Building a best-of-breed customer data platform and martech stack

It’s not exactly controversial to say that businesses live and die by their customer data. In fact, it’s been this way for quite a while.

Back in 2015, 64% of executives surveyed by Forbes strongly agreed that becoming data-driven is crucial to success.

Today, data-driven teams are six times more likely to be profitable than the average company in their industry. A recently released white paper by IDC showed the power of truly data-driven organizations.

• Such organizations saw two and a half times better results across multiple business metrics.
• They were also three times more likely to have smaller time-to-market times, and twice as likely to report improvements in profits, customer satisfaction and operational efficiency.

Given this, it makes perfect sense that more than three-fourths of marketers continue to invest more money and more resources into their data-driven initiatives.

In this blog post, we’ll take a look at the key investments your team should make as you become more data-driven and pay special attention to the new tools you will want to add to your tech stack.

Facing the challenges of modern customer data usage

As with any new marketing initiative, becoming more driven by your customer data comes with its fair share of challenges. The major ones include:

1. Sourcing customer data

Most marketers can agree that they have access to more data about their customers today than ever before.

Unfortunately, there are a few caveats here.

For one thing, this customer data doesn’t just knock on your door; you need to know where to find it. With so many potential sources of data, it can be easy to overlook a platform or channel that could end up being a goldmine for your marketing team.

Along with that, reliability is always an issue when collecting data from third-party sources. In many cases, the data you collect may be incomplete, taken out of context, or altogether inaccurate. This, in turn, can cause your team to draw inaccurate conclusions based on what they’ve learned — which can have a negative impact on your customers and your business in the future.

With these issues in mind, ensuring that you have a reliable way to collect accurate and comprehensive data on your customers should be a top priority for your team. As we’ll discuss, this is a key reason that first-party customer data has become more important than ever.

2. Organizing customer data

With so much customer data now coming in, keeping it organized and ready for use is another major challenge in itself.

Again, today’s marketing teams collect customer data from a number of different channels and sources — each of which typically exist in isolation from one another. This can cause incoming customer data to remain siloed and makes it easy for teams to lose track of the data they’ve collected.

Every marketing team operates in multiple channels, from website to email to ads, SMS, apps, and more. Each channel creates its own data stream that exists in a silo.

Another challenge stems from the need to create a systematic, sustainable, and repeatable process for organizing customer data as it comes in. With so much data arriving from so many different channels, taking a manual approach can easily overwhelm your team and lead to data loss.

Simply put: Disorganized data is unusable — period. 

That said, your marketing team needs to create a plan to ensure the customer data you collect is always right where you need it to be.

3. Using customer data to inform marketing decisions

The entire point of collecting data about customers is to use it to make informed marketing decisions. If this piece of the puzzle is missing, you may as well have never collected the data in the first place.

Of course, this is a major challenge for all data-driven marketing teams, especially those who are currently collecting more data than they had ever had in the past.

For one, teams need to understand not just what the data means, but what the data means to them. In other words, they need to go beyond the “on paper” data in front of them and figure out how to translate it into useful knowledge that will help them take decisive action.

Teams also need to systematize this process to ensure they are using customer data productively and getting the absolute most value out of it. The goal is to take as much of the guesswork out of the equation as possible. 

You want your data-driven processes to run like a finely tuned machine.

An incomplete solution plagues data-driven hopefuls

In recent years, the go-to solution to these challenges has been to invest in an “all-in-one” martech suite to help streamline data-related processes.

Unfortunately, these solutions end up being “all-in-one” in name only. Typically, these tools help teams overcome some of the challenges we’ve discussed — but fall short of delivering on all fronts. In turn, these teams must also invest in point solutions to address the other issues plaguing their operations.

Because of this, rather than investing in marketing suites, today’s marketing leaders are opting to build their own best-of-breed customer data platforms.

Exploring the key components of a best-of-breed CDP solution

David Raab of the Customer Data Platform Institute defines customer data platform as a marketer-managed system that “creates a persistent, unified customer database that is accessible to other systems.” Actually, to be exact, Raab specifies that a CDP is a “packaged software,” a “prebuilt system that is configured to meet the needs of each client.”

By today’s standards, though, we would argue that such a prebuilt, tailored system doesn’t necessarily need to come in the form of a packaged software. It comes in the form a a flexible, composable solution like Lytics CDP, that includes:

• Customer Data Infrastructure or Data Pipeline Management (Lytics Conductor)
• Cloud Data Warehouse Solutions / Reverse ETL (Lytics Cloud Connect)
• Data Activation and Decisioning Tool (Lytics Decision Engine)

First, using a CDI, the focus is solely on collecting first-party customer data. More specifically, it’s on collecting behavioral first-party data. Then, a cloud data data warehouse, is used to host, visualize, and secure all incoming customer data within a single digital space. Finally, as the name suggests, a decisioning engine generates actionable insights based on existing and incoming first-party customer data.

More than just taking customer data at face value, decision engines analyze the context in which said data exists to draw informed conclusions and provide practical advice for how a marketing team should proceed.

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