{"id":119017,"date":"2025-08-20T06:44:00","date_gmt":"2025-08-20T03:44:00","guid":{"rendered":"https:\/\/www.aboad.fi\/?p=119017"},"modified":"2025-08-19T12:58:19","modified_gmt":"2025-08-19T09:58:19","slug":"measurement-mindset-7-simple-and-satisfactory-ideas-for-marketing-analytics","status":"publish","type":"post","link":"https:\/\/www.aboad.fi\/en\/blog\/measurement-mindset-7-simple-and-satisfactory-ideas-for-marketing-analytics\/","title":{"rendered":"Measurement Mindset: 7 Simple and Satisfactory Ideas for Marketing Analytics"},"content":{"rendered":"\n

In marketing, there’s no shortage of data and tools to track and report this information. Yet many marketers still wonder what’s truly meaningful in the data and how to interpret it. There are certain rules of thumb and tactics that everyone can use when analyzing data. Here I’ll explore several approaches that are valuable to keep in mind in your daily work.<\/p>\n\n\n\n

1. Occam’s Razor: don’t take the longer way around<\/h2>\n\n\n\n

This scientific principle suggests that the most straightforward answer is usually the correct one unless there’s compelling evidence to the contrary.<\/p>\n\n\n\n

In our day-to-day lives, we apply the principle quite well. When we wake up in our hotel room during a vacation in a foreign city and our feet ache, we know it’s because we walked 15km on cobblestones the previous day, not because of a one-in-a-million freaky disease.<\/p>\n\n\n\n

Yet when our search traffic drops on our website at the same time an algorithm update rolls out, or when we make changes to what we think is better for our marketing and our results drop, we want to find as complex a reason as possible with all the data tools we have available.<\/p>\n\n\n\n

Unnecessary.<\/p>\n\n\n\n

Occam’s Razor works here. If your website performance drops at the same time you renew it, why is that? Often, it’s because of the renewal itself. Start from there. Remember to keep things as simple as possible. Then you can begin solving the actual problems.<\/p>\n\n\n\n

2. Do the unmeasurable<\/h2>\n\n\n\n

When marketers evolve from treating marketing as a belief system toward viewing it as a science based on data, we sometimes become fixated on measuring everything, refusing to implement what can’t be measured.<\/p>\n\n\n\n

This is the wrong approach.<\/p>\n\n\n\n

While you shouldn’t invest a massive portion of your budget into something that can’t prove its ROI, you might recognize strategies that can’t be properly measured or attributed, yet intuitively you know they work.<\/p>\n\n\n\n

In these cases, just do it. If your company spends 50,000 \u20ac to attend a convention selling your 5mn euro product, don’t obsess over measuring booth visit attribution or tracking subsequent sales. Simply spend the few hundred euros more for hyperlocal marketing and maximize the opportunity. It’s a negligible amount in the grand scheme.<\/p>\n\n\n\n

Sometimes you need to believe a little, even in scientific approaches.<\/p>\n\n\n\n

3. Keep an eye on the behavior metrics<\/h2>\n\n\n\n

I’m a big believer in looking at what people do rather than what they say. Just listen to anyone starting a healthy lifestyle and check back in three months. Most likely, actions didn’t follow what was said.<\/p>\n\n\n\n

This is why measuring the effectiveness of your marketing should also be based on behavior. Reaching an audience isn’t a success. Getting them to take action afterward is. The closer the action is to showing and executing commercial interest, the better.<\/p>\n\n\n\n

4. Set clear KPIs before starting<\/h2>\n\n\n\n

One of the biggest mistakes in marketing analytics is starting campaigns or initiatives without clearly defined Key Performance Indicators (KPIs). When you establish what success looks like before launching, you avoid the trap of retroactively choosing metrics that make your results look good.<\/p>\n\n\n\n

5. Understand correlation vs. causation<\/h2>\n\n\n\n

Just because two metrics move together doesn’t mean one caused the other. This fundamental principle is often overlooked in marketing analytics. For example, your website traffic might increase at the same time as your social media engagement, but that doesn’t necessarily mean your social media activity caused the traffic increase.<\/p>\n\n\n\n

To establish causation, you need to run controlled experiments where you change one variable at a time. A\/B tests are perfect for this purpose. They allow you to isolate the impact of a specific change and determine its actual effect on your results.<\/p>\n\n\n\n

6. Turn your first data into questions<\/h2>\n\n\n\n

One of the best parts in my work is when a new division of a legacy company begins to embrace digital. First data comes with enough statistical significance and we can see what worked and what didn’t.<\/p>\n\n\n\n

So, the question comes: “Why did that ad work better?” And the answer is “I don’t know\u2026yet. But now we can start narrowing our testing and find out why it worked better by testing different parts of it in A\/B testing while driving ever-better results.” At this point a lightbulb usually goes off and people get excited about the potential road to learning and results ahead.<\/p>\n\n\n\n

7. Consider the difference between local and global maxima <\/h2>\n\n\n\n

Understanding the concept of local versus global maxima is crucial for marketing optimization. This theory, borrowed from mathematics, explains why sometimes our improvement efforts actually limit our overall potential.<\/p>\n\n\n\n

A local maximum is a point higher than all nearby points but may not be the highest possible point overall. A global maximum, however, is the absolute highest point possible.<\/p>\n\n\n\n

For example, you might A\/B test different headlines for an ad and find the best performer among those options\u2014that’s reaching a local maximum. But you could be missing out on entirely different ad formats, channels, or messaging strategies that might perform dramatically better.<\/p>\n\n\n\n

This is why it’s important to begin testing with significant differences rather than minor tweaks. By testing radically different approaches initially, you increase your chances of discovering where the true global maximum might lie before refining your approach to reach that peak.<\/p>\n\n\n\n

Conclusion: The power of a measurement mindset<\/h2>\n\n\n\n

Adopting a measurement mindset doesn’t mean getting lost in data or overcomplicating your analytics approach. Instead, it’s about finding the right balance between data-driven decisions and marketing intuition. Remember that the goal isn’t measurement for measurement’s sake, but rather to gain insights that drive better marketing outcomes and business results, whether you do it the easy way or the hard way.<\/p>\n\n\n\n

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