Let’s Talk About Member Data
Alex Mastrianni: Welcome to the Member Engagement Show with Higher Logic, the podcast for association professionals looking to boost retention, gain new members and deepen member involvement.
Heather McNair: Throughout our show, we'll bring on some experts, talk shop about engagement and you'll walk away with strategies proven to transform your organization. I'm Heather McNair.
Alex Mastrianni: I'm Alex Mastriani and we're happy you're here. Hey everyone, welcome back to the Member Engagement Show. Hope you're having a great summer so far. Today's episode is all about member data. Data is such a popular topic because data is so important to us and sometimes it can be a mystery. But data backed decisions are strong decisions and when you understand your data, it really enables you to be more competitive in today's highly personalized market. You need to put your members at the center of everything you do and data can help you do that. From customized program offerings, resources for member onboarding, content you're creating, data really is the fuel that helps power member centric programming. We recently hosted a three part webinar series that dove into the world of member data and I'm so excited to share one of our panel discussions with you. And whether you are a data novice or a seasoned pro, you will definitely walk away with some tips to improve your processes and learn how to leverage all of the great data that you're capturing on your members to create better member experiences. Without further ado, I will turn it over to Higher Logic's own Josh Slyman who's moderating today's panel. Take it away, Josh.
Joshua Slyman: Hello everyone. Who's ready to talk about member data? My name's Joshua Slyman. I'm a senior consultant on the strategic services team here at Higher Logic. We are very excited to kick off the first session of our three part webinar series. Higher Logic has teamed up with our partner at Wicket and the Canadian Society for Association Executives, to bring this program to you. Just want to give a moment for our data experts to introduce themselves. Beth, why don't you kick it off?
Beth Arritt: Hi. I am Beth Arritt and I'm a product marketing manager at Higher Logic, but a couple months ago, I came over here from Association, so I spent a number of years in Association Nonprofit, most recently the last almost seven years at the American Association of Airport Executives and have done a lot with data particularly there.
Joshua Slyman: Great, thanks. Welcome. Jeff?
Jeff Horne: Hey everybody. My name's Jeff Horne. I'm the co- founder and CEO at Wicket and we're the world's first member data platform. This topic is very close to our heart, being an organization that does nothing but focus on membership data and really transforming how associations can manage that data on a day to day basis.
Joshua Slyman: That's super exciting. Welcome. Thank you for being here. Nicole, you're with Wicket as well, maybe you could introduce your role.
Nicole Covello: Yeah, so my name is Nicole Covello, I'm a data analyst at Wicket, so it really, my role spans a lot of different things as things with data tend to do. Lots of just helping clients get their data into Wicket and then also working on the reporting and dashboards and our kind of internal data focus as well.
Joshua Slyman: And then, Vasan, you're with this CSAE, a lot of acronyms. Why don't you tell us a little bit about your role with the organization?
Vasan Selliah: Sure. I'm the member insight and engagement manager at CSAE. And it's been today seven years that I'm with the organization. I started off as the membership coordinator.
Joshua Slyman: Congratulatons.
Vasan Selliah: Thank you. I've been involved with data from the very beginning. Working with member data and then working through as a membership manager. Today it's been six months almost that I am in my new role. Happy to be here and share what I have to say.
Joshua Slyman: Welcome. Let's kick things off with some definitions. Sometimes it's easier to start with defining what something is not. There often seems to be a lot of fear and hesitancy around data. Nicole, maybe Jeff, is there anything you could help in sort of setting some context around what is data driven and what isn't data driven?
Nicole Covello: Yeah, for sure. Just there's a lot of resources out there right now that focus on becoming data driven and a lot of the time that seems like a very large thing but really data driven when you really think about what it is, it's just using data in the things you're actually doing day to day. It doesn't have to be a really advanced thing that you're doing or anything that's complicated mathematics or investing in big programs or anything really, the other examples we have there are having a full data team or something like that. It's really just about understanding what you have, what data is available to you, making sure that you're thinking about what you need in terms of reports. And when you're looking at metrics, making sure that you really are defining those key metrics. There's tons and tons of metrics that are out there and really the key is just defining them.
Jeff Horne: Yeah, absolutely. And well, I'll just jump in with, really I think fundamentally it really does come down to using data to make decisions. It doesn't have to be complex. It doesn't have to involve algorithms. It's really just finding ways to use data to make decisions day to day strategically.
Joshua Slyman: Right, right. Absolutely. Any other insights, Beth or Vasan on sort of what data driven means to each of you?
Beth Arritt: I would say that it's not just using it to analyze and get and make decisions, it's using it to then turn that around and automate, serving a lot of the information up to people that they need.
Joshua Slyman: Yeah, absolutely. Sort of getting it to using that data, activate member actions. I'm sure Vasan, that's something that you have a lot of familiarity with.
Vasan Selliah: Exactly. The data has to be practical use for us. When you pull that information, it has to be useful immediately for the team or the member. That's what we're looking for ys when we look for data driven.
Joshua Slyman: Yeah, great. Beth, you and I were talking about this earlier. Why collect this information? Who uses it? Why is it important? What are the right team members? Who should have it?
Beth Arritt: Everybody. Democratization is great way to put it. The trick is you have to make sure when you give that data to people that it's not just accessible, it is understandable, relatable. They don't just need to know what the data is, they need to understand why it's important and how it impacts them. If you gave somebody a dollar and they had no frame of reference for what money was worth, they'd be like," I don't know what this is. It's a piece of paper. Great." It's the same kind of thing with data. You have to know what it is and how it relates to everyone else to understand the value and the importance.
Joshua Slyman: Right. Connections, making connections, understanding. The analysis part is really a lot of the fun of it. What are the connections we can make? What can we learn about our members? And then how can we use that information to serve up a better value to help them meet the motivations and goals?
Beth Arritt: And the bigger question though, is just why should associations dive into it in the first place? If it's relatable and can do all that work, why bother?
Joshua Slyman: Right. I see, oh, sorry. The value for me a lot of times is about driving the member action and the member motivation and speaking that member's language in order to activate them, to serve the organizational goals.
Jeff Horne: Yeah, absolutely. And I think you want to leverage your data and turn it to action. But I really think fundamentally as an association, you really have a responsibility to know your members. And to know them as a whole, but to show that you know them individually as well and that you are properly collecting the right data and sharing that data back to them in the right place at the right time to be able to deliver them the resources and information they need. I think the importance of data, really for me, a lot of it comes down to that extreme importance of just you need to know your members and that's just critically important to deliver your mandate as an association.
Beth Arritt: I saw a great quote about that the other day. It's not about personalization, it's about personal. It's great if you can put my first name into something but then if you go to tell me that you know nothing else about me, why bother?
Vasan Selliah: Creates the relationship you want to build with the organization and the member and the more you know about, the more about the preferences, you can make that personal absolutely.
Joshua Slyman: Yeah. If it's not, that's a great point. Let's talk a little bit about your day to day work.
Vasan Selliah: Sure. I work with a team of 14 people and they're in different teams so they have different need to accomplish what they want and they have also their preferences on how they want to use the data. For me day to day, that means keeping our database maintained, clean so that we have proper data. Go to the sources to make sure that they're still working properly, link the information to get the proper information that can be used directly by our team. That basic part is very important. Otherwise, the rest, the insight that we can get out, the information provided to our team members, they'd get error in it so it won't be accurate enough. And then based on that, I pull reports or lists, reports that can be used and give insights that they can better their performance in what they're doing. Basically at the end, it comes to providing a better service to members because associations have very limited resources and everybody's in the same boat on that and trying to do the best as we can we have to understand what's going on. And my role is to make that information accurate and share it in a way with my team that they can interpret it and offer the service that we need to do with a member.
Beth Arritt: Josh, I'm going to turn that question back around on you, how do you work with data on day to day?
Joshua Slyman: I think segmentation is critical. And again, it sort of goes back to exactly what we were saying earlier, by delivering the right message to the right people, as painlessly as possible we have that better opportunity to activate that member but also sort of this idea that we're moving into this world of sort of scaled relationship building and the ability to activate and talk to membership at a scale and a percentage and a ratio that is much more effective in terms of driving internal efficiency. One of the things that we've actually recently done was, and some of the results here are on the screen, by segmenting a call to action based on role, we were able to really open up a great open rate, but also move our conversions earlier into our communication process. By understanding the targeting the member by based on their role, delivering them value based on that so it was professional development opportunities, sessions within the annual conference. We're delivering very specified content based on their role and using that, we were able to drive up our open rate. We were able to really drive early conversions and get people committed earlier. Another great opportunity we saw in activating exhibitors who had worked with us previously and trying to communicate the value, the additional value of taking that upsell full member, full exhibitor package. And again, COVID sort of threw off the end result of a lot of these things but our early results really, really showed a nice bump up 13% of people basically in those early communications moved into that upsell package and then really generated additional revenue just by understanding what they had done before and delivering the upsell on value.
Jeff Horne: Yeah. That's awesome, Josh. I think an interesting thing that you often is a great data story that we see is that once you do start segmenting, once you get away from the spray and pray approach of communication and you start delivering more tailored messages, you immediately start to see those returns on whether it's open rates, registrations, whatever it might be. And I know, I think with Sonnet CSAE, you've seen some of that as well as you've segmented the data a lot more.
Vasan Selliah: We've been switching from spraying, like you said, to all our members, everything that we do to trying to cater for what people would be interested in and we do see results in open rates, especially the unsubscribe rates lowers each time because people are interested in what they see so they keep going.
Joshua Slyman: The other thing we've had great success with is identifying members of value and using cross- platform data points in order to sort of filter down and better understand behaviors that happen across these platforms. And so there's been some great opportunity in understanding how the platforms talk together, what data points indicate an observed or understood behavior across those data points and how do we compile them into one spot and make them actionable and automateable, to Beth's earlier point.
Beth Arritt: Well, and you I bring up a good point about cross- platform because if you've got multiple platforms, if you've got email, you've got, say a newsletter, you've got community, you've got your website, if you have enough data and then write down and you understand it, then you don't have to email people as much. You don't have to send out the message because you're putting the right message in front of them at the right time across the multiple platforms.
Jeff Horne: Yeah. And the connectivity there is really key. And I know we're going to probably get a bit later in the talk about talking about the technology side of all of this but the connectivity of the data across your ecosystem of software is just so critical here today.
Joshua Slyman: Yeah, yeah, absolutely. And ultimately, we're looking to serve and Vasan, you can probably speak to it, we're looking to serve the members' motivation. It's their goal, it's their purpose. I know that a lot of times people get touchy and scared of data and what does it mean? But ultimately our purpose is pure. We're looking to serve the end motivation of members to drive advocacy, to drive learning, to drive highlight and spotlights on our members and the works that the organization is doing. Ultimately understanding that data and delivering it to them, drives their end purpose.
Vasan Selliah: Absolutely. As an association it's toward the benefit of our members always. When we get this information, we always find a way to benefit the member through it. The more we gather, like you said, we can filter, we can segment and deliver what we can to them so they benefit.
Joshua Slyman: Exactly. Jeff, let me move a little bit into process. Any advice you would give to organizations that don't currently have any formal process in place? Maybe where they should begin?
Jeff Horne: Yeah, it's a good question. I think when I think of this and really starting from a fresh place where you don't really have much process in place yet, I think starting with identifying a champion internally who can really kind of own or take responsibility for data initiatives within the organization. And that doesn't need to be a data scientist. It doesn't even need to be the person that's going to do the work around data but you just need somebody who has an interest in this type of work and is willing to kind of lead it and move it ahead because you really do need a champion that's going to push on it. And I think from there, I think really your next steps need to focus around, what are your metrics? What are the words you use internally? When you say active member in your organization, what does that mean? If I ask everybody, are they going to say the same thing?
Joshua Slyman: Not even close.
Jeff Horne: Probably not. It's always, it can be surprising to realize just how important something like a data dictionary can be just to get everybody on the same page of what does it mean? What does an active member really mean? What does a lapsed member really mean? What does an engaged member really mean? With something like that you're going to get all kinds of different answers and perspectives too. I think working towards standardizing on a data dictionary is a really good first step. And then from there it's, then you can start talking about, okay, what are our goals now? What do we want to start doing with this data? Which then is going to lead to, well, okay, now what data? Where's the data? And what data do we need to answer these questions? But I think I'd start a few steps back and the software and the tools, you have to resist the temptation to start there. It's easy to be like, oh, I'm just going to go find a piece of software to do this for me. But the reality is the tools is last. It's you got to start much earlier.
Joshua Slyman: That's very interesting. I love that idea of the data dictionary and really sort of defining the semantics and the understanding across the organization. I think that's so often forgotten in this process. I think that's a great place to start. I love it. Beth, sorry.
Beth Arritt: Yeah, it's great. That's okay. I was going to say, it's critical. You have to all be, it's that whole dollar thing again. How do you know what a dollar is worth if you don't know what a dollar is? It's critical. I was just actually wondering Nicole, what are some common mistakes that you've seen people do?
Nicole Covello: Yeah. Related to what Jeff was just saying too, it's a lot more fun to kind of start with building the dashboard or doing that kind of thing and not doing the defining the key terms and that kind of more monotonous, tedious stuff at the beginning but that really does end up being the most common mistake that ends up coming. Because I haven't really run into a lot of things lately where there is no data anywhere and nobody has any ideas of what to do. There's lots of data and lots of things that you can do with it. A lot of the mistakes that ended up coming or not kind of taking that time at the beginning to really work together and figure out what it is that you're talking about. And Beth's been saying, with the dollar, make sure that everybody is thinking the same thing about the dollar. Those four kinds of examples, there are just starting a project with just kind of thinking it would be cool if and then kind of leading in from there. I'm a data nerd myself. I fall into all of these mistakes all the time and you end up a week down into this project and you realize that there really isn't a lot of value in what you're doing. It's cool but it's not really going to drive that value. There's also a tendency to think that with doing these side data driven projects, you really need to start from nothing and then reinvent everything and go completely away from the processes you already have. But really just adding data in as a component to the things that you're already doing is going to be a really good way to make it be effective and to actually be successful in the project you're trying to push forward. There's also, there's so many visual things that can come up when you're working on these data projects. And again, this is another thing that I fall it prey to as well, where you come up with something that's just really visually appealing and you kind of focus on that more, which in certain circumstances might be the right goal to get to. But a lot of the time you really want to be more focused on the content of what it is and not focus on kind of the nice to look at dashboards. And the last one there is really just like I was saying, there's so much data and there's so many things that you can do with it, that it's really tempting and hard to stay away from huge projects that are going to incorporate a ton of different things and they might all be things that you eventually do want to get to, but starting there is very, very difficult to really get moving and really starting with those smaller projects where you can take a bite size piece, have a clearly defined goal and really just get to that goal and be able to show other stakeholders that progress is really quite valuable.
Joshua Slyman: Go ahead, Beth.
Beth Arritt: I was just going to say, what's funny is that as I'm listening to the mistakes you make, I think that the reason why we all can identify mistakes is because we've all made them several times over because we got, oh data, pretty, shiny and then thought oh no, don't start there.
Nicole Covello: 100%. Pretty sure, started on these recently.
Joshua Slyman: Totally. I might've just come from one right now. One of the things that I have run into with customers is exactly that, that monolithic thing. That they just want A, they have a whole list of metrics they want but they don't know why or they have a purpose but they don't know what ties to it. I think it really, to your point, connecting the goal, the desired outcome, the organizational mission to a metric, I think really does help identify how those things connect. And then it really helps simplify the metric that needs to move. If you can see the one and if you sort of bring it down to one or two metrics, then we can focus and we can concentrate our efforts. We can direct our calls to action. I think that really helps sort of funnel down on what we're looking for. Vasan, I wonder if you have any experience sort of filtering down on the types of data you had to consider on your day to day.
Vasan Selliah: We do, especially now that we transitioned towards a new system. We had a lot of wishful thinking. It's we want this, we want that. And then when we think about it hardly, is this really going to be useful? Or we were asked that question and said," Maybe not," because at the end, it's more work, more data that's not useful and we already have so much data to work with. That way we filtered down to some basic things that are really useful that we can follow through and that will actually indicate what we want to measure.
Joshua Slyman: Do you mind giving an example of one or two of those metrics that is that you guys find interesting or valuable to move?
Vasan Selliah: Sure. One of those things that we ask our members is when they join is to fill out their profile so there's a bunch of questions we ask, their preferences. And what we do is it permits us to segment depending on their interest, depending on their responsibilities in their roles so we can share information. And we came up with a lot of lists of interests that might be useful. But at the end, when we look at the role of, let's say a membership manager, what do they actually really need? And we filtered, go through their interests. We said,"Hmm, maybe they don't need 25 different interests." They need to engage members. They need to communicate with member, they need retention ideas, recruitment ideas and we stick to that. We don't need to expand that beyond to further pollute our database basically.
Joshua Slyman: And it must drive your efforts because now you know exactly what you're delivering at least a to whatever degree possible.
Vasan Selliah: Exactly.
Joshua Slyman: Yeah. That's great. Beth, you were largely responsible for maintaining and maximizing data during your time at AAAE. What's your favorite example of a goal you were able to achieve utilizing member or communication data?
Beth Arritt: We did. We did a lot with data. And I could pick a lot of things that we did in terms of outgoing information. And there was one, I think one year that we actually sent a million less emails, but we had a 1% increase in both our open rates and our click throughs. That was pretty good.
Joshua Slyman: Wow.
Beth Arritt: Because I remember we got an open rate. But I think my favorite one is going to have to be the website personalization. It was just sort of the culmination of years of work with data. We took all of the data that we had in our database on our members. We took meetings, certifications, trainings, committees, all of that and then we added in write backs from web tracking. We've added in the write backs from community search. That's that whole cross platform thing that we were talking about and what people had posted. And then we scored each piece of data. It was an engagement score only instead of an engagement score, we were scoring subjects. Each person in the database has a subject score and whichever of our 12 main subjects they scored the highest in, that drove what news, resources, events and advertising they saw across the website. That was really just the ultimate use of data for me. We'd been building that data for so long and using it to analyze our members and figuring out what we should offer and for promotion, but to get to the point where we could turn it around and let that data work for them every time they visit the site, to integrate their web visits and community activity, to help them give them the information that they need automatically even better, that was just next level. Obviously that's kind of an ultimate goal, but it was amazing. I was really in my office when I figured out, we had finally got it set up, the day I finally got things working right and I was screaming into the void, but it was awesome anyway.
Jeff Horne: That's awesome, Beth.
Joshua Slyman: That's great.
Jeff Horne: I think that power to give, to be able to present users with the information that is most relevant for them when they need it and in the place that is most relevant, it's very, very powerful. And there's no question it's going to result in increased engagement.
Beth Arritt: That's what we're all aiming for. That's the ultimate dream is to just be able to turn around. Our CEO used to ask for the Amazon experience and I was like," No, no, no, no. We're going to give him the Amazon plus experience." Amazon, only we're actually going to understand that they're not interested in that one artist that they bought a CD for their grandfather for 10 years ago. Yes, I'm talking about our own experience. But you should know them better than Amazon knows them.
Joshua Slyman: Yeah. Right, right, absolutely. And the nice part is it's sort of the next step is once we identify and sort of filter down on those members of value, then we can you utilize in automation tools or other parts of the scaled relationship management tool set in order to scale those relationships and really increase and filter down on our effectiveness, look at parts within that communication chain that aren't as effective and tweak emails, subject lines, calls to action based on looking at that data sort of across all of its applications. All right, we're going to talk a little bit more about exploring data in session two but I did want to talk about some important considerations laying the foundation in terms of technology, technology stack. How do we start that conversation? Many associations have different systems, member data platforms, AMS, email marketing, online community, how does it all come together?
Jeff Horne: Yeah. This is it's a really interesting area and it's an area that we certainly spend a lot of time deepen at Wicket. I think the reality for an association here in 2021 is you have many, many systems. I think there was the AMS systems of old tried to do everything in one platform. And I think more and more, you see the trend towards, yes, you need a system of record for your membership data but you're going to use other great best in class tools across different functional areas. You do end up with data in many systems. Then it just raises the question of, okay, well with data in many systems, what now? And how do you tackle that problem? And I think that's really where the incredible importance of integration and luckily, integration here in 2021 isn't what it was for you to go five years or 10 years ago. We've come so far in the technology realm of what it looks like to connect systems together. And I think that's just incredibly important. You can today connect your data together. You can use disparate software tools and whether it's a member data platform like Wicket or funneling all of your data into a data warehouse, there's just a lot of different approaches that can be used to give you insights and leverage technology to do so.
Joshua Slyman: Vasan, how have you worked out some of the smooth out some of the ways the processes in between systems or for your members, how do you sort of account for the user experience in the different platforms?
Vasan Selliah: Integration has some very far but it's still not perfect. we still feel that but the experience can be made easier for members when you make the branding same, if there's different platform, connect them in a way that the member doesn't feel that they are in totally different where they're like, oh, this is not CFD anymore. That helps a lot. And in terms of data, what we do is we work around, we find solutions that are a little bit later on when they really need it, but for most part what's integrated helps a lot. And pretty much what Jeff said, we have a very good integration for most of the tools so the data that we gather, we can put them together from different pieces, events, membership, education, we can put all together, make a profile of a member and build segments from that.
Joshua Slyman: That's super exciting.
Beth Arritt: Trying to take a computer chip and turn it into a pretzel when you're doing something like that. Sometimes vetting that data can be really difficult to get something out of it.
Joshua Slyman: Yeah. No, it's definitely, it's a lot of things to take into consideration but there's so much behavioral data in these points. Understanding how people are learning, what events they're going to, how and when they renew their memberships. One of the things that I was able to do with a customer was note when people flowed out of the renewal process. And we found a significant number of people were renewing 90 days before their membership was due. Now it's an interesting piece of information. It to us indicates a valuable group. A, it also allows us to start doing some understanding and forecasting for future efforts. It also showcases some of the places where the campaign may fall down if we see increases on the other end of things. Just the ability to see these data points and sort of understand how they tie together has been really exciting.
Beth Arritt: Well, we had the opposite experience of that at AAAE. It was routine to let things go. We had your renewal is over today. This is your renewal day. And then there was a full month after where they would still have their membership because airports are government entities and so it took forever to get things paid. We just always went on the assumption because our data suggested it, that our members just it took a while to get things paid. And when we turned it around and we created our campaign to start 90 days before the end date and it's a 120 day campaign, 65% of the people renewed between the 90 and 45 day window because they'd gotten the information ahead of time. We were able to tweak all kinds of other processes too. We tweaked how many print invoices we were sending. I think when I left, they were down to a fourth of the invoices that they used to send, which is so much savings on paper and processing and everything else. We went the opposite where we just assumed because of the data we had told us that, I think that's a whole other thing about the data is that you have to kind of look and see, okay, is it the data because that's true? Or is it the data because of something you've done? Sorry, I just opened a can of worms there, didn't I? Sorry.
Joshua Slyman: It's a good thing this is a three part series.
Beth Arritt: Yeah, because I'm like, oh, that's a whole other thing.
Nicole Covello: Oh, sorry. Do you want to go ahead, Jeff.
Jeff Horne: No, you go ahead.
Nicole Covello: Yeah. I was just going to kind of say in regards to just data silos in general, even just kind of the diagram that we have there is a good example of how much more visually empowering it can be to kind of actually sit down and draw it out even if it's just on a piece of paper, where your data actually is and even just little notes about what data is in there. What data might be in your member data platform that isn't in a particular service? Or kind of just having that as a visual way to understand it is going to help with the slides earlier about data democratization and data literacy and all of that kind of stuff. And I find just drawing it down on a piece of paper really is enough to make a pretty huge difference in understanding where those gaps are.
Jeff Horne: Yeah, that's great advice, Nicole. And I think it is so critically important to understand what data is in a system? What data do you need in another system? In the world of Wicket, we think of the member data platform as this hub. And then we say," Okay, for these integrated systems, what data does that system need to have in order to deliver the best experience let's say for the members?" Does it need to know that their membership has lapsed as an example so that when they go to register for an event, they no longer get the member discounts so then now you can prompt them to renew their membership. You don't need all the data in all the systems, but you need to map it out and understand what is there.
Joshua Slyman: Well, and I think that's an important point because this is very much a member lifecycle or workflow or user experience here. If these are our touch points with our memberships and our opportunities to engage and to offer value, then what are the data points that represent that value and that engagement so that we can clearly define where we can improve and where we're strong? I know there's obviously everything would work together if possible, so there's definitely a lot of benefit in that integration. We've talked about some of it around improved member experience and cross- platform behavioral understanding. I wanted to tie in the Spiderman reference, with great power comes great responsibility. I think that does really come into play when we're talking about A, member data but also how we're activating membership and how we're speaking to them, how we're motivating them. I think there's been examples across recent history of both the positive and the negative effect of community and what that data, that responsibility holds. And so sort of back to the point about really looking at this data in order to drive advocacy, to drive learning, to drive member motivations, I just think it's an important point that that responsibility does lie with us.
Jeff Horne: Yeah. I couldn't agree more there, Josh and I think that it's incredibly important information that you're storing on your members and use an association, have responsibilities from a data security, from a privacy standpoint and to really understand how that data is being used to understand what data does need to be cared with other systems and why. And even just asking yourself the fundamental question as an organization, why are you storing certain information? I think often what we'll see is if it's at a point of transition to a new platform, that's a great time to stop and say," Okay, well do we really need this data? Are we just collecting it because we always have?" And it's really not even being used anymore and Vasan, I know that here we went through a lot of that with ESA.
Vasan Selliah: Absolutely. There's a lot of historical data that we tend to keep it because it's been always done like that. And then when you do this reevaluation, you're like, I don't think people would want us to do this. And I don't think they want us to keep this data anymore because either the laws change or the privacy concept changed and these things evolve with time. We have to be up to date as well, in terms of that. And reevaluate the needs. Are we going to use it to accomplish our mission as an organization? And if this is not a question we can answer yes, we don't need that data.
Joshua Slyman: Yep. No, I think that's a really, really important point, the transparency piece and the understanding that the data is being used to drive that end goal, that end purpose really does add the legitimacy and the purpose behind them. All right, let's talk a little bit about small teams. I know we're getting a little tight on time, but we'll keep them moving. Jeff, Nicole, I personally have worked with multiple organizations that are teams of two or four people handling everything. Any advice, thoughts on that?
Jeff Horne: Yeah, I think just to quickly jump in, I know Nicole, you have some thoughts here well. I think it's just start small. I think that, the old saying Rome wasn't built in a day, you don't have to take on A, a huge initiative and B just find something small to start with and try to find a success story. And I think, if you want to work towards a data driven culture within your organization, you need to get buy in from your colleagues. And so finding a small project where you can go through some of those things we've already talked about, coming up with some of those data dictionary terms, get something standardized, find a single key metric to focus in on and then see if you can pull that data together and see if you can do it. Nicole, I know you have some other thoughts too.
Nicole Covello: Yeah. Just similar to a lot of the other things that we've been saying the whole time, too, it really is just like Jeff was saying, just starting small. And there are already so many things that people are doing every day, especially in those really small teams of people, they're going to be wearing a lot of different hats during the day and there's a very good chance that there's something that you're already doing each day that you could just incorporate the data into. It really isn't something that should be considered totally separate from the stuff you're already doing and really focusing on picking something that's going to eliminate time spent in the future. You might have to do a bit of that time investment upfront, but a lot of the data, again, like we've all kind of been saying, when you can actually get it into people's hands at the right time, it can eliminate a lot of other work. Really just picking those right projects to get started with. And just focusing on that goal. And just a quick note too, about the data dictionary stuff, because I know that sometimes that can seem intimidating to people as well. And you might find examples of data dictionaries that are huge things with dozens and dozens of columns that you have to fill out for all the stuff but it can really be as simple as a little spreadsheet that's 10 rows and it's just a term, a quick thing about what the definition for that is and whether there's any acceptable values or something like that. It really doesn't have to be a really big undertaking. It's just getting those key parts in there.
Jeff Horne: Yeah. Yeah. I think the only other thing I'll throw in there is, if budget permits, I think looking outside, of hiring a consultant. There's lots of great people that are working in the association space that have great insights on data, whether it's data governance and just how to overall manage data or getting more into the weeds. There's lots of resources out there. And so, finding a way to get started, that's what it's all about.
Joshua Slyman: Awesome. Well, I think we want to leave with some next steps maybe. Beth, maybe you can weigh in on some ways to keep things rolling.
Beth Arritt: Sure. If you're really looking for a place to dive in and get started, I think that there's the first thing you want to do is choose your goal. Data without goals is like a hockey puck without a net, you got the tools but where does it go? How do you know what's successful? I like my analogy, sorry, but once you know your goal, once you know where the net is, that's when you can figure out which pieces of data would be the most important, what's going to help you actually make a plan to get there. That way you're not just wandering aimlessly through a data mine field, you know what you're looking for. And you know what success is going to look like. Now you've got your goals and you've got your measurements, so you need to make a plan. And then once you've got your plan, you want to measure your goals routinely because you want to see, are you on the right path? What do you need to tweak? And then once you get that set, there's more data to be had and more goals to be had so you just go right back to the beginning and start all over again.
Joshua Slyman: Renovate and build another one.
Beth Arritt: That's right.
Joshua Slyman: That's very exciting.
Jeff Horne: Yeah, I think it's a great point, iteration is so important in this whole thing. You think in an agile mindset, you're going to try something, gets you an end result, you're going to iterate on it and keep refining it. And that's good. That's a good thing.
Joshua Slyman: Yeah, absolutely. That is the exciting thing is that this data really does serve that concept of continual improvement. We always have the opportunity to do better and to better serve our membership and using data to do that ultimately, it sort of hits on all of those high level organizational goals.
Beth Arritt: I think, Jeff, we're ready for questions. And we have one in already. We have a question for Vasan. The question is when CSAE worked to segment your membership, did you contact all of your members and ask them what information they wanted to receive? Or how did you go about that?
Vasan Selliah: We do have in our membership forum, when people go in and sign up and it's available after that, so they can update their information there. And when we did the transition, we sent an email to all our members saying," Hey, we're switching to a new system. It's a good time to go and update your data and indicate your preferences." Because we did expand what they could say to us in terms of preferences. And they also have it in their communications they receive. There's an option in their communication preference where they can update if they want newsletters, they want the magazine or what subject they want to receive them and then these kinds of things. We did ask. We did not though send an express email or communication just asking that question in particular, but yes, they did have always the possibly to update this.
Joshua Slyman: That's a great tactic.
Beth Arritt: Looks like we have another one. Does anyone have an example or a template for a data dictionary?
Nicole Covello: Yeah, so we do have one that we use at Wicket, but just kind of has pretty high level things. I think we have about six or seven columns and we just use that as our general template. And then if we have to tweak it for a particular cause then we'll go from there. I'm not too sure if we have the ability to just send links or something through this. But really if you just, if you Google data dictionary examples, you're going to see a ton of different ones and you can really even just start from any kind of template and just start filling it in and then delete rows or delete columns.
Beth Arritt: I found out first, just in experience that anything you need a template for it's helpful to Google and look at multiple options because you're going to find one that resonates really well or you're going to find two or three different ones that have different components that you want. It's always good.
Nicole Covello: Yeah. And again, that kind of iterative process is really important there as well because you might make a data dictionary and then try and use it for something and realize that you're missing some crucial piece of information and you just have to pull it in.
Beth Arritt: What are some tips for getting organizational buy in from everyone for tracking those touch points in the CRM?
Joshua Slyman: I've found most effective is isolate a couple of pain points for some key stakeholders, find some things that we can use the data to solve and then and go do a little bit of that analysis, a little bit of that research, dig around a little bit and show those key stakeholders how you can solve that problem using this data, especially if they open up the door a little bit more.
Jeff Horne: Yeah, I'd agree. It's all about showing the outcomes, the potential outcomes. If we have that data, here's the positive impact it's going to have either on you individually as a stakeholder or on the organization has a whole. It speaks to just quality of data, completeness of data. The data of course is only as good as its quality. And if you're only getting certain touch points, but not the whole picture, the data's just not as valuable.
Beth Arritt: Here's a good one. I'm interested to see what people have to say about this one. That's particularly around the whole discussion right now about Google and cookies. How will the trend of personal data protection laws and regulations affect the member data collection and utilization business?
Jeff Horne: Yeah, it's a really good question for sure. I think associations and many organizations walk that fine line of wanting to collect data so that they can better serve their members, but walking that fine line around data privacy and how that data is being used. I feel like associations too, you have a different type of relationship to your member. It's different than Google tracking me as a consumer. The member has more of a vested interest in the organization and tends to be giving more consent but it does come back to the critical importance of being very clear about why you're collecting data, what data you're collecting and how you're going to use that data. It's absolutely important.
Joshua Slyman: To some degree it's a transaction. We're asking for something in return for something. And so that needs to have equal value at least. And so the data that we're collecting to Vasan's point earlier, if we don't need it, we shouldn't collect it because we're taking something we're not using and therefore we don't want to pay for it in this transactional approach. There should be this concept that the the data we're collecting is purposeful, it's understood and it's clear in its intent.
Beth Arritt: The next one just happened to be a somewhat related question or at least generally, where does data governance fall in the overall strategy and use of data across the organization?
Jeff Horne: Yeah, that's a great one. And data governance, it's such a great topic and it is so critically important to really look into it. Data governance sits very early overall, in the overall strategy of data use. Really understanding what type of data you're collecting, why you're collecting it, who's responsible for that data? Who owns it? How you're going to use it, those are all key things. And I think depending on your size of organization, you can go really, really deep on data governance and build and entire data governance policy and strategy or you can just at least acknowledge that data governance is important and take at least some initial steps towards it. Not sure if others have thoughts.
Joshua Slyman: Yeah. I'll default to Jeff on that one for the most part. I think it's definitely, clearly early in the process is key and defined and transparent, I think are definitely key pillars in a good policy.
Beth Arritt: This is a little bit long of a question. Bear with me, because it's a really great question. When you're using different platforms, all with different useful datasets, how do you avoid spending too much time reviewing the data? Knowledge is power but do you have a suggested approach for prioritizing the approach towards becoming a more data driven organization? How do you decide where to start first, particularly as a small organization?
Nicole Covello: Yeah, so I think, sorry.
Joshua Slyman: No, you go ahead. Go ahead, Nicole.
Nicole Covello: Yeah. I think one of the really valuable things is just when you have all this data and you really don't know where to start, because you don't know what's in there, there's a lot that you can do on just kind of getting a summary out of the data. That could just be getting a bit of an idea of each field that's in a data set and what values are in there. And there's a few things that you can do to get that data summary. If you do any programming, there's lots of Python libraries, there's lots of other Excel things that you can do there too. And that could be a really great step towards that data driven organization as well because you're bringing visibility into that data set without putting too much emphasis on that specific project or a specific thing to pull out of it, other than that summary to start.
Joshua Slyman: Yeah. I think that's a great point, benchmarking in sort of understanding where we are currently is such an important part of the process and I do sometimes think that gets overlooked.
Nicole Covello: Yeah, for sure. Just exploratory data analysis as a starting.
Beth Arritt: It's again, going back to those goals, pick your goals and then start there. Some really good questions and there are some that we've gotten to. But we are reaching the top of the hour. If we didn't get to your question, someone from our team will reach out to you with a response. We just want to thank you for being here today and especially thanks to our partners, Wicket and CSAE, Vasan, Nicole, Jeff, thank you so much. We will all be back with you on May 20th at 2: 00 PM Eastern for session number two.
Nicole Covello: Can't wait.
Beth Arritt: Where we're going to be talking about how to explore your data. If you haven't already signed up, please make sure that you do and we hope you have a great afternoon. Thanks.
Joshua Slyman: Thanks everybody, this was a lot of fun.
Jeff Horne: Bye everybody.
Joshua Slyman: Thank you. Take care.
Beth Arritt: Thanks.
Data-backed decisions are strong decisions. If you want to compete in today’s highly personalized, specialized market, you need to put your members at the center of everything you do, from program offerings to resources to their onboarding journey. What's the fuel that powers that member-centric focus? Data.
In today's episode, you'll hear a panel discussion from our three-part webinar series about member data. Joshua Slyman, Sr Consultant Strategic Services, moderates this discussion with data experts who take you through how you can make data a part of your team culture.