Increasingly, companies have turned to customer data platforms (CDPs) to help them run more relevant marketing campaigns using the large volume of customer data at their fingertips. Learn why software company Atlassian made the leap and how that decision transformed their marketing in this VB Live event!
“Customer data platforms are very hot — they’re at the tip of the hype cycle at the moment, according to our friends at Gartner,” says David Raab, Analyst and Founder at The Customer Data Platform Institute. “Which of course begs the question of why are CDPs a popular topic? And the short answer is because they’re needed.”
The slightly longer answer is that marketing is about the data. And data in its natural habitat is not naturally integrated, but created in silos. These aren’t just technical silos, but organizational silos, which can be an even bigger obstacle than the technical issues.
“So we struggle as marketers, because we have this fractured view of a customer, rather like the elephant and the blind man,” Raab says. “That makes it really hard for a marketer to do their job because they don’t have any place to get complete information.”
For Atlassian, the software company behind Jira, those data silos were undermining customer-centered focus: Understanding intrinsically what a customer is trying to do, the problem they’re trying to solve, and connecting them with the value in the Atlassian products that help them do that in the quickest method possible.
They’ve currently got about 89,000 customers finding value in their tools, says Jeff Sinclair, Product Manager, Engagement Platform at Atlassian.
“While that’s great from the company’s perspective, it presents a unique challenge for me as a product manager trying to deliver a great user experience around our messaging,” says Sinclair. “Because that’s 89,000 unique problems that I need to identify and help customers solve quickly in the use of our products.”
They identified a number of opportunities in their existing messaging program to make communication with their users better, and to help them realize the value in their products more quickly, Sinclair adds.
Those areas could be broken down into three key top-level challenges: Personalization, timing, and velocity.
In the past, messages were sent to larger customer segments, not personalized or targeted. Timing never even came into play. They were engaging in a lot of batch sending, Sinclair says, as well as time-based sends where they would send one message every day for a customer’s first week to try and help them onboard.
“Those led to a very corporate-feeling engagement model, and ultimately meant that our messages weren’t very successful,” he says.
Velocity was more of an internal issue: Not in terms of how many messages they were getting out the door, but how much they were learning from the messages they were sending to make their program better as a whole in an ongoing basis.
Double-clicking on each of these high-level issues, it turned out that the problems were mainly system-focused, and centered around data: Too many data sources, too many of them siloed, and too many of them incompatible.
And finally, a lot of the sources that they would connect to weren’t real-time. Because of the nature of batch-based or time-based sending, they were doing a lot of extractions from data warehouses using SQL queries and batch processing, which could create a one- to two-day lag in user messaging.
“Put simply, we couldn’t address the problems that we had with the systems that were available,” Sinclair says.
The solution of course is the CDP, the very smart spider sitting right in the middle of the web of data, gathering it all up from different source systems. It does important things like load the data, and then standardize, transform, link identities, aggregate, index, re-format, and expose the data, and then make it available to applications.
They were able to mesh a CDP — Lytics, in their case — into their internal infrastructure, and integrate data streams from the company’s in-product behavior data, marketing behavior data, and entitlements data, and aggregate it into a user profile.
A marketer can then identify a segment of users that have behaviors that they want to target. For example, users that have treaded projects in Jira, but have not created issues, and have signed up in the last week.
Once an audience is created, a trigger is enabled that pushes messages to engage in the platform in real-time, as users meet that criteria. And then they close the feedback loop with data from their in-product and email channels to ensure they’re taking into account how people are actually engaging with those messages — which influences future messages to that audience.
“It enables us to do a whole bunch of stuff around behavioral messaging in real-time — we’ve removed the data filers, we’ve empowered marketing, but that’s not the end, and there’s a lot more capability that we can take advantage of,” Sinclair adds.
That includes things like predictive scoring looking for likelihood to churn or likelihood to convert, look-a-like modeling that allows a marketer to understand which customers in a source segment are most likely to become customers, and keeping track of the messages that matter.
A CDP is also designed to support every part of the buyer cycle, from prospects, which then become leads, which are nurtured, passed them to sales, and engaged with, Raab adds.
“You’re going have different uses for your CDP, depending on where your problems are, where your needs are,” he says. “What do you need? What do you want to look for specifically? Because there are lots of CDPs out there, and they don’t all do the same thing.”
For more on the myriad use cases that enable complex marketing strategies to figuring out exactly what you need from a CDP – and who can give it to you – catch up now this VB Live event!
In this webinar, you will learn:
- Key considerations when deciding to build versus buy a customer data platform
- How companies are taking advantage of CDPs for more relevant communications
- How data science can improve marketing efficacy
- The trends and market factors driving the need for customer data platforms
- Jeff Sinclair, Product Manager, Engagement Platform, Atlassian
- David Raab, Analyst and Founder at The Customer Data Platform Institute
- Jeff Hardison, VP of Marketing at Lytics
- Stewart Rogers, Analyst-at-Large, VentureBeat
Sponsored by Lytics