Blog How to become a revenue-driven marketer with dbt Cloud

How to become a revenue-driven marketer with dbt Cloud

It’s important to show how your marketing campaigns impact the bottom line. For that, you need good data. Here’s how we use dbt Cloud to drive revenue-driven marketing. Read now
How to become a revenue-driven marketer with dbt Cloud

Our unique economic times are challenging us to do more with fewer resources than ever. Marketing departments in particular are being asked to reduce acquisition costs while also boosting pipeline and revenue. 

At dbt Labs, we’ve always held that marketing’s job is to tell a story and drive revenue. But we take that a step further. It’s everyone’s job to be data-driven, whether you’re a data engineer or a marketing campaign manager. Let’s review why this mindset matters—and how we built it into our daily workflows using dbt Cloud.

The challenges we face as marketers 

For years, low interest rates drove heavy investment into the tech sector. But with tech multiples rising, the winds are shifting. With growth down around 4%, more companies are focusing on profitability metrics like free cash flow margins (up around 2%). 

The days of “growth at all costs” are at an end. Companies are now focused on growth and profitability. As a result, many are looking for efficiencies wherever they can. 

Marketing teams face multiple daily challenges no matter the economic conditions. Without concrete numbers, they have no way to show the value they’re bringing to the organization. It’s also hard—if not impossible—to know where best to invest resources and where to trim the investments that are not working. 

Proving this value and finding these efficiencies requires data. Making this shift would be nearly impossible without the insights that your data teams provide. 

This shift has led us to focus on a few key areas: 

  • Reducing customer acquisition costs. We work with data teams to understand exactly which campaigns and channels are driving growth—and which aren’t. 
  • Increasing average contract value. It’s easier to expand within your existing customer base than to drum up new customers. We identified which customers have the highest propensity to buy and which have the highest All Commodity Value (ACV). This enables us to close more deals at higher dollar values with fewer leads. This requires sales, marketing, and data team to work together to identify the customers most open to expansion and find the best campaigns to entice them. 
  • Unlocking profits via efficiency. We’re all being asked to do more with less—fewer people, less time to invest in automation, and less budget to invest in tooling. How do you continue to innovate in these conditions? 

Watch how dbt Labs uses dbt, from Coalesce 2023 in Sydney:

Why dbt Labs has a revenue-driven mindset

When we started our first few campaigns, we knew we needed a way to track revenue and efficacy especially as a startup. As a result, we peppered our data engineering team with question after question. 

“How many pipeline opportunities have my campaigns driven?” 

“Which of my leads from my events have received follow-ups?”

“How many contacts from this campaign came from paid media?”

The experience was frustrating for both parties—because we had to wait on answers—and for the data team, which found itself flooded with our one-off requests. 

We eventually realized that ‌we needed a self-service campaign analytics dashboard. We wanted a tool that enabled us to drive data-driven ROI without bothering the data or ops teams. 

We had three goals with this project:

  • Empower us as marketers to understand our data—without specialized technical skills
  • Reduce dependencies on the ops and data teams so they can focus on higher-level task that move business strategies 
  • Improve data consistency through testing and validation to yield more accurate and consistent reporting 

Our embedded data engineer took these requirements and worked with team members to create the Campaign 360 dashboard. Using dbt tools such as macros and tests, he gave us an all-encompassing, self-service tool with data that we knew we could trust. 

With a few simple clicks, we can now answer detailed questions about campaign performance. For example, let’s say you and your manager want to get super-specific on a metric. Your manager might ask, “Which of your campaigns was the most effective at driving pipeline last quarter?” 

Without a self-service tool, you might make a request to the data team and get back to your manager a day or two later. With a self-service tool, you can pull the tool up in your meeting and see instantly which campaigns drove the most pre-pipeline opportunities in a single period. 

Or let’s say you have questions about conversions. Where are our conversions coming from? How many accounts have we converted to sales? How many have received follow-ups?

Without a self-service dashboard, you might wait days or weeks for someone in data engineering to pull these numbers for you. But with a self-service dashboard built around your team’s needs, you can drill into those numbers on demand. With a dbt Cloud pipeline running to update these numbers with transformed data every 24 hours, you also have the confidence and trust that the data you’re seeing is current and accurate. 

With a well-built, self-service tool, you can see exactly how much money each campaign has made. This enables your team to make informed judgments about what to fund or cut. 

Beyond driving revenue-based results, a self-service dashboard can make your team members more confident and engaged in data conversations. Most marketers aren’t data engineers by trade. But with a self-service dashboard, your team can effectively participate in discussions of data and advocate for their customers. 

We are now enabled to move from discussions about data accuracy to focusing on actions and results.

How to become a data-driven marketer 

So how do you make the shift to data-driven marketing? 

To be sure, this isn’t a shift that happens overnight. It requires knowing what data you need and working closely with your data engineering teams to build the underlying data pipeline that will power your analytics. 

It also requires a culture shift, which can take time. The best way to make this shift is by making the data a daily part of your workflows. 

Here are a few tips on how to get started: 

  • Align on your goals. What will you measure? What metrics are the most important to you? Identify your most important metrics—engagement, clickthrough rate, conversion rate, cost per lead, etc.—and use them to drive the design of your dashboard.

    Work closely with your data engineering team or embedded data engineer. Before writing any code, your marketing and data teams should sit down and hash out a standard nomenclature, set of metrics, and segmentation. Without this pre-work, you’ll spend numerous cycles circling back and clarifying your needs—an unnecessary waste of everyone’s time. 
  • Start tracking campaigns. To set baseline goals and KPIs, you need data. Start tracking all of your campaigns in your self-service dashboards and report out. The more data you accumulate over time, the easier it is to see trends. As data comes in, you can establish a base performance level against which you measure all future campaign improvements and investments. 
  • Focus on continuous improvement. Start a document that tracks what’s working and what isn’t in any given area. Which campaigns are most effective? Which should we cut? Where can we benefit from targeted experiments to see what might work better? 
  • Collaboration is key. Work with your team to decide where to make cuts and where you should increase your investments. Continue to work with your data team ‌to identify new metrics or new ways to filter data for better insights into campaign performance and customer behavior. 

Get the right data with dbt Cloud

Of course, this all assumes that you have good, clean data that is accurate, up-to-date, and easily accessible. Unfortunately, most companies struggle with what we call analytic debt. 

Analytic debt occurs when stakeholders don’t have a clear understanding of the data available to them. This happens because data is inconsistent, opaque, or slowed down through process bottlenecks.

You may have some marketing data available to you now. But if you find yourself wondering why the data doesn’t look right, or if you don’t know when it was last updated, then you don’t have the tools you need to make informed, data-driven marketing decisions. 

This is where dbt Cloud comes in. dbt Cloud is a data transformation platform that shifts your teams from an expectation of waiting to an expectation of knowing. It does this by ensuring that the answer to every data question you have will be answered the same way, no matter who asks it. 

Modern data requires two tools: something to ingest the data (e.g., into your data warehouse) and something to transform it. dbt Cloud uses SQL or Python to transform data where it lives. It provides tools such as version control and easy-to-use Integrated Developer Environments (IDEs) to speed up development. 

With dbt Cloud, you can create analytics that drive value, so that your stakeholders know where data comes from, when it was last updated, and how to access it in a format that fits their needs.

Self-service marketing data with dbt

The marketing team at dbt Labs benefits from numerous dbt Cloud features that make it easier for us to self-service answers for our marketing data questions—and even fix problems ourselves. 

dbt’s documentation feature, including its Directed Acyclic Graph (DAG), enables data engineers and others within the company to ship dbt models with rich descriptions describing a model’s origins, business purpose, and structure. This provides yet another avenue for users to self-service answers to critical questions.

Say that you have a question about a metric and how it’s defined. Without documentation, you’d probably ping the data team on Slack. They get back to you with a definition. You realize that wasn’t how you wanted to construct the metric. So you log a support ticket…which may get looked at. Eventually. 

With dbt documentation, you can read the docs for a metric and then use the DAG to see how the metric was sourced. If it’s wrong, and you know SQL, you can use the dbt Cloud IDE to develop, test, and submit a fix directly. That means the data team only needs to approve a Git pull request instead of investigating and resolving the issue themselves. 

These self-service tools make a huge difference. Over 94% of Campaign 360 dashboard usage is from non-data team members. In other words, over 94% of the questions previously asked of the data team can now be self-serviced. The data team saves time, and marketing can make decisions faster—a clear win-win.  

This isn’t the only way that dbt Labs leverages dbt Cloud to streamline our operations. From revenue recognition to headcount analytics, we are constantly looking for ways to eliminate manual interventions and increase company trust in data. 

When marketers have goals to hit (like the sales team does), they need the right tools and data to track their performance. Moving everyone in marketing to a revenue-driven mindset drives improvement in campaign performance. And that leads to increased revenue and decreased costs per lead for the company.

By working with your data engineers, you can create a self-service marketing data ecosystem that drives revenue through continuous improvement. These tools provide the foundation you need to shift from guesswork to a revenue-driven, data-driven approach to marketing. 

[Check out dbt Labs Founder Tristan Handy’s observations using the new dbt Cloud.]

Last modified on: Jan 3, 2024