Data Lake: The Machinery Behind the Marketing Strategy

Nowadays, data has a special place in the digital environment, especially when we talk about marketing strategy. The data is present from the planning process, the execution, and even the measurement of the implemented strategy results.

On the other hand, a critical digital evolution is taking place: the disappearance of third-party cookies, which leaves many advertisers without data on users who have interacted in some way with their brand.

It opens a dilemma: the need for data versus the gradual loss of cookies. How can we deal with this? Technology gives us the answer: develop a data lake.

Usually, a company has different data sources available; for example, CRM, e-commerce CMS, digital analytics tools, such as Google Analytics, paid digital media like Google Ads, etc. Traditionally, these data sources are separated from each other, which makes the analysis and synthesis process of data an extensive and sometimes complicated task.

A data lake gathers  all these data sources in one place, so we will no longer have the data of each phase of our user's interaction with the brand separately and we will be able to unify data. This is crucial because being able to identify our users throughout their digital, even physical, journey with our brand makes us less dependent on cookies and provides us with more reliable data.

Thus, a data lake helps us to have greater reliability of the data of our users and their interactions with the brand and to know in depth their interests and behaviors.

How do we take advantage of the data from the data lake to grow our brand? Well, the information is invaluable when considering new marketing strategies, and even optimizing the strategies that are currently underway.

When we have a reliable data source that provides us with data on the behavior of our users, temporality, products and services best valued by each user profile of our brand, more robust will be the foundations of any marketing strategy that we want to carry out.

Begin with planning. When we have identified what the objective of our marketing strategy will be, whether it is to generate brand awareness, introduce a new product or increase the sales of a specific product, we can turn to our data lake and obtain the necessary information to start this process. Planning without bearing in mind how our users have behaved is the same as going to the open sea and throwing out a fishing net: we can obtain results but not necessarily the ones we are looking for.  Bearing in mind the data processed in our data lake when planning will help us target exactly the profiles we want to impact and that we know will give us the best possible result.

During execution. The process doesn't end with a created and approved strategy; measuring the results during execution is a fundamental part of the process. Once the marketing strategy is in operation, we must make sure that we are achieving set goals, and if not, rethink the actions to correct course and achieve these goals. Although the results can be tracked in each data source, for example, a strategy to get leads can be measured with the CRM or a strategy with a focus on traffic can be measured with the volume of sessions in Google Analytics, We must always consider the unified data provided by the data lake. This will help us to understand which profiles are interacting with the communication in the way that was expected, which ones are not accepting communication (which opens a message optimization window) and which profiles are interacting positively with messages that are not planned for them (which opens the possibility of rethinking goals).

Measurement of results. Once the strategy has completed its life cycle, it’s necessary to review the outcome of the actions and optimizations that were carried out. At this point, we can have a complete picture of the results of our strategy in the data lake and evaluate it. With all the information available, we will be able to have powerful findings on user behavior with the brand and generate lessons that we can take to new marketing strategies. If we have a data lake with a unified view of our users' interactions with the brand, we can generate different attribution models to quantify the impact of each phase of the funnel of our marketing strategies, among other use cases.

Like everything else, data also has a cycle, so it is essential to evaluate the life cycle of the data lake's data and the impact of that data on the generation of our marketing strategies. Ensuring our data lake has relevant and up-to-date data is essential for success.