As I return to the research and insights industry, I notice that a lot has changed in my brief 18 month hiatus. A surprising amount of change. New tools, platforms and technological advancements are abundant, supported by a shift in industry conversations. The hot new trends of neuroscience and automation fill the forward-thinking discussions that were once centred around agile and mobile research.
Innovation is important, and it encourages me to see how adaptive the insights industry is – always scanning the horizon to see what technological innovations are on offer and how they can be applied. A lot of questions and scrutiny are part of this process. How will this improve data quality? Will it speed up data collection? Will it make analysis easier? Does it offer access to new participants?
However, one question often appears to be missing from these conversations; how will it affect the decision-making process? Admittedly, this is not an easy question to answer. But it is an important one. If research is pared back to a single, unified objective – it would be to inform decisions. So it makes sense that every new technological innovation, process and trend should be benchmarked against how it contributes to this objective.
To be able to inform any decision, research must meet three basic criteria: it must be of a high quality, delivered to the right people, and arrive before the decision is made.
Quality, Relevance and Speed
Quality and speed are often characterised as opposing ends of a scale. Research can be fast, or it can be good and decision makers need to choose one. It’s easy to see where this view stems from, as expectations of research can be unrealistically high. This metaphorical scale (which sometimes also includes cost) is a good way to get stakeholders evaluating priorities.
But, in my opinion, this scale has become a crux; a rod that the insights industry has made for its own back.
Truth be told, research should always be of a high quality. Basing decisions on poor quality data is worse than not having any data to begin with. Research also needs to be delivered at the right speed. The right speed may not always be fast, but it is governed by business needs. An honest conversation about when a decision needs to be made should form the basis of a timeline. From here, it becomes easier to recommend the research strategies and methods that can be employed to gather quality data before the deadline.
However, there’s also a third aspect that needs to be factored in – relevance. Researchers need to find out who will be making the decision their data will inform and ensure it is presented to them in a format that is engaging and useful.
As an industry of agencies, independents and client-side researchers, we should all be striving to find ways to improve how we can deliver on all three of these core tenants. Technology, process and culture all have a role to play in this. But the most important first step is simply remembering that the goal is not to deliver a research project, but for that project to inform a decision.
Turning Data into Decisions
Regardless of the data collection and analysis methods, there are a number of steps that can be taken to ensure data informs decisions - all centred around how the story is told. And perhaps no-one has mastered the art of storytelling quite as well as journalists. The Guardian’s Peter Cole published an article over a decade ago on the principles of news writing that not only remains relevant today, but also offers researchers food for thought.
Peter starts by suggesting that before writing a single word, a hierarchy of information should be established. With the target audience in mind, or in the case of the researcher, the decision and decision maker, information should be ordered from most to least relevant. Next consider the decision maker’s assumed level of understanding and standard vocabulary. A piece which the reader can understand with ease that resonates with their existing knowledge will always be more effective and engaging.
When it comes time to put pen to paper, it is important to use all the data types at your disposal and carefully consider placement. Does an emotive vox-pop qualitatively support the trend displayed by the graphical representation of a survey question? Does a customer quote accurately describe what’s happening in an image? Does a heatmap explain the scores of a wider poll? As journalists know, it is the way in which information is combined that often tells the most impactful story.
Finally, Cole’s article highlights some key rules of newspaper writing that almost certainly apply to the research industry. An active tense is faster and more immediate than a passive one. Stories are more engaging if they describe what is happening, rather than what is not. Officialese, jargon and acronyms alienate (unless of course it is the language a decision maker is most accustomed to). Last but not least, adjectives should be avoided unless they add relevant information – otherwise they can pose more questions than answers.
Creating a Culture of Insight
So what do I recommend next? My advice to any researcher looking to become more effective would be to review their impact on decision-making processes. Look for ways to improve the way insight is used throughout. Perhaps that involves collecting more data, improving the quality of information (or perceived quality), distributing it faster or finding more engaging ways in which to present it. There may be a role for new technologies and industry trends, but it may also be as simple as reviewing very human processes and culture.
Creating a culture of insight is no easy task, but by focusing on decision-making processes, I believe there is a real opportunity to create highly competitive, responsive and most importantly - customer-centric companies.
