During her nearly 20 years at HBO, HBO Max, and Warner Media, Diana amassed a portfolio of quantified achievements and critical contributions, particularly as a key catalyst for the successful launches of HBO NOW and HBO Max, as well as leading the transformation to build a data-informed marketing team to support a direct-to-consumer model. With expertise in brand management, e-commerce, digital media, subscription, and performance marketing, she has worked across every stage of the marketing funnel to drive customer growth.
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We hear it everywhere. It's this sort of semi nebulous thing that we all know is important but struggle sometimes with how to make it impactful for our work or for our business. There's a quote out there about data that I think is a meaningful to me, which is, the goal is to turn data into information and information into insights. And it's those insights that should drive your actions.
I think the first and most important place to start is to understand that data strategy should be about enabling your business strategies. It should be about helping you to achieve your business goals and your business priorities. And at every stage of the customer journey, there are distinct goals.
For example, at awareness, you might be most interested in getting people to know your brand, to know your service and identify you. And at that stage, data can really enable decisioning around what audience to target, what value proposition and what creative might be most meaningful for them. I think in the same way that data can enable strategic marketing decisions, data is key to measure the performance of the strategies that you put into place. The metrics are harder.
On the awareness side, they're not the same as the more performance-based work, but there's a lot you can do with brand tracking to measure awareness interest in favorability, and there's a great deal you can do to measure the impact of your media on those metrics. Now, as your customer moves further down the funnel, your goals might be more about educating them or building interest favorability. And at that phase, you're probably a little bit more concerned about where are they going to get their information, what information is valuable to them and moving them closer to the transaction. And that data will help you optimize that journey and the messages along that journey.
Once you get to the conversion phase, you're going to be really concerned with sales, volume and positive return on investment. Here, data will tell you how well you're doing against that, and it'll help you evaluate other promotional offers or promotional partners. And then once you acquire the customer, then your goals are different. You want to keep them.
We all know it's much more expensive to acquire a customer than to retain them. And the amount of data that becomes available to you increases so dramatically. There's lots of ways that data can enable you to build retention, upsell, and eventually gain loyalty and advocacy. And if you're doing this right and consistently, those loyal consumers become your advocates and they start promoting your brand and your services for you. And then you start to earn media rather than having to work so hard solely on owned and paid media.
Once it's collected and analyzed, it can be activated to build an asset that generates great value by helping generate insights that inform anything from product selection to audience identification to promotional offers. It can greatly aid in reducing waste to become more efficient with media spends and more effective with creative resources. I think one of the most powerful parts or uses of data is its ability to help you develop messaging that's more bespoke and tailored to various target audiences to aid in building awareness and acquisition, as well as the ability to deepen audience engagement by promoting retention and upsell and furthering your revenue goals. And I think why this area is probably the most impactful is just because of the world we operate in today. It is so cluttered. There's so much competing for our attention. There's endless videos to watch, things to read, emails to sort through. And there's a lot competing for not only our attention, but our share of wallet. So developing the right message to get to the right person at the right time is critical to breaking through that clutter, and is really where I think data starts to be a competitive advantage. And then lastly, and probably most obviously, data is essential to experimentation and with experimentation, you get better. It's all about how you optimize and get greater return out of marketing efforts.
Well, you start by collecting it. And, easier said than done. There's a lot of different types of data out there. There's third party data that you can purchase. There's data that consumers tell you themselves. And there's data that you observe on your own based on how customers or prospects interact with your paid media or with your social profiles or with your websites. I think that the strategic consideration here is really articulating a data collection strategy that is coordinated and is around identifying what are the most valuable data collection points, and then doing it in a consistent way that prioritizes data quality and being thoughtful about data quality.
You'll see that at this stage, the data is messy. It's not very organized. It's hard to really understand what to make of it. So the next phase is really about organizing. Organizing the data. And the goal here is to really create a single source of truth around a customer where you understand their identity, how to reach them, you know about their various behaviors, and you have a sense of their preferences and their tastes. And this is challenging to do. And it's particularly challenging in larger organizations where there are multiple business units and they're interacting with customers in different ways and collecting data. Without unifying that and creating that single source of truth, you may be missing opportunities, or you may be oversaturating customers with different offers that aren't relevant to them because you don't have a robust view of what really is important to them.
Next, once you've organized the data, it's time to analyze it and really derive insights. I think that the real strategic consideration here is that you want to empower the people that are responsible with performance or with the marketing strategies with the capabilities around insights. So maybe that's giving them resources to give them analytics, help, or maybe it's technology that can speed access to insights like big data visualization tools. But there is a tendency in larger organizations to keep analytics and data very separate from marketing. I think the more agile approach, and I think the approach more organizations are moving towards is moving that function closer to the marketers. I think those insights are essential for driving action.
And again, there's so many ways that you can activate data. It can be focused on product, it could be focused on assortment or experience, or it can be focused on marketing. And I think the key here is, you want to focus where you think you're going to get the most value, because to do everything all at once is difficult. You want to build your capabilities over time and build a roadmap into how you branch to the next arena. Now, all of this is essentially enabled by people process and technology.
I wanted to give an example of how data can be used to develop and refine audiences to aid in awareness and consideration. I once had to give a presentation to a group of programming and production executives at HBO about how we're using data to market their shows and particularly how we're using it in media campaigns, partly as a budget justification. We came up with this example. It's hard to talk about data to anyone, but it's especially hard to talk about data to programmers. It's an analogy that we loved at HBO and we used it a lot, which is that building a data audience, particularly to activate in media, is akin to Daenerys building her army in Game of Thrones. So bear with me if you're not a Game of Thrones fan, but the way we did this was we would start with a piece of content that had broad appeal, something like a trailer, and we would put it into a digital platform with some money behind it. And we would layer in a whole host of audiences. Broad audiences like entertainment enthusiasts or TV lovers, as well as some of our own data that we might have. If it was people who had visited our site or existing subscribers or fans of shows that were similar. And we would run the trailer and then we would analyze it, and we would determine what audiences thought about it, how they engaged with it. And just by running the trailer, we built our own audience. We built people who interacted and viewed the trailer. And that was an audience that when we went back to because that platform we could activate again, already knowing that they had a level of interest with our content. The other thing it helped us do was refine our targeting for the next campaign, and get smarter the next time about who we wanted to try to reach. In addition, it helped us tailor more unique messages to those different audiences. Danny talked to her dragons in a very different way than she talked to Jorah, or the rest of her council. And no, not talking about creating ads in Defrocky, although I'm sure we tested that at some point too. But essentially, doing this and iterating, the concept is that you want to leverage that audience again and again. You want to leverage the learnings from each of those beats. And whether you're leveraging it throughout a long campaign with lots of beats, or you're leveraging it the next time a Game of Thrones initiative is coming to market, or the next time a show that is in a similar genre comes to market, you don't have to start from scratch. You have a sense of what audiences may perform well for you. So that's the extent of my Game of Thrones example.
I want to talk a little bit more about the underlying data that can be used to build prospecting audiences to help particularly when you may not have any customers of your own. Maybe you're building a new business or you're launching a new product that's completely different from something you've done before. So the best way to go about that is to cast an initial wide net to reach potential customers. I had one client that was in the pet care business, and they were looking to build prospect audiences to come and check out their app and their site. And eventually buy. We started really broadly with them. We started with, in some of these digital platforms, primarily Facebook and YouTube, we started with broad interests like online shoppers, pet lovers, dog lovers, and then we layered in some more custom affinity audiences, or audiences that when after intense a little bit, particularly in Facebook and Instagram, there's lots of ways to do this. But not everyone that likes a picture of a dog is interested in buying a product for a dog.
So we looked at some competitive conquesting. We looked at followers of the Chewy brand and of Bark, and we, with not a lot of money, went out and put some assets out. And we were able to understand who was interacting with the content and who was coming to visit the site or the app. And then once they visited the site or the app, with the help of Google Analytics or campaign manager, you can really see, you can really get a full foot view of the funnel and understand where people are coming in, where they're falling off, where they're spending their time. And in that sense, you're building audiences also that you can go back to, probably with different messages because you're trying to move them a little bit further along towards transaction. But you're building prospect audiences that you can go back to. And as you acquire customers and visitors, you can start to utilize more of your own data. A really great way of doing that is you can take high value customers and you can feed a sample size of them into the same platforms and build look alikes. Platforms let you say you want them to look a little like them, you want them to look a lot like them, and those are other ways that you can start to leverage and build out prospects, and hopefully in a more efficient capacity because hopefully you're getting to people that look a little bit like the people that already liked your products and services. And then as you think about a roadmap of sophistication, there are ways to bring in third party data and combine it with your first party data. And there's a lot of AI tools, and there's a lot of modeling that can be done to help you build high propensity audiences to give you a fuller view of the prospects beyond what you might already know.
You may start more broadly and then you may get more sophisticated with your own first party data and modeling as you go along. Once you acquire the customer, then the data that becomes available to you is much more significant. And it's critical to fuel your lifecycle marketing approach.
I think the key here is really the analysis, nd understanding what are the triggers in terms of their interactions with your product or your service that drive a good customer, and what are the triggers that create a situation where you might lose the customer. And that's how you want to build your life cycle program.
We had a particular client who had a fitness app, they had the insight that if they could get someone to join one workout on the app within a week of opening it, that they were more likely to retain them for a longer period of time. So they developed an omni channel program utilizing email push, and they even experimented a little bit with paid to really help the customer achieve that behavior. As soon as the customer opened and registered, there was an email waiting in their inbox telling them about the features of the app, suggesting some content that they maybe want to try. If within three days, they didn't use the app, they got a mobile push that suggested that they may try this workout. And then once they started using, they again, did a combination of emails and push to really try to get them to restart if they stopped or to move to another exercise that they thought might be useful or interesting to them based on the profile of stuff that they were already engaging with. And this is all something you can test into. Which channels you should be in, how often you need to go back to someone. Some people never open email, so for that particular audience, you may need push or paid. For people that are in the app but not doing the functions that you really want them to do to get them more vested in the products, an app could be a great option.
Now, as marketers, I think creative is still the one thing that we hold very dear to ourselves. It's being driven by aesthetic sensibilities, and more about gut and instinct than about data and formulas. I don't think anyone disagrees with that. But I think data plays a really important role in informing personalization, even just giving you a sense of what direction should your creative go in.
Here's a really simple example, based on very limited interaction. I went to visit the ASOS site, I browsed a little bit, and then I left. And then I came back and I had a whole new homepage, and this homepage was a little more tailored to me. It was only showing women's products, it was primarily showing denim because that's probably what I was looking at, and they knew I was a repeat visitor and they put an offer on there to hopefully entice me to spend a little more time looking and browsing and eventually buying. Now, when you know a lot more about your customers, you can do things like email and it can be inform your paid and all of your other creative outlets too. But here's an example of an email that I got from Hulu. The subject line was Diana, Picks Just For You. And they attempted to show me content that they thought I would enjoy based on what I browsed or what I watched. I think my daughter may have gotten in under my account settings because she's the Real Housewives fan, not me. But you get the idea. It is informing the creative, it is informing the assets they want to feature. And in doing so, it should be more relevant to me.
As I mentioned, it goes through every phase of the customer journey. We talked already a little bit about how you can test audiences in prospecting. You can test devices. We did a lot of tests back in my streaming days on mobile and app inventory versus mobile, web and desktop. There are no end to the ad partners that are out there. You should be testing multiple ad partners. In fact, we always left room. And I advise clients to leave 10% of your budget to test someone new, besides your tried and true performers.
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