In Chapter 2, we took a step back from the nitty-gritty of our bold new plan to talk about who is best suited to champion it. And as you read, I'm quite passionate about the fact that CMOs are the perfect people to lead the charge. It's clear to me that it's time for us—the chief market officers—to transform how we treat our prospects (aka future customers). It's time for us to pivot to playing offense and establish ourselves as the experts who will pave the road to a new, more effective approach to customer engagement. (Imagine my impassioned fist-shake here!)

To be clear, I know that the changes I'm describing in this book aren't small or easy. But I also know that they're 100 percent necessary if we're serious about treating customers differently and reaping the benefits that come from putting customers first.

In theory, customer-first sales and marketing isn't a difficult idea to get behind. After all, who's going to defend practices that piss off our customers and prospects, right? For me, as I've explained, the customer-first approach starts with no forms, no spam, and no cold calls—because we know that those practices don't just drive people crazy, but they also drive them deeper into the Dark Funnel™. And that makes it even more challenging to know when they're in-market and ready to engage.

So we know the risks of providing a bad customer experience. And I explained in Chapter 1 what a crappy customer experience looks and feels like. But when we flip the script to define how to provide an awesome customer experience, we get a wide range of ideas and no real consensus on how to do it. We might realize we need to use more personalization, customize content by persona, and focus on delivering value with every interaction. Or we might look to improving our websites, implementing a chatbot, and reaching customers across channels other than email.

All of those tactics can be effective. But they're all piecemeal, which can result in a lot of work (and sometimes a lot of guesswork) and not much reward. What's missing is an overarching strategy for improving customer experience based on insights and knowledge. And that's often missing because sales and marketing teams don't know where to start when it comes to understanding the key capabilities needed to truly deliver on customer-first experience.

The truth is that we can light up the Dark Funnel™. We can identify the exact moment accounts move in-market. And we can determine exactly what our prospects are interested in—without making them fill out annoying forms. All of that results in the engaging and positive customer experience we're trying to achieve. But the trick is you need the right technology to make it all happen—and that's where a lot of teams fall short.

In this chapter, we'll roll up our sleeves and talk about exactly what capabilities you need in your tech stack to gain the kinds of insight, collaboration, and data in order to provide a truly customer-centered buying experience.

The business case for customer-first sales and marketing

Before we get all techy, though, I want to ground us in the why behind all these efforts. Yes, being nice to your prospects is an admirable cause. But we're not just doing this to be nice. We're also doing it because it's good for business—and for the bottom line.

Remember how much I love Club trips? And fancy shoes? Well you've got your personal financial goals too, and regardless of what they are, they rely on the success of your organization.

So all these efforts are not for our health. They're not just for the feel-good factor (though it does feel good to provide a kickass customer experience). They're designed to achieve the goal everyone is ultimately focused on: growing revenue and doing it predictably. In other words, we're continually reimagining and improving our customer experience because our goal is predictable revenue growth.

But the sad fact is, while we're all after predictable revenue growth, only 18 percent of organizations met more than 100% of their revenue goals in 2019.¹ So clearly there's work to be done, and improved customer experience is key for improving those numbers.

Here's why: A buyer's experience with your brand spans from their first touchpoint to customer success and renewals. A thoughtful, comprehensive experience yields solid, quantifiable rewards across the journey—from accelerated decision-making in the early stages to increased customer loyalty after the ink has dried.

Accounts > Leads

A primary hurdle to overcome in providing a customer-first experience is recognizing a key way in which buying has changed. We know that there are now 10 or so people involved in every big B2B buying decision. So a key step that many companies take when seeking to identify and target the right customers—and to connect with them in the right way at the right time—is to make the switch from lead-based marketing to account-based marketing (ABM).

ABM emerged as the hot new trend in marketing, with the promise that it would rescue us from the unpredictability and frustrations of the lead-based world. ABM of course necessitates a shift to an account-centric approach, where knowledge and insight about an account are used to create targeted, highly personalized outreach. And the good news is that this shift puts us one step closer to a customer-first approach, because now we're talking about accounts, people, and personalization rather than leads, volume, and conversions. So all good, right?

But even after making the switch to ABM, many companies struggle to reap the rewards. We know that lead-based marketing doesn't surface the best leads, yet nearly 60 percent of account-driven organizations are still most focused on generating leads (MQLs or SQLs) despite the role an ABM strategy plays in their marketing mix, according to “The State of Predictable Revenue Growth,” a report we recently conducted with Heinz Research.² Why? Because even with an account-based focus, 50 percent of respondents believe their lead scoring processes don't even surface the best leads accurately or consistently.

And for good reason. Simply shifting focus from lead-based to account-based isn't enough to ensure sales and marketing is working the right leads—those from accounts that are in-market and ready to engage. This is where intent data and predictive modeling earn their pay (two key capabilities we will talk about later). How do we know? We commissioned Forrester Research to conduct an independent Total Economic Impact™ (TEI) study to evaluate the potential financial impact of marketing with these types of insights.³

Forrester analyzed multiple 6sense customers both before and after using 6sense to generate and work MQLs. They found that leads from accounts identified as being in-market had a 75 percent higher MQL-to-opportunity conversion rate, a 40 percent higher close rate, and 50 percent higher contract values than MQLs identified without in-market insights. The proof of focusing on in-market accounts is undeniable, but breaking old habits is hard.

So sales and marketing leaders often fall back on their old, comfortable ways—even though those are proven not to work—and they end up with the same old results. Just take a look at content gating as an example. Ever since marketing crowned content as king, we've been trying to lure prospects out of the Dark Funnel™ by gating the content we think they need in order to make an informed decision. The thinking has been that a prospect will step out of the shadows and give up their anonymity in exchange for some exclusive, or even relevant, content. But as we know—and as I explained in detail in Chapter 1— B2B buyers aren't willing to sacrifice their anonymity to access information that is ubiquitously available online.

Why is it that even with this knowledge, gated content still persists? Sure, it makes sense that it's still front and center in lead-based marketing, but how do we explain the fact that even 53 percent of account-driven companies continue to gate their content?⁴ Iy'd argue that it's because they don't have the tools needed to make an account-based approach work—so without content gating, they can't identify or learn about their accounts.

Where ABM falls short

Now I don't want to give the impression that ABM is a recipe for disappointment. It's not. In fact, it's a step in the right direction if you want to escape the lead-based marketing grind.

The problem is that while plenty of marketers see positive results from ABM, the results have been inconsistent. And that's in part because it's really difficult to scale. Ask 10 people what you need to do ABM at scale, and you'll likely get 20 different answers.

So even with ABM, digital marketing teams continue to struggle to target the best accounts and orchestrate consistent, engaging experiences across channels that meet their ideal buyers where they are on the customer journey. At the same time, sales teams lack the account data and territory alignment they need to engage the best accounts.

That disconnect shows up in various ways in organizations that are trying to implement ABM strategy:⁵

• Nine in 10 account-driven organizations say that orchestrating their ABM program across multiple channels and tools is challenging.
• Half of all marketing and sales leaders are not confident that their data enables them to either make strategic account-driven decisions or to meaningfully engage prospects at all.
• Most sales and marketing leaders lack a singular view of their accounts, including their in-market readiness as well as what they care about, and 50 percent of sales and marketing leaders lack confidence in the structure and alignment of their sales territories.
• Sixty-four percent of organizations implement ABM as at least half of their entire marketing mix, yet more than one in three account-driven organizations don't even know which messages will best engage target accounts, what channels to reach them through, or which accounts to prioritize in the first place.

In short, even sellers and marketers who are fully on board with the philosophy of ABM feel overwhelmed when it comes to actually implementing it. They have to wade through a vendor marketplace filled with hype and confusion. Nearly every sales and marketing tech company out there hails ABM—making massive, too-good-to-be-true promises about how it will revolutionize your sales and marketing practices (and results!). But that's where most of them stop. They don't give a clear roadmap for how to make it work for your business, what specific results you should expect, how you'll know if it's working, and what other solutions you might need to stitch together to make it all happen.

That's what we're going to do in this chapter.

My own turbulent ABM journey

I hope I've made it clear that I'm not some marketing guru who does no wrong. I've had more than my fair share of ups and downs and eff-ups in my seven years as a marketing leader. And if a year of marriage counseling taught me anything, it's that sharing experiences is way more valuable than spouting off advice. So with that in mind, I'll go ahead and tell you that my first attempt at ABM was a colossal failure. I was exactly like the marketers I described in the last paragraph—totally on board with the ABM philosophy and ready to jump in with both feet. I had read the hype, and, shame on me, I bought it all. I was convinced that if we adopted an ABM strategy, it would soon be raining pipeline, deals would be huge, win rates would be off the charts … and we'd all be packing our bags for the Club trip. (Can you tell I really love Club?)

Instead, here's what happened. We followed the advice of aligning with sales for account selection. We looked at accounts on which we'd done well previously and collaborated with the sales team to come up with a list of 25 accounts we were sure we could win. But what we ended up with wasn't our most winnable target accounts. What we got instead was a hodgepodge of everyone's individual priorities instead of a cohesive and strategic selection of accounts.

I now fondly call this the “potluck dinner” approach to account selection.

Because here is what really happens. Marketing says they are doing ABM, and everyone wants in on the action. So everyone brings something. The top rep gets to bring the biggest dish—let's call it the sushi platter—since he has the best success. That random geo that complains a lot gets to bring an account, because you want to throw them a bone. So there's your cheese ball (which, of course, no one is going to eat). Each RVP gets some accounts—oh look! That's 10 desserts. What about your lookalike analyst? Sure, she yields some accounts, so let's invite her too. Oh and here she comes with her grocery store veggie platter. Yum.

You look at your banquet table and wonder … What kind of a meal is that?

But you're stuck with the potluck you've got, not the potluck you want. So we took our 25 mismatched accounts and again followed the standard ABM advice: Personalize! So we set to work creating content, web experiences, direct mail—you name it, we did it. And since the accounts were all over the place, we went after each of them with a fully customized, personalized approach. What we designed for each account was gorgeous! Real award-winning work. But we were spinning our wheels, for two big reasons: First, the potluck approach had not yielded the right accounts. Second, this boutique approach to customer experience was way too labor intensive. So we called it off.

Even if most ABM pilots fare better than mine did, sooner or later, a lot of organizations come to my same conclusion. It's why people have created double funnels (as if one funnel is not enough). It feels way too hard or unattainable to do account-based everything.

Back to basics

Without clear definitions of what ABM entails, the technical capabilities it requires, and how to successfully scale an ABM program, many companies will stay on the ABM struggle bus. In order for ABM to be successful—and in order to achieve predictable revenue growth—it needs to uncover a complete picture of customer demand, provide rich account insights and meaningful metrics to sales and marketing, and have comprehensive orchestration capabilities that meaningfully engage buyers. It also requires sales and marketing to align on the full funnel rather than operating in silos.

In short, successfully implementing ABM comes back to that five-step process I introduced in Chapter 1—and each of those five steps can only work if you have the right technology. These steps are fundamental to any account-based strategy, so let's go over them again, but this time with an eye toward the tech needed to make them happen.

STEP 1 

We know we need to start with the best accounts. But half of all sales and marketing leaders only somewhat agree (or don't agree at all) on their target account list, and one in three account-driven organizations lack a defined strategy to select their target accounts.⁶

Maybe that's how so many of us end up with potluck-style account lists. What we need to do is select accounts based on data—not opinion and personal preference.

And we need to be looking at the right things. As we discussed in Chapter 2, that means we need to pick the accounts we can successfully sell to—the ones that are in-market and most likely to buy. Whether you call it your total addressable in-market (TAIM), target account list (TAL), or ideal in-market customer profile (IICP), you need to know your best targets.

The data we use needs to be reliable, but according to a B2B marketing survey from Forrester Research, only 12 percent of B2B marketers have confidence in their data.⁷ So we need better data than what most organizations are currently working with. And specifically, we need to select accounts based on real buyer intent and activity data that's in the Dark Funnel™. We also need to model out the buyer journey that leads to a closed/won opportunity based on previous behavior, which takes data crunching power we don't have without the right technology.

STEP 2 

Once we've selected the accounts, we need deep insights into them—beyond their industry, number of employees, annual revenue, etc. It means understanding their tech stack, who is on the buying committee, what they are researching, and (most importantly) where they are in their buying journey. But collecting that kind of account data can be difficult, time consuming, and expensive without the right tech.

STEP 3 

ABM relies on understanding your buyer personas and ICP in order to create compelling, personalized content delivered over the right channel, at the right time. From display to direct mail to BDR cadences to website experience, everything needs to be personalized by account, persona, and, most importantly, timing. But only 48 percent of respondents to the State of Predictable Revenue Growth survey believe they can deliver personalized content experiences—maybe because it requires so much technology to make it happen.

The kind of engagement we're talking about requires predictive analytics to understand where accounts are in the buying journey and what they care about. Which means we need to be able to glean insights from millions of signals to understand buying patterns and predict when an account is ready to engage.

Without the right tools in your tech stack, you're going to fall back on manual coordination, detailed project plans, and dedicated teams … and soon you'll discover that you're simply not able to implement top-level account-based engagement at scale.

STEP 4 

And no, this does not mean simply inviting them to the potluck. Collaborating with sales means sharing the same set of data, being aligned on goals and priorities, and knowing when they should engage with accounts for maximum success. If you're using spreadsheets, weekly calls, handoff processes, or outdated information, you're not going to have real collaboration. The same is true if you have multiple systems to track, report, and manage your sales and marketing efforts. I'm telling you, when sales and marketing achieve real collaboration, magic starts to happen. But to get there, you need the right technology so everyone is on the same (unbiased) page.

STEP 5 

Marketing teams typically have the MQL. (I've told you my opinion on those, but for now they persist, so we need to talk about them.) We typically separate MQLs from unqualified leads using some kind of lead-scoring system, often based on points assigned to actions a person might take, such as reading an email, downloading an ebook, or filling out an online form. Once the prospect has tallied up enough points, they are considered an MQL and thrown over the wall to sales. Not only is that a subjective measure of a person's intent, but it only accounts for one of potentially 10+ individuals participating in a complex B2B sale. Not to mention it provides zero insight at the account level with regard to intent, engagement, or where they are in their buying journey.

Instead, we need to be tracking and reporting on things that actually matter and affect deal velocity and pipeline acceleration, such as new accounts engaged, new personas engaged, opportunity rate, and account win rates. Knowing which accounts and personas are engaging can open a whole new prospecting pool that you didn't know existed.

An orchestrated path forward

These five steps toward account engagement can truly transform the way you do business—as I saw personally once I implemented an account-based strategy the right way. When you have the right tools, it's absolutely possible (and scalable) to deliver amazing prospect experiences. Predictable revenue growth is achievable when you nail account engagement. It just takes something more than ABM. It takes orchestrated account engagement.

Now, let's take a look at the technology that's required to make that orchestrated, scalable, and repeatable account-based approach possible so you can say goodbye to forms, spam, and cold calls for good.

Time to get Techy

The no forms, no spam, no cold calls philosophy is transformational, and as you will find in Chapter 4, it reliably delivers revenue results, but there are some very specific capabilities needed to make it happen. This is where the rubber meets the road—and it all starts with core capabilities. There are 13 essential things your tech stack must be able to do, and I'll outline them in the following sections.

Eleven capabilities may sound like a lot to add to your plate—and it would be if you had to reinvent the wheel for each one. But the fact is every capability I recommend here is available, either a la carte or with a comprehensive platform. So take your time and evaluate different solutions for your tech stack, making sure you have all your essential capabilities covered. Getting it right with step one makes step two (and beyond) that much easier as you grow and scale your program.

1. Customer Data Platforms

Customer data platforms, or CDPs, are the modern marketer's solution to managing big data. With buyer journeys, channels, and campaigns getting increasingly complex, we're generating (and relying on) more data than ever before to do our jobs. And it's not easy to mash together data from 10 or more different platforms in order to better understand our customers, what they care about, and where they are on their buying journey. (Remember, we're putting the customer first now, so it's important that we know more about them—without making them fill out forms or open emails!)

The whole idea behind CDPs is to break down data silos, de-duplicate and normalize records, and, ideally, cleanse data along the way so we end up with a single source of rich, accurate account data that we can take action on. And CDPs are definitely built to do that. The problem is that standalone CDPs take a boatload of work to implement and integrate (think multi-quarter implementation timelines), so they generally don't make sense for all but the largest and most complex businesses.

But that doesn't mean smaller businesses don't have similar needs to aggregate and manage their own big data. Consider all the types of data businesses of any size need to be able to capture, integrate, and normalize in order to put the customer first:

• Firmographic and technographic account data from internal systems and third-party sources
• Buyer intent data from first-, second-, and third-party websites
• Known and anonymous website traffic data
• Email, call, calendar, and meeting data from customer relationship management (CRM) and marketing automation platform (MAP)
• Opportunity data from CRM
• Digital ad campaign metrics like accounts reached and results
• Psychographic data
• Market intelligence data

Depending on the nature of your business, the list could go on. The point is that all businesses generate and rely on lots of big data today, and it's essential to have a single source of truth in order to gain deep insights about accounts—whether that's via a standalone CDP or one that's embedded into a more comprehensive solution.

By combining, cleaning, and organizing this critical but disparate data, we can then extract meaningful trends and insights to guide decisions. I think of it as an Alexa for my business. For example, if we're considering hiring a new AE in Canada, it takes me just a few clicks to understand our TAM, identify trends in that market, and even zero in on the specific in-market accounts we might assign to the rep. This type of analysis would have taken a colossal amount of work in the past.

Additionally, with a single source of truth for all of my critical account data, I can slice and dice the universe of accounts into audiences with similar attributes—whether that's by industry, location, buying stage, intent, engagement, or a combination of factors—to conduct segment-specific analysis or run targeted campaigns. In the world of account-based selling and marketing, everything starts with the data.

CDPs aren't the sexiest topic, but they're truly the foundation for the other 10 core capabilities we cover in the following pages. And once we layer in intent data, company identification, AI-driven predictions, and dynamic segmentation, the Dark Funnel™ is truly lit, and the critical step of account selection becomes a breeze.

2. Intent Data

We've talked about the fact that today's customers remain anonymous until late in the buying journey. Instead of raising their hands and identifying themselves, they instead hang out in that dreaded Dark Funnel™ and conduct their own research until it's too late for us to have much influence. This is one of the biggest challenges for sellers and marketers today. Nobody (myself included) wants to fill out a form only to end up on yet another email list—or worse still, be on the receiving end of cold calls. It's just easier to remain anonymous until you're sure you want to talk to a vendor. We all do it.

Intent data offers a path forward for sellers and marketers trying to hit their numbers in this new, anonymous world because intent solutions are designed to capture buying signals from both known and anonymous buyers. In other words, it doesn't matter whether an account or contact showing intent already exists in your CRM or other systems. The whole idea is that we're lighting up our Dark Funnel™ and uncovering the entire universe of buyers we might want to sell to.

Intent signals can come from multiple sources, and there are several different types of intent. The largest source of first-party intent signals is typically from buyers browsing your company's website. And if you have multiple websites (for instance, a primary “front porch” website as well as a separate help center site), intent can be captured from all of them. Other first-party intent signals include data from CRM and MAP, like buyers interacting with marketing campaigns, opening emails from sales reps, and attending meetings.

Second-party intent data comes from sites you don't own, but whose content and conversations are about your company. Think review sites like TrustRadius, Capterra, and G2. Knowing that someone is researching your company, your category, or even your competitors can be an interesting signal. It may or may not indicate intent to buy, but it's a piece of data that can add to your overall understanding of a potential customer's buying journey.

Third-party intent data encompasses the research being done elsewhere on the web—not on your site or a review site. It includes specific keyword and topical research that you know to be significant signals from the prospects most likely to purchase your product. Third-party intent data are important at all stages of the customer journey, but particularly early on since they point you toward potential customers who may not have even visited your website yet. That's the stage when they're educating themselves on the problem they have and the solutions that exist—and that's when you want them to start thinking about your brand.

Intent is generally captured only on relevant websites based on a set of customer-defined keywords, categories, or topics.

It's also helpful to have what we call pre-intent data, which signals that a buyer may be entering the market at a predictable future point. Based on historical data, AI can make predictions about what companies will buy and when. These predictions are based on three types of data:

• Technographic data. We live in a connected world, especially when it comes to technology. By understanding a company's tech ecosystem, including their current tech stack, what integrates with what's already in that stack, and when they're up for contract renewals, we can predict not only what they'll be in market for, but also when.
• Psychographic data. Buyers have conversations across the web that can give us insights into their pain points and their plans for fixing them. By combing through vast amounts of content across the web—including annual reports, web pages, social media, and more—AI can sort through all the chatter to pick up on important psychographic data that can inform our marketing and selling.
• Market updates. Certain market changes can be significant revenue moments because we know that even if buyers are not in market yet, they might be as a result of a big change. These moments can include new product launches, relevant hires, funding updates, acquisitions, events, and more.

Regardless of the exact method used, it's critical that you are able to capture first-, second-, and third-party intent signals, as well as pre-intent signals—and that you get them from as many sources as possible. You never want to miss a signal, and the more data points you have, the more you can create and engage audiences with relevant experiences, as we'll discuss in the next sections.

These signals give you visibility into the accounts you should be selling to—those that are showing buying intent today or are going to be entering the market for what you offer at a predictable future time.

But just as importantly, these signals help you understand what, specifically, your customers care about. Whether you're analyzing a segment of accounts or a single account, knowing which keywords or topics they're researching gives you a significant advantage when it comes to crafting campaigns, creating content, and developing outreach strategies. And when you know exactly what an account or group of accounts care about, you can easily develop resources that help your team market and sell, like value-based messaging, topical sales cadences, and highly relevant content and ad copy.

It takes the guesswork—and personal opinions and biases—out of the equation. For instance, I think CDPs are about the least exciting thing in the world. They're the last thing I want to write about or create content about. But our intent data shows it's a top keyword for us, so I put personal preference aside and dove in. I know our customers and potential customers are interested in it, so we produced a ton of content to meet that need. This serves as a great reminder to me that we really have to check ourselves—and intent data makes it possible to do that.

Intent data are also a critical input to most of the other capabilities we'll talk about later in this chapter, like AI-driven predictions, personalization, and sales insights. If we don't know what buyers are doing and what they care about, we can't predict where they are on the buying journey, personalize their experiences, or give sales reps insights into their journey with our brand. Like CDPs, intent data are foundational.

The catch with intent data is that you need to be able to easily store and glean insights from it, which stems back to our last section on CDPs. And this is not a small amount of data we're talking about; intent data can constitute billions of rows of prospect activity data each month from across the B2B web, including search engines, industry trade publications, blogs, forums, and communities.

The other trick with intent data is that you need to be able to accurately match those buying signals to accounts and personas because it does you no good to know that 100 unknown companies are anonymously researching the keyword “widgets”; you need to know which companies are conducting that research. And this leads us to the next core capability.

3. Account Identification

With today's buyers remaining anonymous through 70 percent or more of the purchase journey, it's impossible to reorient ourselves toward customer-first experiences if we don't have visibility into who our B2B buyers are and what they care about. Capturing first- and third-party intent signals is the first step in that process, but before we can go any further we need to know who they are.

Matching anonymous intent signals to accounts is fundamental for an orchestrated account engagement strategy because the quality and success of your campaigns is directly tied to the quality and completeness of your account data.

I want to pause here for a minute to point out that this is an area where we need to be cognizant of the current regulatory environment. Between GDPR, CCPA, and other pending legislation around the world, sellers and marketers have to be more careful than ever with personally identifiable information (PII). You as the “controller” of your PII (personal data from your web pages or CRM) need to ensure you've followed the appropriate steps to share the data with your solutions providers, including obtaining “consent” where required. And similarly, you should expect that your account identification solution provider have in place the appropriate safeguards to process your PII and source third-party intent data in compliance with applicable privacy laws.

With the appropriate safeguards in place, intent providers will match an intent signal to an account. For example, we may know from intent signals that Acme Industries is conducting research on widgets, what keywords were viewed, when the research was conducted, and other relevant account-based insights. The intent signal doesn't identify anonymous visitors at the “person” level, but only surfaces account-level activity.

So now that we know what we can and can't do when it comes to identification, let's talk about how it works. The most common (and obvious) way to match intent to an account is via the IP address, but mobile advertising IDs and cookies can also be used for matching.

However it's accomplished, account matching is critical because it's part of how we light the Dark Funnel™; otherwise, we're just left with a bunch of anonymous intent and still need forms for buyers to (fingers-crossed) identify themselves. But we know that in order to put customers first and help them buy from us, we have to be able to ungate our content with the confidence that we aren't losing out on critical information needed to engage accounts. We want those early funnel buyers to learn from us without any friction.

It almost goes without saying, but the accuracy of match rates is also critical. If an account is misidentified—say, Acme Industries is conducting the research but your tech stack thinks it's Beta Company—you'll end up wasting precious time and budget pursuing the wrong account. Plus, there's nothing worse than personalizing a message or experience based on incorrect information. Acme Industries isn't likely to convert or engage if they visit your website only to see the chatbot, content hub, or other elements personalized for their fierce competitor, Beta Co.

As you investigate adding account identification to your tech stack, be mindful of both the comprehensiveness and accuracy of account matching capabilities. Check out the tips in the sidebar on page 74 for more information.

4. AI-Driven Predictions

In the past few sections we've spent a lot of time talking about capturing and storing big data. The whole reason we need this data is to better know and understand our customers. When we have deep insights into our current and future customers, we can deliver incredible experiences that put them first, and that in turn help us improve revenue success.

But a CDP filled with big data doesn't give us deep customer insights on its own. For that we need AI and machine learning to analyze historical and real-time behavioral data, understand which signals are relevant, and predict future outcomes.

However, a CDP is critical to AI and machine learning because predictive models rely on really large amounts of data—the more data, the more accurate the predictions. With account and person-level data coming from multiple systems, it's vital to continually merge, master, cleanse, and de-duplicate those records so that AI can accurately score the complete dataset, not just the most recent interaction.

So what does this look like in practice? The easiest way to think about AI and predictive capabilities is to break it down into four essential predictive models, each of which provides a different level of insight into customers. You'll remember some of these ideas from Chapter 2, but now I want to outline the tech capabilities you'll need to bring them to life.

5. Data Enrichment and Acquisition

Between all the different types of data we've already covered, it's hard to believe that there could possibly be a need for even more—but for segmentation you will want to further enrich your data with third-party data sources. But before we get to the why, let's cover the what. In short, third-party data is generally a catchall term for several types of data relevant to every business:

• Firmographic data, like an account's industry, location, revenue, and size
• Technographic data, like platforms and other technology an account has invested in
• Contact data, like title, phone number, email, and address

While you may have SOME of this in your CRM and MAP, if you are like most sales and marketing teams, it's full of holes and ages quickly. This is why you want to refresh your data constantly with third-party data. Now, I fully understand that it sounds a little crazy to hear that you need to acquire even more data that will eventually be stale and useless. But stay with me as we move back to the why question.

Remember that the stack we're building includes intent data to pick up anonymous buying signals, account identification capabilities to match those signals to accounts, and AI-driven predictive capabilities to help us identify the right accounts and contacts to pursue. What happens if we uncover a major account that matches our ICP perfectly and is in market to buy, but that doesn't exist in any of our systems of record? Or what if it does exist in CRM, but we have only one contact created, and it's based on years-old engagement? Or what if we have tons of contacts on the account in CRM, but we have no idea whether that data are still accurate?

No matter which of these cases we're dealing with, we need to enrich our database with accurate information before we can unleash our sales team with outreach campaigns geared toward key personas. That's where third-party data providers come in.

It's important to understand if your platform comes with data, how much, and how many types—and whether it can dynamically enrich and acquire records in your systems (like CRM) at exactly the time your team needs it. Best-in-class account-based tech stacks use voting algorithms to determine which data to use when enriching records, and that removes the guesswork (and manual effort) from the process.

To be clear, I'm not suggesting anyone should begin purchasing lead lists or enriching every account currently in CRM. What I am advocating is targeting your data acquisition budget toward enriching and refreshing data on the right accounts at the right time. And with the right tools in your tech stack, this type of strategic, targeted data acquisition can happen automatically as soon as accounts hit certain thresholds of predictive scores, fit matches, or other criteria.

Now that we have all this data, predictions, and a CDP to aggregate and cleanse records, let's figure out how to make use of it.

6. Data Segmentation

With a CDP chock full of rich account, contact, intent, and predictive data, we need a way for sales reps and marketers to easily slice and dice it into meaningful audiences for analysis and activation. This is where segmentation comes into play.

Unlike some of the other capabilities covered in this chapter, data segmentation will likely be a feature within a platform or system that covers one or more other capabilities. In other words, you can usually get data segmentation capabilities from vendors that specialize in CDPs, intent data, and orchestration. However, I'm calling it out as a separate capability because it's vitally important for your sales and marketing team to be able to quickly build target audiences—and also because there are several key requirements to keep in mind as you consider segmenting the data within your CDP.

The first requirement for data segmentation is that it should be entirely self-service. Sales and marketing users must be able to create segments on their own rather than submitting requests to a data science or services team. If the process is slow and cumbersome, your team can't be agile in meeting customers where they are on their journey with timely campaigns and outreach.

Second, the segmentation process must be simple. Users should be able to create segments with just a few clicks—and without going through hours of training. Nothing kills adoption like a cumbersome, non-intuitive process, and you'll also likely see configuration errors that result in poor customer experiences if it's overly complicated.

Next, the segmentation process should be fast. Remember my Alexa metaphor from the CDP section above—and my example of quickly understanding in-market accounts in Canada before hiring a new AE? Data segmentation is how we get quick answers to these types of business questions, and it shouldn't take more than a few moments for a new segment to be available for analysis and use. If users have to come back later after configuring a new segment, you can bet adoption and usage will suffer.

Segmentation needs to be endlessly flexible too. Users should be able to build segments using any combination of filters on technographic, firmographic, intent, behavioral, and predictive account data. For example, it should be easy to create a segment for all manufacturing accounts in the central region that are a strong ICP fit, are in the consideration buying stage, are actively researching the keyword “widgets,” and haven't been to the website in the past 45 days.

And last but not least, users must be able to create both static and dynamic segments. A static segment might be created based on your current customer list, a list of accounts registered for an upcoming event, or all accounts with closed-lost deals in the last quarter. No matter the source, a static segment can be useful for analyzing or targeting a fixed audience as well as creating look-alike lists.

Dynamic segments, on the other hand, use filters to continually refresh a list of accounts based on any criteria. In the previous example, two of our segment filters are based on account behavior (researching a specific keyword and not visiting our website in the past 45 days). If we use this segment to target accounts with a campaign designed to generate website visits, we'll want to remove any accounts from the campaign as soon as they do visit the site. With dynamic, continually updated segments, we don't have to worry about whether our segment data is up-to-date because it's updated automatically.

As we'll cover in the next sections, segmentation capabilities are indispensable for giving your sales and marketing teams access to insights about specific audiences and also for seamlessly orchestrating engagement across channels as buyers move through the purchase journey.

7. Orchestration

Moving further up in our tech stack, it's time to start putting all of our data, predictions, and segments to work, because ultimately our goal is to deliver meaningful, timely experiences to customers. And that requires the ability to seamlessly engage the right buyers from the right accounts with the right message at the right time—and do so across every channel at scale.

The problem is that this level of orchestration is impossible with traditional marketing automation and journey mapping tools. Those legacy tools were designed for linear, one-size-fits-all journeys, and they require us to guess at the timing and path buyers might take. But the reality is it's impossible to perfectly map the ideal journey for our buyers, even if we lock the whole department in a conference room for the rest of the year. With 10 or more buyers across thousands of accounts—plus hundreds of different tactics, messages, and assets to leverage—it's easy to see why the complexity is too great. There aren't enough whiteboards, sticky notes, and dry erase markers on the planet to cover all the options.

Unfortunately, ignoring the customer journey isn't an option either. We simply need to embrace the fact that buyers are in control of the purchase journey today and that each person on the buying team will take his or her own unique journey. And if we want to put customers first, we need to up our technology game to truly meet them where they are and deliver great experiences.

Orchestration really begins with the dynamic data segmentation we discussed in the last section. This is a core way our solution is architected to allow tactics and personalization to fire based on buyer behavior, which is not static—it changes all the time.

Remember, our intent and account identification capabilities are lighting the Dark Funnel™ to expose the whole universe of accounts we could potentially sell to, but we want to focus on the best accounts. AI-driven predictions help us understand where to focus our resources, and segments are how we organize those accounts into meaningful groups to analyze and engage. So let's put 'em to work engaging.

Depending on the size of your sales and marketing teams, you might have dozens or even hundreds of segments—some for ongoing use (like your dynamic ICP segment), some for use in a specific window of time (like a segment of accounts attending an upcoming conference), and some just for quick analysis (like how many manufacturing accounts in Kansas meet your ICP, and what keywords they're researching). The orchestration layer of your tech stack is what takes these segments and does something with them to engage target buyers.

Examples of actions we might orchestrate for accounts within a segment could include the following:

• Automatically serving a dynamic display campaign
• Personalizing the website with industry-specific elements
• Alerting sales reps by Slack or email when one of their accounts is showing increased engagement
• Customizing content hub experiences with company logos and industry-relevant content
• Recommending content based on buying stage or intent keyword
• Automatically acquiring missing buying center data from third-party sources
• Adding accounts to other systems like CRM, MAP, or a sales engagement platform
• Sending a gift or other direct mail to key personas

Unfortunately, orchestration is easier said than done; as I pointed out earlier, our research revealed that orchestration is a challenge for 9 in 10 organizations.⁸ So it's critical that the orchestration layer in your tech stack seamlessly connects with other systems, from display advertising and web personalization to third-party data providers and sales engagement tools.

Of course, there's initial setup required as your orchestration layer comes online, but the idea is that most campaigns are “always on” and require zero day-to-day tweaks and adjustments. This frees your team to get creative and productive in other ways, whether that's running a competitive takeout or creating different multi-channel campaigns each quarter based on trending keywords.

One of the key reasons orchestration is so crucial is that it seamlessly weaves together personalized content, campaigns, and actions based on real-time data and predictions about your buyers—who they are, what they care about, and where they are on the journey. Let's take a deeper look at one of the ways orchestration can engage buyers.

8. Display Advertising

Digital display advertising is one of the most important channels for reaching B2B buyers. Which makes sense, considering most buyers conduct anonymous research online well into the buying journey. If someone is researching my company, product, or competitors on the Internet (and if I know about it, thanks to my intent and account identification capabilities), display ads are an ideal way to introduce my brand, associate my solutions with the problem they're trying to solve, and invite them to learn from me as they proceed on their journey.

As everyone knows, it's possible to reach potential buyers with ads just about anywhere across the Internet, whether they're checking news and weather sites or posting an update to social channels like LinkedIn. Regardless of the specific site on which an ad appears, the key is to make it relevant, timely, and personalized based on all the data and capabilities we've been talking about. To that end, there are several key requirements to keep in mind as you consider account-based display advertising solutions.

First, it's important that your display advertising solution be entirely self-service. It's good to have the option of managed services, but a self-service solution enables you to save on budget, and also means the platform is simple and intuitive enough for anyone to use. There's just no reason your display capabilities should be the domain of a single person or team within the organization, or that it should take days to launch a campaign. My field marketing team launches their own display campaigns, and I've personally launched display campaigns while hanging out in the Admiral's Club before flights.

Second, it's critical to hyper-target ads to the right accounts, so your display solution needs to integrate with your orchestration layer and leverage the dynamic segments your team builds. The whole idea here is that you're fishing with a spear rather than a net. You only want to invest your time, budget, and energy in the accounts that are a good fit and in-market to buy. The old “spray and pray” approach simply doesn't deliver relevant and personalized messages that help us put the customer first—nor does it result in a strong ROI.

In addition, your team needs to be able to experiment with multiple campaigns running simultaneously with some hitting a handful of accounts while others may be targeting thousands. The point is flexibility and the ability to target ICP accounts in a customized and creative way all the time. I'm an ideas person. I want to be able to experiment and test new ideas quickly, invest more in what's working, and kill what isn't. Should we test the funny Valentine's Day campaign? Hell yeah! What about going all-in on a particular deal and providing air cover? Let's do it! My philosophy is “limits suck,” and there are enough of them out there. My display capacity shouldn't be one of them. In Chapter 4, I'll walk you step-by-step through what this flexibility enables in terms of agile campaign planning and execution—but for now just know that it's transformative.

It's equally important to engage the right personas within target accounts. If the vice president of finance isn't part of the typical buying team, there's no reason to spend precious budget on ads geared toward that role. Similarly, it's crucial to speak to the unique needs and concerns of the personas that do matter, so you might have two campaigns targeting the same segment—yet delivering different creative and messaging for different personas within those accounts.

Third, you need to be able to use this targeting across advertising channels like social. Ensuring that your brand safety standards are embedded wherever your ads are served is table stakes. Beyond that, you want to make sure that you can use your business audiences across advertising channels, whether that's targeting specific types of publications, contextually advertising, or using your audiences across social channels like Facebook and LinkedIn.

And finally, you need to be able to monitor success and continually refine campaigns, but metrics like click-through-rate just aren't as relevant in this new world. So in addition to traditional display metrics, we also need to be mindful of new metrics like view-through rate, accounts newly engaged, accounts with increased engagement, and pipeline and revenue impact of ads. Additional metrics include which personas a campaign reached, new personas reached, and personas with increased engagement.

In short, display advertising is critical to warming target in-market accounts and taking the buying team on a journey without forms, spam, and cold calls.

It's unlikely that account-based ads alone will generate instant conversions, and that's okay. Because the goal of account-based advertising is to educate and engage buyers in order to help them proceed on the buying journey and, ultimately, move into the decision and purchase stages of the journey where our sales team begins outreach. And with warmer accounts that are familiar with and learning from our brand, it's easier to ramp new AEs and set them up for success when they pick up the phone and call future customers.

9. Email

With all the advances we've seen in marketing and sales technology, one place we've been stagnant is how we deal with emails. How long have we been stuck at the “Hello [FirstName]” stage of email personalization? And how long have we been stuck in the linear path of decisioning based on rudimentary measures like whether an email was opened or a link was clicked?

Too long.

A world of possibilities is now opening up to marketers and sellers that goes far beyond standard MAP and mail merge capabilities.

That's thanks in part to the major leaps in machine learning and natural language processing that have occurred in the past couple of years—specifically with the advent of GPT-3 (short for the third version of Generative Pre-trained Transformer). With massive datasets from which to learn, GPT-3 processes input and then generates shockingly intelligent language in response.

In the context of email, that enables us to seriously up our game. We can now automate emails that are responsive and conversational. AI can manage the timing and pacing of our email sends, understand responses, and then make smart decisions about how to route, respond to, and multithread—and when to get sellers or marketers involved in threads.

For instance, if we send an email and get a “Sorry, I'm not the right person,” response, the AI understands that, crafts an appropriate response, and then loops in the correct person. Or if an email kicks back an out-of-office reply, the AI can pick up on the person's return-to-office date and send a follow-up then.

Next-generation email capabilities have GPT-3 baked in. Not to replace humans, but to lighten our lift and allow for personalization at scale. Instead of writing emails from scratch, we can provide a prompt and let the AI do the rest. AI can also leverage real-time data like intent, technographics, psychographics, and market insights to inform the emails it drafts, bringing together a universe of signals to create relevant, timely campaigns. We can, of course, approve or make changes before sending, and the AI can learn from that too.

Of course, even with next-gen email, some old rules still apply. It'll still need to sync up with your CDP. You'll still need to make sure you're not sending spam. But this new technology will make it possible to have much more conversational, relevant, and well-timed interactions without much more effort.

10. Personalization

Just like display ads, personalized web and content experiences are vital for engaging with B2B buyers. And they go together well. Imagine a perfect-ICP-fit account that suddenly starts showing buying intent, yet our predictive models tell us that they've just started their journey. There's no point in sales calling or emailing now because they're still learning, so instead we target them with display and invite them to learn from us with content tailored to their current buying stage and the keywords they've been researching.

If our ad strategy is successful, we eventually get them to click or view-through to our website, content hub, or landing page. But what then? If we're truly serious about putting the customer first, this is when we put our best foot forward by personalizing their web or content experience based on all the rich data we have in our CDP.

Remember, our goal is to generate predictable revenue growth. We know that the best way to make that happen is by putting the customer first. And that means we're going to meet them where they are on their journey and help them learn by offering curated content and experiences with no forms in the middle. And who needs forms anyway when we have first-party intent signals? When they come a-knocking at our door, this is our time to shine and begin building a relationship.

Now, there are a lot of ways we can approach this experience, from customizing imagery based on industry to recommending content based on the buying stage to throwing the account's logo on top of the page with a welcome message. There's a delicate balance— you want to add value to every interaction, but you don't want to seem creepy. On that front, it's best to start with more generalized levels of personalization (like industry, company size, buying stage, and location) and experiment as you work your way up.

Personalization doesn't have to (and shouldn't) stop at web and content experiences. Targeted, personalized experiences should be the bread and butter of every organization focused on a customer-first approach—and should extend to every channel: email, direct mail, advertising, web, content, sales engagement, chat, and so on. Depending on how you choose to build and grow your tech stack, some of these capabilities may come later, so be sure to think about how data will move between systems (hint: open APIs are essential) to enable personalization and targeting at scale.

Remember, personalization isn't a parlor trick—it's about being helpful.

Here are some of the ways my team uses personalization to deliver great customer experiences:

• Chat: Our chatbot knows who's visiting the website, so it initiates conversations and recommends content based on an account's buying stage and interests. And if they're ready to talk, we immediately connect them with someone who knows about them and can help—the account owner.
• Content hub: Every marketing team is a content engine, but unless you're Netflix, nobody is binging on your content. The best way to make use of your content is by curating personalized experiences for buyers based on data. And when it comes to great content experiences, less is more. Think a curated tasting menu rather than the Cheesecake Factory mega menu. By serving up content personalized for our visitors, our content hub has three times better time-on-page than industry average.
• High-value offers: Free trials, discounts, and other special offers can help boost engagement with late-stage buyers, but offering those kinds of deals to every web visitor can bring all the window shoppers to your door—and bog your team down. However, with web personalization linked to our dynamic segments, we ensure that high-value offers are extended only to accounts for which we want to offer high-touch experiences.

When your tech stack has the capability to facilitate personalized web and content experiences, you bring customer experience to a whole new level. It's no longer just about having a few versions of your website or content for different verticals. Now you're recognizing accounts and providing the exact digital experience that's most useful for them, based on all that rich data like intent, buying stage, and more.

11. Sales Insights

As I said at the beginning of this chapter, revenue is the goal we're all after, and every company is on a journey to grow revenue in a more predictable and repeatable way. I also outlined the five steps of an account-based strategy, and this capability is all about step four: collaborate with sales.

In short, the goal of sales insights is to enable sales reps to prioritize the leads, accounts, and contacts they spend their time on, and also to ensure that their outreach is timely and relevant to customers. And to do this, we need to give sales reps direct access to the data and predictions delivered by the other key capabilities in our customer-first tech stack. But of course this doesn't mean inundating reps with all the data in our CDP; the insights delivered to reps must be relevant, intuitive, easy to access, and inspire action.

The challenge with sales insights is that it's difficult (if not impossible) to get sales reps to use yet another platform or tool, no matter how great it is. It can be hard enough to get reps to fill out all the fields on an opportunity record. So asking reps to log into the account engagement platform the marketing team uses is almost certain to be met with resistance.

The best way to deliver sales insights to reps is to do so where they spend the bulk of their time: within your CRM or sales engagement platform. This way, anytime a rep looks at one of their accounts, they can quickly see critical information like the current predicted buying stage, the keywords the account has been researching, the level of account engagement over time, and the complete buying center, including which contacts have been engaged and which have not.

These data points can also be stored in custom fields on account and contact records to enable your operations team to build dashboards and reports that help reps understand where they should spend their time on a day-to-day basis. Every single sales rep at 6sense—from BDRs to our most senior enterprise account executives—starts their day with a prospecting dashboard that shows which of their accounts are in the decision or purchase buying stage and how many days they've been there. These dashboards also enable sales managers to identify unassigned accounts showing buying intent so we don't miss potential opportunities as a result of account assignment.

Knowing which accounts to prioritize and having insights about them is super important, but our sales team takes it a step further by using AI to recommend the next best action a rep should take to increase engagement with an account. For example, the AI recommends specific contacts for a rep to add to the sales engagement platform and begin outreach, and even offers suggested talking points based on the contact's interests and behavior. It also recommends new contacts to add to CRM to flesh out the buying center, enabling reps to acquire relevant data with a few clicks. In other words, this helps reps both know and do everything they should in order to deepen relationships with the accounts they're working.

When sales reps and managers have instant, easy access to these kinds of insights—and when marketing is looking at exactly the same data and insights—alignment between sales and marketing comes naturally. Everyone understands which accounts and contacts are most valuable and why, how, and when they've engaged with the brand, and where they are on the buying journey. With this level of alignment, it's finally possible to put customers first and orchestrate engagement with the best accounts for the business.

12. Analytics

The 12th and final key capability in our customer-first tech stack is all about measuring success. Again, we're moving to a customer-first approach because it's the best way to generate predictable revenue growth, but we can't assume that reorienting ourselves toward customers is going to result in immediate success. We need to continually measure, test, optimize, and improve.

Account-centric measurement capabilities in your tech stack will make it that much easier to start baselining and improving results, which you will learn I'm really big on in the next chapter!

As I mentioned in the display advertising section earlier, there's a built-in disconnect between some of the traditional metrics sales reps and marketers are used to (like cost-per-click and click-through rate) and the reality of how we need to connect with customers and sell today. It's not that these legacy metrics are totally irrelevant; it's just that they don't paint a full picture of how accounts are engaging with our brand. And this is because we've switched from fishing with a net to fishing with a spear, so our ad spend is now highly targeted to the accounts and personas we care about.

Additionally the account engagement tech stack I described over the past sections is all about using big data and AI to uncover the best accounts and engage with them, so other legacy metrics like MQLs, SQLs, and SALs are suddenly no longer relevant. And believe me, I understand the heart palpitations that may cause. As I said in Chapter 2, I've had this conversation with hundreds of CMOs over the past 18 months, and many get nervous when I tell them they should ditch the metrics they've grown up with.

But think about it. If we know what accounts and contacts are doing as a result of our intent and identification capabilities, and if we can predict what those signals mean to our business using AI-based models, and if we can understand who they are and what they care about due to our limitless segmentation capabilities … well then, we simply don't need to arbitrarily score leads anymore based on clicks, form fills, page views, and downloads.

Here are a few ideas to help you start thinking about the new kinds of metrics and analytics that will change the way you think about sales and marketing alignment—and how you measure success.

One of the leaders on my marketing team often talks about how he was one of the first users of Pardot (waaaay pre-Salesforce acquisition) and was a big fan. But he remembers the first time he set up a lead scoring model, and it was as finger-in-the-wind as you could imagine. Hitting our pricing page is worth a solid 5 points. And surely downloading our ebook is worth 10 points. And SURELY if you have a score of 50 points you're an A+ lead.

He was guessing. But we're now in the world of knowing. And when we know everything, we can do anything to meet our buyers where they are, deliver amazingly engaging experiences, and help our companies deliver predictable revenue growth. But we need the right account-centric analytics to help point us in the right direction.

We'll get even deeper into analytics in Chapter 4 when exploring revenue operating models, which ultimately are all about measuring and improving success—from tracking and improving average deal size to boosting win rates, amping deal velocity, and generating more renewals and upsells.

13. Pipeline Intelligence

Pipeline is the lifeblood of any revenue organization. Simply put, if you want to future-proof your revenue, you need to get right with your pipeline. In a recent survey, 88 percent of CMOs said pipeline or closed-won business were their most important metrics. Sadly, only 25 percent of those CMOs said they hit their pipeline targets most or all of the time.

That disconnect has a serious impact not only on business performance, but also on our credibility and alignment with our sales counterparts.

After all, sales leaders have long had the tools to accurately manage and predict revenue. They've had the formal processes and tech that allows them to manage opportunities throughout their entire funnel and forecast with a high degree of precision.

Marketers, on the other hand, have had to deal with disconnected data and a lack of tech capabilities both in forecasting and measuring pipeline. That's beginning to change with new technology that provides pipeline intelligence to marketers as well as sellers.

This tech uses AI to allow us to do three important things: plan, track, and forecast pipeline. Let's dig into each of those a bit.

Reminder: We're doing this for a reason

Okay, that was a lot of information, and your eyeballs hurt, but I promise it's worth it! And remember what I said early in this chapter—we're not doing all this hard work for our health, and we're certainly not doing it for the feel-good vibes we'll get from a beautiful tech stack. We're doing it because it's key to achieving that all-important goal we're all after: predictable revenue growth.

The path to predictable revenue growth is quite a journey—and old lead-based technology isn't going to get you there. You need a shift in both your thinking and your tech stack so you can buckle up and get ready to reach a level of sales and marketing that's been unattainable until now.

Now, are you ready to take this thing for a spin? In the next chapter, I'll show you how technology, process, and customer experience come together to create the real business results we've been talking about.