Dubbed the 4th industrial revolution, Artificial Intelligence (AI) is here to stay. But the speed and intensity with which AI has invaded our lives in recent years has been a source of terror and excitement for people around the world. Uncertainty abounds as employers leap to explore the benefits of widespread automation, while employees anxiously stress about job security. So, the question remains: how do we take advantage of AI tools effectively, efficiently, and ethically? Below, we highlight the transformative ways AI has impacted market research and brand strategy, its dangers and shortcomings, and its role in the future of branding. 

5 Ways AI is Impacting Market Research 

AI is more than just the next step in market research – it is the dawn of a radical frontier. Its potential to increase productivity, amplify our ability to predict consumer behaviour, and enhance targeted marketing capabilities is immeasurable.  

 1.  Automation of Data Collection and Management 

With the support of AI, companies can automate data collection and management to streamline the research process. Gone are the days of manually gathering and cleaning endless oodles of data. Adaptable conversational chatbots like ChatGPT can help design and administer surveys tailored to targeted segments of the population. Parameters can be set to automatically catalogue the responses by demographic, psychographic, or behavioural qualifications, making it quick and cost effective to extract meaningful insights down the line.  

AI algorithms can also monitor data quality by cleaning up irrelevant, incomplete, or duplicated responses. Companies like Synthesized and Private AI are trained to remove Personally Identifiable Information (PII) from survey results, effectively preserving data integrity, usability, and ensuring the information complies with privacy regulations.  

 2. Optimisation of Data Analysis, Visualisation and Reporting 

Analysing consumer data from the internet has morphed from a mere Herculean task to an epic Sisyphean nightmare. An estimated 149 zettabytes of data were created in 2024. That’s 400 million terabytes of data generated every, single day! 

Luckily, generative AI software is well-equipped to extract key insights from mountains of information. Researchers no longer need to dredge through the digital slog of social media posts, product reviews, and public forums. Natural Language Processing AIs (NLPs) analyse qualitative data online to uncover important market trends and sentiments towards a brand. They can also find patterns in survey responses and focus group interviews to increase our understanding of target markets and consumer behaviour. 

 3.  The Rise of Predictive Analytics 

Accurate and efficient predictive modeling is the holy grail of corporate strategy. Imagine the ability to anticipate what consumers want, when they want it, and producing the exact amount needed to maximise profit, while minimising costs.  

Today, AI can analyse industry patterns and real-time information to create models that forecast market trends. Machine learning algorithms like Pecan use historical data on consumer behaviour, preference, and spending habits to stay ahead of demand. These predictive models can determine which customers are most likely to be swayed by targeted marketing, to be repeat customers, or to switch to a competing product. As a result, brands will know which sectors of the population they should focus on. 

 4.  The Wonders of Synthetic Data 

AI’s generate synthetic data by mimicking existing consumer profiles. The potential here is limitless, offering valuable opportunities to enhance market research and brand strategy. Researchers use the artificial data as a supplement to real data when they conduct market studies or form predictive models because it increases sample size and diversity, leading to stronger findings. It can also extrapolate results from hard-to-reach segments of the population to draw more robust conclusions.  

Practically speaking, firms can use this technology to test the performance of new brand campaigns on artificial markets without fear of leaking intellectual property. Finally, synthetic data is far more efficient than collecting and managing real data, requiring less time and fewer resources because market profiles can be generated automatically.  

Read more about synthetic data from this Qualtrics article 

 5.   Personalisation and Market Segmentation 

Market research will become highly targeted in the years to come. Traditionally, market segmentation relied on surface level demographic qualities like age, gender, income and location. Now, browsing histories, political views, social media posts, and past purchases can be used to create tailored marketing strategies and product recommendations fit to each person’s behaviour profile.  

Such a degree of personalisation allows brands to elevate the customer experience. For example, Netflix uses AI to recommend shows based on accumulated user’s tastes and preferences. Personalised market research optimises a firm’s resources and strengthens their customers’ engagement with, and loyalty to, the brand.  

For more insights on how AI is affecting market research, check out TGM Research’s article. 

How does AI Improve Brand Strategy and Decision Making? 

 A.  Make More Informed Decisions 

The effect of AI on brand strategy is akin to the impact radio and radar had on military tactics a hundred years prior. Accurate, real-time information is the most important ingredient in smart decision making. Corporate leaders can leverage AI’s automated data analysis and predictive analytics to reveal important correlations in real-time, transforming a trove of trivial information into deep insights on market trends. In doing so, companies can develop long-term, data-driven brand strategies that position themselves uniquely within their markets.  

Consider mergers and acquisitions. This report from the Harvard Business Review found the failure rate of mergers and acquisitions to be around 70-90%! AI’s powerful data analysis capabilities can boost success rates by efficiently identifying risks, synergies and acquisition targets that most align with each firm’s strategic goals. It streamlines the due diligence process, leading to smarter choices.  

 B.  Mitigate Risk 

In terms of risk mitigation, AI can identify vulnerabilities in a company’s operations or branding strategies with predictive analytics and automated data management.  

Real-time sentiment analysis informs branding decisions. By understanding the choices their customers are making, firms can take preventative action and pivot the company in the right direction. For example, social media posts and customer reviews can be compiled to determine whether a firm’s current campaigns are working or if they should consider investing in a rebrand to better align with their values.   

C. Optimise Operations 

Automated data collection, management, and analysis paired with the use of synthetic data can free up human capital traditionally used for overseeing research. Furthermore, AI can optimise operational tasks such as inventory management, logistics, and customer support to lower costs and increase efficiency. With these advantages, employees will have more opportunities to explore innovative brand strategies.  

D. Capitalise on New Business Models 

Complete understanding of real-time consumer preferences coupled with predictive models allows companies to develop proactive brand campaigns and product strategies. They can also use predictive analytics and synthetic data to anticipate market trends, informing the development of brand messaging that can tap into the cultural and industry zeitgeist.  

Read more about AI’s impact on brand strategy and decision making from Mckinsey. 

AI is a Tool, Not a Replacement (Don’t Worry, Humans are Still Required!) 

Shortcomings of AI 

Despite its advantages, AI has 3 major blind spots: data quality assurance, information bias, and ethics and legality.   

First, AI programmes are highly dependent on the data they are trained with. Poor information and inaccurate data inputs will result in flawed outputs, and ultimately, unreliable conclusions. 

 Second, AI algorithms, like any code, can inherit bias from their programmers or from the data sets they were trained from. Any such biases or limitations in contextualisation could lead to discriminatory outcomes against minority populations or an overvaluation of certain groups. Any potential for discriminatory practices could cause irreparable damage to a firm’s brand image.  

Finally, training AI programmes raises important legal and ethical considerations. Their heavy reliance on large swathes of data indiscriminately scrubbed from the internet has the potential to violate numerous data privacy, ownership, and security laws. Already, writers and artists are banding together to file lawsuits against AI companies to protect their work from unfair use.  

To combat these risks, companies must be vigilant in their practices. AI must be trained on diverse data sets and given comprehensive inputs with relevant contextual frameworks. And most importantly, the work must be overseen by a trained human. Firms must also be prepared to collect data in ways that respect the law and store the information responsibly. This means they must ask people for their consent and adhere to data privacy laws with the utmost sincerity.  

Learn more about the challenges with AI from Forbes.  

Humans are Irreplaceable 

The future of market research and brand strategy lies not in complete automation. At least, not yet. Blindly accepting an AI’s conclusions can be dangerous, leading to superficial insights at best, and harmful biases at worst. While AI excels at analysing  information to create predictive models and synthetic data, they still lack the deep contextual understanding, flexibility, and critical thinking abilities of a human mind. Indeed, AI cannot empathise with our stakeholders. And it cannot match our creativity for pioneering visionary solutions. 

Instead, the future of branding rests in the harmonious integration of artificial and human intelligence. Human analysts and engineers are still vital to the research and decision-making process. They must verify and validate results to ensure the data is accurate, complete, and unbiased. AI is nothing more than an incredible tool to augment human capabilities. But when used correctly, it can help extract truly meaningful conclusions from previously inaccessible information, paving the way for humans to develop innovative, well-informed brand strategies. Only by combining AI efficiency with human decision-making can we realise AI’s full potential. 

To return to our earlier analogy, radar and radio can tell us where our problems are, but they lack the complexity and empathy to formulate a plan and execute a solution.   

Photos taken from Unsplash, Freepik and Adobe Stock.

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With the series of redundancies by digital platforms (such as Meta’s Facebook, Snap’s Snapchat and most notably, Twitter) resulting from the decline in their digital advertising revenue, brands should be questioning if there is still any credit left in spending large sums of money on paid ads.

The truth is, the decline in digital advertising revenue is indicative of the increasing ineffectiveness of paid ads in recent years. In other words, brands who are still allocating high budgets for paid advertisements might be wasting money. Fortunately, there are better, more efficient methods in digital marketing which brands can, and should, look into to further their brands’ growth organically; the prime of which is to develop their in-house content marketing capability.

Understanding the decline of digital advertising industry 

So far, more than 20,000 employees were laid off in the slew of retrenchments by Silicon Valley in 2022. Among which, Facebook’s Meta had axed around 13% (11,000 staff) of its workforce, while Snapchat’s Snap dismissed 20%. Further job cuts, hiring freezes and other contingency plans were also executed widely across the Valley.

Meta and Snap are examples of some Big Tech companies in Silicon Valley, whose main source of revenue is generated from ad placements on their platforms. So the decrease in their revenue means brands are spending lesser for ads to be placed on Big Tech’s platforms. The seemingly obvious reason behind this is the threat of an imminent economic crisis; when companies tighten their belts in preparation for upcoming crises, advertising budgets are usually the first to be cut. But assigning blame only to the economic downturn seems too easy a way out. As with all things, there is more than what meets the eye. 

The fact is, the digital advertising industry already hasn’t been performing well since years before the looming economic crisis. Albeit yearly projected increases in revenue, a closer look at the number reveals that the industry’s growth rate is actually on a declining trajectory.

As 19th century businessman, John Wanamaker famously said: “Half the money I spend on advertising is wasted: the trouble is, I don’t know which half.”, a statement that is especially relevant in the age of digital ads. 

During the nascent stages of the digital advertising industry, companies jumped at the opportunities and potential it seemed to offer. But because the industry’s operation has always been opaque with its obscured metrics, the effectiveness of paid ads remains questionable. Therefore, it is difficult for brands to track the return on investment of their digital ad spend. To that end, brands are starting to realise the money pit they’ve been in, and have increasingly stopped betting on paid ads.

The situation for the digital advertising industry has been made worse due to the rise of privacy concerns in recent years. Paid ads can be accurately targeted the way they do because of data collection by Big Tech. Fearing that their privacy will be compromised, consumers have grown to be less receptive towards paid ads. Hence, when Apple introduced its App Tracking Transparency (ATT) feature– which gives Apple users the choice to stop apps from tracking their online activities– it was praised by users for taking a step in the right direction towards privacy protection.

Undoubtedly, the limitations placed on data collection have had a huge impact on the digital advertising industry, since advertisers would not be able to track critical data that make ad placements more accurate. With lesser accuracy, the industry loses its appeal and is unable to continue charming brands to spend on paid ads. Apple’s ATT has been estimated to result in a loss of USD 10 billion in Meta’s revenue, and USD 546 million for Snap. 

What’s next? Build an in-house content marketing team. 

In facing the decline of the digital advertising industry, it is crucial that brands venture into other aspects of digital marketing to ensure further growth for their brands. So, what should brands start doing? Instead of spending large amounts of money in paid ads with lost credibility, brands should start investing in building in-house content marketing through hiring and capacity-building. 

What is content marketing, exactly? Content marketing is the development and distribution of valuable and useful content and information that are relevant to target audiences through various means. These include emails, blogs, videos, podcasts, etc. Simply put, it is the creation of strong content that evokes the emotions of target audiences. 

The current shift of focus to content marketing is also timely, as the social media landscape simultaneously moves into a content-driven economy. The proliferation of social media and technology in the last few decades has placed consumers in the driver’s seat to decide for themselves the kind of content they wish to see. With the abundance of information users come across daily, they’ve developed mental shortcuts to sieve out only meaningful and impactful content deemed worthy of their time. Hence, information put out by brands must be meaningful and impactful– they need to tell stories that consumers can resonate with to boost brand loyalty and conversions.

Having an in-house content marketing team has several advantages that can greatly benefit brands (this article does a great job explaining them). But to summarise, an in-house marketing team does a better job at ensuring sustainable business growth, especially for brands with a strong customer base. Of course, building a strong in-house team takes time, effort and capital. But the investment in building in-house content marketing teams can certainly yield better results than paid ads.

What is also crucial is that, with an in-house content marketing team, the marketing process is made dynamic, and brands are able to better adapt to changes as the business needs evolve. 

Unlike paid ads hiding behind obscured metrics, in-house teams are able to embark on a process that lets them continuously see what are the strategies that work, adjusting them accordingly in the right direction to ensure brand growth. In other words, having in-house teams means brands are able to put out more dynamic content that can definitely connect with their ever-evolving target audience in a more effective way.

(Check out this article to find out the roles in an in-house team, areas of focus, and how to ensure efficiency of the team.)

Conclusion

Safe to say, the digital advertising industry has lost its grip on brands. While paid ads may have been effective for brands in the 2010s, the decline in its growth rate in recent years clearly indicates that it’s time for brands to stop relying heavily on paid ads. Instead, the future of brand growth calls for brands to start developing and strengthening their internal capabilities, and going beyond basic advertising to adopting a holistic approach for more sustainable and meaningful brand growth.

Tangible is a brand strategy and business consulting firm, synergising our consulting and creative offers to give clients comprehensive solutions. We believe brands are strategic management tools that should go beyond simply delighting customers. For more information on how Tangible can assist you with your branding, or other related digital services, please contact us here.

Issue 39: Q1 2022

Issue 32: Q2 2020

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