AI in Marketing Research: Avoiding the Pitfalls of Past Tech Booms

Q2 Insights is a strong advocate for using AI in marketing research. It has undoubtedly transformed how we conduct research, particularly when AI’s speed, efficiency, and accuracy combine with our human ability to analyze and understand emotions. We embraced AI research platforms early, and while not our only tools, they are important tools in our toolbox.

However, we would like to offer a word of caution.

What is happening now with AI in marketing research feels reminiscent of two periods: the dot-com era of the late 1990s and early 2000s, and the Marketing Technology (MarTech) stack expansion of the last 15 years. 

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What Are the Parallels?

Dot-com Era

There was tremendous promise associated with the dot-com era. How does it compare to AI in marketing research today?

• Just like back then, there is massive hype and disruption, with many companies rushing to adopt AI solutions. There is a sense of urgency, and while some will succeed, many will not.
• The dot-com era involved a lot of experimentation, uncertainty, and ultimately failure. This will likely play out again in AI research today.
• There is also a shift in required skills, as businesses adapt to the new landscape created by AI, just as they did during the dot-com era.

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MarTech Stack Expansion

Over the last 15 years, the MarTech stack has grown exponentially, with thousands of tools for everything from CRM to automation. This has created a crowded marketplace, and there have been challenges with tool integration. More importantly, marketers and marketing researchers alike must seriously consider the strategic value of adopting these tools.

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Dot-com and MarTech Stack Failure Rates

So why compare the Dot-com Era and the MarTech Stack to AI Research? The statistics speak for themselves:

Dot-com

• By 2000-2001, 48% of dot-com companies had failed.
• Dot-com stock prices fell by 78% from March 2000 to October 2002.
• The capital burn rate was huge as 90% of dot-com IPOs did not survive the bubble burst due to poor management, unsustainable growth, and lack of revenue.

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MarTech Stack

• Over the past 15 years, the number of MarTech tools skyrocketed, from around 150 solutions in 2011 to over 11,000 in 2023. However, it is estimated that 25% to 50% of new MarTech tools fail within their first year.
• Every year, about 8% to 10% of vendors disappear due to failure, mergers, or acquisitions.
• Even when tools are adopted, only 58% of marketers use their full MarTech stack effectively.
• Many companies fail due to overcrowding and lack of differentiation in the market.

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AI Research

While there are no widely available statistics on failure rates for AI in marketing research there are indications that the challenges are similar to those seen in the dot-com era and MarTech. Looking at AI development in general, and according to o a 2019 report from Gartner, 85% of AI projects fail to deliver on their intended goals due to various reasons, including lack of skilled personnel, poor data quality, unrealistic expectations, and difficulty in scaling the technology. McKinsey & Company have also highlighted that while many companies invest in AI, about 50% of AI projects stall at the proof-of-concept stage, failing to scale or provide meaningful returns on investment.

Many AI startups are funded with high expectations, but according to research from various sources including Harvard, 70% to 90% of tech startups fail within the first few years. Additionally, within the crowded field of AI, many tools offer overlapping functionalities. With variable performance across these platforms, AI-based startups face issues in developing sustainable revenue streams, particularly when they focus on technology rather than on practical solutions that meet market needs.

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Characteristics of the Winning AI Marketing Research Platforms and Tools Approaching these challenges as seasoned marketing researchers rather than a programmer or AI expert, our advice to fellow marketing researchers is to proceed with caution as you adopt AI platforms and tools. 

Our advice to the developers of AI platforms and tools for marketing research is:

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Areas in Which Marketing Research Can Assist

• Ensure you are meeting a market need.
• Ensure your platform or tool is differentiated and not just a “me too”  in a crowded AI environment.
• Segment your market and design your technology accordingly.
• Consider how your platform or tool can align with available, successful MarTech platforms.
• Commit to continuous improvement.

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Other Recommendations

• Consult marketing research experts. Marketing research is nuanced, and your tool will benefit from real expertise.
• Make it easy to use. Marketing researchers need tools that are intuitive and do not require a steep learning curve.
• Keep costs realistic. One of the benefits of AI is that it can lower costs compared to human analysis. Tools should be priced accordingly.
• Avoid overhyping. There are countless AI tools on the market. Overpromising and underdelivering will damage your reputation.

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Summary

As we have seen in the past, rapid innovation can bring both incredible breakthroughs and significant failures. AI is no different. The tools that will stand the test of time will be those that offer real value, are easy to use, and continue to evolve. The lessons from the dot-com era and the MarTech explosion are clear in that success lies in strategy, differentiation, and delivering on promises.

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Kirsty Nunez is the President and Chief Research Strategist at Q2 Insights a research and innovation consulting firm with international reach and offices in San Diego. Q2 Insights specializes in many areas of research and predictive analytics, and actively uses AI products to enhance the speed and quality of insights delivery while still leveraging human researcher expertise and experience. 

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