Reducing risk to create a competitive advantage

Editor’s note: Jacob Gascoine-Becker is a partner at STRAT7 Advisory, London.

The market research industry, more than most, understands the rapid changes occurring worldwide and their profound impact on businesses. 

We also recognize that technological changes, including the rise of big data, artificial intelligence and unstructured data analysis, are radically transforming our sector. While we are adapting, notably developing new skills to leverage these technologies, have we fully considered the long-term implications on jobs, morale, trust and leadership?  

The bigger question – and echoing the mantra we take to clients: Are we successfully positioning change within our own sector as an opportunity, not a risk?

Perhaps not as much as we should. 

There’s a fair context for this, slowing down to take stock is challenging when we’re so often sprinting to keep pace with what our clients and stakeholders require of us. However, allocating sufficient resources to evaluate our current position and to define and communicate our future direction is business critical if we’re to maintain our position in delivering market intelligence and insight. 

Part of the problem is that “change” – in its various forms – is coming with increased frequency and impact. Macro trends such as post-pandemic challenges, geopolitical instability and climate change are significant factors. Additionally, at the individual business level, market research firms and brand-side market research teams must navigate disruptive technologies, emerging business models, new competitors and volatile markets each quarter. The range of issues is broad, diverse and growing.

Putting the customer first: Creating and maintaining a customer-centric strategy

It is for these reasons we encourage businesses to adopt a customer-centric model, where all core activities are focused on understanding, predicting and acting on current and evolving demands of customers effectively and efficiently. This strategy marks an overhaul of legacy models which tend to be much more reactive, slow and riddled with intelligence blind spots.

Yet to make customer centricity a lasting reality for clients, market research businesses are required to dig deeper than ever before when gathering the insights they require to act on fast-changing audience needs, and to deliver these at speed. 

This has required an overhaul of our own methods. Principally, this translates into using AI and machine learning to analyze unstructured data, and in the process forming a “sensory system” capable of detecting subtle or hidden trends or unmet customer needs – the stuff humans have little to no hope of uncovering themselves.

This process typically starts by analyzing vast troves of data – from diverse and unstructured sources such as website logs, social media or e-mails – to understand at scale, which in turn allows businesses to predict and make decisions with confidence. It also allows businesses to act on insights at pace, with high frequency and short lead times. The overall effect of this strategy is to gain a deeper understanding of customers, and thus use tech to become more customer centric.

Adapting to AI: The importance of balancing automation and human capabilities 

The challenge for leaders in our sector, however, is in maintaining trust – both internally and externally – as our sector adopts these quite radically new approaches in an industry that is necessarily risk-averse.

Internally, for instance, employees might harbor fears about AI encroaching on their roles – dubbed FOBO – because so much heavy lifting can now be done by a machine. The apprehension is not just about job security but also about their ability to adapt to and work alongside new technologies.  

From a client perspective, there are concerns around data security – the safety and privacy of their data when subjected to AI-driven analysis. Equally, a lack of clarity about how data is sourced and the inherent biases it might carry can make clients skeptical about the reliability of the information derived from such analyses. After all, the prospect of chatbots assisting in the research process, abetted by synthetic respondents is quite the leap!

To reduce perceptions of risk, a reasonable rule of thumb would be for AI to never do more than 80% of the work, with 20% reserved for human consultancy. As we further integrate AI into our workflows, and as challenger businesses enter the market with AI hardwired from the start, this will be important to adhere to.

The role of leaders in the market research sector is crucial in navigating these complexities. The priorities must be to find the right cadence for introducing change and to reassure all parties that market research will remain, above all else, a human-centric industry. 

Market research firms must therefore find a balance between leveraging automation for efficiency and maintaining rigorous human oversight to ensure the accuracy and reliability of their findings.

In practice, mitigating any fears begin by underlining the primacy of human expertise in complementing AI to employees and by introducing robust training programs and mentoring that help staff build the necessary skills to leverage AI tools effectively. For clients, leaders must ensure transparency about data handling processes and educate them on the data sources and methodologies used, thereby demystifying the analytics process. 

This will not only enhance trust but also encourage a more enthusiastic adoption of the new technologies we are required to understand our increasingly complex world.

Reducing future risks in market research

To mitigate further uncertainties, especially when incorporating AI and other emerging technologies, both in-house and supplier-side businesses should adopt a strategic and cautious approach. Initiating small, manageable proof-of-concept projects allows for the controlled testing of AI solutions, minimizing risks before broader implementation. Early success in these projects builds a strong foundation for expansion.

Measuring success is also crucial; companies should establish clear metrics such as accuracy, speed, user satisfaction, ROI and impact on client objectives. These key performance indicators track progress and ensure AI implementations meet their goals.

The investment strategy should also concentrate on solving real client challenges, delivering tangible value and avoiding the hype around new technologies – a mistake made by many businesses when the shiny and new arrives. This strategic investment balances development costs with budget constraints and focuses on areas where AI has a distinct advantage.

Finally, adopting an agile approach by starting small, iterating and continuously measuring success, along with embracing ongoing learning, helps firms stay technologically ahead. This significantly reduces risks and enhances competitive edges.