How to become a data-driven marketer: a practical guide

Marketers can become data-driven without having a background in analytics, a point often overlooked in our industry. While many professionals know that data is crucial, the emphasis tends to be on volume rather than relevancy. Yet, accumulating vast amounts of data without focused analysis can lead to paralysis by analysis. For instance, a digital campaign might generate millions of impressions, but if these don't lead to conversions or something valuable for the business, the volume of the data points becomes meaningless.

To overcome the issue of data overload, marketers should prioritize a centralized approach to integrate data across all channels. Such an integrated approach provides the dual advantage of centralizing data while also streamlining the decision-making process. However, the key to unlocking the potential of this approach lies in aligning the gathered data with desired outcomes. For example, if the aim is to increase customer lifetime value, metrics like customer retention rates would be more valuable than merely tracking click-through rates.

While the industry tends to focus on short-term metrics—like the number of leads or impressions—a more holistic and deeper view is necessary. This involves looking at older data to identify trends over months or even years, as well as conducting cost analysis to understand ROI better. Say, if your paid ads from last year performed exceptionally well during a specific season, reallocating more budget during that time this year could yield better results. Equally crucial is comparing current data with set objectives and key performance indicators to understand where you stand in relation to your goals and budget.

Lastly, let's not forget the importance of a well-designed, user-friendly dashboard. The dashboard should prioritize simplicity, whatever platform you use to do it, featuring only metrics that are relevant to achieving the business's goals. A cluttered dashboard with an overwhelming amount of metrics can be counterproductive. By focusing on essential data points, like customer acquisition cost or lifetime value instead of just impressions or clicks, marketers can make more informed decisions that align with their business objectives.

By adopting this more nuanced approach to data, marketers stand to not only improve the efficiency of their campaigns but also contribute to best practices that elevate the field as a whole. Ignoring these aspects risks perpetuating common but flawed techniques that can give our industry a less-than-ideal reputation. Therefore, a more thoughtful approach to data isn't just a recommendation; it's a necessity.

Redefining Attribution: Strategies for Low-Quality Data

I am going to share one of the methods I have used to make a good attribution of sales data, leads, etc. when the data is not very reliable or of questionable quality.

The method consists of the following: first, I classify the channels from most reliable to least reliable, according to the history of the quality offered in the data of each channel, or whatever criterion is considered most appropriate. Then, I assign the results to each channel in the established order, starting with the most reliable channels, until I reach the last channel and assign the rest of the results that could not be attributed to the previous channels.

This method has worked quite well for me when I have an internal database on the website that tells me the total number of sales, leads, or whatever KPI I need to measure. The ideal scenario will always be to try to get as much accuracy as possible in the data we get from the tools, but there are cases where this is not possible.

The main limitation of this method is that it does not consider the possibility that multiple channels may have contributed to a single conversion.