Polina Buchan

One of the themes we explore in my Marketing Analytics course is how easily data can be misinterpreted or misused by well-meaning marketing professionals. Would anyone from my network be willing to share an example of a data analysis blunder they have witnessed?

Marketing is more data-driven than ever, but with more data come more opportunities for misinterpretation. From my experience, here are a few of the biggest pitfalls, and how to avoid them.

Mistake #1: Chasing Vanity Metrics
It’s tempting to focus on numbers that look impressive (likes, impressions, website visits), but if they don’t connect to meaningful business outcomes, they can be misleading. Instead, focus on metrics tied to revenue, conversions, or customer retention.

Mistake #2: Ignoring Data Context
A sudden spike or drop in performance doesn’t always mean success or failure. Seasonality, external factors (like economic shifts), or changes in platform algorithms can all influence results. Before reacting, look at trends over time and consider external variables.

Mistake #3: Over-Reliance on Averages
Averages can hide important insights. If your average time on site is 3 minutes, but half of your visitors leave within 10 seconds while the other half stays for 6 minutes, you have two very different user behaviors to analyze. Segmenting data provides a clearer picture.

Mistake #4: Misattributing Conversions
Last-click attribution is still widely used, but it ignores the role of touchpoints earlier in the customer journey. A prospect might have seen multiple ads, read a blog post, and received an email before finally converting. Multi-touch attribution models provide a more accurate view of what’s driving results.

Mistake #5: Assuming Correlation = Causation
Just because two data points move together doesn’t mean one caused the other. For example, an increase in website traffic at the same time as a new campaign launch might seem like a success, until you realize it coincided with a major industry trend driving organic interest. Always test assumptions before drawing conclusions.

How to Avoid These Mistakes:
✔ Clearly define success metrics that align with business objectives
✔ Look beyond surface-level data and segment results
✔ Use attribution models that account for the full customer journey
✔ Always ask why before acting on a data trend

Marketing analytics should inform strategy, not just generate reports. Marketers must develop solid analytical skills to turn data into truly actionable insights.