The debate around automation and how it impacts the future of insights is ongoing. Some think the insights function has the potential to completely change with the advent of more DIY SaaS platforms and automation. While others think DIY and automation should be more focused on helping speed up some tactical, end-use cases, leaving the insights function focused on what it does best – strategy.
The reality is that it’s probably a combination of the two. Insights automation tends to lend itself better to DIY services. However, there are elements of automation used in more complicated studies to help democratize analytics. Regardless, the main point of automation is to speed up repetitive and predictable manual tasks so you can get insights faster.
What should insights automation look like for DIY?
Right now, automation in DIY mainly means ‘automating’ the survey creation experience; many people think of automation within insights as templated surveys. These templated surveys are a simple button click away, but of course require you to customize the templates to your own use case and unique business context. In this way, templated surveys are only partly automated.
The challenge here is balancing the flexibility to ask what you want to ask, in a way you want to ask it, while still maintaining the ability to automate it. Templated surveys aren’t particularly novel anymore, and while many software solutions used to offer rigid, inflexible versions of these templates, they’ve come a long way from where they used to be; We should know, we allow for a combination of flexibility and new methodologies within our Upsiide Templates.
What’s most interesting is where the power of automation can go in DIY research; as an industry, we need to look at further ways of automating insight generation once the data comes in. This should happen almost instantly if you are touting automation. You can’t tell someone their study is done but come back in two days to see what it all means. If the study is complete, the analytics should be too.
How we automate within DIY reporting
A good example of how we took the complicated analysis of source of volume/share of choice and automated it is the introduction of our Market Simulator within Upsiide. We could automate this process because we controlled the collection of data and built algorithms specifically around that particular collection process.
Prior to building a self-serve source of volume simulator, our client experience team had to take the data collected and send it to our analytics team for analysis. Even though we already had an algorithm, the data sometimes needed formatting to enable successful analysis through our in-house algorithm. There are two potential problems here – human error and capacity.
When formatting data tables, there is always the opportunity for human error, which could lead to less accurate data. But, even if the data is formatted correctly, you still need someone to actually run the analysis. Waiting for an expert to do some advanced modeling, where you have to run models to get that data visualization, is something that might, in theory, only take a few hours. However, if everyone needs them, the bottleneck could make it take days.
However, because we’ve controlled the data and formatting with our new Market Simulator, we can transform the data the same way every time. Because we designed the data capture and built algorithms around it, a complicated methodology can be offered up on a DIY platform. You can now access an accurate market simulation in minutes.
We might be biased but Market Simulator is a great example of finding a happy medium on the speed vs flexibility debate. It’s objectively fast (a report takes only a few minutes to run) and the modeling adapts to your use case seamlessly. It enables researchers and insights professionals to make a go/no-go decision on new products, flavors, and concepts.
Remember: automation is not just about self-service platforms
The automation trend for insights shines a spotlight on DIY. This makes sense when the industry is trying to find the quickest way to make product innovation decisions. You want something you can test yourself and get the answers to within minutes. This is why DIY insights automation is great for tactical decision-making.
But we continue to lead research projects for things that DIY platforms simply can’t shoulder the weight of, which means we’re focused on automating analytics and analysis for much more than quant study data. We take it a step further by automating analytics – connectivity visualizations, TURF analysis, virtual shopping, etc. These algorithms enable us to build tools that democratize analytics. By doing this, our analytics folks can focus on higher-level thinking (developing new algorithms, testing new methodologies, etc.) instead of grinding through analytic requests. And our consultants can quickly and easily run complicated analyses, spending the majority of their time on strategy and knowledge sharing.
Insights automation is all about speed
We expect to continue to see more automation built into restech platforms in the coming years. Whether you’re using automated elements within a DIY service or it’s us helping you reach a decision because we’re automating analytics, it is all about speeding up the time to insights or actionable decision making. The quicker you can make a decision or the faster we can deliver strategic inputs, the happier you’ll be.
