Using the Meaningful x Actionable Grid to Evaluate Your Insights
How do you evaluate whether your research is successful? It’s an innocuous question, but one that is surprisingly difficult to answer. Not because we, as researchers, can’t intuit whether our data and analyses are making an impact — but because, on an objective level, success is difficult to define.
By comparison, our colleagues in adjacent fields have it relatively easy. Marketing campaigns have clearly defined goals. User experience and customer service teams work to clear benchmarks against previous data. But what is the right measure of success for a research project? There are a number of variables we could choose to evaluate:
- Volume of research requests processed
- Return on research investment (or cost)
- Number of projects completed on time
- Quality of analysis and findings
- Impact on business decisions
- Participant experiences
While this is only a small sample of metrics, it illustrates the breadth of options available. And the choices aren’t simple. Is the amount of research projects your team undertakes more important than the impact each one has? How important is quality in that matrix? It’s easy to say that these are all factors that should be considered. But that’s not entirely practical. After all, when every variable is scrutinised — we end up with a fairly balanced scorecard. Most likely performing well in certain areas and below average in others. And that’s fine. It just doesn’t help us align directly with what the business prioritises.
So, how can we narrow down the criteria and select two or three KPIs that will form the basis of our insights evaluations? Perhaps it’s worth taking inspiration from a similarly complex field — where there are a wide variety of variables — consumer segmentation.
A Simple Matrix for Consumer Segmentation
I’ll start by saying that this comparison isn’t about building a market map. It draws from the stage before this — how marketers decide which variables will sit along the x- and y-axis of their maps. That’s what the ‘Meaningful x Actionable’ grid helps us narrow down. And I have to thank Marketing Week’s Mini MBA course for introducing this method so eloquently.
Primarily, this matrix is a tool for customer and consumer segmentation. But it’s one that can easily be adapted for our purposes. The concept is simple. Start by drawing a table with four columns and as many rows as you need. In the first column, we’re going to list all the possible variables that you might want to consider as possibilities. You can use the ones in this article as a starting point, but be sure to ask your team and stakeholders what they consider to be important as well. We’re then going to mark the second column the ‘meaningful’ and the third column the ‘actionable’ column. Leave the fourth blank for now.
Next, we’re going to score each of our variables out of ten. It’s important to remember that these aren’t precise or scientific calculations, but a representation of our estimations. Here’s what we’re scoring against:
- Meaningful — To be meaningful is one that has relevance to what you’re trying to achieve. If you’re aiming to build a customer-centric culture, then the volume of stakeholders reached will be an example of a meaningful variable. On the other hand, if customer journeys are a top priority for your business, then perhaps the participant experience has greater relevance.
- Actionable — Some variables will naturally be easier to work towards than others. To provide a score that measures actionability is to evaluate how easily you’ll be able to change what you’re doing to improve the score of your variable. As a generic example, measuring and impacting the cost of research is easier to achieve than a more subjective measure such as quality.
The final step in our journey is to multiple the two scores together to give us a final value. This final value can be placed in your fourth column, creating an easy-to-understand ranking of the different variables you started out with. By the end of the process, your table should look something like this:
In this example, based on the knowledge of our (in this case, fictional) business, key management priorities and existing research competencies — we can see that there are two stand-out winners. We want to be measuring the volume of requests processed and the cost of research. Those are the two variables that will determine the performance of our insights team.
At FlexMR, the ‘meaningful x actionable’ grid provided an important litmus test for our insights empowerment framework. As the structure that underpins our product and service offering — helping insight, product and marketing teams to improve the efficiency, reach and influence of their research data — it was important that each of the pillars we addressed would be both relevant and achievable to our clients.
Adapting the Formula
One last point to highlight about the ‘meaningful x actionable’ grid is the flexibility of the method. Not only are the variables and applications easy to switch up, but so are the two measures used to score each row. In principle, the degree to which a potential measurement is relevant to your business and how easy it is to affect are universal indicators of how useful it will be. But, of course, every business is different. And perhaps there are criteria that better reflect what you need in an evaluation. So, change it up! All you need to do is swap out column headers, or even add one or two more, to create a completely bespoke evaluation system.
However, no matter how you adapt the formula, always be sure to be ruthless with outputs. The ‘meaningful x actionable’ grid is, at its heart, a decision-making matrix. While it may be tempting to take lots of measurements of success forward — try to narrow down KPIs to only the top two or three top performers. In the long term, this is what will keep your team focused, aligned and constantly striving to do better.
This article was originally published on the FlexMR Insights Blog and can be accessed here.