What can you do about that? As a marketer, your success often depends on the smallest details in the total customer journey. Knowing the consumer of your product or service is the foundation of your work. The data you need to work in a targeted manner is usually readily available. Once you have the tools in house, performing a good analysis is a matter of combining the right data and running an analysis with it. But marketers are often hesitant when it comes to data analytics. The threshold to start often seems higher than it actually is. The crux of the matter lies in how you can obtain clear insights from those analyzes that you can then take action on. Because an analysis can yield interesting figures, but if you still have to look at an unclear graph and make your decisions based on that, it will not help you much. Tablet with numbers and analytics.
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5 tips that help draw conclusions from a data analysis In this article I share 5 tips that help you draw conclusions from data analysis. Unfortunately, there is no “magic bullet” to make statistics and data analysis understandable and Peru B2B List useful for research. But there are some things you can do to make sure things run smoothly and avoid pitfalls. Many marketers try to put the cart before the horse and perform intricate analysis before spending time examining the data from a basic perspective. A data analysis will not always give you the answer to all your questions. Look especially at the opportunities that you can use from the research to take follow-up steps. Also read: Get rid of gut feeling, start with data-driven CX [handy framework] 1. View data with an open mindset It is good to consider in advance what results you expect from a study.
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For example, based on previous studies or certain ‘gut feelings’. This way you can search more specifically for the data that is relevant for your data analysis. But it is important to be open to the fact that the data can also produce unexpected results . Don’t see that as a wrong analysis. If you are open to learning new things from the data, you will draw the best conclusions from a data analysis. 2. Clarify what you can conclude: correlation or causation? A question that statisticians have been asked for years, but that does not make it any less important: can you also draw a causal relationship from a certain analysis or is there only a correlation ? This is an important difference, because causality is often concluded too quickly from correlation. But what do these two terms actually mean? In short, a correlation says how likely it is that there is a relationship between 2 variables. A causal relationship, on the other hand, has drawn a conclusion from this.