What Good Data Means for B2B Marketing Strategies

March 23, 2017 Kyle Harper

b2b marketing strategies

It’s been a long day at the office for our heroic marketing manager.

She’s found herself sandwiched between pressure from corporate leadership to meet performance quotas and constant production hiccups from her team when it comes to actually executing on their B2B marketing strategies. The marketing-tech mix at the enterprise-level SaaS company she works for is still a mashup of outdated legacy platforms and expensive new services that aren’t used nearly enough. There’s clearly an enormous amount of data coming in—data that costs quite a bit of budget to gather. But rather than meeting her goals, it just keeps her—and her team of marketers and analysts—away from well-earned rest.

Clearly, something has got to give. And if it’s not the company’s data mix, it’s likely going to be the company’s employees giving their notice.

B2B brands have distinct needs, strengths, and weaknesses when it comes to working with data. Contrary to the mid-2000s, these problems no longer stem from having too little data; rather, it’s how they deal with having too much of it.

So what is our intrepid manager to do?

Image attribution: アラツク

Image attribution: アラツク

Your Brand Isn’t a Farm—So Why All the Silos?

“The paradigm shift for me is—as opposed to guessing and extrapolating—knowing and interpreting,” said Andrew Swinand, CEO of Leo Burnett, in a recent interview with the AMA. “Data without insight is worthless, but where do you find the points of tension and points of interest and use that as a way to inspire creativity?”

It’s a hydra of a problem, stemming from issues that span the gamut from technical inconsistencies to shortfalls in keeping staff up to date with training. But the primary obstacles preventing B2B brands in particular from gaining useful insight from their data appears to be more technical than anything else.

Dun and Bradstreet’s B2B Marketing Data Report 2016 aggregated data from the firm’s year and combined it with survey data to find exactly how B2B brands are working with data. When asked what the biggest obstacle was for receiving ROI on their data, the top two responses were “inconsistent data across technologies” (41 percent) and “integrating technologies” (39 percent). These findings are mirrored in Openprise‘s State of B2B Marketing Data Management, which, similarly, found that the leading obstacle for brands to be “poor data use, accessibility” (54 percent).

So the data is there, the tech is there, but the ability to make them speak to each other in an actionable way is failing. Why?

From organizational inefficiencies within departments to an overload of marketing technology, B2B data is proving itself to be more of a hassle than a help. If our SaaS marketing manager is ever going to get some sleep, she’ll need to come up with a way to rectify inconsistencies across all the data she’s pulling, and come up with a streamlined way of putting what data remains to work.

That sounds simple enough . . . but where should she begin?

A Stronger B2B Data Practice

Keeping your brand’s data clean, accessible, and actionable will be a constant practice in review. There will always be new technologies your team will want to add, pivots in your B2B marketing strategies, and other curveballs you just can’t predict at the front end. However, building your team’s infrastructure on these three principles can help set your team up for success and easy updates well into the future.

Image attribution: WoCinTech Chat

Image attribution: WoCinTech Chat

1. Team Organization

One of the most difficult problems when it comes to data is that it doesn’t actually live in a computer or on the web. It’s people. Just people.

Before you do anything to try and reorganize your data stack, you need to understand how your team’s organization might be getting in the way of healthy data practice. Take a few minutes to map out who in your company works with data you use, and any procedures involved in communicating with them. Wherever your team has the power to shorten lag time in communication or improve the consistency with which your teams correspond, do it. Having all the best data in the world does you no good if the people who can affect its integrity or give you access to it aren’t on the same page as you.

This is also a great time to gather insight into how people across your organization are using data and the goals they’re working to achieve.

2. A Funnel-Forward Approach

Outline all the data sources, data points, and key performance indicators your team works with (and the platforms they’re pulling them from). Then, sketch out your brand’s whole funnel, from discovery to relationship. Now, drag and drop every data point you listed and associate it with a step in your funnel, taking the time to answer one simple question: “What does this data point actually inform about this step?”

You might be surprised how much of your data your team keeps because they “think it might be important.” But if it isn’t helping you grow your audience, generate and score leads, and eventually make a sale, why are you spending time looking at it?

This is an easy, yet powerful review process that helps your team identify where you have useless data and where there are gaps in your process that could use more informed decision-making. Consider doing this once a quarter, and keeping your team informed about what you find.

3. A Centralized Data Practice

Inconsistency in data integrity happens when you have too many “cooks in the kitchen,” so to speak—whether that’s in the form of multiple platforms that are all trying to measure a single metric in different ways, or in the form of people from numerous teams all messing with the same data source without communicating with each other.

Good, actionable data science is only able to happen when your team can trust the integrity of the data they are working with, and when you are able to easily and accurately associate points of data from across all your tracking sources. Taking the time to set up a system for centralizing data storage (data lakes are a powerful and simple solution to this) and analysis can ensure powerful data practice for your brand, and oftentimes pulls the pressure of reporting off professionals who would rather use that time to execute on their marketing tasks while letting your data experts focus on what they do best.

Our heroic marketing manager from the beginning of this story isn’t a one-off example in a single tale. Her long nights and frustrating meetings about inconsistent marketing technology are problems that plague marketers around the world every day. To tackle these issues, B2B brands in particular need to take the time to break down the institutional and technical silos they instill upon themselves, condense their data gathering and communication, and then align every bit of that refreshed practice with their marketing funnel. From this simplified and focused foundation, B2B marketers should find it much easier for their brands to grow—and to clock out when 5 p.m. arrives each day.

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