Marketing analytics: measuring corporate communications with Looker

  • 28 March 2022
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This content, written by Tamara Ford John, was initially posted in Looker Blog on Oct 11, 2019. The content is subject to limited support.

I’ve always preferred words to numbers. I’ve fallen asleep with my nose in a book since I learned to read, so a career in Corporate Communications (Corp Comm) felt like the perfect fit for me. My interests and strengths seemed pretty predictable until I found myself joining a data company where everyone—even the English majors—relied on numbers.

Still, I saw myself as a marketing person at a data company. The Looker marketing team has used Looker to manage marketing analytics since there was a Looker marketing team (this all started with ). We use Looker for , , , and much more.

But as a Corp Comm team, we didn’t expect access to data would enable us to analyze the impact of our efforts so easily.

Looker—both the platform and the culture—changed that.

While much of what communications teams do can’t be measured easily (or happens behind the scenes), we wanted to find the best way to track, analyze, and share the things we CAN measure. We wanted to go beyond metrics such as “share of voice” and “number of social media followers,” which are helpful but tend to look backward rather than using data to suggest improvements and focus on communications metrics that provide actionable insights.

Because, at Looker, we believe data isn’t just about reporting—it’s about improving.

Measuring communications analytics with Looker

Building our team a high-level communications dashboard seemed like an excellent place to start. To do this, I worked with our Marketing Analytics Manager. He and I began by thinking about what data in our internal Looker instance would hold the most valuable insights for our team.

Because we have Google Analytics 360 (GA 360) and Salesforce (SFDC) data accessible via Looker, we knew we could analyze activities on Looker.com as well as activities associated with campaigns and leads in SFDC. From here, we thought about our available team data in three buckets:

  • Awareness
  • Content (i.e. press releases & blogs)
  • Analyst relations

For this first dashboard, we focused on the activities that we drive on Looker.com.

Measuring awareness

Awareness is a common Corp Comm goal, but how do you measure it? Is it a brand survey? A count of external mentions or impressions?

It can be all of these things! And while a combination is probably the best approach, we use organic search and direct web traffic as a proxy for general Looker awareness.

Our entire marketing team tracks this change over time by region, but I wanted to identify the Corp Comm impact on this metric.

Years ago, when we first built a social media strategy at Looker, we started adding into our links that we leverage in both GA 360 and SFDC. We have UTM tracking in our various social media channels, employee advocacy tools, press releases, and large campaigns.

Using GA 360, we can see what channels drive the most traffic and web activity. Using SFDC, we can see what acquisition campaigns drive downloads and progress down the sales funnel.

By combining this data in Looker, our team can identify how we contribute to the increase in web traffic as well as how we help create Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQLs), pipeline activity, and closed business. This information allows us to identify what channels are the most valuable.

For example, while we have the most followers on LinkedIn, we see that Twitter drives more traffic to Looker.com. We also see that press releases and employee advocacy social posts lead to more form-fills than corporate social posts.

We believe this is because folks following our corporate account are already familiar with Looker while connections of our employees may still want to learn more and download content. We’ve taken action on these insights by putting increased effort into our employee advocacy tool and program.

Measuring content performance

Most of the content our team produces is published outside of the Looker domain, which makes it much more difficult to accurately attribute our content’s influence on the business.

In the future, we will share how we analyze some of that data in Looker, but for this post, I will focus on an analysis of our newsroom and blog pages, as published on our website.

By analyzing page data, our team can identify our top-performing press releases and blogs as well as spot trends to help us understand what types of news are most interesting to our audience and what kind of blogs they want to read.

One interesting finding is that our newsroom and blog readership have very different interests. For press releases, we see the most traffic for corporate announcements sharing business growth and major hires.

Meanwhile, our blog audience is most interested in detailed and how-to pieces as well as reading about insights from data (, , and , etc.).

Our team uses these insights when deciding what belongs in our newsroom or on our blog as well as how to focus our time on side projects (ex. analyzing interesting datasets).

Measuring analyst relations

Some of the richest data available to our team has to do with tracking the ROI on content derived from our work with Industry Analysts (think Gartner and other research firms).

When we license the rights to a report or a webinar with one of these firms, we create a custom campaign in SFDC to track the leads who interact with each piece of content—as well as how they move through our sales funnel and (hopefully) into our customer funnel.

By analyzing this data in Looker, we’re able to easily compare pieces of content across campaigns and firms to identify the impact and value on our business. Our team uses this data to help determine which firms we hold seats with, which reports we license—and how we prioritize our time (filling out requests for information (RFIs), researching, and creating content for webinars, etc.).

This in-depth analysis has helped us identify which firms and reports bring in the most MQLs and which reports create the highest value MQLs (leads who are most likely to move through our sales funnel). Interestingly, we have found that the highest volume and highest converting pieces of content are not necessarily the same.

All of this data helps our team determine how we spend our time and money and ask for additional resources and support.

When we’re getting ready to launch content that we KNOW is high value but that may require additional support from other teams, we can share this data as proof that their effort will be well spent. And, when we test something and see a low return, we’re confident in passing on similar opportunities in the future.

For a team of ‘word folks’ who are quite new to data, having access to these communications metrics as answers and ‘proof’ of our work has been game-changing.

And we’re just getting started.


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