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At the end of 2023, we teamed up with one of our clients to set up a research experiment to further optimise future pricing and branding insights.  

The study aimed to refine our brand strength metric, also called a brand's pricing power, to enhance its predictive power for key pricing metrics. It also aimed to provide consumers with the most realistic simulation of a shopping process, possibly improving the way we measure price elasticity. 

Methodology 

We interviewed a large group of FMCG shoppers via an online survey but divided them into 8 different "experiment cells". We explored three aspects related to measuring price elasticity via a conjoint method. 

• The impact of the conjoint shopping shelf: an advanced shopping shelf vs. a 2D, more flat looking shopping shelf  
• The impact of promotions on measuring price elasticity: a conjoint where none of the shelves shows promo vs. a conjoint where several shelves have brands on promo  
• The impact of the device: filling in on a laptop/tablet versus on a mobile phone 

On top of that, we measured brand strength using a variety of metrics.  

Key Questions: 

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• Can we optimise the way we measure brand strength? 

The study aimed to determine whether our current definition of brand strength could be improved, i.e., which short list of brand characteristics best measures a brand's pricing power. By knowing how well a brand scores on these aspects, we have a good view of how price elastic the brand is, or in other words, how easy or difficult it will be to raise prices.  

• Does an advanced shelf design better reflect how consumers choose real-life products versus a simpler 2D flat design? 

A standard 2D graphic conjoint design is often criticised as not sufficiently representative of a shelf simulation. Hence, we compared choice behaviour between two designs to explore the following: if a more realistic 3D shelf design is presented, do shoppers behave differently, i.e. select fewer or more products, spend less or more time on the choice process, choose more realistically...

In addition, we compared the response behaviour related to the device on which the survey was taken (laptop/tablet vs. mobile phone). 

• Does showing promos in the conjoint affect the non-promo price elasticity of a product? 

The study aimed to understand if the price elasticity of a product is the result of its non-promo price in combination with its promo elasticity or if offering competitive promos had no impact on product price elasticities. This question is often raised in promo-driven markets, where there is a need to clearly understand the effects of price changes outside of promo actions. 

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What did we learn? 

Based on the research goals and questions, here are the key takeaways:  

1. Brand strength/Pricing Power is ideally measured through 4 brand characteristics 

While we previously used two brand attributes, which were already predictive of price metrics, we could have enhanced the predictability by extending the definition with 2 more short questions.  

We discovered that adding a specific combination of brand associations linked to the exclusivity & premium-ness of a brand had a subtle but significant impact on the definition. The effect this has on its predictiveness for pricing metrics was substantial. 

2. No need to build an advanced shopper shelf 

While it is essential that a conjoint exercise reflects a realistic shopping experience, creating an even better, possibly even more realistic look doesn't lead to better results, and one should accept this. Despite the more realistic shelf design and purchase simulation, key conjoint metrics did not substantially impact it. Moreover, it would only create additional, often unnecessary, technical complexity. So, "never change a winning horse" really does apply here.  

Furthermore, the research also showed that the conjoint behaviour on a laptop/tablet versus a smartphone was very similar, confirming that smartphones can be used to measure price elasticity via a conjoint exercise. 

3. Showing promotions when necessary is key to representing a realistic shopping experience, as it does not hamper measuring the non-promo price elasticity. 

The findings revealed that showing competitive promos in a conjoint exercise had minimal impact on non-promo price elasticity. This clearly indicates that promos should always be integrated when relevant when conducting a conjoint study.  

A promo landscape is a commercial reality and must be included when aiming for a more realistic simulation. Omitting promos only marginally increases non-promo price elasticities, so there is no reason to set these aside in a conjoint. 

In conclusion, this research-on-research study provided crisp insights into how to and not to optimise branding and pricing research methodologies.  

By refining the brand strength with a well-thought-out extra dimension, conducting conjoint testing in its purest and most effective way and understanding the relative role of promos in price elasticities, our client can now make even more informed decisions when conducting future pricing studies.  

Ready to optimise your pricing and branding strategies? Contact us today to learn how our data-driven pricing and branding consultancy can help you make informed business decisions. 

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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.  

Pricing products and services is one of the most complicated aspects of any product or marketing strategy. It has a direct effect on sales, revenue, and profit. Furthermore, understanding how price impacts brand perception and vice versa is another important consideration. While it’s important not to price too low (which could lead to long-term issues in achieving business goals), pricing too high might limit the trial use of products – leaving companies with limited volume growth potential down the road.  Finally, achieving equilibrium between marketing and sales teams which typically have different goals when evaluating pricing plans, is also vital for success.

Three approaches to price setting

There are many different ways of deciding how to price a product, but broadly speaking, there are three approaches, each with pros and cons.

- Cost plus pricing: Internal focus

Cost-plus pricing involves calculating total production costs (including materials, labour, and overhead) and adding a markup for profit. To use this method, you must identify production costs and analyse market factors to determine an appropriate markup. Cost-plus is well-known and reliable, but it doesn’t necessarily reflect the value of the product/brand to the consumer. This could lead to missing out on high-value sales, i.e. leaving money on the table.  On the other hand, if too much profit is accounted for in the cost-plus approach, anticipated sales could be misjudged.

- Competitive-based pricing: Competitive focus

Competitive-based pricing is a model where other businesses heavily influence your price points in the same market. This outward-facing approach differs from cost-plus pricing, which looks at your costs. It’s a popular option as it feels like the ‘least risk’ approach. It definitely has its advantages as competitors are an important factor for any setting prices.  It is typically used by ‘price followers’.

- Value-based pricing: Customer/consumer focus

This approach considers perceived value rather than cost or market competition to set price points. It depends on customers' needs and desires, as well as their willingness to pay. People don't think about a product's cost to produce but what value it gives them. Consider handbags, shoes, perfumes, or brands such as Apple, Nike and Starbucks; customers are willing to pay more as they attach high value to these. The cost of these products is often only a fraction of the retail price. This method helps identify a brand/product's pricing power and value. The stronger the brand, the higher the pricing power and prices, and products with a lot of price power are less influenced by inflation.

So, what would be the best pricing approach? 

Unsurprisingly, value-based pricing has the most potential to create a successful long-term strategy. However, costs also need to be taken into account, as any price should result in profit, and keeping an eye on what competitors do is a no-brainer too. We recommend a combination of the three with value-based pricing as the foundation.

5 steps to successful pricing

1. Use the overall company strategy as the goal for pricing

Should your organization win on volume, revenue and profit margin? Winning all three is usually impossible since each requires a different pricing approach. For example, focusing on volume often means lower pricing for less premium products, while profit margin goals require higher pricing.

2. Decide on being a price setter or follower, and act accordingly

Achieving the status of price leader requires dominance in the market, strong branding, or a superior product. Market size is not always a reliable indicator of price leadership, nor is brand prestige. Carefully choose your strategy and maintain consistency in your channel partnerships and marketing communication.

3. Look beyond the price and the business KPIs

The company strategy and the vision is the starting point of any price setting; however, there are other key factors which contribute to the success: (1) your brand, (2) your product/service and (3) the consumers/customers.

Companies often assume adjusting the price will improve sales, revenue, and profits, but it is not the only factor of success. Brand recognition, product quality, and the target audience’s ability and willingness to pay are all key components that should be considered – in some cases, more so than the price.

4. Listen to the customers/consumers

Analysing consumer feedback should guide any pricing strategy. Digging deeper to understand how consumers perceive your brand/product, if it meets their needs, and the value it brings are essential questions to answer. It will tell you, e.g. if your brand is strong enough to sustain a 10% price increase.

5. Educate and create a win-win situation for the different players in the sales channel

Price changes often require approval from multiple levels between the manufacturer and shopper, such as wholesale, distributors, retail, installers, etc. Without approval, there’s a risk of delisting, reduced visibility, and weaker negotiation power. Showing your understanding of consumer needs and what they are willing to pay is key to establishing trusted long-term relationships with sales partners.

Next up

In the following weeks, we’ll share with you more on each of the above 5 aspects and how to choose the best pricing techniques and tools for your business. 

In the meantime, if you need help optimising your brand's portfolio pricing, get in touch with us. Our team is here to assist you in developing your ideal pricing strategy.