Dear Analyst #104: Creating a single source of truth by cleaning marketing analytics data with Austin Dowd
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I’m always fascinated by how different people on the podcast find their way into data, and this episode is no exception. Austin Dowd has always enjoyed photography. He was associated with the American Marketing Association and was always curious in the metrics for his photos. After pestering the analytics person in the AMA in terms of how to analyze his photo metrics, he eventually decided to do a career change into analytics and received a Nanodegree in data analytics through Udemy. This career change coincided with the onset of the pandemic as his photography business started slowing down. We talk about Austin’s experiences working for a big conglomerate to a startup, working with messy data, and how photography is just like data visualization because you’re telling a story.
Marketing analytics at a startup vs. a big conglomerate
As you can imagine, working for a big company comes with its pros and cons. You have a ton of resources to tackle large problems and projects, but the change management can be quite the process. Multiple teams and stakeholders are involved, and changes can take months to years depending on the type of change you are trying to make.
Austin worked at Cox Automotive and talked about how the data stack at Cox Automotive was custom built years ago. That means even small changes to the system were very hard to adjust. Then you have the issue of clients wanting custom edits to their reporting. If you don’t have a data engineering team that can build an infrastructure that allows analysts to edit reports on the fly, you’ll start getting into being a consulting company providing custom data analytics solutions.
Austin moved to a startup called Blues Wireless where they built a robust data stack, but they didn’t necessarily have the marketing team in mind when building out the stack. Product usage analytics were top of mind for the small but budding analytics team. Austin was brought in to coordinate web analytics projects so that the marketing funnel–from a website visitor to a conversion–could be better quantified. Getting accurate data, however, is paramount to this project because you can’t make decisions on bad data.
Website data platforms fighting each other
Austin is currently enrolled in a data analytics masters program, and he talked about how cleaning “dirty” data is very different in the academic world vs. the real world. This is true for any discipline, I suppose. In the academic world, the problem space is confined and there are “right” answers, so to speak. In the real world, surprises and nuance are littered all over the place. In the marketing analytics world, you are working with a data format that a Google Analytics or some CRM platform forces upon you. In rare scenarios, cleaning data is as simple as using Python to get rid of a bunch of
Austin realized that page data was being counted twice. I’m sure all of you have dealt with double-counting data and coming up with a system to de-duplicate data. The problem came down to Google Tag Manager fighting with Segment to report on page data for the website. Austin uses GA to view early marketing funnel activities and Segment pulls late marketing funnel activity. Once he got these two systems working together and the output is clean data, he then pipes the data into Google Data Studio so that the data is accessible for Blues’ content creators and business stakeholders.
On the topic of de-duplicating data, I feel like all analysts have to go through solving this problem at some stage in their career. Finding the root cause of duplicate data requires a lot of spelunking through different systems, curiosity, and down right determination.
Austin’s team came to a point where they couldn’t de-duplicate the data anymore since the data was simply not reliable. There were two systems and around the new year, their team just said we’re going to create a “point of trust.” This is a specific time where they say the data coming from one of the systems is clean, and everyone will trust the data coming out of that system going forward.
How a background in photography helps with data storytelling
When Austin was working with businesses as a photographer, he talked about being a visual storyteller. His clients wanted to deliver a message with the help of Austin’s photos, so Austin’s job involved working with different people and departments to figure out their goals. He then came up with a strategy on how to capture the right photo to meet those goals. In his new world of data analytics, he says he takes the same approach. The only difference is that he’s working with data instead of photos.
In terms of translating his analytics work to business outcomes, content creators at his company want to know some basic metrics on what links are being clicked on. This way, content creators know how which content resonates the most with site visitors. Austin created a dashboard for these content creators so that they always know what content is doing well on company site.
Austin’s boss also wants to know what the ROI from their various marketing campaigns is. This is where setting up tracking on the entire marketing funnel is important. Through Google Analytics (what Austin uses to track top of funnel metrics), you only get anonymized data at an aggregate level. With this data, Austin can see which web sessions lead to a sale or conversion.
Austin’s company is a B2B company which sells a product to data or engineering teams. Customers don’t just go to the website once and decide to buy their product. It might take several visits to the homepage, a blog post, or a webinar before they finally convert. For these bottom of the funnel metrics, Google Data Studio helps Austin map out the flow from someone who visits the website to the eventual conversion. Austin also uses Tableau to visualize Segment data which tracks bottom of the funnel metrics as well. In marketing terms, you might hear this type of tracking called the “360 degree view of the customer.”
Adding visitor scoring to marketing analytics stack
In my opinion, B2B content has been going through quite a transformation over the last 5 years. Long white papers, case studies, and webinars are being replaced with shorter TikTok-style content, podcasts, and Instagram Reels. There is still a world for this older type of content in some industries, but I’m seeing more content being created by employees at these B2B companies which allows for more personalized and “real” content. LinkedIn newsletters like this one, for example, allow you to reach a “business” audience that a traditional white paper gated behind an email signup would not provide.
Content creators at Austin’s company are also creating B2B content. Are the people consuming the content interested in Blues Wireless as a hobby or would they actually become paying customers? Austin helped created a dashboard that scores a site visitor to help the content team differentiate between a hobbyist and a power user of their product. For instance, if the visitor read a blog post they would get 1 point, but signing up for a trial might be 5 points. Creating a scoring system like this requires constant tweaking. Austin realized that a lot of the points were “front-loaded,” meaning people who consumed an educational resource on their website usually didn’t go back to that resource. This resulted in scores dropping substantially a few quarters later.
What a perfect marketing analytics future looks like
I asked Austin if he had a magic wand, what would he change to get the perfect marketing analytics system? He said at a startup, the landscape changes every week. A few weeks ago, the sales team might be ramping up so he was doing research on which companies would be good to target and putting those analytics together for the sales team. Another week, he might be doing late-funnel stage marketing analytics work working with Segment data. This sounds like the typical lifestyle for anyone who works at a startup :).
In the future, Austin hopes that the systems they’ve set up will stay fixed, and they can focus on doing more predictive analytics. Using machine learning, they might be able to figure out which visitors will most likely convert to a customer. Or which feature used in the product might lead to more usage and higher revenue in the future. At the end of the day, all this requires not just a solid system, but also clean data so that you can make solid predictions for the future.
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