Speaker 1: Hey friends, Katherine Korostoff here from Research Rockstar, and today we're going to continue talking about data, and specifically I want to start by asking this very important question facing a lot of Insights teams today, and that is the question about what tools they're going to be using for data analysis in 2018 and beyond. So think about this for a second. What about your Insights team? Is the team that you work in, or the team that you lead, is it a team that's primarily used SPSS in the past? It's really common, right? A lot of Market Research and Insights teams have been standardized on SPSS for a really long time. Sure, there are some folks who use SAS and other tools, but in Market Research and Insights, we usually have used SPSS. Personally, I've used SPSS throughout my career. Now a lot of organizations, though, are thinking about moving to R. How about your team? Have you started to think about moving from SPSS to R? Well, the reason why a lot of teams are thinking about making this transition is really rooted in what's going on in terms of organizations these days. So these days, a lot of Market Research and Insights teams increasingly have other types of data analytics peers and colleagues that they're interacting with, and as a result, very often the Market Research and Insights team needs to have a central role in using many sources of data, not just survey data, but using data that exists in other parts of the organization. In fact, there's a wonderful quote here that I'd like to share with you from Stan Stannanathan, who is the very well-known VP of Consumer and Market Insights at Unilever. Previously, he ran Insights at Coca-Cola. Stan speaks at a lot of industry conferences, and a lot of us in the profession find his articles and his speaking very inspiring. He's a real thought leader in terms of the role of Market Research and Insights. So in a September 2016 article that he co-authored in the Harvard Business Review, he said, for any insights group that serves as a data aggregator, interpreter, and disseminator, the first challenge is to integrate massive and disparate sets of both structured and unstructured data from such sources as product sales figures, spending on media, call center records, and social media monitoring. This may amount to tens of millions of pieces of data. The data sets are customarily owned by different teams, sales data by sales, media spending by marketing, etc. And of course, Market Research and Customer Insights, right? We own typically the survey data. So what Stan is talking about here is really important and reflects why some organizations are moving from SPSS to R. When the Market Research and Insights function was doing survey data analysis in a silo, SPSS was fine. But the reality is that if you are working with colleagues and data sets from other functional areas, a lot of these groups use R. They might be using Python and other tools that are a little bit outside of the typical comfort zone for Market Research and Insights. But when Stan talks about the importance of being able to take different disparate sets of data and really bring them together, this is something why a lot of organizations are moving to R because it's kind of becoming the language that all of the different data functions are speaking. Now I know that it can feel a little intimidating if you are an SPSS user and maybe you've heard about R but you've really not gotten hands-on with it yet. I just want to assure you that R has a lot of options and you can keep it as geeky or as graphical as you like. So you can use R and usually with R people will use RStudio. So RStudio is sort of an interface for R that is something that makes it a little bit easier to use. Also if you use Q, Q Research Software, they also act as a front end to R. So you can use Q Research Software which has a really nice graphical user interface to basically have a graphical user interface function or front end so that you can easily use R. And it's still, it's using actual R to do a lot of different statistical techniques including max diff, various other types of multivariate data analyses, and even data visualizations. So again, you can use R and it's pure sort of geekiest form and those of you who grew up using SPSS syntax may be comfortable with that. But those of you who have used SPSS primarily as a Windows product with drop-down menus, you may find some of these other options a little bit friendlier. So again, just because it's R and it's an open source coding software, you don't need to be intimidated. Again, you can use either RStudio or Q to really create a much friendlier experience while still using the power of R. Because part of the power of R is that it is an open source product. And so there are thousands and thousands of modules available that have been developed by a lot of academic researchers and different types of researchers and statisticians. Now clearly a lot of this stuff that's been developed for R is related to things outside of market research and consumer insights, right? A lot of times these are products that are more geared towards people who are doing pharmaceutical data analysis, for example, or government data analysis. So there are a lot of different applications. But within specifically the world of market research and insights, it is absolutely applicable. And thinking about how you are going to become more of this aggregator of different types of data sources, thinking about taking a survey data set and appending it with variables from other data sources within the organization to do more robust analysis, that's a hot trend, right? Analyzing a survey data set by itself is something that, yeah, it's still done and will be done forever, I suppose, but more and more survey data projects are actually then appending that data set with variables that are from other data sources within the organization. So you can do things like capture customer attitudes in the survey, but then append that with actual customer purchase behavior as one example. All right, so if you are going to learn R, how do you do it? There are lots of places to learn R for free. There are awesome YouTube videos. There are free classes on Coursera, edX, Datacamp, Udemy, and of course here at Research Rockstar we also have an R course that's really tailored towards examples from survey research. So in our R course, I have to announce that carefully, in our R class we specifically use a mock data set from a survey data project so that survey researchers can try out R with our instructor Andrew Rothman in a way that's going to be very comfortable and, you know, with types of variables and data that you're used to seeing. So it's got a data set that has a lot of rating scales and that semantic differential scale, et cetera, et cetera. So do check out training for R whether you take a free class or whether you decide to take a paid class with Research Rockstar. But if you are thinking about the future of what your team's going to be doing, if you think you might be moving from SPSS to R, I would suggest that take a little time to learn it. You don't have to become an expert in it, but just try it out. See if it's something that you think will actually add value to your data analysis and your ability to have your insights team really be that central aggregator of all data sources. I hope that conversation was useful. If you have any questions, please add it in the comments. And if you did like this video, please do like and subscribe. Thanks so much. Have a great day.