Can you do better than ChatGPT?

Create a blog in one minute: does speed reduce quality?

Being impatient by nature and an optimizer who can work endlessly on texts, OpenAI’s ChatGTP seems a great solution so I decided to give it as try. Signing up cost me 10 seconds and entering a first prompt another 10 seconds: “write a blog on the value of AI in marketing with the keywords content, efficiency, quality” in a happy style.” And hardly a minute later, the blog was there. I had ChatGPT regenerate the content twice, resulting in the below three versions.

How do you value the text? Could you have done it better? Certainly not faster I guess. What is missing? Do you see a development from response 1 through 2 to 3 and which version do you rate as the best and why?

The value of AI in Marketing – Response 1

Hello there, dear reader! Are you curious about the ways in which Artificial Intelligence (AI) is revolutionizing the world of marketing? Well, you’re in the right place! In this blog, we’ll explore the many ways in which AI is adding value to the marketing industry, with a focus on content, efficiency, and quality.

First and foremost, let’s talk about content. Content is the lifeblood of marketing, and creating high-quality, engaging content is a time-consuming and challenging task. But with the help of AI, marketers can streamline their content creation process and produce content that is optimized for their target audience. AI tools can analyze data from social media, search engines, and other sources to identify the topics that are most relevant and engaging to a particular audience. This means that marketers can create content that resonates with their audience, leading to increased engagement and conversions.

Next, let’s talk about efficiency. With so much data available, it can be overwhelming for marketers to manually analyze and interpret it. But with AI, marketers can automate many of the tedious and time-consuming tasks associated with data analysis. AI tools can analyze data at lightning speeds, providing marketers with insights that they can use to make informed decisions about their marketing strategies. This not only saves time, but it also allows marketers to focus on the creative aspects of their job, such as developing campaigns and content that resonates with their audience.

Last but not least, let’s talk about quality. AI can help marketers ensure that their campaigns and content are of the highest quality possible. AI tools can analyze data to identify the most effective marketing channels, messaging, and creative elements, helping marketers optimize their campaigns for maximum impact. Additionally, AI can be used to ensure that content is error-free, grammatically correct, and optimized for search engines. This helps to ensure that the content is high-quality and engaging, which in turn leads to increased engagement and conversions.

In conclusion, AI is adding tremendous value to the marketing industry. From streamlining content creation to automating data analysis, AI is helping marketers create high-quality, engaging content that resonates with their target audience. By leveraging AI tools, marketers can work more efficiently, making informed decisions that drive results. So, if you’re a marketer looking to take your campaigns to the next level, consider incorporating AI into your strategy. Happy marketing!

The value of AI in Marketing – Response 2

Hello there, dear reader! Today, I want to talk to you about the amazing benefits of AI in the field of marketing. As we all know, marketing is a critical aspect of any successful business, and staying ahead of the competition is key. With the advancements in artificial intelligence (AI), businesses now have access to powerful tools that can help them create, manage, and optimize their marketing campaigns with greater efficiency, quality, and content.

First and foremost, let’s talk about content. Content is king, and businesses need to create high-quality content that resonates with their target audience. With AI, businesses can leverage powerful tools to analyze customer behavior and preferences, to create personalized content that engages and resonates with customers. AI can help businesses create content that is tailored to their target audience’s interests and preferences, resulting in more effective and engaging marketing campaigns.

Efficiency is also a critical aspect of marketing, and AI can help businesses improve their efficiency in several ways. For example, AI can help automate tedious tasks such as data entry, freeing up time for marketers to focus on more creative and strategic tasks. Additionally, AI can help businesses identify and prioritize high-value leads, enabling marketers to focus their efforts on leads that are most likely to convert.

Finally, let’s talk about quality. Quality is a critical component of any successful marketing campaign, and AI can help businesses create higher quality campaigns by leveraging powerful data analysis tools. AI can help businesses analyze customer behavior, identify trends, and make data-driven decisions that result in more effective marketing campaigns. Additionally, AI can help businesses optimize their campaigns in real-time, enabling marketers to make adjustments and improvements on the fly.

In conclusion, the value of AI in marketing cannot be overstated. With the power of AI, businesses can create high-quality content that resonates with their target audience, improve their efficiency by automating tedious tasks, and optimize their campaigns to drive better results. So, if you’re a business owner or marketer, it’s time to start exploring the amazing world of AI-powered marketing and take your marketing campaigns to the next level.

The value of AI in Marketing – Response 3

Welcome to the world of AI in marketing! Artificial Intelligence has revolutionized the marketing industry, making it more efficient, personalized, and high-quality. In this blog, we will explore the value of AI in marketing and how it has transformed the way we approach content creation, efficiency, and quality.

Content Creation

Content is king, and AI is the perfect tool to help marketers create high-quality content that resonates with their audience. With AI, marketers can analyze large data sets to determine what type of content their audience is interested in and tailor their content accordingly. AI can help you determine the most popular topics, keywords, and even the right tone of voice for your audience.

Moreover, AI can also help with content creation by generating high-quality content quickly and efficiently. With AI, you can automate tasks such as article writing, video production, and social media content creation. This helps save time and resources while still maintaining a high level of quality and creativity.


Marketing is all about efficiency, and AI is the perfect tool to help streamline the process. AI can help automate routine tasks such as lead scoring, lead nurturing, and email marketing campaigns. This saves time and allows marketers to focus on other areas that require a more human touch, such as customer interactions and relationship building.

AI can also help with data analysis, allowing marketers to quickly analyze vast amounts of data to identify trends and opportunities. This helps in making informed decisions and optimizing marketing strategies to achieve better results.


When it comes to marketing, quality is everything. AI can help ensure that the quality of your marketing efforts is at its best. For instance, AI-powered chatbots can provide quick and efficient customer service, leading to higher customer satisfaction rates. AI can also help with personalization, enabling marketers to tailor their marketing efforts to each customer’s unique needs and preferences.

AI can also help with quality control by automating tasks such as proofreading and editing. This helps ensure that your marketing content is of high quality and free from errors, leading to a more professional and trustworthy image.

In conclusion, AI has transformed the marketing industry by improving content creation, efficiency, and quality. By utilizing AI tools, marketers can streamline their efforts, save time, and resources, and produce high-quality content that resonates with their audience. With AI, the possibilities are endless, and the future of marketing is brighter than ever. So let’s embrace AI in marketing and discover new horizons of success!

Can Artificial Intelligence reduce the bias in Marketing and Market Intelligence?

In June 2019, Jake Silberg & James Manyika of the McKinsey Global Institute (MGI) published the essay ‘Tackling bias in artificial intelligence (and in humans)’ [1]. In the below article, Philip van den Berg shares his experience with this phenomenon in Marketing and Market Intelligence. He shares some thoughts about reducing relative bias and the state of ‘lack of bias’ or ‘absolute fairness’, including conventional ways on how to reduce bias and conclusions from the MGI article on how to apply AI to do so.

The Bias Dilemma for Marketing and Market Intelligence

An important dilemma for Marketing and Market Intelligence practices is often to identify, quantify and communicate bias, while maintaining credibility and business justification. Bias is defined as ‘the action of supporting or opposing a particular person or thing in an unfair way’ by ‘allowing personal opinions to influence your judgment’ [2] and includes ‘prejudice’, ‘statistically unexpected deviation’ and ‘systematic error’ [3]. The consequence is that market data, market insights and market segmentation, as well as marketing plans, marketing content and marketing actions remain debatable or even questionable. This occurs especially when business results are under pressure and marketing impact is below expectation.

I have seen senior management using a mix of three approaches for decision making and communication: data, stories and intuition. The first is often dominant: data driven managers use numbers to align people and to reduce bias. The phrase ‘data don’t lie’ is used regularly, but is this true? Silberg & Manyika show that not only data interpretation can be biased but also data itself is often obtained from a non-representative sample, with a subjective methodology.

In a more ‘siloed’ organization or partnerships, departments don’t trust the ‘fairness’ of each other and declare their own data source and insight as the best. The market intelligence analyst defends his research, the marketer or agency his competencies and expertise and the sales person his experience and customer relations.

Increasing fairness bias by transparency, omni-data and feedback

What a person does not know, he tends not to trust. A first step to create confidence and thereby to increase fairness is transparency. This starts by questioning: a) the data, b) the algorithms and analytics that turn data into intelligence and c) the interpretation or insights, in order to understand the bias. Here is important to document the findings and communicate them to the stakeholders that use the data, the intelligence and the insights. Most of the time, being open about bias and data quality limitations creates more trust, than just stating the are ‘great’ or ‘sufficient’. Transparency also encourages stakeholders to bring suggestions how to improve quality and to start co-owning the topic of improving fairness.

A second way to reduce bias is an omni-data approach, by efficiently extracting value from multiple data sources. With every source added, more data quality checks can be built in and insights become richer, deeper and better. Stakeholders who demand using another source to take away their remaining distrust, can in the in most cases be satisfied.

A third part which is often missing, is the thorough post-cycle or post-event feedback loop. It allows stakeholders to review, to what extent data and insight assumptions were biased and to agree with them, on where to improve and to take joint action.

Bias transparency, an omni-data approach and feedback loops lead to a better understanding of and more cooperation on how to increase fairness. This is not only valid for Market Intelligence but also for Marketing activities, from the market insight, the market segmentation, and the persona definition, to the marketing plan with the messaging, the marketing mix and the metrics.

To make the organisation bias-aware and capable of reducing it, a data-driven strategy and a culture of openness on data quality are essential. For this, leadership has to understand the value of fair data, to map where the organization is and should go and to start a transition project with a midterm horizon.

Reducing bias by experimentation

Advantages of starting a strategy and culture shift are, that they may take too long – the market, competition and customers don’t wait – and that they don’t state well, what fairness is. Silberg and Manyika conclude, that this last topic is so complex, that ‘crafting a single, universal definition of fairness or a metric to measure it will probably never be possible’. Instead they see different metrics and standards to be used, which each depend on the use case and circumstances.

Reducing bias however, means one still needs sort of an understanding of fairness and how to improve it. I see experimentation as a quick way, to determine how relatively biased for example a marketing campaign is. Testing and trying out different small scale scenarios in parallel on persona definitions, messaging and marketing actions, will provide useful insights and learning. The scenario with the best business result is likely to be the least biased one.

Reducing bias with Artificial Intelligence

Still, even the best scenario could still be biased and far from the point of ‘ultimate’ fairness. In seeking to identify this point and reduce bias, human behaviour and judgement have clear limitations. This raises the question, to what extent Artificial Intelligence, which has the promise to overcome human limitations, can help.

Silbert and Manyika see it as a challenge, that the underlying data are often the main source of the bias, rather than the algorithm itself. This is because the algorithms are often trained on data that contains human bias. The authors observe three main approaches to increase fairness in AI models, but conclude technical progress is still in its early stage. The first is data pre-processing for accuracy and independency reasons. The second is post-processing to transform AI model predictions to less bias. The third is including fairness constraints on the optimization process or using so called adversaries to reduce bias from for example stereotyping. Also adding more data points, innovative training techniques, like transfer learning and explainability techniques [4], can help.

Moving forward with Artificial and Human Intelligence

While clear definitions and the above approaches can certainly reduce bias, they cannot rule out fairness restrictions in the data collection or in the social context into which an AI system is deployed. Therefore the Silbert and Manyika state that ‘human judgment is still needed to ensure AI supported decision making is fair.’ This means that an adjustable mix of human judgement and AI judgment is needed. To find the best balance, in order to maximize fairness and minimize bias from AI, they recommend ‘six potential ways forward for AI practitioners and business and policy leaders’:

  1. Be aware of the contexts in which AI can help correct for bias as well as where there is a high risk
  2. Establish processes and practices to test for and mitigate bias in AI systems.
  3. Engage in fact-based conversations about potential biases in human decisions.
  4. Fully explore how humans and machines can work best together.
  5. Invest more in bias research, make more data available for research (while respecting privacy) and adopt a multidisciplinary approach.
  6. Invest more in diversifying the AI field itself.


The availability of almost ‘endless’ amounts of customer and business data, as well as the fast growing capabilities of Artificial Intelligence-powered data analytics, have brought Market Intelligence and Marketing into a new era. Companies were never more dependent on data as well as data analytics, and thereby on data bias and data fairness. These topics have become strategic and require a paradigm shift in the way organisations deal with them, with deep consequences for their strategy and culture.

This calls for the need to define the state of ‘ultimate’ fairness and to quantify the bias gap in both Market Intelligence and Marketing. This can be partially obtained by transparency, omni-data, feedback and experimentation, but these approaches have their limitations. While AI-powered data collection, analytics and enrichment solutions are still in an early stage, they add substantial value in reducing bias. As AI-generated data and insights also use biased data and biased algorithms, a flexible mix of human judgement and AI judgement is required. Although defining the ‘biassless’ or ‘ultimately fair’ state might still be difficult, this approach is an important step towards it.

The business value of AI will continue to increase in the near future. This will strengthen the competitiveness and the business results of companies and organizations. Therefore it is of strategic importance, that their C-suites embrace ‘Data Bias and Fairness’ as a strategic theme and start utilizing the ‘six potential ways forward’ of Silbert and Manyika.

[2] Cambridge Dictionary
[3] Merriam-Webster
[4] While the high-performance and accuracy of Artificial Intelligence, that is Deep learning and Machine Learning algorithms, are generally valued, the models are often applied in a black box manner. This makes it difficult for researchers and data scientists to fully understand how the algorithms work, to understand how to assess the bias and define the point of ‘absolute’ fairness and to communicate the reason of the outcomes to stakeholders or customers. ‘By providing an explanation for how the model made a decision, explainability techniques seek to provide transparency directly targeted to human users, often with the goal of improving user trust.’ They consist of ‘ Local explainability techniques’ that ‘ explain individual predictions, which makes them more relevant for providing transparency for end users.’ and of ‘Global explainability techniques’ that ‘refer to techniques that attempt to explain the model as a whole.’ [5]
[5] Several authors; Explainable Machine Learning in Deployment, 13 September 2018;

Book review: Strategy-In-Action: Marrying Planning, People and Performance

Transition-to-Success Framework for strategists & implementers, management & employees

Why do many corporations but also SME’s, scale-ups and start-ups struggle and even fail? It’s not because the initial business idea wasn’t good or the mission or organizational setup wasn’t good. The older companies get, the more they can create an environment where its employees want to crawl into the comfort zone of what has been achieved. One becomes defensive of historical ideas and positions. In the meantime, the business environment changes at what seems to be an ever faster and more disruptive pace. Strategies or often just tactics are adjusted more and more reactively and the responsible managers or consultants move to the next challenge before they can be held accountable.

Thomas Zweifel offers a methodology that joins strategy with planning and binds planning, people and performance. The goal is to be always open for the future and for transformation and capable of implementing it. The Strategy-In-Action methodology empowers the people in the organization to be future-focused, to get the relevant intelligence, to give room to deviating view, maximize buy-in of all relevant stakeholders, to get so-called quick wins, which are key to involve the organization in the transformation and to identify early people that my block the transformation.

The book shows how to bridge the gap between strategy and actions, and as I have experienced at large, inward-focused companies, between tactics and metrics. The methodology of Thomas Zweifel is clear and logical and will bring success. Implementing this will not only transform the organization to become more agile and future aware but also helps to get stakeholders involved. Therefore his book is a recommendation for both strategists and implementers and both management and employees at any organization, from established corporations and governmental organizations to smaller and younger companies in growth pains or decline.

Strategy-In-Action: Marrying Planning, People and Performance (Global Leader Series), Dr. Thomas D. Zweifel & Edward J. Borey