“You must have a customer-centric approach to data in order to get it right,” said Gaelyn Almeida, Sr. Director, Tech Advisory for customer experience management software company Merkle, speaking at our spring MarTech conference.
She added, “Because there’s so much variety in the fidelity of the information about your customers and types of interactions, it’s very complex. You have to think of your data as a unified data ecosystem.”
With so many digital touchpoints to engage customers and prospects, marketers have to assemble a customer-centered strategy for organizing and acting on data. The truth in the data comes from individual customers. And they’re found across the entire marketing funnel.
Pulling together data in the right way leads to informed customer engagement and revenue. Implementing a data solution that’s up to the challenge, however, can seem daunting. There are also some myths about data that can gum up the process. The important thing to remember is that through the clutter of all this data, real customers are to be found.
Table of Contents
Don’t forget about prospects
A successful data strategy doesn’t leave customers behind. It must always remain customer-centric. And this means accounting for all of your customers and future customers.
“Oftentimes, organizations think of just existing customers,” Almeida said. “When you think of customers from a marketing or customer experience lens, you’re looking at it across your prospects, who are in your upper funnel, right down to people who are about to convert at the end of your funnel or who are your existing customers.”
Looking to take control of your data? Learn about trends and capabilities of customer data platforms in the latest edition of this MarTech Intelligence Report.
Also, before you start the process of bringing all your data together, realize that the data you have from prospects and customers are essentially the same kind of data used for a common purpose.
“Everybody uses the same data, whether you’re doing analytics or trying to activate,” said Almeida. “At the core, you still need to know about the customer. You might transform that data differently, but eventually it’s the same data. So think about it as a supply chain.”
Use a hub-and-spoke model
Investment management company T. Rowe Price found that the best way to set up the flow of their data was through a hub-and-spoke system, according to Frank Hong, Software Group Manager. They consulted Merkle for data technology solutions that helped with lead management, sales outreach and relevant messaging.
“With this hub-and-spoke model, what we’re anticipating is pulling data from one source, instead of all the different various sources, especially with the challenge of trying to go through the hybrid model of mainframe database versus in-house versus cloud-based solutions,” said Hong.
Just because data is on the cloud, doesn’t mean it’s fully centralized or actionable either. The cloud alone won’t solve all your customer experience data problems, Almeida said.
“The volume and the velocity of that data is very complex,” she explained. “When you look at the cloud as a customer experience person, make sure you have a seat at the table with your IT department to make sure your needs are being taken care of.”
Early in T. Rowe Price’s transformation to a cloud solution, they ran into problems with sharing data across different business units.
“Unfortunately, the cloud solution didn’t solve all our issues,” said Hong. “One of the big issues we ran into was, how do you share data between business units? How to share with marketing, which uses all the business units’ data, to help them get the data? We had a lot of point-to-point integration between the systems. Our architecture looked like a spaghetti of different systems.”
The new centralized data lay strategy helps in building a hub-and-spoke model that organizes all the confusing intertwined data flows in the previous layout, which was wasteful and costly.
“We’re actively working at improving our planning, so that we now have a centralized source to pull the data and we can reduce the cost of implementation (of the data).”
Curating data
The data strategy isn’t finished just because it’s centralized, however. It has to be ready for consumption by different areas of the organization.
“What we’re looking for is what we call a curated customer domain that is ready for business consumption,” said Almeida.
What this entails is having the data team create some common areas that are properly catalogued and aligned with the right security policies, and are ready for integration and distribution into other areas of the organization. Also, this data has to be ready to be updated in real-time.
T. Rowe Price ran into problems with duplicate customer profiles and other wasteful data issues from individual business units that had their own data lakes, Hong said. Data had to be properly catalogued with domains to curate and unify the data into a shared data lake for the entire enterprise.
CDPs aren’t a single solution
Unifying the data company-wide is a challenge without an easy one-size-fits all solution. Almeida cautions against marketers assuming that a CDP is the quick fix to handle all data curation.
T. Rowe Price uses the Adobe Experience Platform (AEP) as their CDP, along with other solutions in the Adobe Experience Cloud stack. “Since AEP has strong integrations between platforms for data ingestion and activation, we wanted to reduce the cost by getting all these different sources into one central place.”
But as they built out the integration, they realized it was too expensive to move every data point from the individual business units into the CDP. Instead, the AEP data is being moved under its own domain within the enterprise data lake, to make this data accessible to the rest of the organization.
“Now we have a true customer 360 database that allows us to curate data and stitch it against any other datasets within the other business units without having to load everything in AEP,” said Hong.
This strategy allows for the best of both worlds. Individual business units have the flexibility to pull what they need, creating a true hub-and-spoke model for all of the organization’s customer data.