Sharon Flynn is a Digital Analytics Pioneer with over 15 years’ experience driving business decisions with data. Experienced presenter, data advocate, model builder from Excel to SQL and RStudio. She specializes in Digital Analytics-Adobe Analytics (Omniture,) Adobe Audience Manager, Test & Target, A/B Testing Comscore, Google Analytics, SEO/SEM, and Customer segmentation.
She is the Lead Digital Analytics Consultant at Infotrust, and formerly worked as a Senior Digital Analytics Manager at BMO Financial Group in Canada.
S.F.: I could list out individual actions or decisions that I'm proud of personally. But, I think what really warms my heart is when you demystify digital analytics for an organization. What I'm most proud of is when an organization or a team pulls the data themselves through either the Google tools or the Adobe tools and feel accomplished and confident. It's almost as if they're doing it without knowing that we told them to do it. It becomes almost natural.
One of the things that is really interesting to me in digital analytics - when I think the reasons why I really stick with it, is that its challenges are around culture in the organization and culture change. As a digital analyst working with data analytics in financial services, being successful is about engaging with that culture and getting that culture to adopt. It is about being passionate about or empowered by data rather than it being something that disciplines and restricts people. It also about being respectful, since digital analytics touches a lot of creative individuals.
I come from media and I'm now in financial services, yet in banking, we have designers and people with a creative mindset. And math can be frightening, math can be intimidating or, at best, boring. When numbers actually tell a story and get people in the organization feeling like ‘this will help me do a better job’, ‘I'm looking at a report you produce for me’ or ‘I'm bringing you into the process’, that's when I feel the proudest. Because it's not so much about the pretty visualization or the discourse it asserts. It's when you have that sense that you are genuinely contributing and your work is integrated into the organization. It is something I encourage people to be conscious and proactive about in their roles and leadership. It's not just the deliverables, the output, or the tools, it's about helping people understand the value and impact that data can bring.
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S.F.: Well, I have a lot of mantras being Irish. One of my sayings is that we do insights here, we don't do math. Especially with the advent of big data and the wonderful resource we now have with the cloud where we can just gather everything and decide later what to do with it, focusing on ’Can you make your insight human-readable?’ is essential.
It doesn't necessarily have to be a chart or graph. There is a tendency to create these 37-page PowerPoint showing all these wonderful ways to slice and dice the data. That's math, not insights. Insights mean it worked and this how you can pivot or it didn't work. None of that necessarily requires a chart or a regression analysis. There is some self-serving to that strategy, as well. It's not entirely altruistic, but that approach of integrating yourself into an organization is by far the most rewarding for everybody involved.
S.F.: Well, when people join my team, I always ask them to put a post, a note on their computer that says ‘you're not stupid’. One of the things we struggle with as digital analysts is trying to make data as accessible, human-readable, and as low stress as possible.
However, there is a definitive skill set that is required, there are things that take years to learn. We are so busy trying to explain to people that we will make it easy for you to get the insights that when we can sometimes confuse that message with ‘it is easy to get the insights’. For organizations, understanding the role of that subject matter experts is very important. Digital data analysts can ensure that your organization and your enterprise tools like Google and Adobe Analytics are being properly leveraged because that's their job and it's their passion.
Yet though, I think organizations like the Digital Analytics Association help organizations understand that. The elevation of the data scientists in the data science role is also helping because there are more voices around the table talking about data governance, deep data cleaning, the whole ETL process.
S.F.: There is a tendency going around, especially with vendors that promise: ‘you just plug it in and next thing you have is insights.’ There’s still a lot of real work going on for implementation teams. A tough truth in our industry is the fact that implementation is an operational ongoing activity and not a project. There's a lot of misunderstanding in thinking that you can simply buy a tool, you deploy it, and you know magic data fairies produce insights.
The reason why I think that's a problem is that it doesn't allow us to roadmap out what we need today, where we need to be tomorrow, and what our current budgets and skillsets would allow us to do to be successful today and evolve.
When you put energy into obtaining high-quality datasets that are well documented and cared for, that's where your subject matter, expertise and cost come into place. If you have a very good digital analyst in one department that is able to create calculated metrics and can transform the data within a tool, their immediate team is going to love them to pieces. But it doesn't scale. Scaling comes when your data governance and your subject matter experts are as close to the data collection point and distribution point as possible. That's where you get your ROI.
S.F.: I think that one of the challenges of financial organizations is their shift size. They’re embedded in the community and, with the closure of branches, we have seen a lot of activity being shifted onto digital. In the digital world, we typically have the digitally-engaged customers who were maybe only interacting with the brand on the website and through the app, and rarely went into a physical branch. But now we have this cohort who maybe were light digital users or never used digital who are kind of forced into the digital space and have lost that sense of community. I think that that has significantly opted for the need in the digital analytics behavioral space of understanding who these people are so we can design empathetically, understand them, and understand their digital journey.
I'm speaking with great generalizations, but we used to separate the website into the public website, which was maybe quasi-marketing with funnels of purchase, had a little bit of information in there, and the secure site which was the virtual bank. We’re now trying to really understand and answer essential questions: what is our website for? Whom does it serve? I suspect most organizations, I certainly know mine is, very comfortable and has a very clear understanding of each of the components of our site to the very last button and pixel. Yet, with the larger step back of removing the channel of the physical branches and with all the changes that the current pandemic brought, we are really asking a lot of digital that we literally weren't asking about seven months ago.
A big challenge right now, which I don't think it's unique to financial organizations, is the complete scrambling and rethinking of our priorities. We have no time where we could go back and ask: ‘the last time we shut our branches, what did we do digitally?’. There's no history for this we can't even look at other sectors and say: ‘the last time supermarkets shut down, what happened? We have to be very nimble in figuring out how to bring some of these datasets or these understandings together that maybe we hadn't put together before. We need to try to figure out what the various keys for our database are and how we connect them, how to bring teams that may not have had any need to interact with each other before together and make sure that we have the right subject matter experts. It is a strategic triage.
Martech is really coming to the fore and it's all about attitude. Taking a very humanitarian and empathetic approach is key. Finding out what can we do effectively with the resources we have without overpromising or underdelivering when we have a gap in our knowledge that we cannot fill. How do we, which is what data really is, minimize the risk of making the decision? And how do we test when once we've rolled something out and we weren’t able to have a focus group, for example. Let's use behavioral analytics to test that initiative very clearly, learn, and iterate.
If you're not agile now, think about an agile way of doing work and how to break things into tasks. It always comes back to putting as much work in the front as you possibly can, and then, once it's out there and live, trying to be humble in understanding how it's working. Those organizations that explore failure and embrace and learn from are going to do very well. Right now, the customers of financial organizations are stressed. There's a financial pressure on people and many would like to meet with the bank contact or the bank manager in order to have that human connection while in distress. Now, it has to be delivered digitally or at least have digital as a first stop as a way of communicating. And how does one do that? How can you embed that care into digital and how do you measure? Obviously, we do have our call centers, but call centers are under tremendous pressure right now. We want to be able to say to our customers: ‘OK, you're on hold, but you're also on the computer and you can get some of the conversation going there and feeling that we're here for you’. With data alone, you can't be on the call with the call center individually. It's much more challenging. But we can literally walk in the footsteps of our customers digitally by looking at the digital data. And that's a very powerful way of thinking about what's happening.
S.F.: I think we're going to see a lot more consolidation between teams, which I personally welcome. In terms of tools, I believe we’re going to see organizations asking themselves ‘What is this tool for? What problems are we trying to solve? Do we want to know how people came to our website, understand what they do, or what they do once they've arrived? Asking ourselves these questions will significantly inform whether people go down the Adobe or Google Analytics route, how they choose to deploy it, and what their priorities are. If that's happening in harmony with the consolidation of teams you may get an elevation of digital analytics since it's becoming more focused. As a result, you will ideally avoid the duplication of data creation or collection.
Preparing for the future of cookies or a post-cookie world is also going to be on the agenda. If you’re sitting in your (home) office right now and you’re not thinking about ‘what will I do when those cookies no longer exist’, you are in for one hell of a shock pretty soon. Organizations should at least have a strategy to straddle when those changes take place. Cookies have become so important in marketing and marketing retargeting, and we've almost taken them for granted. It’s possible and very credible that they're going to completely disappear at some point, so we're going to have to seriously rethink what we do. They might not disappear formally, as in you know they're removed from the ecosystem, but I think a lot of companies will be more explicit about why and how they are using or not using them. They will deploy them where they're valuable for their shareholders and their customers.
That's a very elaborate way of saying 'I don't really know'. Lots of things are going to happen, there are lots of things to think about, but it's very challenging to point to one particular thing.