How to use data context to build a better digital marketing story

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Anyone executing performance-based marketing for any length of time understands the importance of a report. It shows you what you’re getting, who you’re reaching, and what you’re paying. However, whether your marketing is in-house or executed by a digital marketing agency, you have to trust the experts closest to the campaigns to guide you to what data is most important, meaningful, or actionable. Even the best reporting dashboards are near useless if they don’t deliver numbers in a way that you can understand. Thus, without this guidance, numbers are just numbers, with little to no inherent meaning. 

But they don’t have to be. Your reports don’t have to be – and shouldn’t be – row after row of clicks, impressions, conversions, etc. Those numbers are important, but they’re missing the context that is needed for someone to get the story behind the data. It makes it hard for digital marketers to truly understand the campaign and audience. Additionally, without data context, narrow reports make it difficult to show how best to get results. By going the extra mile and building context and insights into key performance metrics, you can better contextualize data so that anyone can understand what’s good, what’s bad, and what can be improved. Here are a few tips to get started contextualizing your data. 

Why data context is important

Even if you have all of your systems integrated and talking to each other, there’s still a hurdle that can hinder you from seeing actionable data: the context of your data is missing. Context can make your data easier to interpret and it can show you actionable takeaways. 

Data two-ways: A study in context

For example, the two below tables show the same data in different ways. They’re both a measure of conversions by location for a paid search campaign. 

Data table example
Table 1 shows data in the most straightforward way – with typical conversion metrics straight from an advertising platform. However, Table 2 reflects a custom metric for representation. In this data set, representation measures conversion data as a proportion of that city’s population. A location with a representation percentage above 100% is overrepresented relative to its population, while a location under 100% is underrepresented. The city population aspect adds context to the data.

The importance of metrics with built-in context

It may seem simple and intuitive, but creating custom metrics with built-in context is often ignored due to how much work it can be. However, it’s important for any marketing team to improve the relevance of their data. In fact, it’s so important to weight performance metrics that most digital advertising platforms already do this by default when looking at internal data.

  • If you measure clicks in relation to impressions, you get clickthrough rates (CTR), which can be used to compare digital ads to determine what resonates the best.
  • If you measure conversions in relation to clicks, you get conversion rate (CVR), which can be used to compare landing pages, relevancy, etc.

Metrics that don’t have built-in context aren’t as actionable because they don’t say anything about potential or what could be. Clicks, impressions, and conversions should never be the focus of a report by themselves because those metrics alone can’t be evaluated in a vacuum. Metrics must be rooted in what’s known about the campaign or market. Without this, your numbers are just numbers, not insights. 

Creating a data narrative through insights

With the information from our table example, you can go deeper with analysis and campaign insights. This includes the ability to draw comparisons between the locations, even if you know little to nothing about campaigns otherwise. This additional context can help inspire deeper-level questions. It also enables us to ollect more information to build a narrative around the aforementioned paid search campaigns. For example, you might explore:

  • Do people in Evansville just not search as much as people in Elkhart? 
  • Are the keywords not as relevant to underrepresented places?
  • Do the underrepresented locations have smaller markets relative to their size? 
  • Are people in Fort Wayne seeing the digital ads or organic materials less often than people in Indianapolis? 
  • Is something in the messaging or landing page more interesting to some places over others? 

The questions are endless. However, we can only ask those questions because we have formatted the data. This formatting allows us to make meaningful comparisons. Our campaigns will not be data-driven if we can’t format performance data in a way that can be understood well enough to drive action.

If contextualizing your data is new to you, there are a few places you can start. Look to internally provided market research, industry-standard data sets, or publicly available sources depending on your business, product, and target audience. Any of these data sources can be tapped to help you begin to transform raw data that comes from digital marketing campaigns into contextualized data.

Interested in learning more about how to contextualize your data for better, actionable digital marketing insights? Reach out to our experts today. 

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