Turn Metrics Into Momentum: Engaging Your Leaders With Data
Top three takeaways:
- Consistency and a “single source of truth” in data across all aspects of your marketing campaigns and potentially even across brands in your organization is crucial to comprehensive campaign evaluation.
- Understanding what lies beneath the topline campaign data is essential to bringing an elevated view of results to leaders. They want a high-level, coherent view that connects the dots between the data and strategic impact.
- Use metrics that matter to your business and your leadership.
When your insights are founded on elevated data and analytics, you’ll present more sound marketing recommendations and investment rationales. In this blog, Kit Burkus, IQVIA’s senior principal and measurement innovation lead (and self-confessed “data nerd”), shares her insights on ways that brand marketers in pharma can turn metrics into marketing momentum. Sarah Brown, head of marketing at IQVIA Digital, interviewed Kit, and this is a condensed version of that interview.
Sarah Brown: Brand managers continue to look for new ways to present data and analytics to leaders on the business side. What can elevate the conversations that brand managers may have with their leaders?
Kit Burkus: The key to having elevated conversations lies in being able to connect the dots between what the data says and the strategic objectives of your marketing campaigns. Understanding what lies beneath the topline metrics gives you the ability to make better strategic decisions to say, we can do this better and here’s why. Maybe your connected TV (CTV) ROI wasn’t great this year. You can usually find the “why” in all the data points that went into your mixed model.
Beyond that, results derived from individual-level data can give you important insight on why a campaign wasn’t as successful as expected. As an example, maybe CTV didn’t work because everyone exposed to CTV was already exposed to digital.
Look at the assumptions that guide your mixed models. They make the results more or less accurate. Are your assumptions sound? Why or why not? Act on your analysis. You can then tell leadership that you’ve honed your model assumptions and you’re more confident that they’re accurate. But you also need to know why a campaign is performing a certain way. Consistent data enables you to dig in and diagnose what’s happening. Then when you speak to leaders, your commentary has depth and accuracy. You can also better justify shifts in marketing investments.
SB: What are some specific metrics that brand managers should be looking at?
KB: Important metrics that you should be looking at regularly include:
- Audience quality
- Net impact
- Statistical significance/confidence levels
- Unique reach across channels
These metrics can help strengthen your model assumptions and make it easier to adjust your campaign as you go.
SB: What are some of the challenges and pain points of brand managers who are using marketing campaign data to tell their stories to leadership?
KB: One challenge is consistency. A group of brand teams may report to the same person, but their data may come from different sources. Leaders can often find this a challenge, as well, when data is coming to them differently across marketing teams. Even when the data is consistent, it can be difficult to get a holistic view of what’s happening with your brand.
Understanding the analytics is another pain point. In elementary school, it was cool to say you hated math, but now here you are, having to understand these reports AND discuss them with your leaders.
SB: Why is consistency so important?
KB: Consistency in data makes it easier to pull it all together at the end of a campaign and give leaders the high-level, coherent view they’re looking for. It also gives you a more solid basis on which to make marketing decisions. Using different data sets to measure what’s happening across your ecosystem makes that far more challenging.
Let’s take measuring for diabetes as an example. Well, one data set could cover 95% of people with diabetes, but another covers only 70%. Another one covers 65%, and it doesn’t have the same people as all these other data sets. It’s very hard to have a holistic view when everyone’s pulling on data that doesn’t align.
SB: How do you resolve that inconsistency?
KB: You want to have the same data set underlying all of your analytics. Sometimes analytics companies that don’t have strong data come to us, and we’ll do a third-party agreement so they can use our data to create their models.
You want your mixed models and regular reporting to serve as a “single source of truth.” You don’t want different analyses telling you different things about campaign performance. A single source of truth also makes for better discussions with leadership because you know your analytics come from a single reliable data set.
It’s also important to use analytics regularly. In addition to your mixed models, use weekly or monthly analytics to get a directional sense of brand performance and make budget pivots as you go. If you use a consistent data set, you can use your weekly analytics to hone the model assumptions related to that campaign. Your models are at the national level; they don’t look at individuals like consumers or healthcare professionals.
So why not take that individual-level data and work with it? Do some additional analytics to inform what your model should do, and connect the dots. Have truly data-driven inputs for the assumptions that you’re baking into your model. When you make more accurate assumptions, the data will be more actionable.
SB: What should brand managers do if they see data in their reports that they aren’t sure about?
KB: Most people can sense when something’s wrong in their results. If something doesn’t seem right, pull in an expert. Ask your analytics team questions and try to understand the “why.” Ask questions like: What test did you use? How did you choose a control group for this? Are your metrics aligned to the business question you’re answering? Use this as an opportunity to deepen your own understanding and get more savvy with the data yourself.
SB: Thanks for sharing your insights, Kit.
Watch for more posts like this one in the weeks ahead. In the meantime, you can learn more about data and measurement with these resources:
- Burkus, K. Significance testing: What brand managers need to know. IQVIA Digital. 2024 Dec 14.
- Two metric “must haves” for healthcare marketing: Audience quality and net impact. IQVIA Digital. 2024 Oct 18.
Or watch our recent webinar featuring Kit: Our resident data nerd spills the tea: Confessions from a methodologist. IQVIA Digital. [cited 2025 Feb 4].
