Ryan Vong

President & CEO

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“What advice would you give marketing practitioners looking to maximize the use of their marketing automation systems in 2019?” It’s a question we get on each client engagement.  The answers vary based on some fundamental differences within each company. A better question isn’t necessarily ways to maximize their use of an MA system but should be about how to show incremental results of marketing with the investment they’ve already made in a platform.

I often hear questions that begin with “can I do this with it?” or “does it have ‘x’ feature?” As a practitioner, it’s hard to avoid asking those questions because they are necessary for the day-to-day operations. You’ve introduced a system and now it’s a living, breathing ecosystem of software and tools that can consume all waking hours on top of the context of a “normal” marketing job. However, when I ask for the initial reasons why they purchased marketing automation, the answers aren’t as clear as they need to be. “We want to nurture…” — OK, but why? What made you think that was necessary? What does nurture mean? And most importantly, what is the business outcome you want to achieve?

My point is this: to understand how to maximize the system, you must first understand what to optimize. I say incremental results because doing the right things and getting 5% improvements month over month leads to a 20% improvement over the course of a year. That’s a huge increase for any meaningful objective you’re measuring. Get clear on what moves the needle for marketing and leverage the “machinery” to accomplish this. The idea that “marketing automation” solves marketing processes or automatically provides better alignment will lead to disappointment.

For some of the more “rubber meets the road” sort of advice, I would say stop sending so many emails. It’s said in jest but there’s definitely a bit of truth in it. It’s a difficult behavior change because we’ve been so conditioned to do so, and MA makes it so easy. The intent of these systems was always about being smart about what you say to whom: the right timing, right audience, right message and right signals. However, MA users tend to miss this basic tenet. Instead of thinking more along the lines of what-to-send for this, remind-for-this, send-again and follow-up with another send, I’d invest in getting a solid understanding of the type and level of data you must see, how to do this effectively and resist the temptation to look at the world through email metrics. Do you have segmented data on your audience and understand how to address their needs? Can you determine if you are getting the right signals from your MA about this person? Invest in the time and effort to do this since that level of information is what should be driving marketing automation.

And further into 2019, we’ll continue to hear more about personalization. From a practical MA level, it tends to be about how many fields can be customized to display the user’s data back to them to “be personal.” However, it tends to be a mixed bag of wrong tokenized information like “Hello, First Name” to company and title details, which is not really engaging and sometimes just odd given the context of the message. I think consumer marketing is light years ahead on this front with well-targeted and timed events for engagement, and there’s a lot to carry over to the B2B side. This might even be one of the most direct paths to maximizing your marketing automation investments as it crystalizes the right message, right timing, right audience and right signals idea.

Lastly, and this may be the biggest one: put your marketing analytics to work for you, driving actionable improvements.  MA is great at collecting data — lots of data to make you smarter about your marketing investments all the time.  We see so many customers fall short to get to a place where everyone agrees on analytics, trusts them and uses them to improve.  This ties back to what I said earlier about data. So much of what it takes to get analytics working falls to data the right data collected the right way, normalized and de-duped. All these data hygiene and structural requirements must be dialed to get to great analytics.

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