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    Episode 6: Building Future-Ready Marketing with Data and Analytics

    All businesses have greater access to data today. So, organizations that keep evolving their marketing analytics capabilities are the ones that successfully customize their customer experience down to the individual level. Analyzing the right data to generate actionable strategies helps them navigate through digital shifts, visualize trends, and comprehend ever-changing customer behavior patterns.

    From eCommerce to healthcare, organizations that embrace data-driven agile marketing, build efficient marketing analytics teams, and have the right technology infrastructure for marketing analytics would be better poised to deliver the desired results.

    And this is what’s summed up in detail in the sixth episode of our marketing analytics podcast series – Building Future-Ready Marketing with Data and Analytics.


    What You’ll Learn:

    • Understanding the Need to Make Digital Shifts Based on Data
    • Streamlining Marketing Processes by Utilizing the Right Data
    • Having the Right Marketing Technology Infrastructure in Place
    • Adding AI and ML to the Marketing Analytics Mix
    • Building an Efficient Marketing Analytics Team

    Featured Speakers

    CMO Analytics Podcast – Episode 6 – Transcription

    Shayla: Welcome back to the final episode of our six-part marketing analytics podcast series, Marketing Analytics Central – Conversations on Blazing Ahead with Data. My name is Shayla Wentz and I’m your host. In our last episode, we talked about building a highly efficient marketing analytics team for organizational success. We discussed the must-have people on the core marketing analytics team, empowering your team with the right tools to measure and manage marketing performance, as well as the features and capabilities that these tools must have. We also touched base on privacy concerns, data security, and more. Today’s episode is a little bitter-sweet. Bitter considering that this particular discussion and the incredible insights we’ve gained as a result are coming to an end. But it’s also sweet because I can assure you that we’re not stopping here. The larger marketing analytics conversations have only just begun and we are going to continue building on these insights. With that in mind today, we’re going to do a recap of all of the amazing, relevant, and myriad mission-critical topics that we’ve touched upon over the last five episodes with some additional pro-tips for each one. And here to provide his invaluable expert advice, I am once again joined by David Edelman. David is the former CMO of Aetna, a global executive advisor for digital and marketing transformation, and a member of Grazitti’s advisory board. Thank you for joining us today, David

    David: Great to be here once again, Shayla.

    Shayla: So David in an earlier conversation, we discussed how data analytics, which is primarily known for its problem solving and predictive superpowers, has become an essential navigational tool supporting several high-priority tasks that companies are now faced with and how quickly organizations have implemented analytics solutions. So, with all the disruption that has occurred in the last year and a half, how would you sum up the role of marketing analytics in today’s digital-first world?

    David: Well, marketing analytics has become a critical navigator helping to bring together data increasingly from a broader and broader range of sources that are collecting interaction data from all the digital touchpoints that we have. These systems are also adding artificial intelligence to interpret that data, find new patterns, think about predictions and scenario modeling going forward. All of which is critical, not just for marketing, but for the broader organization as well, to keep their finger on the pulse of what’s going on and use that to make dramatically more precise decisions about where to focus their investment, what to do in terms of next steps for specific customers, getting a sense of what’s working, what’s not, learning how to test, um, and provide new kinds of support for innovation because, with analytics behind it, you can test and learn at a pace you just have never done before. And it’s also frankly, reshaping specific industries. Um, we talked earlier about telehealth, and in those areas, marketing analytics is bringing all kinds of new capabilities to understand much more about what people need, how to make better matches between those on the phone and those who are calling in, helping to prioritize investments in rounding people to the best place for care.

    So marketing analytics is not just something in the background, it’s fun, it’s learning and it requires frankly, a very explicit strategy for how you want to invest and build even more capabilities. Because this is just simply, as you said before, Shayla, is not going to end, we probably are just beginning.

    Shayla: Right! And we also talked in detail about how businesses can cope with ever-changing customer behaviors and how to streamline marketing processes, utilizing the right data. And it all makes perfect sense. Do you have any additional pro tips on how marketing teams can arrive at the best possible outcomes of their efforts using data and analytics?

    David: Yeah, as I started talking about it before, I think one of the most important things marketers can do is step back, look at their processes and ask themselves, are we keeping up with the pace of the market, the pace of the data, and the pace of what we can learn and act on? There are all kinds of new tools and I talked about several of them ranging from ELSY that provides a real-time sense of what’s going on across your marketing investments to a pointer-list that gives you a real-time sense of what’s happening in terms of individual customer journeys. But the question is- can you act on that as fast as the data comes in? And this is where setting up one’s processes to be much more agile, having small focus teams, probably on the scale of six, maybe seven people cross-functionally of marketers, analytics, insights, all kinds of technology capabilities, maybe even legal operations to drive execution who are looking at that data and constantly figuring out what to test and learn. Coming up with new ideas and then learning what, which ideas work for which customers or which work for which general channels to invest in and then using that to constantly innovate.

    And then the other area that’s going to become more important than we started talking about is building up first-party data. As third-party cookie deprecation becomes more prevalent, especially because of the actions being taken by Apple and Google, companies are going to need to build their own information arsenals. And that really comes from getting customers to interact with you more and provide value in those interactions. That’s gonna drive them to give you permission to hold and use that data. And again, all of that requires much more testing and learning to figure out the best ways to engage customers and get that data, and the best ways to get their permission to use that data. So all of this is around not just what should the data be and what should the analytics be? But thinking about how we can align our operations around the most important uses to be dramatically more agile.

    Shayla: In one of our episodes, we talked about how organizations can achieve peak performance in a post-pandemic world by adding AI and machine learning to the marketing analytics mix. David, do you think there’s one safe way for organizations to implement AI and machine learning? So as to extract maximum value, while saving time and money?

    David: You know, AI as with any kind of investment, has costs and changes that are involved in it. And you’re not going to start buying all these systems and putting them all over your organization. It’s about ruthless prioritization. And generally the way I think about where AI can make a difference is where there is a high-stakes decision such as media allocation or a large-scale learning and testing that has to happen. Such as how to best personalize across the customer. And those are two examples of pretty high stakes use cases where AI can make a difference.

    So pinpointing those areas where just basic analytics, even the best data scientists are not going to be fast enough, and they’re not going to be able to digest and use the range of data that’s available at the right scale. This is where AI can make a difference. So I would prioritize it in terms of media personalization and understanding what’s going on with customer journeys and for different businesses across those three things, different issues are going to matter. Um, you’ve got to make that choice, but it is around where the scale and speed is going to make a difference in the impact of how you spend and focus money.

    Shayla: We’ve also discussed the need to enhance data visualization and truly unleash the power of marketing analytics to improve health care. David, do you have any additional thoughts on how healthcare organizations can utilize data visualization to improve decision-making and operations?

    David: Yeah. And it’s not just about healthcare. It’s also just more broadly keeping data in the black box doesn’t help people on the front lines making decisions. And this is one of the most important things. I think that distinguishes healthcare actually from a lot of other different sectors. Um, but it’s also true for, for example, financial advisors, if you have people on the front lines who are interacting with customers, providing them with the best insight and support makes a huge difference. So helping a nurse on the front line, see how the trends have been in somebody’s test results. Understanding the prevalence of certain issues in the zip code, somebody is in, those are kinds of data that you can bring up out of the system and bring right onto the front lines. If someone calls into a call center, using data that can track their journey beforehand. So you can understand, Hey, this person was just on the app and couldn’t get something done. Or this person just had a claim that has been kind of bumpy in terms of our willingness to pay it because of out-of-network issues. These are all prompts, ways of that data can be brought to the front lines that you can color code it in terms of red, green, yellow, in terms of hot heat and prioritization, what should be the next recommendation – all of that can arm the frontline dramatically better, but you can’t just simply throw numbers at them. You’ve got to make it rich. You’ve got to make it simple to see.

    Shayla: Now, shifting gears back to, you know, the human aspect of data for just a minute here to power all of the, uh, activities that we’ve covered in this series, organizations need a high-performing marketing analytics team. So David, would you please share with us the CMOs secret sauce to building a highly efficient marketing analytics team for organizational success?

    David: Well, we’ve taught earlier about specific roles, um, which I won’t repeat now, but what I’d really like to talk about is the importance of analytics working in a completely integrated way with the marketing team. Analytics is not just a back-office capability. It is a complete, critical partner with what’s happening in marketing itself with strategists who are trying to decide what campaigns to run with creatives, who are trying to understand what are the best renderings that make a difference. And the more analytics can actually see how people use the data to make decisions – what is important to those people, whether it’s understanding campaign ROI, attribution, segmentation, or competitive analysis. These are all things that marketing analytics brings, but just simply doing an analysis doesn’t necessarily help people make a decision. And the more analytics can be side by side with the teams, they can tune what they’re doing to not just bring out differences in numbers, but to actually provide recommendations and implications.

    Shayla: Thank you so much, David. I could continue this conversation for ages. This is a topic that is, you know, near and dear to my heart, as I’m sure it is to everyone tuning in. Um, but unfortunately we do have to wrap it up. So before we go, do you have any final thoughts for us?

    David: I think just wrapping up in terms of final thought. The key concept that we have been talking about is the importance of data in and of itself, the quality of the data, the robustness, the nature of the data, the way you’re using that data in analytics, the technologies that collect, store, and manipulate that data. All of which doesn’t just happen. It requires explicit strategies, thoughtful investment prioritization and for many CMOs, you know, it stretches them into areas that they themselves may not have the depth of experience. So it takes a village to build these strategies. And it’s important for leading leaders in marketing and similarly leaders in, um, chief digital officers, chief analytics officers to work together as a team to prioritize their use cases, be explicit about the goals that they want to seek, and then build the teams and test and learn cause there’s going to be operational changes besides the technologies as well. So it’s a team sport but it’s an essential sport and it’s one on which the competitive playing field is going to become more and more vicious. So you gotta get out there and get on top of it, but don’t just do it alone. Um, you gotta see the bigger picture and work with others.

    David: Fantastic advice. Thank you so much, David. And thank you all for tuning in. This has been our six-part podcast series Marketing Analytics Central – Conversations on Blazing Ahead with Data. Thank you so much!