Have you ever looked at a spreadsheet filled to the brim with analytics about your latest marketing campaign, and thought “now what?”
It’s a common problem. Marketing resources like email campaign platforms and social media behemoths make data easily available to their users, but turning that data into actionable strategies can be tough—especially if your organization doesn’t have a dedicated data analyst on hand. In fact, Gartner recently reported that 48% of marketing leaders identified marketing analytics as the most difficult skill to recruit and retain.
Turning spreadsheets into revenue doesn’t have to be such a pain. Understanding a few basics can go a long way. It’s all about setting clear goals and using the right tools to draw accurate conclusions that can then be presented effectively to decision makers in your company.
Let’s start with goal setting.
Why am I collecting this data?
Let’s say you’re running an ad campaign on Facebook, and your goal is to drive sign-ups for an event your company is hosting. You’ve written compelling copy, designed attractive images, and determined your budget. But how will you know if that budget is well spent?
Start by identifying the performance metrics that truly matter. Often, marketers get lost in the vanity metrics—impressions, reach, click-through-rate. The thinking goes, if 5,000 people saw this ad in only one day, it must be a successful campaign!
But did anyone sign up for your event? Were the people who “clicked through” and visited your landing page part of your target audience? What benchmarks are you comparing your success against?
Focusing on metrics that align with your goals will help you unravel the complex matrix of data you unearth. In this case, your goal might be to sign up 25 people for every $500 spent. With this in mind, conversion rates will matter more than click-through rates. For instance, if your first ad had a very high click-through-rate, but didn’t drive any actual sign ups, your audience might be too broad. This means that your ad piques people’s interest, but once they’re on your landing page something went wrong. They may realize your event isn’t relevant to them or the content on your landing page isn’t compelling. This insight will allow you to write more targeted copy and to narrow your target audience.
Should I use more than one analytics tool?
No one set of marketing analytics tools will help you paint a complete picture of your data. Instead, use a variety of complementary tools. For example, if you download analytics from your latest email campaign meant to drive visitors to a landing page, you could compare that to a heat map of actions on the landing page. The email platform you use might reveal that 130 people visited your landing page from the email you sent. However, your back-end data shows that only 20 people scrolled past the top of the page. The problem could be that the hero image of your landing page doesn’t give visitors a clear understanding of what they’re about to find.
Tracking links are another important source of data that can offer key insights, especially if you are running multiple campaigns at once. These help identify which campaigns are driving what kind of traffic. With information gleaned from tracking links, you can enlist A/B testing to ensure you are running ads that best move the needle.
Likewise, Google Analytics can help you paint a comprehensive picture of your campaign(s). Use the audience center to confirm if the leads you are generating are from within your target market and, thus, likely to engage further. Or, create surveys to gain fast, reliable insights from consumers.
When you develop an integrated campaign across platforms, and draw data from multiple data sources, it’s easier to draw actionable conclusions.
Will my conclusions drive results?
Drawing conclusions from a large data set can feel like finding constellations in the stars. “That looks like a ladle, but it could also be a spork.” Decision makers want to be sure there’s a high degree of confidence in your conclusions, but often there’s more than one way to read your data. This is why it’s crucial to adopt an experimental mindset for your marketing campaigns.
If you conclude that your google display ads aren’t driving enough page visitors because the copy is too broad, then change your copy to test that conclusion. If you think the audience your Facebook campaign is targeting is too narrow, try implementing a different audience for each ad in your set, to test which performs best. Once you’ve optimized your ads several times, you’ll have a clearer picture of what elements drive the best results, so you can present your conclusions with confidence.
How can I distill my insights for decision makers?
Remember, the goal of data analytics is to paint a complete picture of your marketing campaigns. You’ve gathered the colors you’ll need, selected the brushes that suit your task, and identified what the subject of your masterpiece will be. Now it’s time to choose the right canvas.
There are many data visualization programs out there, and often the best option will align with the story you are trying to tell. At Extra Mile Marketing, we’ve found that Microsoft’s Power BI is a robust and interactive visualization tool for the data we collect. It’s easy to use the free-form, drag-and-drop canvas to paint a beautiful, and more importantly, accurate picture of the campaigns we run. But we don’t stop there. Sometimes you want to show how you came to your conclusions, in which case a well-designed spreadsheet works perfectly. Or if you’re tasked with condensing your information into a one-page document, a professional infographic built with Adobe InDesign might suit your purpose.
Do you need help telling a complete story of your data? Get in touch, we’re ready to build a solution that fits your needs.
Ready to start? Email me at email@example.com.
Gartner recently reported that 53% of marketing leaders identified marketing analytics as the most difficult skill to recruit and retain.
In August 2015, over 1 billion people used Facebook in a single day.
Marketers are expected to spend over 11% of their total budget on analytics.