Understanding the Scope and Pitfalls of End-to-End Insights Automation

If nothing else, COVID-19 has been a catalyst for accelerating digital transformation in nearly every industry, including the insights space. Undoubtedly, the world of market research right now is in the midst of an extended and unprecedented period of major change, especially as we seek to automate processes, minimise time-consuming manual tasks, and respond to pressure to become more agile and efficient.

Automation is undoubtedly the best course of action when it comes to freeing up valuable human resources to focus on driving innovation and value for customers, instead of drowning in labour-intensive, repetitive tasks.

Looking at the end-to-end research process, much progress has been made in automating the start and end of the process, such as scripting, reporting, and analytics. But the delivery process, the sample “bidding” and fieldwork, remains by far the biggest bottleneck to really feeling the benefit of automation. It is the lowest common denominator in achieving speed and reducing human workload, which is why we now see an uptick in companies seeking to address this issue.

But how can companies pursue adaptable data collection architecture without being paralysed by technical debt? A good starting point is to utilise solutions that are future-proofed, adaptable, and, where possible, limit upstream and downstream dependencies.

Trying to develop these solutions in-house can create a massive amount of technical debt, strain on internal resources, and a loss of focus on critical company goals. Innovation dies, and companies who attempt this feat are quickly left behind.

This approach undermines the possibilities that can be accessed through smart market research automation. I recommend asking yourself the following questions before embarking on your data collection automation journey:

• Is the path I’m taking adaptable? You need to architect a solution that is future-proof and facilitates ongoing innovation, not crush it under a complicated web of technical debt.
• Is configurability part of the equation? Standardised platforms, along with best-in-class configurable APIs and plug-and-play solutions, will keep you from painting yourself into a corner with custom code.
• Am I properly leveraging outside expertise? When you engage a partner or outsource part of your process, make sure you truly understand the complexities. Ask about what is automated and what is still manual. Once vetted, lean on your partners and leverage their knowledge to help you grow and look to the future.
• Does this path support my goals? Don’t be fooled and think that automation is a simple undertaking. It can actually require significant investment – in both time and money – and can remove your focus from your main business proposition and value. Tapping into a SaaS partner with an established, tested infrastructure may put you in a better position for growth and innovation.

I encourage you to read more about this in my new paper, “Transforming Data Collection Without Paralysing Your Insights Business.” In it, I lay out critical considerations for successful insights automation, all within the context of the current insights landscape.

In so many ways this is, arguably, the most exciting period in the history of the business, with multiple opportunities to use data in more sophisticated ways to derive insights that can be converted more directly to action and outcomes which are more easily measured. But it can be daunting working out how to adapt and what technology to adopt. And the pace of change is not going to slow any time soon.

New ways to engage consumers will emerge, new technologies, new data, changing regulations, evolving demands from marketers, and new insight products. As both suppliers and buyers shift their identities to fit the changing competitive landscape, there is no doubt that automation and technology will have a big role to play in differentiating levels of service and the ability to meet current – and future – demands.

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