How to Get Your Marketing Team to Drive with Data

Creating a culture of analytics will help you get the most out of your marketing efforts, writes Justin Grossman.

Over the last decade, marketers―including those in pharma―have been drowning in a sea of data. With the development of business intelligence tools, sophisticated martech solutions, and intelligent machines, marketers are under pressure to extract value from the wealth of information available.

Why? Harnessing data to make better marketing decisions vastly impacts the bottom line. It’s estimated that by 2020, 1.7 MB of data will be created every second for every person on earth. An Accenture study reported that 79 percent of enterprise executives surveyed said that companies that do not embrace big data will lose their competitive position and could face extinction. What’s more, 83 percent of their organizations have pursued big data projects to gain a competitive advantage.

With those stats in your back pocket, it’s easier to understand why savvy organizations have already started investing billions of dollars on technologies to capture a piece of this data pie. They know they need to focus on data-based decision making, and many have access to valuable insights - at least in theory.

While many marketing teams are eagerly collecting data, an unhealthy portion are failing to analyze and act on it in a timely manner. In worst-case scenarios, they’re doing nothing with it.

I get it. Even for tech-savvy marketers, the vast amount of consumer data out there can be overwhelming to say the least. So, let’s take a layman’s look at how to assess your company’s level of data maturity, why to build systems and processes triggered by specific information, and best practices for creating an analytics culture that not only generates discussion, but also action. It’s time to drive with data.

Popping the hood: Take a good look at your data

The first step in getting a fair shake out of your information is understanding exactly what you’ve got, how it’s organized, and whether it’s accurate, compliant, and accessible. 

This begins with benchmarking your data maturity, which will fall into one of four stages:

●      Stage 1: Baseline―You’re not intentionally collecting, managing, or analyzing data.

●      Stage 2: Identifying―You’re using data to identify key KPIs, but not contextualize them.

●      Stage 3: Contextual―You’re using data to paint a bigger picture of your marketing efforts, but not to guide strategy.

●      Stage 4: Predictive―You’re able to optimize AI and machine learning to pull data, forecast, and make better decisions.

Once you’ve assessed your maturity level, it’s important to examine how you’re evaluating your data. Are your data sources connected and linked by a single platform (like a CDP) that provides a comprehensive customer view? Or are they siloed across multiple applications, which leads to an incomplete customer view due to data discrepancies?

Next, analyze whether or not your data is healthy. How old it is, how it’s collected, whether or not it’s fragmented, and―of course―whether it’s even correct or not makes a huge difference. Remember, bad data in, bad data out.

Don’t forget to evaluate how your data is shared, including who has access to data reporting, how the reports are compiled and disseminated, and the speed of your overall turnaround time.

Finally, make sure you’re staying compliant. Dig deep and confirm that your data checks all the boxes for opt-in regulations and other guidelines.

Data roadblocks: What’s causing the hold-up?

Now that you understand the data you’ve got and how you’re using―or not using―it, let’s talk about overcoming barriers to data visibility, and how to source information you can really use to move the marketing needle.

One roadblock to many an organization’s data maturity is that when it comes to data, teams tend to only look at single metrics instead of considering holistic business results. There are good reasons for this, including:

●      Decentralization: Typical pharma teams are structured by channel, such as website, display, and offline, instead of having a key player or centralized function responsible for 360-degree customer profiling.

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