When Data Doesn’t Deliver: Building a Better Road Map to Drive Business Success
We recently commissioned a study with Forrester Research to help us better understand something that we’ve heard from customers since the first day we went to market over six years ago: Why is innovation so hard around customer marketing and engagement? Why are emails still bland and unpersonalized? Why isn’t messaging fully omnichannel? Why is revenue falling short of goal? Yet despite having seemingly the right people, processes and systems in place, why aren’t things improving?
To answer these questions, Forrester interviewed 465 marketers to better understand the root causes. It focused on MarTech capabilities, and then asked marketers to talk about associated outcomes or lack thereof, before diving into limitations around these capabilities.
When the survey came back, the headline confirmed what we were seeing: the vast majority (90 percent) of respondents said that they had issues delivering against their core strategic initiatives. This wasn’t at all surprising to us.
Yet digging into the causes of these shortcomings was in fact quite surprising. While 90 percent said they had issues hitting their strategic goals, 90 percent also believed they had all the right capabilities in place to affect these goals. But if the goals are aligned, the strategy is sound, and the tools and processes are in place, then marketers should be hitting their goals every quarter. So, where’s the disconnect?
As it turns out, the problems aren’t in the capabilities but in the glue that makes them all work together. Without the right cohesion across systems, personalization just can’t happen. It’s like a bulldozer running low on gas, or a sports car in need of new tires. You just can’t do as much with them as you’d like to.
The first form of missing “glue” is data. Customer segmentation is only as powerful as the data that powers it. Lifecycle messaging automation can only be as accurate as the real-time behavioral signals collected around customer touchpoints. Without the right data in place, these capabilities simply can’t perform. Over a third of respondents (34 percent) said that they don’t have the right data in place across their customer marketing systems.
The second form of glue lies around getting these systems to work together. To execute true omnichannel messaging, your systems need to know about each other, and they need to drive messaging in a systematized way. System connectivity was the other big bucket of shortcoming in the survey, with 35 percent of marketers saying they’re unable to properly integrate data.
There's an old adage that rings true across all data applications: garbage in, garbage out. And MarTech is no exception to this rule.
So what does the world look like when “garbage” is no longer in, and high-quality data becomes accessible and actionable? Below are a few stats that we’ve seen across our customer base today:
- Customer segments that are defined using data coming from more than one source convert on average 2x higher than their single-source counterparts, and segments that also incorporate event data convert 3x higher.
- Dynamic content that’s populated by not just contact-level data but also real-time event-level data converts an average 130 percent higher.
- Behaviorally triggered messages that incorporate more than two real-time customer actions have an average of 2x engagement and 6x conversion rate.
The benefits of having the right data in the right context are clear. However, without requisite systems and support around the management of this customer data, MarTech systems will continue to look great on paper (or in PowerPoint decks), but will fall flat on outcomes.
Jason Davis is the CEO and co-founder of Simon Data, a customer data platform that enables you to drive marketing results faster with a solution purpose built for growth.
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Jason Davis is the CEO and Co-Founder of Simon Data. A data scientist-turned-entrepreneur, he previously founded Adtuitive, a retail adtech platform that was acquired by Etsy in 2009. While at Etsy, he led several engineering teams including data science, analytics, and big data infrastructure. Davis holds a Ph.D. in machine learning from the University of Texas and spent time developing search algorithms at Google.