Marketing Metrics Made Possible by Unifying Data

How well do you think you know your customers? At Velir, we work with people at businesses who collect lots of customer data but struggle to assemble a cohesive picture of their customers' needs and behaviors. Often, the root of the issue involves data silos: individual systems that collect — but don't share — important customer data. What we've found is that by unifying data across silos, marketers can unlock a fuller understanding of their customer journey and the impact of their campaigns.

Unified data often takes the form of a central customer data repository like a data warehouse or customer data platform (CDP), which combines your data sources. In this post, I'll share several marketing metrics that are only possible through data unification. First, however, let's get aligned on the challenges of working with siloed data.

Challenges of Data Silos 

A 2024 survey conducted by Okta cited that companies with 2,000 or more employees use an average of 231 different software applications. With this being the case, it should be no surprise how data silos multiply. Marketers feel the impact of these silos more than other departments given the explosive growth in the MarTech vendor landscape and rising consumer expectations for tailored experiences across their entire interaction with a company online. If you've ever had to log in to multiple systems to pull together campaign reports or build out audience segments using multiple data formats, you've felt the pain of data silos.

Help for Unifying Data Has Arrived for Marketers

If the increasing number of data sources and the expectations of consumers are the headwinds, then the rising tide of increasingly sophisticated and easy-to-deploy cloud data systems are the tailwinds at marketers' backs enabling data unification. Data warehouse tools like Fivetran, Snowflake, and dbt are designed to aid your data unification process by bringing critical business data sets together in one place. CDPs like our partners at Segment, Rudderstack, and Hightouch have the same mission but specifically focus on customer data. These tools have become increasingly sophisticated yet easier to use, solving many headaches for marketers by allowing them to unify customer data. Deployed correctly, these unified data platforms put real-time, trustworthy, cross-channel metrics in the hands of marketers, eliminating data silos. Let's review some metrics that are only possible with this unified approach.

Unified Metrics for Marketers

Return on Ad Spend (ROAS)

Definition: The revenue attributed to marketing spend measured as a multiple of that spend.

When calculating ROAS, there are two primary components: 

• How much revenue did customers generate from your campaigns?
• How much did it cost to acquire those customers?

Paid media platforms like Google and Meta are quick to deliver their own take on this metric based on the conversion signals they receive. Unfortunately, they offer only limited visibility into the value of a conversion. Therefore, many important questions are left unanswered. For instance, does the user go on to convert into a loyal customer? Or do they abandon your brand after their free trial ends? To understand the true return on ad spend, you need visibility into two types of data: cost data from the ad platforms and revenue data from your product. Unifying this information allows you to improve attribution accuracy, identify the most cost-effective channels, and optimize your campaign performance.

Experiment/Personalization Lift

Definition: The incremental improvement in key performance indicators (KPIs) resulting from A/B testing or personalization efforts.

Experiments are only as useful as the data you get out of them. Did your design change make a positive impact? Or the reverse? Experimentation platforms are often equipped to answer simple answers out of the box like, "How many users saw an experiment and took an action in the same session?". They don't, however, provide an understanding of an experiment's impact on core business metrics such as LTV or churn. These same limitations exist when measuring the effectiveness of personalized experiences.

By combining experiment and personalization exposure data with back-end business metrics, you can measure the business impact of experiments, identify the most beneficial strategies, and optimize based on long-term rather than short-term outcomes. Optimizely, a company that makes A/B testing software, capitalized on this opportunity in its acquisition of the product analytics platform, NetSpring, to unify experimentation and business metrics. We wrote about the benefits of this acquisition in our blog post, here.

Attribution Score

Definition: The contribution of a particular campaign or channel towards another metric such as conversions or revenue.

Paid media vendors love to assign credit to themselves when measuring conversions. A problem arises, however, when multiple channels attribute all revenue earned from a single action towards themselves. How is it possible for Meta, Google, and TikTok to have produced $300 in revenue when $100 was earned? To gain clarity, marketers require unified engagement data and a multi-touch attribution model that can produce attribution scores based on the user's cross-channel journey. Easier access to this information was part of our 2025 predictions, as we feel marketers are ready to break free of vanity metrics and move towards unified metrics that are fully aware of the customer's complete journey.

Customer Segment Metrics

Definition: Key business metrics measured across different customer segments (e.g., subscription tier, engagement level, loyalty status)

Marketing tools often provide their own reports and profess to make marketers "data-driven." Problems arise, however, when marketers need more granularity from these metrics to inspect the customer segments they care about most. Pulling a report on "orders over time" isn't helpful when you need to see that data split by new vs. returning customers or loyal vs. lapsed customers. 

By unifying data at the customer-grain (meaning that each data point can always be tied back to an individual customer), you gain metrics that can highlight a specific slice of customers. Investment in this approach has the added benefit of making customer data useful for personalization. We write more about the benefits of personalization here.

Benefits of Unified Metrics

The metrics above are only possible when data silos are broken down and data is unified across various MarTech systems. Accessing these metrics is valuable, but there are other benefits to this approach as well:

• Centralized Visibility: Imagine a single dashboard or tool where you can access all your crucial marketing metrics. No more switching between multiple platforms or exporting CSV files.
• Improved Data Quality: Data unification processes often involve cleaning, standardizing, and validating data, leading to more reliable insights.
• Collaborative Insights: By making data centrally available, multiple teams can access the same information, fostering data unification and a culture of data-driven collaboration. Instead of conversations that boil down to "my data says this," everyone will have one common version of truth.

How to Get Started Unifying Data

It's never been easier to start down the road of unified data. Advancements in cloud data warehousing and CDPs have placed better data management within reach of both startups and enterprises. At Velir, we specialize in bringing our customers a fully functional modern marketing data stack in as little as three months. Our customers are often amazed by how quickly they can access and explore production reports containing data from what used to be very disparate data sources.

Ready to deploy your modern marketing data stack so you can start using a unified data platform to drive better insights and decision-making? Contact us. Our experts would be happy to help you get started.