The answers are in the data.
Question is, does your team have the culture—and tools—to find those answers?
Zontee Hou gave a powerful example of how effective data-driven marketing can be in her B2B Forum 2024 presentation.
Get Zontee’s insights from the video, or read the transcript below.
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How we can empower our teams to explore data proactively and cultivate a hypothesis driven culture:
It’s not enough to have data, right?
You have to do something useful and interesting with that data that impacts the business.
I want to share with you a story about Target.
In 2002, the marketing director at Target went to their Data and Insights team and said, “I have an idea and I want to know if you can pull the data for it…
“Could you figure out a way to identify people who are pregnant in their first trimester who have not yet signed up for one of our registry lists?
“So they have not identified that they are pregnant yet, but they are pregnant.
“And can we tell that from the way that their shopping behavior changes?”
Now, the director of their data and insights team, he said, “sure, let me think about that a little bit.”
And it took them about six to eight months of trial and error and hand-processing a whole bunch of data to figure out the pattern.
What they eventually figured out was, yes, they could figure this out before someone had signed up for a registry
If you stopped buying scented soaps and lotions and you went to unscented. If you of course stopped buying alcohol. If you started buying certain vitamins. If you started buying certain other product…
There was a basket of behaviors that indicated that you were a person who was likely in their first trimester.
And the team of analysts went back to the marketing team and said, “here’s your segment!”
They went and they ran a marketing campaign against it…
And it was so effective.
They saw immediate lift.
And in fact, it was so effective that there were people who were coming into the store complaining about how Target knew that their family members were pregnant before they did. That’s how good it was.
I bring this up because they came in with a very powerful hypothesis, and that’s something that we have to think about as marketers:
That, in order to have a good data-driven culture, we need to have a culture where we can ask these kinds of daring questions and then go back and look at the data and have access to the data.
So I want you to think about this.
Cultivate questions, not just answers, within your own marketing team.
Lead by example, by bringing the data into your marketing meetings saying, “here are the things that we have access to and that we can track.
“What are the hypotheses or experiments that we actually want to be running?
“What is it that the data actually tells us? And what data is missing?”
And then go out and run these different experiments.
And don’t be afraid of some of them failing, because not every single one of them is going to be like Target’s test, where it’s immediately successful.
Some of them, it won’t show anything at all.
But if you start to encourage your team to build this culture around cultivating questions and looking at the data, then that becomes a huge opportunity.
But more than just looking at the data, we need to think about, how can we empower our teams to really use the tools at hand, right?
Looking ahead at the next five years, the majority of marketers anticipate that AI and machine learning is going to be really impactful.
And one of the places it’s going to be impactful is giving us this access to the data in a way that even people who are not data analysts can go and query against it.
In fact, I want you to think about that Target example that I just shared. So again, it took them six to eight months of hand work done by a whole team of data scientists.
Now, if you had a data layer against that same database of shopping behavior, you could go in there and say, “build us a segment of our customers who’ve identified as pregnant in the last year. Find all of the commonalities in terms of what they bought or stopped buying within those first 12 weeks of their pregnancy based on their due date that they’ve provided us. Now, build us a segment that looks exactly like that.”
In three queries, we’ve done the work of six to eight months of this work in 2002.
So what I’m saying here is that you need to prepare your team for AI augmented work, but to really identify again, where are the places where we the people are doing the work of identifying the important hypotheses?
And doing the work of really thinking about personalization, and what matters to our customers, and where AI can augment by helping us to streamline some of our processes and make it easier to do things like the queries.
That means that you need to strategically identify the AI use cases, right?
Are our best opportunities in places like segmentation and targeting or predictive analytics?
Or are they more customer facing within content creation or curation, and then customer service and personalization?
Identifying the places where you guys need the most help, that becomes part of the exercise of preparing yourselves to use AI strategically and not to waste time.
We cannot do it all.
Nor should we expect ourselves to have AI that does it all.
Our teams need to be really thoughtful about where we invest our time and energy to fill in the gaps that we have, where we can have the most impact on the customer lifetime value.
I want you to think about different tools that are out there that can help you to scale impact as well.
Tools like Optimizely and Persado are actually leveraging third party data that’s on top of your customer data so that they can offer personalization based on what they know about your customers.
So you don’t have to have every single piece of data in order to personalize.
Persado, they call their tool “Motivation AI,” and they’ve identified based on how your audience has engaged with content on a variety of other websites.
Are they price sensitive? Are they motivated by a sense of purpose? Are they motivated by a sense of urgency?
And they actually will change your headlines and your display language based on the motivations that they know are true.
Optimizely allows you to do similar things, but they also are adding in a layer of personalization based on the data that you have within your system and can run A/B experiments on that as well.
So there are a lot of different opportunities here to identify tools that fill in the gaps, but again, these tools don’t do the work of the strategy.
Published July 29, 2025
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