude fit perfectly with the boobook's approach and framework: "At boobook, we tend to keep asking questions until we reach the very end of the road. Our work is based on in-depth business analysis and strategic thinking. We need to understand the business questions and get to the core of the client company and its processes."

Speaking of challenges, 2020 proved to be one of the most difficult years for everyone. Pandemic impacted our lives profoundly, but there is always a silver lining. 

For boobook, the calm period was beneficial for reassessing how the company operated. "At the beginning of the pandemic, it was quiet as many projects were on stand-by. We took that time to discuss the processes within the company and how to restructure our team. Some decisions were hard to take, but this time was precious because it helped us find focus again," says Eva.

Businesses of the future will be more data analytics oriented

Even though she already has a wide range of skills and considerable market research experience, Eva is a firm believer that there is always room for improvement and new skills. "That's the beauty of this type of work; there are always new things to know. It's never a dull moment, and I like it because it keeps me agile and curious," she confesses.

Nobody can tell the future, but Eva believes it's looking bright for data analytics and data science.

"Data can help companies make decisions about their customers by understanding who the customers are, what they do and especially why.

As more and more data is available, I expect companies' mindset to evolve and become more 'data analytics oriented' from the start of every new development.

Companies will think about existing data and reporting tools, the ideal measurement framework and KPI's they want to track, privacy rules, etc. This way, companies will be able to get the most out of their data, and struggle less with all kinds of different systems that are not designed with data analysis in mind," Eva explains.

Companies need to think big regarding analytics, and by thinking big, I mean long-term planning, moving forward step by step. I believe there are exciting times ahead for data analytics.

Eva believes that data translators will be even more in demand. "I think the need for data analysts will increase, especially if they are business savvy. AI won't replace a human brain to ask the right questions, translate the data into insights and create an impactful strategy. Data scientists and consultants know how to connect the dots, and enable companies to communicate better and more efficiently within the organisations and with their customers," she concludes.