Data Driven Marketer. There is so much buzz around being data-driven. I sometimes wonder if everyone understands what that means, and how folks got into wanting to understand the data. 

My story goes back to when I was the marketing department for the Knowledge Center at Reputation Institute in 2012. At the time, Reputation Institute was a fast-growing global Corporate Communication Consultancy. To help grow new clients, the agency created a Knowledge Center, which demonstrated its expertise in Reputation Management. One of our core products was the Reputation Management Certification program. Our go-to market included social media, email marketing, and partnership marketing. We utilized a homegrown email marketing system to share information about our offerings with past, current, and prospective clients. This was a good-sized database, and leadership believed we should have gotten a better response rate than we were. So I took a closer look was the marketing list size real or was there bad data that made the list size smaller? 

Previously, I worked with third-party data companies to rent marketing lists. These organizations, at the time, also offered a way to check email address validity and quality. For me to answer the marketing list size, I needed to understand of my list what amount was good data, and what amount was bad data. 

Good Data definition when I was running this exercise was that the email address was valid and delivered to a real person. That there were no duplicates in the email marketing list.  Bad data was identifying duplicates and honeypots, malformed email addresses, fake/disposable addresses, and general addresses like accounting@xwycompany.com. 

Hold on Honeypot? Malformed address? What are these crazy words I am saying? 

In the email marketing world, access to an inbox is a privilege, not a right. Organizations are out there to make sure you are not spamming individuals. Honeypots are inactive email addresses that were abandoned by their original user and repurposed by anti-spam organizations.  The purpose is to monitor what the individual is receiving and whether it complies with CAN-SPAM laws. 

Stepping into the data, I realized some issues needed to be addressed. The list size decreased, but the response rates increased. This exercise left me wanting to ask more questions. Why did these individuals sign up to hear more about us? Were we meeting their expectations? How else could we attract individuals into our database? What else could we offer to keep the individuals engaged? I left Reputation Institute shortly after this cleanup and joined CFO Publishing, where I started to answer those questions. 

Marketing Technology was taking off, the CFO was tech-forward, and I worked with several great tools while I was there. I had a fantastic partner with my IT manager, and together we would test and either adopt or move on from those trials. One product we were excited about was going to connect our email and marketing visitor information; the only catch was that each campaign required a tag to be placed on our website. We initially planned to run hundreds of campaigns, but the prospect of managing hundreds of tags didn’t seem feasible, so we decided to move on. (A tag is something you need to allow your web page to load, so having a lot of tags to load on your website will cause you to feel like it’s 1997 and take minutes for a page to load; it’s unacceptable today.) 

In 2013, we acquired a Marketing Automation Platform that connected our email and marketing visitors. And it did so much more. So many of my questions were answered, and new ones were coming. How can we keep our readers engaged and connecting with lead generation programs for our advertisers without fatigue? How can we better target lead generation programs without creating noise for other readers? How can we continue to expand our circulation to the target audience? And then we would dig into the data and tools.

While going into the data and seeing it firsthand helped me understand data, the real reason behind my success is being curious.  By asking questions about my audience and understanding what resonated with them from our offerings, I was able to present them more effectively and continue to see a lift in engagement. While at CFO, we saw a 150% increase in organic leads generated within a year. These numbers had a direct correlation with revenue, which doubled in the year.  

Having data knowledge and curiosity is very important in the age of generative AI. There are benefits from being curious about marketing data that will have a positive impact on your bottom line. But when you power it with generative AI, you will see more lift. Having the computers help answer questions without the potential blind spots and prejudices humans have will result in immediate results. The downfall is that if you don’t understand the data you’re working with and its limitations, you risk making incorrect assumptions.

 If you are on a journey to become a data-driven marketer or a seasoned marketing professional looking to pivot into AI, the first step is to cultivate curiosity. Ask questions and have your colleagues walk you through what everything means.  There is no need to delve as deeply as I did into the data; instead, focus on 50 lines of it. Seeing the data firsthand allows individuals to understand what they are working with. If your colleagues start using jargon, ask them to clarify their meaning or use more common terms.