Marketers still struggle to build a unified source of data

You are here:

Marketing organizations are being tested like never before. Many businesses are being forced to reconsider every aspect of their business model, pushing marketers to realign their strategies while in motion. All the while, marketing organizations, and the marketing operations teams who support them, are trying to get a handle on their data to avoid making disastrous moves.

“We know that our world oscillates from periods of certainty to periods of uncertainty — marketers need to be able to navigate both sides of this coin,” said Emily Hoffman, product marketing manager for Datorama. She and Datorama client Josh Alvernia, CEO of the marketing services company Clue, joined up for a presentation during Discover MarTech last month to dig into the challenges marketers face when it comes to managing and implementing data — and the need, especially now, to tie together your data with so much uncertainty around business strategies.

Marketers ongoing battle with data

When it comes to actionable data, Hoffman says marketers are up against three fundamental challenges.

“The first one is a lack of a unified view across all of the marketing investments they’re making, their performance and their results,” said Hoffman, “Second is a lack of real-time insights to be able to optimize ROI and results in real-time.”

The third challenge: Lack of alignment and governance in order to drive cooperation across teams, regions and stakeholders. Before COVID-19, these challenges were already pronounced, but now they are more pressing than ever as businesses are trying to make massive shifts in strategy without having a clear view of their investments, performance and ROI.

“As a result, marketers are struggling to answer some critical business-driving questions,” said Hoffman, citing questions like which programs should be cut and which should be maintained or what messages are best resonating with audiences at the moment.

“These are the questions that are becoming more and more critical in times of uncertainty,” said Hoffman.

Too many data silos

The challenges outlined by Hoffman are often the result of disparate and siloed systems across not only the marketing organization, but businesses at large. Marketing efforts are spread across various channels — paid ads, social, email, etc. — without the technology or resources to effectively tie together the data from multichannel campaigns. Meanwhile, teams managing various data sets are just as divided with marketing, business intelligence, customer service groups and more all working within their individual silos.

“Marketers really need a transparent and holistic view to see what campaigns are most effective, how their content and offers are performing and how to understand all of their engagement,” said Hoffman, “And then, on the other side, transparency across teams is equally as important because it allows stakeholders to align and to ensure that every everyone is striving toward a shared goal.”

A Forrester report from last year backs up Hoffman’s claims, finding that marketing and business intelligence teams were often too siloed to maintain effective communication patterns, even though they relied on each other for day-to-day operations. (More recent research from Forrester found that less than 40% of customer experience executives even knew where all of their customer data was stored.)

Stitching everything together

To get a handle on your marketing analytics, you have to take the effort to “de-silo” the data. Clue CEO Josh Alvernia says this is the thing his company does when working with clients.

“This is usually a pretty daunting task for marketers who are unfamiliar with this process because they have so many platforms,” said Alvernia, who noted his company sees marketers managing, on average, 24 different marketing platforms. “What we try to do is break it down into something that’ s a little more digestible.”

Alvernia used the following example to illustrate how his team distills data at the campaign level to be more digestible.

“You’re going to have to go through a process even when you’re running a single campaign. You make decisions about what needs to happen from the campaign. You make assumptions about which channels are going to provide it to you. You take actions on those assumptions, and then you look at the outcomes to continue the process again.”

He said by characterizing data sets into these three categories — campaign objectives, channel implementation and outcomes — marketers can more easily exclude data that may overlap, while focusing on data that is vital.

“Once everything is one place, we then need to tie it together — that’s the process of data modeling,” said Alvernia.

According to the CEO, data modeling is a combination of a product and a process. “A product because you are programming intelligence software to understand ‘when I receive a piece of data, where am I supposed to put it and what am I supposed to do with it’ — it’s also a process in that it is a manual naming convention that you’re going to apply to very granular pieces as you traffic your campaigns.”

The benefit of applying data modeling to your campaigns — and at a much higher level to your overall strategies — is that you are stitching together your data in an effective way that delivers measurable business outcomes.

Trust the data

Alvernia emphasized how easy it is to get distracted by the influx of available data across platforms and channels. To build out data sets attached to actual goals, marketers need to have a solid grasp on the data that matters versus data that lacks any real insight.

“We focus on something called an impact matrix,” said Alvernia, “It’s the idea that not everything is as valuable as other things are.”

Once marketers are able to distill the data that will drive outcomes, then they can take action, prioritizing the platforms that have scale and are affordable. “The moment you find something good, and something that’s working, you want to double down on it.”

Most importantly, marketers have to trust the data. If the data proves an assumption wrong — no matter whose assumption it was — go with the data and lean into what’s working.

According to Alvernia, “You’ve only made a mistake when you’re not letting data show you a better way.”