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What Most Marketing Analysts Get Wrong

Marketing analytics are the best way to learn how effective your marketing strategies are, and in the modern era, they’re borderline necessary. Without the feedback to tell you which strategies are working, and how much value they’re earning your business, your marketing strategy devolves to be mere shots in the dark.

Unfortunately, analytics can only provide raw information and high-level insights; everything beyond that relies on the insights, interpretations, and actionable takeaways produced by human marketing analysts. And even the most experienced marketing analysts can end up making critical mistakes.

Key Points of Confusion

These are some of the most common strategies, approaches, and concepts that affect how marketing analysts interpret data:

  1. Obsession with numbers. It’s easy to get caught up in the numbers—it is analytics, after all. Unfortunately, many analysts believe that numbers are everything; low numbers are always bad and high numbers are always good. This isn’t necessarily the case; for example, a high number of social media followers or interactions may not accurately reflect how interested those followers are in your brand.
  2. Correlation and causation. It’s easy to mistake correlation for causation when you’re looking at numbers changing over time; if you change something and see higher web traffic as a result, it’s easy to think that your new addition is responsible. Correlation is important to note, but proving causation requires far more experimental control. Don’t assume cause until you can demonstrate it effectively.
  3. Metric distinction. For the most part, you can rely on your intuition when discovering what certain metrics truly mean, but there are some metrics that are so closely related, they can easily be mistaken for one another. For example, do you understand the difference between a bounce rate and an exit rate? Or between a visit and a pageview? These are distinct concepts, and you need to understand the differences. Many analysts assume they know without foundation.
  4. Failure to segment channels and audiences. When you look at the big picture of your marketing effectiveness, you can glean some important conclusions. However, it’s also important to zoom in and take a look at specific segments and channels that comprise that big picture. For example, you may be seeing lots of web traffic, but how are different streams of inbound traffic behaving differently? Which is most valuable?
  5. Overreliance on visuals to describe or explain data. Data visualization is a major up-and-coming trend in the analytics world, and it’s certainly useful—but there are also a lot of ways it can go wrong. Not all data visualization software produces ideal results, and even with perfect results in hand, visualizations can sometimes mislead you or draw your attention away from important outliers.

Overcoming Challenges

So what can analysts do to overcome these key challenges?

  • Account for your biases. Understand that your reasoning abilities are inherently skewed by biases. The better you understand those biases and account for them, the closer you’ll get to the objective truth.
  • Invest in the right software. The right software can make it easier to understand the data that’s in front of you, with fewer chances for mistakes. SAP offers a host of intelligent marketing analytics products.
  • Get second opinions. Work with other analysts to get a clearer understanding of the metrics, and potentially clarify any of your misinterpretations.

Marketing analytics aren’t going anywhere. Analytics will likely grow to become more sophisticated over the years, but it’s unlikely that the human element will ever be fully replaced. Use these strategies to avoid the major pitfalls in marketing data analysis, and keep your business strategies as objectively founded as possible.

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