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

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

Determining the right price for a product can be daunting for many businesses. Often, pricing decisions are based solely on cost or intuition. However, at boobook, we know how important an informed, customer-first pricing strategy is to achieving business success. That's why we created the "Pricing 360°" series to guide you through the complexity of pricing strategy, one aspect at a time.

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In our previous article, we discussed the connection between brand and pricing. Today, we'll continue the "Pricing 360°" series with a second business pillar: the Product.  

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In this article, we will explore the relationship between pricing strategy and product features. We'll delve into the importance of understanding which product features are most valued by customers when setting your price. Adopting a data-driven approach allows you to develop optimal product configurations and corresponding pricing strategies that resonate with your target audience.

The influence of product features on pricing decisions

Setting the right price involves considering various factors, including external influences like macroeconomics, market demand, brand perception, competition, etc., as well as internal pressures such as production costs, company strategy, or corporate finances. While we will cover some of these factors in upcoming articles, for now it's absolutely crucial to acknowledge the pivotal role played by the range and the level of the offered product features in pricing decisions.  

Product characteristics such as design, durability, specific functionalities, product bundling possibility or other technical specs are vital elements to have evaluated by your prospects when determining the value customers assign to a specific product and what they are willing to pay for it, based on its specific product features combination.

It’s essential to avoid letting internal pressures dictate the product features or the price at which they are to be sold and instead focus on understanding and meeting your customers' real preferences, expectations, and willingness to pay. After all, what makes a product excellent is defined by the customer, not the product manager. This is the essence of a Value Based Pricing strategy, in which we strongly believe.

Get your pricing basics right first

Successfully defining an acceptable broad price range for your product(s) involves multiple factors, categorised as internal and external:  

Internal factors:  

1. Production costs: Understanding the costs involved in producing the product is essential for defining an appropriate price range that ensures profitability.  

2. Financial resources: A company's financial capabilities indirectly determine its pricing strategy. E.g. the level of promotional efforts at launch, the level of investments in product development that come with an impact on its final price, etc.

3. Positioning strategy: How the product is currently positioned in the market and its perceived value influence pricing decisions. A premium positioning strategy may warrant higher prices, while a value-for-money oriented positioning may require more competitive pricing.  

4. Pricing strategy: The chosen pricing strategy directly affects the product's final price.  
   Different methods, such as cost-based or value-based pricing, have distinct implications for profitability and market positioning.  

External factors affecting pricing decisions include e.g. the state of the (national) economy, market demand, level of competition or even seasonal influences.  

How to measure product needs  

Even though it’s important to have these internal and external aspects in mind, the key question you have to ask is what product or service features contribute the most to its value, and therefore, drive willingness to pay?  
Is it its design? Or maybe the width of the colour range you can choose from? Or could it be one of the more technical (and often underestimated) specs that really drives interest and willingness to pay? It is essential to be open to learning and understanding. These important nuances of what the customer thinks are crucial to a product and bring value and what are superfluous extras that they aren’t willing to pay more for.  
To further enhance your understanding of product-related customer insights, at boobook we use advanced methodologies such as conjoint analysis, Max Diff, or key driver analysis.  

Conjoint analysis is particularly useful for understanding the value of each individual product feature.  
In conjoint analysis, customers are shown all possible variations of product features/specs (levels) and are asked to choose which variation of features and specs-level they would be most likely to purchase, given a specific competitive and pricing situation.  
The different product variations, including competition (!), are presented on a simulated shelf to create a realistic shopping experience and the exercise must be done several times to ensure all possible competitive and pricing situations are tested. This is the only way to make robust, statistically significant, data-based insights afterwards.

Let’s take a look at the example with smartphone comparison. A product is characterised by its attributes and levels, which depend on the variations to be tested. A minimum of two attributes are required (for instance, price and brand) to create a design grid.

At boobook, we employ a scenario simulator for conducting key conjoint analysis. The simulator allows us to analyse the following:

• Price sensitivity of different product variations in a competitive market

• Estimates of volume, revenue and profit

• Evaluation of the value of various product features

• Potential cannibalisation effects

• Identification of the ideal product range

By analysing the data obtained, we can accurately measure the value of each product characteristic and identify which product variations customers are willing to pay more for. Combining these data-driven insights with our industry expertise, we enable businesses to make informed, strategic decisions that drive growth and profitability.

In conclusion

Conjoint method is a powerful tool that can address various business enquiries beyond pricing alone. Additionally, it can offer multiple simulation options.

However, it requires substantial set-up time, larger samples and is generally more costly compared to simpler methods. Conjoint analysis is best suited for examining the price sensitivity of multiple products, analysing the combined impact of price and product composition, and optimising price and promotional strategies.  

Curious to learn more? Check out our case study with Center Parcs to discover how we helped them identify which product features customers are willing to pay more for.  

Feel free to contact our team if you have any questions or want to learn more about data-driven strategic pricing. Stay tuned for our next article in the series: "Pricing 360°: Customer understanding as key ingredient of meaningful commercial strategies".

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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.  

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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