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Marketing success begins with good data. This guide shares the steps any organization can take to implement campaign tracking best practices and optimize spend based on richer insights.

This guide will cover important topics including:

  • How to align campaign measurement with strategic marketing objectives
  • How to enforce a consistent classification taxonomy across teams and channels
  • How to optimize digital marketing based on reported performance metrics

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Building an Accurate View of Marketing Performance

If you do a web search for “Build a Marketing Dashboard,” or “How to Measure Marketing Success,” you’ll find plenty of content out there, much of it good. Of course you should determine your KPI’s and visualize your data. And yes, you’ll definitely want to know who your audience is, obtain buy-in from the team, and have clear, achievable goals for how your business will measure success.

But here’s the problem: these articles will generally have a bullet point tucked near the end of their lists. One author might call it contextual analysis, another report consolidation. Regardless of terminology, they describe a familiar challenge for many marketers that can be grouped in a bucket called data integration.

Putting data integration at the end of a list of tips for capturing Marketing Performance strikes me as backward. As if data integration were an afterthought to the larger process of measuring success! The alternative to data integration is data disorganization, and unfortunately that is the place where many teams find themselves (more on that in a future post).

So, what does my priority list for building a view of marketing performance look like? I’d save the top spot (or the top three spots) for data integration, in capital letters. And my focus would be on developing a single source of truth.

Getting to a single source of truth

When it comes to data, cleanliness is next to godliness. Why? A business can’t confidently make data-driven decisions without working from a single source of truth. Getting to a single source of truth is only possible with clean data. That should be your first consideration if you wish to gather insights from the reams and reams of information we are now able to collect. To put it simply: we have the tools to create endless bits of data — making the bits work together is hard. It is a lot more difficult than anyone thought it would be to reap the benefits of all our new data streams.

Most companies that do marketing on any scale will bump up against this reality. They may spend money on premium data capture, with a growing suspicion that they’re not getting any smarter for it. The team may not want to say it in front of management, but the story they talk quietly about among themselves is this: “Here’s what we think our website is doing, and here’s our revenue. There’s an abyss between the two and any correlation seems to be dumb luck.”

This is discouraging for executives who want to increase investment in data as a way to drive the business. They read about how data is going to change their lives, yet when they check in with their teams they are told to check back in six months, because “the data isn’t ready, it needs to be integrated.” The gap between executives’ expectations for data tracking and their patience with the process is considerable. At the first hint of trouble, stakeholders may begin to lose faith in the process.

Being clear about which metrics matter most

So what can you do about it? The biggest drawback organizations face in using data to drive their business — and the thing that creates the data gaps that erode trust and that require integration  — is the manual handling of data. Some manual data manipulation may be unavoidable, but it should be done as little as possible by only a few people. Every instance of manual handling is an opportunity for error to creep in. Remove the manual handling of data, and you’ll eliminate 90% of your data gaps.

It’s well and good to say, “don’t handle your data manually,” but logistically it can be hard to do that without creating unhelpful bottlenecks (and without burning out your analysts). A way around this is to spend a lot more time developing clear data requirements. The average company will spend 70% of their time on implementation, and 10-20% on developing data requirements. They really should spend 70% on establishing agreed-upon data requirements.

Then it’s about planning and process: using available technology to serve the team, instead of asking your team to serve the technology. Finally, it’s about getting the people on your  team to understand which questions need to be asked, and to follow established processes to answer those questions.

With any business challenge, there are problems to be solved and questions to be answered.

The specific metrics you want to derive (e.g. repeat visits, time on page) will help to pare down the reporting possibilities to just a handful.

Using these metrics to drive your business

Of course, even when a business knows what data picture they want, there are many ways to get there. Again, the clearer your team can get about what you wish to measure, the clearer you’ll be on how to use the tools available to you. Then it’s about having a consistent process, and sticking to it. The final step, using data to drive the business, becomes possible only when everyone is 1) working from a single source of data, and 2) focused on shared goals. A team that gets it right can become the executives’ trusted advisor, offering input that helps shape corporate strategy at the highest levels.

If you have questions about how your team can minimize manual data handling and get to a single source of truth, please email us at info@claravine. We’d like to help!

Author: Joseph Riddle
April 25, 2019

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