Is your market research platform fit for purpose?


Fit for Purpose Extended


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In other videos in this series, Geoff talks about 

  • Why having one source of truth is critical for market researchers to deliver insights faster and eliminate errors
  • Why adopting new market research tools doesn't have to be as hard as you may think


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How do you know your market research platform is fit for purpose?

There is a sad truth out there that there are a lot of platforms being used in the market research industry that are not strictly fit for purpose. There is 2 big buckets there. One is that there may be agencies that are using systems or sequences of systems and processes that have been around a long time, and so they're locked into technology that is perhaps a little outdated. Then the other is they might be modern systems, but they are systems that are not architected for market research. So, here I'm thinking of things like BI tools. Tools that are optimized for large volume, relatively simple datasets that aren't architected at their core to cope with those complexities that we talked about that are inherent in market research data. 

We can frame this question about what BI tools do well in terms of fit for purpose for BI tools, right? So if you've got very large volumes of customer data, transaction data, social data, data from sources that are not necessarily primary market research and you want to turn that data into analyses, insights, dashboards, regular reporting, then that's a sign that a BI tool is good for you. And the BI tools that we encounter when they are put to that purpose are excellent, that is what they are made for. It's when you try and shoehorn data that isn't relatively simple from those sources I mentioned, into those BI tools, when you try and do that, you hit up against barriers and that's when you know the platform that I'm currently trying to use for that data is not fit for purpose. 

Let's talk about some of the specifics of market research data that really trip those big BI tools up. As soon as you start to try and do things like you want to weight data so if you want your sample survey to be representative of the general population, it's very, very difficult to get your sample to be representative. You are going to under-represent or under-sample some groups that are hard to get and you're going to over-represent or over-sample groups that are relatively easy to get. When you do that, if you want the insights generated from that data to be accurate, you really need to weight the data so that your sample, once it's weighted, looks like the population that you're looking to represent. A platform that's architected for market research will do that inherently, it has weighting built-in, you can create your weights, you can apply weights if they are created elsewhere, automatically, seamlessly, almost without having to think about it. If you're using a generic BI tool that isn't architected to do that to work with market research data, you're going to have to work really hard to apply that weighting.

There are other things like - in market research you've got generally humans, interacting with humans, finding out information. When you've got humans, interacting with humans in some way, humans are fallible and there are going to be mistakes made, there going to be questions missed, there are going to be errors in the data because of that human to human interaction. There also just legitimate gaps in data. There are things that we call missing values. So if somebody isn't asked a question, there is a missing value and it needs to be treated as a missing value not as a zero. Market research platforms deal with that automatically. Business intelligence systems are usually architected to assume that there is a data point for every variable for every record. When you assume that and you hit up against the missing value, you are going get wrong numbers. 

So there's another element to a platform that plays nicely with market research data, that's designed for market research data, and that is the more you have that architecture right the easier it is for you to apply automation. So one of the things as a researcher that you want is to get insights, get the results (insights) out to your client as fast as possible. And one of the ways that you can achieve that is applying automation. Automate that process so that you're getting the data in shape as fast and efficiently as possible. You can really only achieve that automation if you have a platform that is architected for it. If you're trying to do the same in a BI tool, you've got a heck of a lot of work on your hands to build in all of those things that a research platform takes for granted so that you can then automate those things. Then of course once you've got the analysis sorted out, you want to, as automatically as possible, get those built into and updated into reports, online reports or dashboards that can push out. That's something that BI tools do quite well, but if they aren't playing nicely with the research data, you're not going to be able to get the automated reports that you want. 

There's another key area of benefits of having a platform that is built specifically for market research data and that is, you don't necessarily have to follow the sequential workflow of the classical market research process - and that would be you write your questionnaire, you field it, collect the data, you organize the data, shape the data (data process it), then do the analysis, then you build the reports from that analysis and then you interpret it and then you get it out. So, that very sequential system takes time. It means that you've necessarily got time, from the time the last bit of data is collected and the time you get the insights out to the client. And this is specific to our platform, we've architected it, so that you can disrupt that sequential workflow. If you want to get (and I think researchers do) top line results and insights out to clients as fast as possible, we've architected the platform so that you can identify the key things in the survey data that you want to take through that sequence one at a time. So, there's a particular business question that they have, you know the questions that are going to help you answer that business question - Organize it, analyze it, visualize it, straight out to the client immediately, fast. If you've got a platform that isn't architected for Market Research data, you are going to really struggle to do that. Now that's not to say that that sequential workflow has no value. It does, and the way that our platform works, still allows you to do that, but it gives you that added flexibility of focusing on the really important things and getting those out to the clients first. 

So, if you've heard what I've had to say about platforms that aren't fit for purpose and you're thinking that you've got a platform that isn't really fit for purpose, then you want to seriously consider looking for a platform that does tick all of the boxes that you're going to need. And, I would encourage you to at least consider our platform Infotools Harmoni. 

I'm a market researcher, I've been in market research since the mid 80s and all of my colleagues are market researchers apart from the ones who write the software code, of course. We’ve built this new cloud platform to meet the needs of market researchers so that they can then meet the needs of the clients, which we know intimately. We know about the importance of timeframes of getting things done fast, we know the importance of coping with all of the things that market researchers and market research data throws at us, and we know about all the ways that people need to get their reports, to get their insights.

- Geoff Lowe, Executive Director