When we talk about data-driven marketing, we typically imagine a blissfully quantitative utopia. We measure and track consumer behavior across the lifecycle, and attribute conversions with precision. Every element of our campaigns are A/B tested and refined in real-time. Our writers and designers optimize every creative asset based on past performance. And over time, our marketing strategies and tactics zero in on the ROI bullseye of low-cost MQLs and sky-high conversion rates.
It’s a nice fantasy. It’s also wildly out of reach for most marketing teams in our current age of consumer privacy and increasingly complex buying journeys. Third-party cookies are crumbling, consumer data is kept within walled gardens, and consumers are learning about products and brands through private or offline channels that even the fanciest attribution software can’t measure.
This means B2B marketers are coming to terms with the idea that marketing attribution just doesn’t work anymore — if it ever did.
And that’s a good thing.
Because obsessing over attribution forces talented marketers to over-index on “proven” channels (usually paid) at the cost of trusting their gut and building long-term relationships with their audience.
So if marketers can be freed from the shackles of tracking every behavior and calculating the precise ROI of every activity, that means they can return to obsessing over their customers instead of performance metrics.
In this new era, data becomes a useful tool that serves our marketing — not the other way around.
The real-life principles of data-driven marketing
For today’s leaders, data-driven marketing centers around using the right data at the right time to inform your strategy and improve your performance — while leaving room for creativity, intuition, and experimentation.
We’ll explore some specific tactics in a moment, but big-picture, here are some core principles of data-driven marketing in 2024.
- Prioritize first-party data: Collect your own data whenever you can — like your website, applications, and owned channels.
- Connect data sources: Make sure your first-party behavioral data, CRM data, campaign performance data, and any data supplied by a third-party are centralized — ideally, within a single source of truth.
- Segment your audience: Use the data you have to develop your ICP and buyer personas, and segment your audience according to industry, role, company size, location, or whatever characteristics matter most to your business.
- Personalize content and messaging: When possible, create variations of content and messages tailored to your audience segments to provide a more relevant experience.
- Evaluate campaign performance: Look at engagement metrics and conversion rates to get a better understanding of what’s working, and what’s not.
- Test, experiment, and refine: Do more of what’s working, and tweak what’s not. Try something new to see how it lands. Measure, adjust, and measure again, with the goal of incremental improvements over time.
- Analyze and report: Get the clearest understanding you can of how your marketing activities translate to sales. Define your metrics and your approach, and share them.
The overriding goal here is to use data-driven marketing techniques to figure out who your customers are, and what works to reach them. Do more of those things — but don’t limit your investments to only those things, and keep experimenting to see what innovative and unproven tactics can move the needle.
The challenges of the old data-driven marketing model
Of course, in order to shift your approach to data-driven marketing, you’ll need to convince your C-suite that the notion of data as the end-all, be-all path to good marketing just doesn’t work. So let’s unpack the big picture challenges at play in 2024.
Increased privacy regulations
Since 2018, we’ve seen a seismic shift in the landscape of consumer data privacy. Government regulations like GDPR and CPRA have beefed up the requirements advertisers need to follow around obtaining, using, and sharing consumer data. At the same time, tech companies like Apple and Google have raced to get ahead of additional legislation by introducing their own privacy protections to their web browsers, email clients, and mobile devices. Most notably, Google is in the final stages of eliminating the third-party cookies that allowed advertisers to track and target consumers across the internet.
For marketers, the undeniable end result is less easy access to data — especially third-party data, which probably wasn’t all that reliable in the first place.
Whatever’s happening on “dark social”
You can’t measure what you can’t observe. And an awful lot of your prospects’ buying journey happens on “dark social” — in private spaces like messaging apps, email, or SMS. Or, offline entirely.
Maybe your prospect heard your brand name-dropped on a podcast, or mentioned in a Slack community. They might have asked their actual human friends for a product recommendation. Or saw your CEO speak at a conference, and then looked up your brand in a direct search when they were in-market months later.
These activities are difficult-to-impossible to monitor, let alone measure — or include in attribution models.
Measurement does not equal attribution
That leads us to the problem of provable attribution — the unfulfilled promise of data-driven marketing. This mindset suggests that marketers can track their leads’ behavior from throughout every step of their buying process, using expensive software and advanced statistical models to weigh and quantify how each touchpoint impacts conversion.
But as Rand Fishkin and other marketing leaders have been emphasizing lately, this kind of attribution modeling usually isn’t the best use of marketers’ time. For one, as we’ve just discussed, consumer data is getting harder to access, and much of the buying journey takes place on channels we can’t monitor. Second, there’s a big difference between measurement and attribution — despite what Google, Facebook, and other advertising platforms (whose number one goal is to take more of your marketing budget) would have you believe.
As we alluded to earlier, this all means an organization that becomes over-reliant on provable attribution can overlook important channels and undervalue brand and marketing activities that don’t have easily quantifiable ROI.
The lack of experimentation
There’s a lot to learn from what kind of messaging and content has performed well with your audience in the past. But if historical data is the guiding force of creative decision-making, you’re leaving very little room for innovation and experimentation.
Both quantitative and qualitative data should help your strategists and creative teams understand your audience and inform a thorough understanding of their needs and pain points. But it shouldn’t be a limiting force that takes an “unproven” idea off the table — a real danger of the most devoted data-driven marketing leaders.
The practical difficulties of applying data
Collecting data is hard enough, but our research shows that marketers’ biggest challenge is figuring out how to actually apply data to marketing.
This can happen for many reasons. Some marketers experience “analysis paralysis” — being so overwhelmed by parsing through large quantities of data that it takes a long time to actually make a decision. Others have to deal with internal silos that restrict access to the data they could use. Some marketing orgs lack a clear understanding of how to interpret specific insights into actionable steps.
And some may be so intimidated by the all-or-nothing mentality of “data-driven marketing” that they don’t believe the data they already have can make a meaningful difference — without investing in five or six-figure software.
Despite all of these challenges, data-driven marketing is a worthwhile endeavor. We just need to define and contextualize it for marketers working in real environments with real constraints.
How to improve data-driven marketing today
Let’s put these principles into practice: how can you start improving your data-driven approach right now?
Optimizing campaigns with engagement data midstream
Keep an eye on campaign performance and look for opportunities to intervene and improve in time to impact results. For example, if you notice that your webinar promotion is disproportionately attracting registrations from the finance sector, you can quickly spin up a segmented invite highlighting the relevance for your other ICP targets.
Using lead scoring to qualify MQLs for sales
Lead scoring is a relatively straightforward way to use data to smooth out the marketing-to-sales handoff. Partner with your sales team to develop a lead scoring model tailored to your ICP — aligning quantitative scores with demographic and firmographic factors that matter most. Next, layer in behavior scoring that prioritizes activities that indicate real buying interest (like webinar attendance or a buying guide download). Then, set an MQL threshold that everyone agrees indicates a lead is most likely ready to meet with sales.
Bonus point: implement an AI-powered predictive lead scoring system that incorporates real-time sales conversion data, meaning your model looks for documented patterns of behavior that lead to actual conversions and closed deals.
Analyze how users interact with your website
Behavioral analysis tools like Hotjar or Lucky Orange create heatmaps to visualize how visitors interact with your website and content, and help you identify opportunities to improve usability and conversions. For example, you notice that prospects who click through a testimonial carousel are more likely to book a demo — but your testimonials are buried deep on the bottom of your homepage. Given this insight, you can relocate your social proof higher up on the page and monitor how (or if) the change impacts conversions.
Look beyond email open and click-through rates
Email open and clickthrough rates are losing credibility with marketing leaders, thanks to artificial inflation from mail provider changes and corporate scanning bots (designed to check email security by opening messages and clicking every link). Instead, look further downstream in your email campaigns — when readers click a link to your website, how long do they engage with that content? Where do they go next? What meaningful outcomes — like webinar registrations, content downloads, or completed video views — are your emails driving? Moving beyond early indicators like opens and clicks will focus your time and attention on the data that actually matters.
Combine data sources to identify content opportunities
Looking at multiple data sources about the same content assets can help surface valuable opportunities. For example, say a certain page gets great campaign engagement — like time-on-page after an email click or number of social shares — but has low organic entrances and pageviews. That indicates that while the subject matter is relevant to your subscribers and followers, you may not be optimizing the post for the related keywords your broader audience could be searching for. Time to do some keyword research and optimize accordingly.
Share data across teams
Level up your data-driven marketing efforts by building a data-driven culture across the organization. Your sales reps should know which blog posts are highest-performing, so they can share them with their prospects. And it goes both ways — you should know which questions come up most often during customer success calls, so you can build educational content for customer marketing campaigns.
Use AI to power natural language search
Not all marketers are analytics experts, or have time to go digging for insights within raw data or complex dashboards. But when your analytics layer includes AI-powered search, you can use natural language queries (like “Which email campaign drove the most engagement last month?”) to mine your data for actionable insights, all on your own — no overworked analyst or SQL skills required.
Get the most out of your marketing automation data
Ultimately, knowing when to rely on data is just as important as knowing you need to rely on it in the first place. Don’t get bogged down in data that doesn’t actually matter — focus your time and efforts on data-driven techniques that help you reach new leads, improve conversions, and lower acquisition costs.
And while you don’t need a perfectly optimized mar-tech stack to get started, actionable analytics makes data-driven marketing much easier.
Learn more in our recent webinar about optimizing your marketing automation platform with robust integrations, predictive lead scoring, and impactful analytics that supercharge your marketing — but keep you in the driver’s seat.