If I had to describe my job as a marketing analytics manager in a hashtag, it’d be #nerd. While numbers and statistics may not seem interesting to the average marketer, I love them. Data is my happy place. But it’s not the figures themselves that bring me joy, it’s the insights derived from data that reveal valuable information about our audiences, our industry and our business.
A large part of my role is to glean those insights and share them with my fellow marketers. But another part is to get them to buy into the potential of what data can do for them. It’s my job to help build a data-driven marketing culture where every team sees data how I see it: as a primary driver of brand growth.
Speaking from experience, this process is not quick or simple. Cultivating an organization-wide, data-first mindset takes time and effort. But the tangible business impact is worth it. Erik Brynjolfsson et al. from MIT’s Sloan School of Management found that data-driven organizations have five to six percent higher output and productivity than their less data-driven counterparts. They also had higher asset utilization, return on equity and market value.
Regardless of the size of your company or data and analytics team, building a data-minded culture takes a lot of planning, intentionality and patience. Here’s how to start:
Envision an ideal state
Before marketers implement new tools or strategies, they should start with a vision of what a data-first mindset looks like in their organization. In other words, how will they know when they’re part of a data-driven marketing culture? What’s the end goal?
This first step is a little “meta,” because that’s exactly how someone with a data mindset would approach a new project or campaign. Set a goal, decide how you’ll measure progress (and success) and then create a strategy. I encourage leaders to take a step back and examine the direction the company needs to grow and then work with managers to set goals for the organization and its teams.
It’s helpful to split this ideal state into two areas: 1) the people and 2) the data.
In an ideal data-minded marketing culture, team members engage in both data-driven planning and data-driven decision-making. Members of the data and analytics team are included in the early stages of campaign or project planning. Before ever writing a brief, marketers are already thinking a few steps down the line about measurement and optimization.
And while it’s often the job of a marketing analyst to develop a strategy around those things, data-driven marketers already have a few ideas of their own and partner with the analyst to create the strategy.
In a data-driven marketing culture, decision-makers are held accountable to provide reasoning and basis for their choices. There’s more value placed on insights than on intuition. And while a gut feeling is important, people make decisions more often based on results from A/B testing and grounded in concrete knowledge versus going with their gut alone.
To be a real driver of growth in an organization, data must be three things: accessible, approachable and actionable. Marketers can’t harness the real power of data if they don’t have adequate access to the numbers, can’t make sense of them, or don’t know how to use them properly. The ideal data-driven marketing organization has broad access to data, defined parameters for the use of marketing data tools and a single source of truth for each team.
While it’s important to ensure certain data is widely available across the marketing organization, it’s up to the analytics team to create a cohesive strategy outlining which marketing data source is right for which metrics, as well as which team owns each source.
There are a ton of data measurement tools out there. Tools that measure quality, tools that measure quantity, engagement, etc. If no one knows where to go for a specific metric, it’s chaos. There needs to be a single source of truth for each team and only that team should have governance over that tool.
For example, Salesforce is our go-to source for sales measurement at Sprout, and Google Analytics is what we use for marketing goals like engagement. When members of those teams are looking for specific metrics, they know which tool to access. We also put parameters and centralized administration in place to ensure only one team has the ability to modify a data source, to maintain the quality and integrity of that tool.
Once you have that end goal in mind, it’s time to start making moves.
Align data strategies with team goals
A common challenge for marketing analysts is the lack of common understanding of what data can do for a team. So before they can begin developing an effective data strategy, they must ensure that every team within the marketing organization understands how their work impacts the business’ bottom line.
If you have a team that understands how their individual team’s KPIs ladder up to the broader marketing organization’s goals, as well as the company goals, then they know which drivers they should continually tweak and optimize to hit those goals.
If a company is trying to drive revenue, for example, consider what factors go into hitting that number. For marketing, there’s lead generation, awareness, engagement, etc. When marketers understand the drivers of their bottom line, marketing data analysts can then take a step back and say “If you want to measure trials, leads and impressions, you’re going to need x, y, z tools to do so.”
This process then becomes a top-down approach, ensuring that marketing leaders see the business value of both the tools proposed, and the data they’ll provide.
And while it’s important to show marketing leaders how data can make a positive impact on their business’s bottom line, I personally love when I’m able to show individual contributors who might not always see or understand what their value is to their organization why their work matters.
Empower your teams
One of the biggest mistakes I see people in my position make is when they silo themselves off from the rest of the marketing organization and do all the data work themselves. While analysts may think they’re serving teams this way, they’re actually hurting the org in the long run because that’s not scalable. Especially when you have a small team supporting the needs of a larger organization.
If you take steps in the beginning to train teams in the way of data, it will pay off dividends in the long run. It’s like the old adage, “teach a man to fish…” This is why I consider myself to be in a service-based position. It’s my job to ensure individuals across all teams are empowered to pull numbers, extract insights and apply them to their projects. That’s how you create a truly data-minded marketing culture.
But there’s no one right way to educate marketers about data. Formal training can be effective, both organization-wide and for individual teams. Goals for training should include expanding marketers’ data vocabulary and literacy. Start with basic terminology and a clear list of metrics and their definitions. Then progress to demonstrating how to navigate dashboards, pull reports and analyze for insights.
Another way to empower your teams is to identify several data champions—individuals who have more experience with data and hold a particular interest in how it can positively impact their work—across the organization. Ideally, every team would have at least one data champion, including members of the marketing leadership team. Not only can data champions serve as strategic partners for marketing analysts, but they often assume the role of data evangelists, sharing both their enthusiasm and their knowledge with their own team members.
I’m fortunate to work in a marketing organization that is already ahead of the data curve, but I know that’s not the case for a lot of companies. If marketers are serious about delivering consistent results and providing measurable business value, they need to step back and ensure their culture is ready to put data first.
This piece is part of our series on data-driven marketing in which our experts explore the keys to developing a team and strategic approach grounded in data. Read the next article here.