What is data-driven decision making? The term “data-driven decision making” can be defined in many ways. In this article, we'll focus on how this can be applied to your marketing and business decisions.

A quick tip: the data-driven decision-making framework doesn't just apply to marketing alone. You could even say it can be applied to decisions in your life – especially business decisions.

My framework for data-driven decision making:

Step one: Define the problem you're facing

Step two: Gather the data and evidence directly or indirectly related to the problem. Analyze the data so you understand what it means.

Step Three: Once you've answered the "what" by analyzing the data, develop an understanding of "why" the problem is happening.

Step Four: From your understanding, develop a hypothesis that answers “how” to test your assumption. It is important to note that we are not coming up with the solution here. We simply come up with a case to test whether our insights and assumptions actually solve the problem.

Step Five: Now that I've tested my hypothesis, what are the results and outcomes? Have I solved the problem? Have I made the problem worse? Or has the problem changed and evolved?

If the problem still exists or has gotten worse, repeat these five steps until you resolve the problem.

The most important thing to understand is that the data-driven decision-making process is not linear. Rather than having a specific end point, it should be seen as a cyclical process of continuous improvement.

Once you've launched your website, any changes you consider making should be processed through this decision-making framework.

That way, the changes you make will only lead to continued improvement. At the same time, it reduces the chance of changes that negatively impact the performance of your website.

To best describe this, let me show you some examples of Search ad campaign issues that we've solved for us and our clients at Web3.

Example one – “I'm not making any money from my ad campaign!”

1. Problem: I am not achieving my sales target.

2. Analysis (the what): I'm missing my monthly lead acquisition KPI by fifty percent.

3. Insight (the why): I'm losing seventy-five percent of my search impressions due to a lack of advertising budget compared to my closest bidding competitors.

4. Test (the how): I'm going to test how to increase my ad spend by fifty percent over the course of a month to see if I can increase my search impression share.

5. Result: Increasing my advertising budget resulted in a 100% increase in Search Impression Share, reaching my Lead Acquisition KPI. I compared these results to my return on ad spend (ROAS) and return on investment (ROI) for the last 30 days compared to the previous month, and I'm seeing improvements here too.

An important lesson here is that if you don't have the data, it becomes very difficult to come up with solutions. This applies to both the problems you're trying to solve in your business, as well as your website and marketing efforts.

You can work around this problem by ensuring that all the data you need to make effective decisions is accurately tracked and measured with the right tools.

Once you have the data, process it through a data-driven decision-making framework to solve these solutions faster and easier than if you simply made decisions based on the basics.

Don't get me wrong, there are cases where you can make a decision based on the weight of your pocket alone. I'm certainly not saying you shouldn't trust your instincts!

But when we talk about major decisions like redesigning your entire website because you're not happy with it, consider gathering evidence against your problem and analyzing the evidence to develop insights and hypotheses that you can test before you make a decision that will work. result in a large investment in time and money.

The reality with marketing is that the problem you are trying to solve often doesn't get solved on the first try. In fact, more often than not, your tests will result in failure rather than success.

In most cases, it will take several iterations (and several failures along the way) before you can achieve a satisfactory result.

That's why it's so important to process your decisions quickly and efficiently through a data-driven decision-making framework. That way, you can get the results you're looking for sooner rather than later.

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