This post is the first in a series that gives you a step-by-step guide to planning a marketing data structure that’ll act as the basis for your revenue marketing machine.
Who remembers the first time they saw James Cameron’s Avatar? Like most people, I was blown away by the graphics and SFX. And I was not the only one!
I remember people walking out of the theatre, going gaga over the special effects, the vibrant colors, and how “real” those weird Pandorans looked.
Nobody was saying:
- The programmers over at Maya / 3ds Max / Cinema 4D have created such an amazing tool to enable all of this!
- Those new GPUs are really something!
Of course, folks don’t often know about the hidden tech that makes a lot of this cool stuff possible.
Plus, talking about the “cool stuff” is a lot more exciting than talking about the boring backend.
Something similar is happening with revenue marketing as well.
More and more marketers understand the need for a revenue-focused approach. We’re seeing a tremendous mindset shift toward the importance of connecting marketing campaigns with revenue metrics.
But when we start talking with marketers about their revenue-driven marketing methodology, they want to talk about the exciting “cool stuff.” Terms like attribution or setting revenue KPIs are favorite conversation starters.
These are very important, but just like the tech behind the visually stunning (but a little creepy) Pandorans, there is a vital backend step. It serves as the foundation for all the cool stuff that comes later. What is it?
Building a solid marketing data structure.
Why is a marketing data structure important?
Most marketers are fully aware of how important data is in determining the success of their marketing activities. In fact, 89% of marketers believe that data quality is critical for building effective campaigns. But, only 50% of firms are confident in the quality of their data.
Source: DnB B2B Marketing Data Report
How can you understand what makes up a quality campaign if you can’t trust your data?
Imagine a recipe that keeps switching between metric and the U.S. standard system.
One cup sugar, 226 g butter, 2 ¾ cups flour, 240 ml milk…
You would spend more time converting measurements than you would baking the cake. Even worse, you might just “eye it” and hope for the best.
In most marketing departments, teams initiate each campaign from a different platform. Each provides its own analysis dashboard, measuring thousands of data points, and giving itself credit based on different rules and metrics.
Without establishing a common language for success between all of your systems, it’s impossible to measure what works and what doesn’t.
An iron-clad marketing data structure works the same way as a great recipe. It establishes a shared language between all of your incoming data and gives you a clear understanding of the key ingredients of an effective campaign.
Tying business activities to business outcomes
Attribution, planning, forecasting—important pieces of a revenue-marketing mindset—are all impossible without a cohesive data structure. Still, 95% of organizations struggle to make sense of customer data. Why?
- Different campaign reporting platforms use different terminology
- Important data sits in different systems with no strategy for connecting with one another
- Each department has its own definition of success
White paper downloads, LinkedIn ad clicks, email campaign opens—they should all tie back to revenue. Few want to hear about the .8% open rate on your latest lead nurturing campaign. They want to know how many dollars the business made through each marketing activity—the main goal of a marketing data structure.
Once you have this common language in place, you’re ready to find out how much revenue each campaign generates. And that’s when multi-touch attribution, forecasting and all those cool stuff come into play.
What is a marketing activity?
In marketing, we often refer to activities as campaigns. They’re the “ingredients”—the strategic ways we try to reach new customers. Some examples of marketing activities include:
- Running paid Facebook or Google ads
- Exhibiting at an industry convention
- Sending a sequence of emails
- Sponsoring a conference
- Writing a weekly blog post
As marketers, we deploy each of these campaigns from different systems. Each system has its own structure and speaks its own language. LinkedIn Ads Manager measures campaigns differently than Facebook Business Suite.
When you’re bringing in data from so many different channels, your marketing data structure is how you can establish a common language for success.
What is a business outcome?
In revenue marketing, each business activity is linked to a specific business outcome.
Instead of blindly throwing darts, every campaign has a very specific end target. Business outcomes can vary based on your sales process and funnel velocity, but usually we’re talking about revenue or pipeline.
The business outcome model refers to your company’s funnel. It should have several steps, based on your business model, but what’s important is that it ties to revenue as its main goal.
Note that a common language for “success” is highly important for aligning your organization around a common data structure. So as part of the process of building a marketing data structure, you’d also need to align the revenue org around the definitions of your business outcome model (your funnel). We shared a framework for doing this in our recent post, here.
A clear and consistent marketing data structure is the foundation of revenue marketing
Only when you build your marketing data structure by unifying all of your data and connecting activity data with business outcomes, can you compare apples to apples and move on to evaluating the success of each campaign. And that’s when you can apply an attribution model, forecast future results, and all those cool revenue marketing stuff.
We were all in awe when Jake, the main blue guy in Avatar, first inhabited his new form. In marketing, it’s also easy to get distracted by flashy metrics and creative campaign ideas that we forget to focus on the backend that makes it all possible.
Soon, we will share our second post in the series, where we’ll cover the elements of the marketing data structure, how to plan one, and what to take into account when doing so.
Hang tight! 🚀