How Lenovo Delivers Intentional Content Through Predictive Insights

Mike Ballard, Senior Manager of Digital Marketing at Lenovo, joins the Content Pros Podcast to share how he is using predictive insights to create content with intent that engages, motivates, and closes sales.

In This Episode:

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Full Episode Details

Predicting Impact

Knowing what your customers are doing on your website is great knowledge to have. You can see where they are going, what is drawing their attention, and how long they stick around. We’d all like to think that’s plenty enough to create a content strategy.

But it isn’t.

In reality, the amount of time spent on your website is a tiny fraction of your potential customer’s online activity. The real data gold is in what they are doing before and after they visit your site.

Understanding the greater picture of their online activity helps ensure you are crafting content that appeals to what THEY need to know, not what YOU want to tell them.

Mike has used predictive insights to successfully craft impactful messages that customers want to hear, and his first step is to ditch the pitch at the top of the funnel. Drawing prospects in is about helping them with a problem, and predictive insights can help you diagnose that problem quickly and efficiently.

In This Episode

  • Why understanding your prospects means going beyond your own digital properties
  • How reinforcement of your core messaging can lead to prospective buyer tune-out
  • Why getting the most out of your data means using it to trigger campaigns and craft your message
  • How being helpful instead of pitchy leads to successfully capturing leads and moving them through the funnel

Quotes From This Episode

Lead scoring really only represents a microscopic fraction of a person's day. Click To Tweet

“Now we have the ability to see way beyond just the scope of our digital properties, and with that, we can trigger timely and relevant campaigns and topics and content.” —@mballard5574

The market is very good at sniffing out a sales tech or a sales tactic. Click To Tweet

“If we keep hitting them over the head with the wrong message just because it’s the message we want to get out as a company, then they will quickly tune us out, and we may have lost brand perception right away.” —@mballard5574

“We only engage with those contacts that engage with us.” —@mballard5574

“Data is great for triggering campaigns, but we’re also using this data to make sure we’re speaking the right message.” —@mballard5574

“If I have a pain point around something, help educate me. Don’t sell me something. Don’t push something in my face.” —@mballard5574


Content Pros Lightning Round

What has Netflix been recommending for you that you’ve actually bought into lately? My wife and I, we don’t watch a ton of TV. The only thing we watch, if we do at all, is HGTV.

What is the item that Amazon is most likely to recommend to add to your basket that you’ll buy into? I’m big into fishing, and so I get a lot of fishing suggestions, different lures and such.

What type of music is most likely to come up recommended for you on Spotify or Apple Music or whatever platform you use? I go through phases. I go through a classical phase. I go through a country phase, and everything in between, but usually I’m that folk guy, the old-school like bluegrass folk stuff.

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Episode Transcript

Randy:Welcome to another episode of Content Pros. I'm Randy Frisch from Uberflip. As always, I've got Tyler Lessard with me from Vidyard. Today, we're going to dig into a really interesting hot topic around content on this podcast. It's this whole idea of how do we start to think about delivering content with intent, the right content that people are looking for versus us stuffing content down people's throats, this big idea that we've been talking about a long time, Tyler. I think we've got a great guest, so you can probably intro who's going to be able to set context here.
Tyler:Thanks very much, Randy. It's really great to have our guest here today, Michael Ballard, from Lenovo's B2B digital marketing and demand gen team. I've known Michael for a number of years now. One of the things I've been most impressed with is how Michael leverages the right technologies in his marketing stack, not just to build an interesting marketing stack that can drag attribution and do all sorts of fun and smart things, but to really help identify who's out there in the market, who's interested in what topics, and using those insights to fuel his content marketing and demand gen strategies. Without further ado, Michael, perhaps you could introduce yourself, and give the audience a little bit of background on your role at Lenovo and how you've gotten to this point.
Mike:Yeah, sure. I'm responsible for the B2B digital marketing arm particularly for North America. We've been, I'd say a, somewhat, advanced demand generation shop over a journey of many, many years. We're very fortunate to have a pretty advanced marketing stack, tech stack, award-winning, I guess I could say, with the marquees, if that counts. Over this entire time, we've done some of the traditional techniques. We follow the SiriusDecisions Demand Waterfall and modeled that out for all our KPIs, but I think you guys know a few months ago to change it up on us just when we got used to it, the ... Sirius changed the Waterfall around, and all the nomenclature with that, and rightfully so, and they made the right call, because it realigns how MarTech has changed the industry. You guys have probably seen Scott Brinker's infographic with all the logos five years ago, three years ago even, there's only a couple graphics on there, to today it's over, I think it's 3,000 plus different logos. The market place-
Randy:Makes our heads spin, for sure.
Mike:It does. Now, it's completely confusing. The MarTech spaces has dramatically changed, has allowed us to do a ton of new and creative things. This is really one of them. We went down this path because we've felt that we kind of hit the edge of traditional lead scoring. Some background for those that aren't familiar with that. Lead scoring is a combination of tracking your customers via cookies through your marketing automation system of choice, based on two elements. One is a demographic score, so who they are, you could throw in title, company size, whatever your company targets. Then, the other is digital behavior. It's usually in the form of email engagements, website engagements, maybe it's a display on social engagements. However, where the problem comes with that ... That's been a proven model for us marketers for, gosh, well over a decade, but where the problem exists is that that really only represents a microscopic fraction of a person's day. Let me explain. That cookie, that tracking, that lead scoring is limited to, in my case, Lenovo properties only, so only the emails that I/Lenovo sent, only my Lenovo website, only my social engagements on Lenovo. That's what I'm tracking. Yet, we're all on board with it, and that was the technology we had, but that's how we were basing our entire lead flow structure, was based upon someone just interacting with Lenovo. If we were honest with ourselves, that really only represents a microscopic fraction of a person's entire day, if they even interact with us at all, right? There's all these other things happening out in the world. The internet's huge, right? They're interacting with all these different properties, so how can we take the power of that information, and turn it into for our own good? The cool part was going back to the MarTech component, because there's a ton of companies out there that are doing just that, they're harnessing all those publicly available social data, publisher data, search data, so on and so on, and putting it together in a couple different varieties, so I'm even adding predictive modeling on top of that. Now, we have the ability to see way outside beyond just the scope of our digital properties. With that, we can now trigger timely and relevant campaigns and topics and content.
Tyler:Michael, this is super interesting because you talked about how what people are doing on your website is just a microscopic portion of what they're doing in their broader set of online activities, and in the broader set of content they may be interacting with, which I think is super important. But in addition to that, I also want to be mindful of the number of people who are actually coming to your website is actually only a microscopic portion of those who may have an interest in Lenovo products or maybe part of your target market. We're hearing a lot about that when it comes to predictive and account-based marketing, and how we need to think about how to identify those potential individuals and those potential accounts who may not be coming inbound and raising their hand. How do we identify those who are surging in an interest topic related to what we can do, but that aren't showing activity on our own web properties? With those individuals, I think it can be even tougher from a content and messaging perspective, because they're often very cold potential buyers. If we can go out and identify how to communicate with them, we have to be conscious that they may not be familiar with our brand yet, or if they are, they haven't actually shown active interest. I'm curious, as you build out these predictive models, and you think about how you're hitting people that are surging on different topics whether they know about your brand or not, does that influence the kind of content that you're creating and the kinds of messages that you're building?
Mike:Yeah, it is. It's a tough message. Obviously, Lenovo is a very large global organization. We can sometimes get caught up on ourselves thinking that, one, everyone knows who we are, and they know our entire product line. They know everything about us. They just love us, right? We think like we're Apple. Everyone loves Apple. All the customers love Apple. But if are honest with ourselves, people don't care about us, Lenovo, or even your company, until they need to, until they have a need, until they have a pain that your product can fulfill. How do we find that? It's tough. To your point, I mean, if you think about it, especially in today's age, we are bombarded with marketing messages in our consumer life, in our business life. Walk down Times Square, how many messages are you hit there? I think the last stat I saw was the average person gets 5,000 marketing messages per day, which is crazy. There was a funny thing I saw once, the way we access information is different. Back in the late '90s, we were always taught don't get into a car with ... or don't get into a stranger's car. Don't meet people from the internet, where today we're literally summoning strangers via the internet to get into their car. Everything has fundamentally changed, even to the point of ads. I mean, look at ... people are actually paying to not get advertisements. Think of Netflix. There's, I think, 60 million people that are actually paying not to receive ads. The market is very good at sniffing out a sales tech ... or a sales tactic. They're really good at that. We do that when we go to Car Lot to buy a car. It's just in tuned within us, and they can drown it out, unless ... this is where the content part comes in ... unless we can deliver something that is truly meaningful to them at that point in time. If we don't deliver that, if we keep hitting them over the head with the wrong message, just because it's the message we want to get out as a company, then that person will quickly tune us out, and we may have lost brand perception right away. That's where this third-party data comes in, allows us to really target to the specific individuals.
Randy:Mike, it's really interesting, in fact, it's funny. You were touching on Netflix there at one point. I know you were touching on it from a different perspective, but I was having a conversation with someone the other day who actually talked about how as amazing as Netflix is today in terms of delivering us content that we care about, it can only get better, because today it looks at what content I've watched on Netflix, but imagine when Netflix can start to access, as you put it, that other third-party data, right? Not just the other third-party data on me. This was where this conversation with this person got interesting. It was like, "How do you find the intersect of what me and my wife actually want to watch together," right? How do you start to mesh all the different audiences together from your target audience that you're selling to to figure out what's a combination to appease the different people throughout the buyer stage? It's interesting. I'm curious ... I don't want to get too into MarTech's stack itself, but I'm curious, and I'm sure people listening are curious how you're doing that. I'll tell you after how we've started to do that ourselves, but maybe you can tell us how it's being down at Lenova.
Mike:Lenovo. You almost sounded like a Southerner there for a second.
Randy:I know. I don't know where that came from.
Mike:What we have, I'll just talk about the stack and how we do it. We're a tool for automation with sales force on the backend. Also very fortunate to have a very large data warehouse that we maintain, where we have, I think it's 10 or 15 different data sources, both for marketing as well as just company in general, such as invoicing data, building data all on a single database that we can just crunch and crank out and slice and dice however we want. That allows us to get down to the data that we need to do, the targeting, but now we got to go and find those people and then serve up ads or emails or what have you. Very fortunate to have a very awesome data ... I hate calling them a data co-op provider because they're much more than that, but the company on Boston called MeritDirect, which has access to public contact databases and they really allow us ... This isn't list buying at all. We only engage with those contacts that engage with us, but allows us to augment our database and our targeting. We're an Adobe shop as well for display target. We use Adobe Audience Manager where we have multiple data sets coming in, both first-party, second ... Well, actually, I think we also have second data, second-party data as well as third-party data going in, so we can create all sorts of traits and segments, which again adds an additional layer to serve up the right content to the right people. Then, that intent data that's coming in. We recently went through a very detailed pilot where we went head-to-head .. or we brought in people to go head-to-head ... of different intent providers. I did that for a couple of reasons. One, that intent market's still relatively new. There is a kind of “the one strategy that's the best one yet declared”. Everyone kind of does it a slightly different way. I brought in someone that does ... has the third-party data, and then they add a predictive layer to it. I brought in someone that goes down to the device layer and tracks on the device layer. Whether you're at home, at the coffee shop, at the office, whatever, we're not reliant upon cookie level or IP data. Then, I brought another one that did all three. We literally split them up evenly, three different random sets of contacts, same exact campaign, same exact criteria, or timeframe, all of that, and we hit the go button to see. At the end of it, we had a clear winner. Bambora is our intent provider that we use for their data. What they're doing is they're tracking ... Boy, I think the available list of topics is like 3,000 or more topics. We obviously filter out ... because that's everything from super computing to Ferraris. We obviously filter out Ferraris and consumer type items that we're not targeting. We've aligned each topic with a campaign. So when our accounts ... and we're only tracking our accounts ... when they bubble up or surge on a certain topic, that triggers a full-blown email campaign, this programmatic display campaign, social ad campaign. Soon we'll be adding in some other layers, some additional social targeting as well as dynamic web content, as well. But the other part that we found ... This is really important for content development, is that data's great for triggering campaigns, but we're also using this data to make sure we're speaking the right message. I can now pull a list of all my accounts and see what they're talking about, what they care about at this moment in time. I can make sure that if I'm pushing pain point XYZ, but when I pull up the list, it's on the bottom of the topic list of my customers, why am I putting time, money and effort into creating content that no one really cares about at that point in time? With this, I can now see what is that top layer, was is that top level, and I can create content to them that lines up with that. We're also doing that with ... We're doing a historical lookback. We're taking and finding customers that are closed one deals, so whether they're brand-new or existing customers, and we're doing historical look-ups to see what were their topics that they were surging on prior to buying. Now, we're actually finding a lot of correlation between different topics. This is now adding another ... an additional layer of detail to really fine-tune the machine to again send that timely and relevant content, not just to the right person, but to do so with measurable success data analysis.
Tyler:Am I the only one that's getting giddy over here, guys?
Mike:I know.
Randy:Here's the crazy part. It's funny because they often say the most boring panels are the most boring podcasts, or when everyone agrees, but I think we all agree that this is amazing. The funny thing, and Tyler, you can be witness, it's funny that you talked about that beta test that you did, and then you ended up with Bambora. My company, this stuff I do on a full-time basis, we just signed a strategic alliance with Bambora to embed Bambora data into power our EI recommendation engine. We've launched this earlier this year with Bambora. We're going to be formally announcing it pretty much the week this podcast goes live.
Tyler:I actually think you just announced it here live on Content Pros.
Randy:Yeah. I'm like thinking as we're doing this, we're a week ahead. We're announcing it at our conference in a couple of weeks. It's pretty exciting in terms of the way you can start to shape behavior and ... or more so adapt to behavior, I think, is the better way to put it. I feel like I'm doing an infomercial here, so we'll actually take this point to take a quick pause here from our real sponsors, and then we'll be back to chat more with Mike at Lenovo, not Lenova.
Tyler:We're back here on Content Pros with Michael Ballard from Lenovo. Now, Michael, we spent some time talking about how you're using these interesting predictive insights to fuel the kinds of content you're delivering and hitting the right people with the right message at the right time, but I want to peel it back at a very practical layer. Can you help us explain what are these services using to better understand these buyers in the market? Are they tracking what they're doing on social? Are they tracking what web searches they're doing? How exactly are they giving you insight into what different companies might be interested in at a certain time?
Mike:We hire directly with the CIA, and they're tracking everything and everyone to know. It kind of seems like that, though, sometimes when I talk to people about it. Like, "You're Big Brother." I'm like, "Yeah, I'm Big Brother." But it's all good. It's for everyone's sake, right? I'm not sending you stuff about ... something boring like accounting. Last year, Forrester did a big B2B buyer report. One of the questions I asked is what's the top ways, or types of content, or ... yeah, I guess it's types of content ... vehicles that you consume and that you make decisions based upon? They identified 50 different vehicles, everything from talking with peers, to going to conferences, to visiting the vendor's website, and so on, so on. The cool part is eight out of the 15 are digital properties. Majority of them, we have as marketers, access to that data, whether it's an advertising platform, or just available out on the public internet for searchbots. I can't really speak to how specifically, in this case, Bambora goes out and does their Big Brother stuff, because that's ... even if I knew, they'd probably shoot me if I said anything about it, but we could probably talk to them more specifically. There are algorithms that are in place, because what they do is they maintain a baseline on that account of a topic. So if there are 3,000 plus topics, they're running 3,000 plus baselines on every company well globally, quite frankly. Let's say the topic is Canada. They're creating a baseline on company ABC for Canada, that topic, and how people are searching on it, how ... if they're going to blogs or forums, how they're interacting with that. Then, if that baseline starts to surge above the norm, that's when it's considered a surge topic. It goes there's enough activity above the standard baseline at that account that something is going on there. That's how we determine whether or not to send something, or whether or not to trigger upon. There's a neat thing we did that we took a random control group of topics, we just randomly picked out of the 3,000. When we took the hundred some odd topics that we were monitoring, and we lined it up with close one deals. We found that we are 48% more accurate with our close one deals by targeting on the keywords that we were, rather than just randomness. The randomness, quite frankly is kind of what is the standard today before intent. We had the potential to do some dramatic increases in our revenue and our engagement, which is really what that tells us.
Tyler:Now, that you've proven that this works, and that you're getting a higher hit rate and a better conversion rate by targeting individuals who are surging on different topics, how does that influence your content strategy? How does that change the kinds of content you're thinking about creating to make sure that you're nailing people with a strong message, but also really standing out from the crowd and building the engagement you're looking for?
Mike:I mean, mentioned before, people could sniff out a sales pitch a mile away. Our most successful, if I can call it that, successful content at least at the top of the funnel when someone may be in a research mode, or they just may not be ... they may not have a particular need at that point in time, but they just like to be ... this like things that help them out. If I have a pain point around something, help educate me. Don't sell me something. Don't push something in my face. Now, we find that in a content such as definitive guides to topic ABC, whatever that pain point is, the old clickbait of the top 10 steps to accomplish ABC, but the video content has been really good for us at that upper funnel stage, just because everyone loves video. I mean, look at the popularity of YouTube. Everyone loves a play button, but we've added layers of personalized video, so being able to dynamically create a video, a single video around an individual, and literally do that to hundreds and thousands of people. We found success in that because, one, it's just downright entertaining, even if you do it slightly cheesy and just ... I think the one piece we did, we literally just put the person's name on a banner while everyone was celebrating and dancing, and people were totally into it. I mean, I looked in the stats in our Vidyard platform, and there was some dude who went back and watched that same part 15 times just to see his name on a banner, and so people love that. Now, we can really kick it up to the next step. If I take this third-party data, I can now dynamically create video on the fly, and use that with the personalized systems. That's one thing we do. The other part two that is really ... it's difficult to do right now, but it's extremely successful, is making sure the content lines up to where they're at in the sales stage. If I'm pushing a datasheet when someone's still up at the upper funnel, no one engages with that, but there's still a place for a datasheet, but it's lower in the funnel. When we accurately target those people over in the funnel with data sheets and the like, that's when they become a lot more successful.
Randy:This is really interesting stuff. I feel like this is one of those podcasts people are probably going to have to listen to a couple times just to digest this, take notes, and we'll be sure to have notes for the show for everyone at, but Mike, before we wrap up, we always like to just get to know our guest a little outside. I figured given we've been talking all about personalization, I figured I can come with three rapid-fire questions, because we only have time for couple word answers here, that all have to do with recommendation, okay? I'm going to hit you with them. You're not prepared, but you've got to just fly with it as we go. Number one, what has Netflix been recommending for you that you've actually bought into lately?
Mike:I'm really horrible. My wife and I, we don't watch a ton of TV. The only thing we watch, if we do at all, is HGTV, and it's the House Hunters International, just because we live vicariously through these people who basically pick up their homes and go to some far-off country
Randy:That's amazing.
Mike:I think we've left the country once our entire lives.
Randy:All right. Let's go off TV then. Let's try something maybe a little bit more mainstream these days, Amazon. What is the item that Amazon is most likely to recommend to add into your basket that you'll buy into?
Mike:I'm big into fishing, and so I get a lot of fishing suggestions, different lures and such.
Randy:Nice. Awesome. Awesome. All right, last one for you, last recommendation engine that I could think of on the fly here, Spotify, or Apple Music, or whatever you use, what type of music is most likely to come up recommended for you?
Mike:Oh man, I've been all over the board lately. I go through phases. I go through like classical phase. I go through like country phase, and everything in between, but usually I'm that folk guy, the old-school like bluegrass folk stuff.
Mike:I'm in North Carolina, man. It's the country.
Randy:It's all good. It's all good. Listen, we don't judge, Mike. It's funny. I realized for a while that my Spotify choices were like posting to Facebook, and all of sudden, friends of mine are like, "You're listening to a lot of Moana these days," which is a Disney film, if you're not familiar. I'm like, "Yeah, that wouldn't be me, but that's pretty representative of my house right now, so that makes sense. That makes sense." This has been awesome, though. Mike, we really thank you for taking the time to share with us. It's really great to understand here on Content Pros how Content Pros's are actually taking these challenging problems today and handling them as a day-to-day practitioner, so I think a lot for people to learn from. If you've enjoyed this podcast tuning in, we encourage you to check out where you can find all the other episodes that we've recorded here in the past. You can also find us on iTunes, on Stitcher, on Google Play, wherever you find your podcast. Tune in. Give us some feedback. Let us know what you want to hear so we can continue to personalize great episodes for you. On behalf of Tyler at Vidyard, I'm Randy at Uberflip. Thank you, Mike, for joining us from Lenovo.
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