Video: Full Presentation of Drowning in Data, But Thirsty for Insight?

There’s more marketing data available now than at any time in history – but how do you actually use it to guide content? Learn how in this full presentation of “Drowning in Data, But Thirsty for Insight?”

• What if you’re not sure you can trust your data?
• What if you don’t know where to start with your data?
• What if you’re at a loss as to how to apply it to content?

This 75-minute presentation, by Elizabeth Crinejo of 360Partners and Melissa Mines of Bulldog Solutions, answers all these questions and more. It was delivered at ConnectToConvert in New York City on Monday, August 21, 2017.

Transcript

Session Host: Elizabeth Crinejo is the director of client operations for 360Partners in Austin. In past professional lives, she’s had several roles, including time as a PR exec in the high-tech sector. She worked as a programmer for a large consulting firm, and the one I find most interesting, she did a stint as a professional chef.

Joining Elizabeth is Melissa Mines, who’s the VP of client success at Bulldog Solutions. She recently served as head of brand content for Cisco, and her efforts have been recognized through Kellogg MBA case studies and highlighted publications such as the New York Times.

Ladies welcome. So, thank you guys for being here.

Elizabeth: Okay, so to start off with, this is us. A little bit about us, so 360Partners is a boutique, a digital marketing agency. We specialize in PPC SEO conversion rate optimization and digital business intelligence. Good? Okay, sorry, short factor. I forgot about that. So, we are geeks with spreadsheets. We love our numbers. We’ve great people and work really hard to get good numbers for our people.

Melissa: Great. Melissa Mines with Bulldog Solutions, and we are based in Austin Texas, and kind of our tagline is Wildly Creative and Totally Geek. So, we like to blend both sides of our brain, so looking forward to discussion today.

Elizabeth: Okay, so Melissa is going to talk us through what we’re gonna be up to today and then I’ll take it from there into the first few parts of our session.

Melissa: So, we’ve broken up our presentation, our time together, in a few main chapters. The first one is really an overview of data and, hopefully, with a slightly different approach just in terms of just laying the groundwork in terms of some common nomenclature, a little bit of orientation about where we are, and then Elizabeth is going to take you through really that baseline or that framework upon which we can build our insights and really put data in motion in terms of actually having it work for us in terms of metrics.

And then I’m going to pick up with chapter 3, which is really where we jump in the pool, and we look at some use cases and look at how we can start swimming some laps together and taking a look not at anything that’s definitive but some of the slices and insights that we’re seeing that hopefully you’ll find some commonality in terms of what we’re looking at, maybe some takeaways, and then finally if we have time, we have some advanced maneuvers or some synchronized swimming if you will, just a little bit of off-script in terms of some things that we’re seeing on the horizon, looking at maybe some trends that might be interesting to us, and we’ll only do that if we have time. We want to make sure that we don’t shortchange the Q&A or things that you want to learn or what we can learn from you.

So, that’s a little bit of the orientation about our time together. If at any point you have a question, for sure let us know. We want this to be highly interactive, and with that I’ll turn it over to Elizabeth.

Elizabeth: Okay, great, thank you. Okay, so to start off with, to kind of get a context of where people were, I started by polling some of the people in our company and what they hear and clients to see where are all the many, varied, and usual places people could be around their data, and this is just an assortment of things that we heard and I heard, so things on one side of the spectrum, like overwhelmed by it, to the other side, which is I’m not sure what all the fuss is about. Nothing much has seemed to change because actually we’re all digital marketers of some flavor, and we’ve been dealing in data since the very beginning. So, any of these resonate with anybody in the room? Raise your hand if it’s a yes, continue to say nothing if it’s a no. Okay good.

Yeah, so actually let’s talk a little bit about that. So, what are some of, if anyone’s willing to share, what are some of your biggest data challenges or where are you in your business with your data? And you can go—

Attendee: Access to it.

Elizabeth: Getting to your data is a challenge. Got it, and is it that other people have it or you don’t know where it is? What’s the problem?

Attendee: They’re in silos – siloed areas, different systems.

Elizabeth: Right, okay. Who else?

Attendee: I’d say, like, nurturing and captivating my list without destroying it, you know.

Elizabeth: Got it.

Attendee: Like, when is the right time?

Elizabeth: So, making sure you’re getting the right data at the right time from your list so you don’t disappear your list.

Attendee: Right, yeah.

Elizabeth: Yeah. What else?

Attendee: I would say like bringing it all together. You know, like there’s like different areas like the ESP [email service provider] for your email to analyze the amount, and even Salesforce has a lot of nurture campaigns, and, you know just kind of like—

Elizabeth: It’s everywhere.

Attendee: putting it all together and making sense of it all kind of seems overwhelming. So, we just sort of stick to analysis in GA tools.

Elizabeth: And do you feel like you end up with actionable answers there?

Attendee: Sometimes. I know that there’s a lot that I’m missing as well…

Elizabeth: Got it; got it. Okay, thank you. Yes.

Attendee: So, we capture all this data, but then it’s, well, what do we do with it at that point, and how do I take these numbers and turn them into actionable stuff?

Elizabeth: Okay, got it, yes, thank you.

Attendee: I sometimes have trouble reaching a statistically significant sample in a timely fashion, and then want to make decisions sooner than that allows.

Elizabeth: And is that because, all in, your data is not big or is it because the slices of data that you’re looking at get smaller?

Attendee: It can be both. A lot of times it’s like trying to work only in a segment your traffic or audience, not scrubbing too strongly or not fluffing enough in that or perhaps you’re testing something that’s, you know, you’re not testing big enough so you don’t actually pull out some of the insignificants, maybe never.

Elizabeth: Okay, yeah, looking at that. Anybody else?

Attendee: I’d say which data we use in our plans, what to look at.

Elizabeth: Perfect. That’s a perfect segue. You can feel like a plant, ‘cause that was perfect, so thank you. Yeah, so he said like what data to look at. It’s all there. So, a lot of—everything that you guys are covering here in the situations you’re looking at, we do plan to address.

So, one of the things when we were looking at this process that we have and our approach to data, this is one of the quotes that kind of came to mind, and in a lot of ways it synthesizes what we need to—who we need to be or the kinds of people we need to have around us in this world of big data. So, from here this is really the primer here. We need to be synthesizers looking at the right information at the right time in order to make those important choices that we make every day, which means, you know, dollar signs in or out, making those choices wisely. That’s what data is here to do for us.

So, let’s just take a step back for a minute because when we were looking at this world of data, in a lot of ways it sort of surprised us all of a sudden, and I think it’s something that we often overlook. The technology is very young, just looking at some high points here. So, this is the history all the way from like 1990, and if anyone was marketing back in those days where like SEO was white on white text on your page, which is, you know, now frowned upon, but, you know, that was okay because no one understood, and then we got these smarter systems. It helped us get better content. We got better at delivering better content, connecting better to our people. You know Google was amazing in helping us have a nice match to quality.

Some couple of things that you want to think about that happened: first e-commerce transaction 1994, tablets 2002. So, then our desktop data doubled because now we could get data for tablets. We could get data for desktops, right? IPhone in 2007, same type of traffic in triplicate now because we can get mobile; we can get tablet and desktop, all these different worlds. So, every single metric we looked at in one place we now have in triplicate with some extras, right, because shortly thereafter we started having voice to text, which is adding a whole other way that people search.

In 2015, mobile wins. It’s now people are leaning to their mobile more than their desktops. So, lots and lots and lots of data, and now we’re just in this really amazing scramble because programmatic, artificial intelligence. People are walking around with little wearable data trackers on them, and there’s tons of data. Some of the biggest places where data is impacting things is in healthcare because we now know so much more about people and in advertising in Google, just like in the last couple months—I think in the last month it came out of beta, where you can now track from an impression on a computer to a person walking into a building because—and purchasing— because of the technology on their phone.

So, the thing to get here is that it’s really impressive how fast this data has grown, and if there’s any places where you feel behind the eight-ball, just know that we’re actually growing right along with this, and as Gartner put it, really the news these days is—the news about big data is it’s not big data; it just is what is, and really it’s ours to come and figure out how we’re going to approach it, and, yeah, there’s a lot out there.

You know 60 seconds, here’s a couple stats. One hundred and thirty six thousand three hundred and nineteen emails get sent. How’s that for a challenge for your email marketing efforts, right? Pretty noisy, and that’s in 60 seconds. So, times that by how many 60-second passes we have in a day. There’s 2.4 million Google searches, 1,400 WordPress posts because, you know, Google has told us content is key. So, we put a lot out there, right? So, there’s just a lot that we have to deal with. We’re now in the world where it used to be the case that there was 2.7 zettabytes of data out in the internet universe, and a zettabyte is one trillion gigs, okay. This year we’ve already produced 1.46. So, 2017 forward we’re in the zettabyte-a-year world, and it’s going to be what it is. So, you know, while we may be in this situation every once in a while. This is our joke. We’re not going to actually speak it, but here’s our comic.

So, really what do we do with all this big data? Well, we actually just have to figure out how to navigate in it, so looking at ways to help, looking at our process and the way we think about things.

So, the way the brain works, it really likes things to be categorized and organized because then it can go to the places it needs for information. So, if you look at all the different types of channels, these may even be the silos you’re talking about, in your marketing world pretty much anything you’re gonna do in your marketing portfolio, any contributions to your marketing portfolio, anything that might be a line item probably falls into one of these seven categories. So, now while you see this huge world of big data, there’s this handful of categorical places where they come from, and inside of those categories there are all of these different types of amazing partners that you can work with.

So, when it comes to understanding the data in each of your channels, one of the things that really can work to your favor is having the right partner for you. So, when you’re organizing your marketing world into those seven categories and then you drill down, because inside of each of these you know we have just a couple of examples. Paid can be search, display, paid social. It can be Google AdWords, Bing, LinkedIn, Facebook. Each of these have different vendors that are reflected here.

So, as you’re looking at your categories and what’s going to matter to your business strategy, to your goals, to the places where you need to be to have these conversations with your customers, you can probably pick a handful of these, and they would probably be really great partners to you. So, in terms of understanding your data, knowing where it is, how it’s impactful, if you have the right data, a lot of those problems land on their shoulders because they get to be the partner to you. They get to be the expert in the channel. You get to be the expert in your customers and what they need and all this world of data and what we’re going to talk about here in a few minutes, but the right partner is really key because it allows you to focus on your categories. Categorize what you need. Pick your people and then strategize your marketing portfolio and your strategy accordingly. Does that make sense? Any questions so far?

Okay, so one of the things that we also didn’t want to overlook is that regardless of how big the world of data is out there, the main thing—the marketing game is still the same. It’s know your customer. Now in this world of data, it’s actually irresponsible to not use the data to know your customer better because in a lot of ways, you know, they chose not to opt out of giving it to us, right? They said, “ Okay, cookie me.” Right? It’s okay. So, given that we have all this data that we need to be respectful of and use in all the right ways, knowing your customer is key because it’s what actually—at the end of this thing the punchline is, this is where you simplify your data to your point. It’s starting from knowing your customer.

So, this kind of highlights—the idea here is figuring out that your needle is. We have this big, and I can even give you a chef reference because I love cooking, and, you know, for me like grocery stores are limitless possibilities. My husband feels the same about Home Depot. So, grocery stores are filled with ingredients just like we have this giant pile of data. Now what do I do? You approach it with what you’re out to accomplish. You bring your question to your data and you say, “What do I want to know?” And an easy way for us to look at it is our funnels because we all understand our customers in relationship to where they are in the funnel.

So let’s say awareness questions: Where do my buyers come from? There are metrics associated with any question you could possibly want to answer about your customer, when you’re not asking them directly. So, here I would say, “Where do my buyers come from?” So, I could go get traffic by geography. I can look at cookie tracking data. I could look at shipping information if you ship something out or where you fulfill on different leads that have sold in different areas by zip code, right?

Going on, consideration. How do we look for our solutions? Search query data, sales by source? Where do they go to get their information? Traffic by landing page, on site search data. I don’t know if you use this very often, but if you go and look to see what people are actually putting in on your website, you can learn a lot about what people want from your website, and you can understand how well or how poorly you’re actually delivering it.

Going into decisions, so where do they need more information to help them purchase, and this is data that you would find in a spreadsheet or it might be data that you go inquiring about. Go talk to your call center people or their sales people and say, “What are the top three questions you have to answer before you can get them to the next phase of sales?” Well, why don’t we have a landing page with that information on it? Why don’t we lead with that? Because time and time again both are site search data and what we’re hearing from sales all validate these are questions that people have.

And then looking at the bottom of the funnel, loyalty, advocacy, you know what’s similar about my best customers? How can I take care of them better and then how can I go find more them that are similar to these just based on these people who have an affinity for what I have to offer. Now I just need to go find their friends.

So given that, we do need to address the data part of things because as everyone knows garbage in garbage out, right? So, how many people here would say they trust their data. Okay, so there’s a few of you. Okay, so when it comes to the people who don’t trust their data, can you tell me a couple reasons why? Anyone?

Attendee: It all comes from different reports, different systems, you don’t know what’s updated properly so you can get the right information.

Elizabeth: Got it, okay. So, it comes from a lot of different places, a lot of different reports.

Attendee: They’re not tagging it properly, so you don’t know what’s going on —

Elizabeth: Okay, so you actually know this—

Attendee: –or if there’s one spot that–

Elizabeth: Perfect, okay.

Attendee: Part of our challenge is because a lot of data we have is person to person, insights that–

Elizabeth: Okay.

Attendee: –exclusively affects the company at this point. We’re trying to change that, and so in folks that are reporting end users, so to speak, it’s inaccurate or misinformation and it’s hard to have any kind of trust in–

Elizabeth: Yeah, and I bet in a situation like that when you’re getting someone, you’re almost having to deal with people’s feelings about things. They think they’re further along, and maybe if you were a fly on the wall, you would have had a different interpretation. Got it; that makes sense.

So, considering this we really need to get into the causes of bad data. There are a lot of ways we could approach the subject and I think really understanding at a categorical level because each of these will have special to-dos. So, let’s look at this from the top level, and I have a few examples that I’ll roll through here.

The first one, I think, is competing systems of record, and a couple people already validated this one, and this can be a lot of different places where the data is, and they may be separate channels doing separate things, but it’s also times where a business grew, and they started looking at their data and Google Analytics and then someone else decided to throw some Adobe products in there, and now they have two different ways. Maybe they were looking at it in AAC or GA, but these two different people are worth looking at data in different ways because they attract different ways in each of those systems, and whenever they have to come together to make decisions, it can get very challenging because who’s right and how do you decide what. So, this one can be a real time-consuming challenge.

An addiction to vanity data: so, how many people here really stress out about impressions or clicks? Right, okay. So, there is—this is a little bit of the legacy world of eyeballs in advertising. This is where people worry about things like how many impressions we’re getting or traffic. This is a data challenge that can cost money, I’ll show an example of that in a little bit, and a lot of time because you end up explaining what metrics matter to people and trying to get them into those conversations versus other ones.

Another is no clear owner. This is the gatekeeper world. So, we’ve definitely had situations where we take on a new client, and we ask them for access to Google Analytics, and they don’t know who can grant it to us, and then once we get into the account, we realize there’s maybe five or six people who have high levels of access and can create goals or change things. Some people are making notes. Some people aren’t making notes. The things that they’re noting aren’t always relevant or interesting or notable, but they’re all in there, and then there’s this big mess with different filters, and no one person understands what’s happening in there. So, if there’s no clear owner of your data, you’re going to have siloed experiences that you’re talking about because there’s no one who’s sort of the pivot point, and that can cause a lot of challenges.

A lack of understanding of how system works. Now, this one may not be that the people here in this room should be accountable for at the granular level, but I do recommend you understand enough about your systems to ask the right questions because each of the systems that we’re going to attach to our marketing processes will report things in different ways. Some of them will dedupe in different ways. Some of them will have a shorter or longer cookie duration. Some of them understand cross-device differently. Some of them will label their revenue—it will be cost or spend, and if you pull it into a spreadsheet the wrong way, you’ll see things incorrectly, and you will also not understand what could break a system. One quick example is imagine your really friendly programmer thinks that your confirmation page doesn’t sound friendly enough. So, they turn it into a thank you, mycompanyname.com/thank you because that’s friendlier than confirmation, right? Then all of a sudden your Bing tracking is gone because it measures off of your URL. No one knows that. They may say, “Yeah, that is friendlier. Thanks.” and then 24 hours later someone’s freaking out because all their Bing conversions have disappeared and what happened? Well, we just got friendlier. That’s all that happened.

So, having a non-data-driven culture. We’re going to talk a lot about this one because it’s really important to have your data enculturated. You want people talking about your numbers, and all the relevant people. You know, not everyone should care about your numbers in the company, but all the people that are touching the data, all the people that are making decisions based on the data, all the people that are trying to make you more money should understand the data the same way because then they’ll understand where to put money and how much money they’re going to get out of it, and they can plan accordingly. They can grow. They can expand. They can do all the wonderful things that you can do when money comes in and the money comes out in the right ratios. Yes?

Lastly, just the summary. If it’s just when you have straight-up bad data, things not being tagged properly, a misunderstanding about what a conversion is, someone tracking a conversion based on a lead that comes in on Google and then someone else thinks that it needs to measure at the qualified and then on. So, how are those two people going to have a conversation together about numbers? It’s impossible. So, it’s really important.

Do any of these resonate with you guys? Raise your hand if you see your company’s challenges in one of these boxes. Yeah, it’s pretty much a good number. Is there anything that you guys are challenged by that doesn’t show up here? Yeah, for the most part it will fit into one of these boxes.

So, let me give you a couple of examples of this out in the wild. So, this is in that world of having your data enculturated, having conversations about your numbers be a part of the company. So, this was a client that we had that was switching to a new website, and these are all quotes that I heard at some point on the phone or in person. So, the 83 percent bounce rate was not good news. It was on the new site, so we were at 75/25 moving over to the new website, and all of a sudden we see this bounce rate. Now, this was a big deal.

So, there were high-level people on this call. The marketing team was on this call, and we had to do a lot of our digging amongst all of us to figure out what was going on. So, what happened was they had no fewer than 10 different tools that were hitting this site with a variety of different types of tests. So, here this tool was definitely doing what it was supposed to be doing, and that’s what it knew it was going to cause 100 percent bounce rate, but this conversation wasn’t enculturated. So, one side wasn’t talking to the other saying, “Hey, we’re about to go from 75 to 100 percent. We need to make sure that everything looks good.” “Oh yeah wait. I was about to run a test. Should I wait?” That wasn’t the conversation. So, the site move was delayed another 48 hours while we figured out what all this stuff was. So, don’t have that happen.

So, keeping it part of the conversation. This also lands in the world of enculturating your data. So, this was something I heard from a client not too long ago. “We don’t have keyword level data.” Which it stopped me in my tracks because we had worked with this organization, and it’s a company—they have a very long funnel that can go anywhere from three months to two and a half years, and we had worked with them really closely to build out keyword level data, which they have from the leads all the way to closed/won, and there’s revenue dollars on the other side of these numbers as well. Now, the problem was two people had left the company, and they hadn’t kept the culture of data alive. Those two people were the stewards of the data conversation we found out later, and so when they left, so did this level of granular market, and it definitely hurt them. So, the thing for you guys to think about is make sure you’re keeping the conversations alive.

Here’s another one. This was a prospect, and we do audits with potential customers who are really interested in making sure we can make a difference for them, and, you know, if their agencies are good, we usually recommend them staying. So, that’s what our audit helps us figure out, and in this one we always ask the goals. They said, “Okay, our CPL is really solid. We just need to grow. We just need to scale.” So, when we actually dug into the data, we saw well that’s not really the case. Their whole portfolio, as you can see here, at that $77.23, their whole portfolio was relying on the performance of their brand, but the rest of their portfolio was fairly weak when they were playing against $110 CPL cost per lead here. So, with this we would recommend, no don’t grow yet. Build your foundation, make it stronger in your non-brand categories so that then you actually have something that can really build and scale without the integrity that you need to keep that $110 in play. So, just remember this is the world of like having the fullest picture.

Then another, just to address the vanity data issue. I’m hoping this one doesn’t happen as much as it used to. This is really the legacy world of billboards and eyeballs and all of that. So, this is just an illustration of how vanity data can really cost you money, and this is, you know, those calls, those emails like, “Impressions are way down. What’s happening?”

So, this was a prospect, and we did an audit of their account. They were in the electricity market back east where it’s deregulated, and they were like, “We just want traffic.” So, they got traffic. Now, they have broad match keywords like coal, gas, wind, electric, electricity, and power. So, they were really on point, $23,824 spent helping people find gas the lowest, the cheapest gas price near me. They could have also gotten electricity if they wanted to. I don’t know why they clicked the ads, but they did, and it cost them almost $24,000. Other things were Power Rangers, Transformers. I’m pretty sure these were like 12 or 13-year-old kids because we’re not talking about like transformers on the power line. We’re talking about like Decepticons and those guys, yeah.

Volts. They were trying to give a lot of information to people about voltage for different things, and that was—I mean well it’s only $186 dollars. These are all clicks that are not likely to turn into customers. We could have a whole session on why people look for one thing and then click on your ad. If you have that problem, it can be really fascinating and worrying, but $40,000 and change went to this type of traffic, and I would assert that the line share of this—you know maybe someone who’s looking for the benefits of coconut oil probably might also be upset about their electricity bill. I don’t know, but I do know that none of this money converted. So, not all traffic is good traffic. Any questions?

So, what do we do about it? Creating good data management processes should be a part of that enculturation of your data. You want your people to be pretty protective of it. You want someone in a meeting to talk about a new channel and then say, “Well, how do they track?” Because that person sitting there thinking about how all the tracking channels are gonna sync and where they’re not going to sync and how you have to play with the math to make sure they all can give you a picture that makes sense.

So, the first things you want to do is you want to clean up your data, get the tracking handled, bring someone in to help you if you need to. Sometimes a third party can make a difference when there’s a lot of people at the table who have been talking about the same thing for a long time. So, don’t be afraid to bring in a consultant or someone who can help you. Get to the point where you trust your data.

Now, understand whenever you’re looking at distinct systems you’re probably going to have some slight differences. That’s probably okay as long as you understand what the reasoning is behind those slight differences. So, maybe between AdWords and Analytics you understand first click and last click and those types of things. So, when you’re looking at the data, you can interpret it the way that’s proper based on language of its system and then you’ll understand how they come together.

So, clean your data until you trust it, you understand the differences, you know how they impact you, you know where they’re important and where they’re not to be bothered with, and you also know them to the degree if one of them is off you’ll notice it or one of your people will notice it. Okay? A lot of this may apply to the people that work around you who are the people who are managing and maintaining this. You want to make sure that they have this process in their day, week, month, year based on how often they have to interact with their data.

Then you want to maintain it. You want to be really protective of your data. Not only that, you want to keep your technology on point. Make sure everything’s up to date. Right now one of the biggest ways we’re gonna be managing big data is with really great partners that have super smart tech because there are too many metrics in some situations than we can manage, but if there’s a machine that can pull them all together and give a smart team of thoughtful marketers some good information to direct their tasks and their efforts and their tests, then you want to pull those in. So, stay up ahead.

There’s probably lots of great vendors for you to check out here to understand like what is happening with programmatic, what is happening with AI, and how can it solve the problems that I have with my data. So, stay up-to-date, maybe create some Google Alerts. Also, make sure your technicians, any people that you have touching your systems, make sure they are maintaining them, and make sure they’re keeping their own tools sharp. My people love Excel, and now they’re all starting to learn R and Python on their own because they want to be better at manipulating data in new and different ways. So, give them those opportunities and then enculturate it, as we’ve been saying, “Enculture it, enculture it.” It’s really important.

So, make sure that there is a conversation for data in your company. Having reports is great. Just make sure they’re reporting on the right things and reporting when you want them. So, if you have a team meeting every Tuesday, make sure you have the previous seven days’ data when you walk into that meeting on Tuesday that you’re looking at, and make sure everyone agrees that the metrics that matter are in that report, and there may be a variety. If you’re talking to higher-ups or you are a higher-up, you might want to see data a certain way. If you’re more in the weeds, you want to see it at a more granular level because that’s where you’re taking action. Any questions on any of this?

So, the thing to get is—this is kind of a silly question because metrics and performance management are really integral to your organizations, yes? So, what are you gonna do about it? This is sort of six steps to enculturating your data, and we’re kind of beating this one to death because it is really key, because if we’re going to be people who decide to use data to drive our decisions, you can kind of see why this would be really, really important for us, yes? Yeah, and it’s going to be an ongoing thing.

So, enculturation. This is what we already talked about: Reporting, taking care of it, making sure that if something changes that impacts your data, it’s noted. If you track things in a Wiki, if you track things in Google Analytics, make sure one person is the one who’s being that—or a couple that know what the types of things that get noted.

Then create redundancy. So, I gave the example of the customer that had impression-to-close information, which is—you know, it’s a unicorn in the marking world. It’s hard to get that at the keyword level for such long sales funnel that we have at times. So, make sure that you have redundancy. This really helps if people are just talking about it, because if you’re talking about it, you know where to go to get the data you want, and when that person decides to leave or that department changes, you know where the data needs to move to make sure that those owners and those keepers of, and those understanders of remain a part of your company no matter who the personality is, who’s sitting in the seat at the time.

Then you want to understand the system. You want to understand how everything works. How does it track? How does it think? In our company we do a lot of paid search marketing, and we have gone back and forth in the world of bid tools, and whenever we use them, it’s in the right situation, and it never overlooks the fact that you need to have a smart thinker and someone who wonders about data running the machine or else you’re likely to lose money. So, make sure you understand your systems – how they can break and how they think.

Stay current. I already talked about this. Just really educate yourself. Make sure your people are smart. A good day would be when someone comes to you and says, “Hey, I just checked out this vendor. They’re really cool. I think they really could be a game-changer to the way we understand our data. Can I set up a demo?” That’s a good day for you. Even if it’s going to cost you money, I guarantee you it will save you money to also have those people who are thinkers and researchers, and they’re out ahead of that technology curve because that’s where you want to be.

And then embrace curiosity. You know when data doesn’t—the thing that’s funny about data, and we’ve had clients before who say, “Well, I have this goal. I told my boss I would get this many leads at the same cost this month, and why can’t I do that?” And then we have to say stuff like, “Well, because math.” Sorry, math is impersonal. It doesn’t care what you said to your boss. It doesn’t care what your goals are. So, whenever you have those situations where someone has to come to you with the bad news, you want to really applaud them because as I always tell my people, “The things you measure are the things you move.” So, if we know the bad news, if we know we have an 83 percent bounce rate, well I’m definitely gonna do something about that. So, don’t have it be the case that someone’s afraid to come to you with bad data or, you know, bad news about your data because those are the things you want to uncover, and you want to count on uncovering them. The better you get with your data, the more you’re going to find out the things that you maybe don’t love so much, but you can transform.

So, again, just coming back to this quote, the goal is to have a company or a marketing department or all the relevant people be these synthesizers that know how to put together the data, think critically, and you make decisions about it.

Okay, so now I’m going to hand it over to Melissa. Thank you Melissa.

Melissa: Thank you, and thanks everyone for all the input so far.

So, what we’re gonna take a look at now is really a three-step framework in terms of how to apply and how to put some of this in motion, and we’re gonna look at it through a lens of a case study here in just a little bit.

So, when we think about how to approach putting data in motion and having it work for us, we need to think a bit like investigators. We need to be curious, and as Elizabeth really explained so well, a lot of it is building a culture. It’s communicating. The basics still apply even though we’re dealing with math. It’s great to have a pocket of data sitting over in one part of the company, but if we’re not asking, if we’re not reaching across, if we’re not fostering an ability to look and to listen, it’s good money after bad or it’s good money that often will go wasted.

The second part is to really then analyze and learn. So, again, as Elizabeth mentioned, we should celebrate when things aren’t looking right. We need to applaud the person that’s brave enough to walk in the room and say, “This campaign that we just launched, this program, it isn’t looking so hot. Some of the early indicators are showing us that some of the metrics are a little off.” That’s where we can dig in. That’s where we can ask the questions. That should be celebrated. This is an iterative process that only will get better the more we ask the questions and we throw hypotheses at what we’re trying to accomplish.

And so then the next is to act and iterate. It’s really—if some of you have started to incorporate Agile into the way that your teams are working, I’m such a big believer in it because it really becomes this constant process of quick looks. It really helps foster communication, and you begin to change the mindset in terms of we’re not reaching a goal because of a quarterly ask or what have you. This is a continuous cycle where we’ll always be moving. Because the minute we start to understand the behaviors of our customers, you know what? Their behaviors change. You know what? We have a whole new pocket of customers that we’re going after, and/or we have a whole new psyche or a change of the way that things are behaving, and so there is no end game and destination. It’s something that we’re really just improving, and as we go over time and as we get smarter, our dollars work hard for us. We’re spending less on search terms that don’t mean as much. We’re actually becoming less impressed with the volume of impressions that we have or the volume of clicks that we have, and we’re really looking more towards the quality that we have.

So, we’re gonna go through a series of slides here from one particular customer, and we wanted and we liked the concept of—actually, let’s focus on email a little bit. The great news is that there’s so much that’s out there in terms of predictive analytics, predictive data. There’s so many wonderful ways that we can reach people from digital and online, but you know what? If we can break things down in terms of getting a bit smarter for something as old-school as email, maybe there’s some nuggets in here and some commonalities, and that’s kind of the point, is that there’s some commonalities in the basics that apply that we think and we know actually can be translated into others, and we’d love to learn how you’re applying them.

So, it’s a basic game, right? We’re trying to get attention and drive engagement. Historically, email has been something that’s just kind of has been that annoyance in your inbox. “Hey, hi, pay attention to me.” But there is science now that we can apply where we really look at engagement. It’s now part of the marketing mix. It’s now part of the whole integrative effort. So, as you’re doing something in an awareness level where you’re hitting people from a social perspective, now you can actually—this is all part of the blend of how you can communicate to people in different ways. It’s that digital bird seed that we can kind of drop across.

So, we want to look at measured engagement, really look at—we can now apply the insights of the scoring, the qualification, and start to look at behavior. So, we can start to actually see it’s a bit of a psychological understandings and some of the behaviors of people as they interact with something as old-school as email.

So, again, an overview, and I’ll spare you reading you every word on the bullet, but you can kind of see what we were trying to do. We were looking at purchase contacts. We were trying to see, you know, the emails directly driving to assets. We wanted to decrease abandonment, some of the basics that many of you are working with today. Also, there was no CRM integration. As much as what we’ve talked about the beginning part of this is an ideal state or some frameworks that are ideal, the reality is—we’re working in reality, and we don’t have all of our systems that are connecting. We don’t have everyone who’s talking. We have data that’s actually dirty, and it’s not quite working in the way that we want, and so what we really wanted to do was to look at how we can improve ROI and look at how we can increase by the science that we could apply to some of the different motions and we wanted to run.

And so how do we do it? I’m going to give you the punchline first. The numbers were pretty good. We increase, you know, unique open rates. Our dials-to-deliverables were good, and we had an increase in scheduled meetings. That’s nice, right? Obviously, we picked this for a case study because it’s pretty successful, but I think one of the ways or some of the ways that this became successful is that we didn’t try to solve everything at once. We broke everything up into small bite-sized bits and looked at our numbers, and so I’ll kind of show you how this built a little bit.

So, again, this is a timeline. You may not have this long of a timeline. It’s okay. If you have three months, if you have one month, the point of this slide is to plan, is to look at what are your main goals. What are you trying to accomplish and then break up the different things that you can test back to what we looked at earlier, which is that this daunting amount of data and metrics can actually be categorized. So, you can take a look at overall what are you trying to accomplish, look at the different campaigns or programs that you’re running, and begin to test different variables, and we’ll go through some of these. I’m not going to go through each of the different things that we tested. I’ve just picked about four or five that we can take a look at. I’m happy to go through the larger detail at any time if you’re interested.

So, let’s first look at audience, right? We talked about starting with the audience and starting with the end in mind. The goal was to cultivate an engaged audience who’d consume more content, and so what we first wanted to look at was the product segmentation to see if we could start to look at and get a little bit smarter at the audience based on the product that we were targeting and then look at active versus inactive and then basically do a purge of our inactives. Sounds pretty basic, right?

So, again, the first thing that we did in terms of isolating was if we could actually divide up and begin to look at our customer set, the customer set that we were looking at based on actives and then inactives and those who were new, could we glean some insights in terms of driving better behavior? If you’re to look at the top bar or the bottom bar, it’s kind of hard to tell. You know, if you look at the bottom, performance doesn’t look that great, but if you can divide in segments of your audience in terms of actives and inactives, you can start to see which of your groups are performing higher, where do you need to clean up your data and just purged from the system, and so that’s a good first step, kind of basic.

But then the next thing that we did is we actually thought to ourselves, “Hey, these inactives, the people who aren’t interacting with us, I wonder if we can get them back.” Because the first jump to conclusion was well let’s just purge the inactives. Let’s get them out of our system then we have to clean data, but what if those inactives just needed to be touched again? What if they potentially could yield some benefit for you. So, what we did is we actually sent out an unsubscribe email and just one last touch. Are you interested? And you know what? We actually got a decent response rate in terms of those who were inactive coming back and subscribing. So, you know, it’s never too late to just see. You know what we did is, if they didn’t reply, then we actually purged them from the list, and we got them out, and that helped us with the clean data, but what we found that those that came back actually were interested in being contacted again. It’s kind of like they came back into the family. The results were surprising from that perspective. So, that’s a little bit about audience, kind of flying through this, but I want to get into a few other examples.

So, subject line testing. So, hang with me. This is actually pretty down in the weeds, but it’s pretty interesting, I think, when you take a look at it. We wanted to see if we could create the perfect subject line. Now, what I’m going to show you may not apply to your industry, but you can do a similar type of test in your industry because high tech and technology may not fit if you’re in a consumer business or if you’re going after some other form of B2B, but we went through a series of subject line testing where we did some A/B. We started with the traditional and then we introduced a little bit of personalization. Good news – actually we saw engagement increase. We saw our open rates increase.

So, then we went through a process. We tested over 180 subject lines just to look and see what we could learn. So, the question was, do we have enough subject line testing to write the perfect subject line? So, if we looked at history and we could test to look at type—what type of email was it? Did personalization matter? What if we mentioned a product? What if we blended these? We did a series of A/B testing just to see, and did character length matter? We’re in a 140-tweet world. Does it matter? Does it not? Is shorter better? Is a little longer better? So, the results were very interesting.

Again, what we found was that personalized obviously makes a difference. I think the Delta was the most interesting to us. You can assume it’s better to be treated like a human than not. So, that’s pretty interesting. City doesn’t matter. A lot of things that we hold kind of near and dear to our hearts don’t matter as much, but it also—when you start to look at the number of characters, there’s a science now behind the content and the phrasing. There’s some amazing tools that we can take a look at that help us analyze that, whether or not the tone and tonality is interesting, but what we found is that actually shorter is not necessarily better. For this particular audience, between 70 and 79, not 80, characters made a difference. So, when you add these in, you personalize, you acknowledge the company that they’re with, you acknowledge the brand that they’re with, and then you keep a nice tight subject. Again, I think the results speak for themselves. So, again, there’s some examples of how that can be applied in terms of the email. So, we put this into the wild and let it run.

So, again, if we can take a look at some of the calls to action—so the next step is, all right we’ve looked at the audience. We’ve gotten a little bit smarter there. We’ve looked at the introduction in terms of how we’re going to appeal to you. Now let’s look at the call to action. So, again, if we take a look at—if we can track the clicks and the call to actions to determine how many contacts are engaging, you can actually see that if you apply and take a look at the types of conversions by week, we noticed that certain domains were engaging with every piece of content. So, that helps you get a little bit smarter, and then we wanted to take a look at—because the next thing what we see so often is that we want to put everything in the body of the email. We have you. We’ve appealed to you. You’ve opened it. So, now let me serve up three and four different assets that you can play with.

Well, what we found was that isn’t necessarily the sweet spot, and I know this is a constant tension. Having spent a lot of years in B2B space, working with product teams, and within wanting to put their latest and greatest product in and us trying to put more, I understand and I appreciate the challenge here, but really simple is better, and so if you have this flow of attaching and being smart with going after the right person, making sure, and if you can see over here to your right, if the introduction actually matches to the asset, they’re going to see really you only need one. You don’t need a whole laundry list of your latest and greatest in there. The efficacy just wasn’t showing in the numbers, and, again, we may have satisfy three or four different executives over on the left, but on the right is really the way that people want to apply. So, in terms of what Elizabeth mentioned earlier, yes we all see what would a marketing presentation be without knowing your customer, but the reality is you can now apply science to really understanding how they want to behave and how they’re interacting and what they want to see.

So, I’ll kind of skip through for the sake of time, but, again, personalization. What we found was not—it didn’t matter if you continued to personalize all the way down through the email. It was really hitting them upfront and then you don’t necessarily have to personalize all the way through. It starts to look a little bit like a form, you know. Like it’s kind of like you’ve put them through a personalization generator a little too much. It loses a bit of the authenticity, if you will.

So, finally I’m going to go through just—again, if you think about kind of what we’ve looked at, and one of the main points is in this world of what can be seeming as very, very big, really break up and ask to the questions, to that point of what we looked at a couple minutes ago, of line up your timeline, really work with your team to identify what questions are our customers asking? What questions can we asked and let’s put hypotheses around it. Instead of swinging for the home run, it’s kind of, for those of you into baseball, it’s a bit of a small ball. Let’s go for the base hits because that gives us better insight in terms of what’s really happening.

So, from a time perspective, again, you can take a look at time at when emails are sent. You know, often if we just assume that if everything goes out on a Tuesday at 9 a.m., it’s effective, right? That’s what we do. Don’t ever send anything out on a Monday. Heaven forbid you send out anything on a Friday afternoon. That’s the dead zone; don’t do it. You know we didn’t find much benefit of sending something out on 9 a.m. Eastern Time. Everybody’s getting in the office. There wasn’t much delta between the different time zones either, and then also when it comes to days of the week, at least for this audience—now, again, this is a tech audience that we’re going after here. Look at that, Saturday. You may find something different with your base, but let’s think about ourselves. Let’s look at the data, see what it tells us, and then think about ourselves as humans.

When we’re in the middle of meetings and something hits us at 4 p.m., now we might find, we might click on it, but are we engaging with it? The answer is probably no. When do we do most of our catch-up reading? When are we really looking at the backlog of things and say, “Hey, you know I actually wanted to take a look at that piece that ended up in my email box or something that someone sent me. Nowadays it’s Friday afternoons, it’s Saturdays, and it’s Sundays, and so while we don’t want to over rotate and become annoying in terms of hitting people on the weekends, we also need to realize that people have asynchronous behavior. We save things, we send things, and so clicks and engagement are going to look a little different, and so, again, data don’t lie. Let’s look and see what we have and look and see what your audiences are telling you in terms of the times of the day and think about that. You can really outsmart competition by looking at behaviors, and then, again, this is—I kind of led the point a little earlier, is that what we found is that, you know, people would open at 4 p.m., but they would actually click on the engagement kind of in the mornings before their days we’re starting.

So, a little bit of takeaways from the case studies that we’ve just gone through is that don’t make early assumptions. Be as objective as you can with your team in terms of what are your goals and how do we want to break this out into small bits.

Look at your desired outcomes and develop hypotheses. It’s a science game. Plan the work and work the plan. We’re all under the gun for speed. I get it. We may not have seven months to do something, but you will never regret taking a little bit more time out in front to plan.

It’s an old adage, but you know it’s a battle that we fight constantly, and it’s something that I know as we work with clients to ask them just to spend a little bit more time to think and then, you know, just having been on the client side for most of my career to ask our team to just think and plan, and improvements are guaranteed. Sometimes the numbers come back and you know what? They just didn’t work, and that’s okay. That’s all learning, but the goal is to keep, keep communicating. I cannot underscore or overemphasize the importance of just making sure that left hand is communicating with right hand and that you as managers and as leaders are making sure that you’re talking to your executives and keeping them informed. Bring everyone along to the importance of the numbers that you’re seeing, and don’t be afraid to show when things are red and not green.

So, in terms of key takeaways—I think Elizabeth is—okay. So, I think a lot of the chapters that we’ve just seen, there’s three main takeaways from what we’ve really learned, and one is align. So, it is possible to organize. There is way too much coming at us, but if you line up the framework, something similar to this or what we just showed, I think it’s a good way to align your people for you to look at where you have gaps, for you to look at where you need to bring in technology partners to help fill that gap. Obviously, start with your customer’s priorities and then you can categorize, then you can really take a look at where your business is.

The second is commit. So, this really speaks to a lot of the really great points that Elizabeth made around data. Results are only as good as your underlying data. If you don’t start there, anything that I just showed in terms of a case study is going to be off. It’s sort of that one-degree change ultimately as you go miles ahead you end up miles apart.

So, there are four basic questions that you can ask, and at the bottom of it really is how are you building that culture? That admits where you have data strength and admits where you have data weaknesses, but always with the customer in mind.

And then trust the process. Trust the process of acting and learning, of analyzing, of really rolling up your sleeves, and if you don’t understand how to interpret the data, it’s okay, raise your hand, and ask for help and then iterate and constantly iterate.

So, what we know for sure is that you’re not going to drown if you jump in, but if you only read a textbook on swimming and you never jump into the pool, you’ll know the strokes and you’ll know it beautifully, but you really won’t ever know how to swim, and the first time you jump in it will probably—I don’t know like me it will probably look a little silly, but ultimately you’ll get the strokes down.

Data deluge – It’s really—marketing is no longer anything but really data and anything but tack. It’s kind of going back to the old town square but now we have data and we have drop lines where we can see people’s behaviors out in the wild in ways that we haven’t ever been able to. So, it’s the water in which we swim. It’s the air that we breathe, and it’s where the exciting—I mean we’re really having a renaissance in our profession. It’s never been really a better time to be in marketing.

So, that’s kind of the conclusion of the main part of our presentation. We do have some other things that we can talk about, kind of some things that we’re seeing as it comes to using predictive analytics and marketing or looking at culture and things like that. So, we’re happy to go through some of that, but we wanted to pause and see if you have any questions before you get into that.

Elizabeth: Any questions guys?

Unknown Speaker: So, you were testing with different headlines. How big was your sample size? Were you confident on any of the outcomes being, like, statistically significant?

Melissa: Yeah, we were. You know we had to—again, in the B2B it was pretty targeted space. So, we made sure that we didn’t test anything that we didn’t feel comfortable from a statistical significance. You know, the tighter and tighter you get, the closer you get to that leading edge in terms of being confident. So, I don’t know the size that you’re working with, but you may not be able to test 180, you know, headlines. It may be 20 to start from and then what you can do is then start to see the results, right? Because the good news about something that’s electronic and digital is you can go on and kind of get it out and see if it’s performing or not and see if you can kind of look at the return, just like watching an election poll, and just to see. So, it becomes a bit of art and science, but we stopped at a point where we felt that we didn’t have confidence, you know, so 180 for the sample slides was about the right number to test.

Elizabeth: And remember all these examples that Melissa shared are really about approach, right? So, if it’s the case that that takes you a year that might be fine. It’s just another [case of] polling that process, measuring and iterating, looking to see what you learned about measuring, iterating, iterating, and then you’ll get down to your version, because with any of these, we couldn’t give every example that might come up in the paid search marketing world, but you can test a headline for an ad. You can test a headline for a landing page. It’s good that you have more to your vocabulary that’s statistically significant because then you can—that can be a part of what you bring into your culture.
So, the idea here is just ongoing learning based on the Melissa and everything that she showed here. Like, that’s a really beautiful example of what’s possible when you really just iterate and don’t stop.

Any more questions out there?

Melissa: I think we’re pretty good.

Elizabeth: So, the way we set up this deck is really for you to be able to have it as a take-away. There is some information that we threw into that that if you want to—this is more like in the weeds. Anyone interested in getting in the weeds with data for a little bit? Okay, so I’ll talk through some of this if we’ve got time.

Melissa: Sure.

Elizabeth: So, one of the things I think is really important, and when I look at my role as director, one of the things I’m always having to do is look at hiring. What am I hiring for? What kind of skills am I looking for? It’s really interesting to understand what’s happening in the job market around data analysts, data scientists, and what that means for potentially the types of people we have on our team.

So, how many people have a team that they work with in marketing? How many people are like the guy who wears all the hats? Okay, got it. So, that’s something to understand as you look forward, so the people who are the guys who wear all the hats that you may want to hire in the future. One of the people I would hire is go hire an econ major or a stats major or a math major plus Excel skills.

So these are some of the job requirements, starting salaries, like that can be prohibitive. Like that salary for a marketing team may be a challenge, but what we’ve found is it’s way easier to teach marketing to math majors or marketing to econ majors or marketing to stats and finance majors. So, start looking in those worlds, and you can have your job descriptions pull for those types of thinkers, but what you’re looking for are people who wonder about data. They’re not intimidated by it. They’re really excited when you’re interviewing them, and you give them a math problem. They really like it, and those are the kinds of people you want to have on your team.

So, if it’s the case that you are a team of one or even a team of ten, you want to have some smarty pants math people on your team because they love what they can learn from this stuff, and if you combine it with their understanding of marketing or have them be the voice of math on the team, it can actually really be a multiplier to what you’re up to.

So, these are just some tips that I’ve found. Give them some analytical puzzles. If you’re interviewing them, make sure you understand them and that you can see all the—because partially what you’re looking for is understanding the way they think. Ask them questions like, “Tell me about a time you had to deal with a big set of data and you had to synthesize it down to a recommendation or an observation.” Then mostly pay attention to the way they thought the problem through, okay?

So there’s some other things you might want to look at here. You want people who are really comfortable with Excel. If they’re starting to on their own, which a lot of people do because maybe they don’t have classes for it in their universities, start learning Python and R, and then you want to make sure you keep those people because you’re going to invest a lot as a business into them to learn marketing, to understand how they can really take their talents and have them deliver for your clients. So, make sure you do what it takes to retain them. These people like good problems to chew on. Make sure you give those to them

Also, this end section is just little things you should be doing. So, it’s kind of like a potpourri or a mishmash of different ideas. So, one of the things that I know is, whenever I’ve talked to clients or potential clients that aren’t— that don’t have a clean data world or have a no-data world, the first things I tell them to do is put your Google Analytics code on your page. Set up all the relevant goals. It doesn’t take very long. You can put one person to that task, and most of you are probably beyond this but just in case, everything that you’re spending money on should also give you a way to track it. So, if it’s AdWords or Bing or Yahoo or LinkedIn or Facebook, you have some way to track it, and make sure you have those codes on your page, and you’re doing it properly.

Then set up performance goals. So, for each of those channels where you’re spending money, it may start out as just a thought like, “Okay, I want in general get this type of return or in general I want to get this kind of cost per acquisition.” See how you fare against that.

The other thing I wanted to point out is, so really technology is going to be one of our biggest partners in this world of data. So, you want to make sure you’re using the technologies you have on hand. You may not think, “Oh, I can’t go get like an AI vendor, someone who can really bring some programmatic thinking to my game.” But if you’re just marketing in AdWords alone, there are tools that are already in the tool that do this already for you. These are tools that use Google’s smart analytic processing power to cross so many metrics simultaneously when someone puts in a search and hits go. This tool will automatically say, “Should your ad be there? Should it not be there? Should you automatically bid more because the likelihood that they’re gonna turn into a lead is higher?”

So, I made this pretty actionable. Each of those are hot linked. I know you’ll be able to get these downloads, but there’s also some information about each of them specifically. So going through each of these, and I won’t go through each of these. I don’t know how many of you—how many of you advertise in AdWords? Okay, so it’s a good number of you. How many of you already are working with any of these tools? Okay good. So, a few of you are and a few of you aren’t. Okay, for those of you who aren’t, I hope it actually can be something that’s—it’s easy to implement. It’s already there. No budgets need to be improved. You might need to allocate, but this is all stuff you need to test. In our agency we’ve had some of these do really well for clients and then do really not well for some other clients. So, use the process that we outlined to help you test and iterate on this.

So, here, again, I’ve shared—basically, one cheat sheet is here for each of these products. So, take a look at them, and there’s also links to either—I put pretty much the most useful resource that I came across, either a video or AdWords help that took you all the way through to implementation, but these are all tools that help you find audiences in some cases or help you show better if the likelihood, based on its algorithmic process, in that millisecond after someone hits go on a search says that it’s much more likely that they’re going to click on your ad than another. So, it’s worth a 30-percent bump in your bid. That’s what these tools do.

There’s also great tools that allow you to upload your email lists so that you allow their algorithm to learn from your people. Either you can market them, so maybe it can be a world of nurturing a lead or maybe it’s having them come back with different types of remarketing efforts that maybe you have other product or services you’d like to show to them or you want to go out there and find people just like them. Google will use those email addresses to—they’ll watch them for a little while, see how they behave up there in the internet, and then they’ll go find people that seem to behave similarly. These are all guesses based on a machine not a person. So, act accordingly, but don’t avoid the fact that sometimes there are going to be lots of machines who will think faster than we can, so you definitely want to make them your friends.

Can we talk through—are you guys set to go? Do you want more information because we have a little bit more that Melissa included in here that are just value ads for you guys that we can look at?

Melissa: You know, I’ll just tell you a little bit about what we have here, and you’re—I’d love to learn from you what you’re seeing because this is just but one example, but for those familiar with, like, IBM Watson and that, there’s some really exciting things.

I’ve transitioned from kind of more the content world over into more the data-driven world, and so I see the tension often between this creative “It’s pretty, and it sounds really pithy.” with “Well, yeah that’s nice, but how is it performing?” You know the amazing thing that we can do, and I’ll click through this, is that you can actually break out like just from a sheer content marketing, there’s another side of your marketing house that’s thinking about this, into things that you can measure, and so often you can measure experiences to connect. We just talked about that for the last bit. You can look at your buyers. You can see where they are, but kind of this missing sort of fuzzy thing out there is this whole concept of narratives and storytelling, and there’s some really cool technology out there that’s starting to look at narratives and storytelling and providing analysis compared to where you are on this arc of a story. So one of the things, and, again, you can—and if I can work this—that’s really interesting is that machine learning can now look at some unique rules and apply conversations and storylines and apply some science to not only looking at your storyline and its efficacy against itself but also against your competitors in terms of are you connecting emotionally and of that.

So, this is something we’re starting to play with. It’s still very beta, but it’s actually pretty interesting when we start to look at yet another layer. I know how to overcomplicate things of data to take a look at, because not only do you want to find where your customers are, see how they’re engaging, but then you want to get smarter about the stories that you’re telling, and then you may be able to automate those stories or some fascinating things going on with bots and stuff like that in terms of creating content that’s smarter, and there’s just too much to ingest. So, we’re going to have to begin—I pause it. We’re going to have to start to take a look at some of this machine learning as it applies.

So, I’d love to learn if some of you were playing with that because I think that’s where a lot of these worlds collide in marketing. You know, this is from a company called 8-Point Arc that I’m quite a fan of. Quid, if any of you have played with Quid, looking at some really interesting things from conversation and measurement out there in the social sphere, and I think you’re gonna start to see some of this overlay on top of some of the work that we do.

So, again, this is a lot here. It’s bonus material. So, thanks for staying for kind of the additional part, but any questions or anything that you’re—we’d love to learn from you that you’re seeing. If not, we’ll definitely let you go to happy hour, but any additional questions?

Unknown Speaker: I guess it’s more of a comment. I’m part of a 128-year-old conference based in Germany. Everything is processed perfectly, but trying to get them to understand that yes, it’s great to have the processes, but you want to have the technology, you want to implement that technology to make those processes work. You know, and this really speaks to me. I’m trying to network business units to work together. Getting them past the fact that while we’ve done business like this for 128 years, that’s not going to help us moving forward.

Melissa: Yeah, I’ve lived that world, and I think that there’s a couple ways to approach it, but one of the things, and I’m sure you’re doing this, that might be a suggestion is, as Elizabeth mentioned, some of these tools will actually come into a demo. Sometimes they’ll do some analysis. You know, often you’ll find that people are willing to partner to kind of just take a look, and, again, back to the point of starting small. Finding some of those small wins I have found to be more—I’ve tried both, where I kind of swung for trying to make this big change and doing big executive presentations and then the other side is where we almost treated ourselves like a little incubator, like a little rebel group, but then went and kind of proved out something kind of on the sly and then went and showed the results. That I found much more effective in a big organization than—I mean you should still evangelize, right? But that’s the way culture often, in big companies, changes, is by kind of find—treat yourself like an incubator.

Elizabeth: One of the things we’ve done as well, and mostly successful, is having those people sit down and presenting them with a competitive review. So, successful companies, a lot of times they used the competitive space to guide their actions to figure out if they were ahead of the curve, behind the curve. So, I hate using words like “shame them a little,” but it is an “oh” moment that has them go, “Ah, okay.” And if you have a really good Google partner, if you’re in the paid search, sometimes they can use some really amazing competitive analysis. It helps you just show how much the competitors are spending. We’ll have to anonymize them a little bit. You can usually give them a select set. They’ll throw out a couple extras to anonymize it. They’re not supposed to tell us they know every single thing there is to know blatantly, but they will—those competitive reviews can be really helpful. There are tools out there that will help you understand in general. It won’t be specific, but they’ll be kind of directional like, “Here’s how many keywords our advertisers are advertising on it, about how much they’re spending on it, and then you can see this really big bubble and then you see your tiny bubble, and they’re going to be really interested in becoming one of the bigger bubbles if their competitors aren’t. So, sometimes that can be a least—can get dialogue started.

Any other questions or thoughts you guys? Okay.

Melissa: Great.

Elizabeth: I think we’re good. Thank you for sticking it out as long as you did. We appreciate your time and attention.