Tl;dr: Dynamically personalized content for website visitors is an important element of most ABM programs. While WCMSs provide the capability to deliver personalized content, they can only do so based on rules and insights. Contact level™ intent data provides important information to support this. Real-time monitoring, dynamic segmentation and orchestration at scale are the next challenges, and the typical MarTech stack might have some gaps.
Personalization...but based on what?
You're hearing and reading it everywhere these days it seems. Personalization is the key to marketing, particularly for account-based marketing (ABM.)
We're advised to personalize everything it seems. Email, ad creative, video, special offers and web content.
It makes perfect sense. But what's realistic? What constitutes personalization? How does that compare to customization?
Web content management systems (WCMS) typically rely on "anonymous" criteria for content customization. These include IP addresses and language tags for location adjustments. IP lookups also support customization based on company, which may in turn incorporate industry, size and other firmographic and technographic details through enrichment engines.
Cookie based tracking supports individualized personalization. When you know someone's job function and seniority it's possible to tailor their experience on your site to match what your persona research tells you is important to them.
First party intent data helps companies take the personalization further. Inferences drawn from the velocity and details of engagement can inform more specific web content personalization. For instance, if they've suddenly moved from awareness type content, quickly through consideration, and have taken multiple actions around decision stage content within the last several days it's reasonable to conclude that they are advancing quickly along their buying journey toward a purchase decision. In that example, personalized content could reflect more social proof in that case, and highlight competitive distinctions.
On the other hand if they simply return monthly to consume newsletter type content, then a personalized web content experience is probably less important but might occasionally highlight case studies which quantify the impact of taking action.
With nearly 2 billion websites, however, and seemingly hundreds of competitors in every space, anonymous "customization," and personalization based solely on first-party intent data, miss important information. (That's likely why we've seen several recent transactions including WCMS vendor Episerver's recent acquisition of IDio.)
Let's look at this from a sales angle. After all, in many cases web content is as much about selling as marketing. How do your top sales people "personalize" their interactions? They weave together insights about the involved indviduals, the buying team as a whole, and the department, company and industry. Experience personalization is dynamic.
That raises three common challenges for companies eager to personalize more effectively as part of their ABM efforts.
Challenge #1 - Missing Data
Those top sales people understand that some of the 6.8 buyers on the team (according to CEB's most recent estimate) are at different stages in the buying journey, engaging with different competitors, considering the problem from different perspectives, and probably even more interested in outcomes specific to their department or function.
Sales hotshots are constantly working to understand the dynamics at play, what urgency exists to solve the problem, and what barriers exist. They're both intelligence agents, working to cultivate sources, and intelligence analysts, piecing tidbits together to understand what's happening. And even with that approach, they're only collecting the information their champion knows and is willing to share.
Nevertheless, that's probably much more than your first party data, which only reveals insights for those who take action with you.
While there's no substitute for a top sales person with high EQ and effective questions, contact level intent data can fill in some of the additional insights you need to really personalize content through your WCMS. For instance, understanding who seems to be on the buying team, which competitors they're engaging with, what problems they're focused on solving, which outcomes are their priorities, and even their job function and seniority can be gleaned through third-party purchase intent data and used to support personalization.
Imagine how a great sales person would incorporate all those details into their conversation. Now imagine how you could create amazing experiences with dynamic content that's tailored to those people. For example, almost every buying team has someone in an IT role and someone in a finance role - and they're almost never as enthusiastic about the project as your deal champion. You can't speak directly to them - at least you couldn't in the past. With these insights, you almost can.
Challenge #2 - Monitoring and Segmentation
Before we get ahead of ourselves, though, let's think about how you actually make this happen.
Let's assume you've got a typical, robust MarTech stack. In addition to your WCMS, you probably have a combination like Marketo or HubSpot for your marketing automation platform (MAP), Salesforce.com for your CRM and SalesLoft or Outreach.io for your sales acceleration.
These all sync well.
But there's a gap.
Let's say that you've got several contacts from a key target account in Marketo. One was just added when (s)he converted on a form on your site. The other two haven't opened an email or been on the site in months.
Suddenly though, over the last several days, there's been a significant burst of activity in your 3rd party intent data. Assuming it's contact level data, you'd see that one of those in your database has engaged with three competitors this week. You'd also see that there are four other contacts from that same company, who aren't in Marketo, but who are taking action online including with articles around the problem you solve, the outcomes you help achieve and with key thought leaders in your space.
This raises several issues.
First, how would you compare your first-party data with the third-party? In other words, while there's been relatively little activity with the few contacts you have, on your own site, is there an airtight way to ensure that the surge in activity elsewhere that seems to correlate isn't overlooked? This is trickier than it sounds since it's likely based on a rolling profile of individual and account level activity.
Second, if you detected the activity, how would you programmatically interpret the signals and segment the contacts so that they were identified to receive the appropriate outbound messaging (sales cadence and/or paid social nurturing) and the properly personalized site experience when they visit? Or at least ensure that anyone from that company received largely pertinent content based on the buying team's collective work?
Third, how would you attract them to your site to experience the personalized content? And how would you proactively engage them so that their visit might be as a known visitor rather than just an anonymous visitor from a known company (potentially recognized based on IP)?
We tend to assume that our MarTech stack will let us do this sort of stuff. Often, however, it won't - at least not automatically. Sure, it's possible to create lists and workflows - but typically those only consider a single contact's activity that's captured in the MAP. Activity reflected in multiple third-party data sources is often not incorporated, and account level activity isn't easily incorporated.
But maybe your stack accommodates this. Then...
Challenge #3 - Journey Orchestration
Then you actually have to pull the strings to make this all happen, dynamically, at scale, and personalized in content and journey for each member of the buying team.
That's substantially more complex.
How would you automatically trigger cadences to get effective emails with tracking pixels (so that you can deliver personalized experiences when they visit) in front of the contacts that you're not already connected with?
How would you automatically assign the junior level finance buyer and the senior level IT buyer, who each are engaged with different competitors, to the right lists to ensure that the WCMS delivers the right content?
How would you put each contact onto the right custom audience list to show the optimal nurturing and conversion ad content for paid social? Or into the right segments to make sure that the chatbots deployed when they visit match the personalized content?
How would you decide which member(s) of the buying team get triggered for 3D mail follow up? And then make that happen?
How would you keep your sales team informed so that they could brief the inside deal champion to coordinate efforts? And how would you dynamically provide the right sales enablement content at the right time to support the sales team?
These are the complexities of orchestration.
An Evolving Mindset and Tech Stack
There's no doubt that digital marketing and ABM are getting increasingly complex. That's driven by evolving buyer expectations and increased technical capability. (In fact, it's part of what's fueling the surge in interest in customer data platforms (CDPs) which are purpose built to manage challenges two and three above.)
Not every company needs to be taking a full scale approach to their use of intent data, marketing automation or other tools. (That's why we recently published a Maturity Model to help companies visualize where they are in their intent data journey.)
Companies that are adopting major strategic initiatives like ABM should understand what's possible and what would be required to achieve it. They can launch with a simpler version, and maybe even plan to keep the program simple for the longer term. With all the buzz about intent data and personalization in ABM, though, it's easy to get swept along with rapidly emerging capabilities. Be sure to understand what's involved.