Modern marketers are obsessed with data, and for good reason. Data helps inform strategies and gives direction, but not all data is equally useful or helpful. The wrong data sets can be just as damaging to your marketing program as having none. It’s essential to know how to identify the right data, so your insights accurately guide your decision-making.
At the heart of significant marketing trends, like Apple’s Mail Privacy Protection feature, zero-party and first-party data, and the rise of AI-driven natural language processing models, is data. Data has technological and philosophical definitions, and it can be information a computer can use for processing or “things known or assumed as facts, making the basis of reasoning or calculation.” This “or assumed” part is where we can go wrong with data.
We rely on data daily, both in the obvious ways and the non-obvious. For email marketers, the obvious includes marketing data we use when creating and structuring campaigns, choosing audiences, measuring success and taking the next steps. This is why email marketing is so useful. It generates data we can apply throughout the entire marketing ecosphere.
Data is also valuable in understanding and measuring customers. Our campaigns are like an ongoing source of market research. Because the people we email are our prospective and existing customers, we’re tapping into, tracking and measuring our customer base daily. However, it’s important to know what the right numbers are and what they mean, as data misuse can harm our marketing efforts.
When good data goes bad, there are three scenarios to watch out for. Firstly, optimizing for the wrong success metric. Email is famous for being so easy to measure, but the metric we choose doesn’t always capture the true success or failure of our campaigns. Open rates are often used, for example, but these don’t always translate into the metrics that matter, such as campaign revenue, orders, basket sizes, repurchases and other campaign-related numbers.
Secondly, changing direction based on one-off testing. This happens when you run a single A/B test on a single feature, like a subject line, call to action, offer, image, body copy or time of day. These tests are easy to do and might give clear-cut results. However, a single A/B test only gives you results for that campaign, at that time, with that audience. You need to keep testing and testing different components and making sure your success metrics reflect your campaign goals.
Thirdly, relying on ad hoc testing instead of scientific methods. Ad hoc is a fancy term for guesswork, and when you test on the fly, you open yourself up to the problems people encounter when they test a single component and then change direction based on that data. Scientific testing using a hypothesis is more likely to deliver meaningful data because it gives you a framework for deriving workable insights.
To ensure your data is telling you the right story, try this litmus test. Create three lists: the top 10 campaigns with the highest open rate; the top 10 campaigns with the highest click rate; and the top 10 campaigns with the highest conversions or other campaign goals. You should see little overlap among the three sets of campaigns. Now, look at the campaigns in each category. What do your top-converting campaigns look like compared to the ones that got the most opens or clicks? This will help you understand what works over time and optimize for the right result.
Data is essential to marketers, but it’s important to know how to identify the right data and determine what it means. If you misuse your data, it can lead to disaster. To glean better insights, use scientific methods and take a holistic testing methodology so you have a broader understanding of your audience and what motivates them. With this approach, you can “squeeze out all the crap and be left with the stuff that’s gold.”
Originally reported by Martech: https://martech.org/3-ways-data-can-steer-you-wrong-and-how-to-glean-better-insights/
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