Smart Data Marketing is presented as a strategy to transform useful data into better marketing results. Instead of collecting large amounts of information, Smart Data focuses on getting actionable data.

Smart Data Marketing: Transform Your Data Into Better Results

As companies move towards a stage of maturity in terms of harnessing customer data, Big Data strategies are giving way to Smart Data strategies. In marketing, gathering useful data that answers the right questions is the key to better marketing results


In a previous post in this blog we already referred to the difference between Big Data and Smart Data and the reasons that have led to the clash between these strategies.

Until a few years ago, everyone was talking about Big Data and the massive collection of data was perceived as the most efficient strategy to obtain useful information, knowledge and business insights to support business decision making.

However, as the amount of data we generate has increased exponentially, the overabundance of information has gone from being an advantage to becoming, on many occasions, a disadvantage.

Smart Data is positioned as a strategy for harnessing data that solves the problems of massive data collection or Big Data. Instead of obtaining large amounts of information, Smart Data focuses on the collection of useful data.

What is Smart Data Marketing?

Smart Data Marketing is a marketing strategy that focuses on the collection of useful data (as opposed to massive data collection) about the customer, the activity itself, the competition, etc.

In other words, Smart Data Marketing prioritises the collection, processing and analysis of data that will be of some kind of use to the company's marketing team, whether it be to get to know the customer better, to carry out a customer intelligence & analytics strategy, to segment customers or to measure results.

The dichotomy between Big Data Marketing and Smart Data Marketing is similar to the usage of waste generated in recycling processes.

As we generate more and more waste (or data), it is necessary to carry out a classification process where only useful materials will be used for reuse.

The same thing happens with data. Having immense amounts of data is not only completely unproductive, but it can also end up harming the company.

Some of the risks we face when storing too much data are:

  • The abundance of data assets hinders and delays processing, treatment and analysis.
  • Having too much information makes it difficult to get insights.
  • Useful data can be camouflaged and discarded among so many useless data.
  • The storage and processing of large amounts of useless data is a waste of resources and time.

For a company to carry out a Smart Data Marketing strategy, it must be in a stage of maturity in terms of data management and analysis, and also be in the beginning of adopting a customer-centric perspective that allows it to truly know its customers and move towards building omni-channel customer experiences.

In other words, the collection of useful data or Smart Data involves a previous strategic approach linked to defined marketing objectives that, in turn, will be linked to performance indicators and KPIs for monitoring and tracking.

Smart Data Marketing is a strategic approach whose ultimate goal is to support business decision making and, in this case, marketing decisions related to the customer universe.

To make better marketing decisions, customer data and analytics are essential.

Why should you base your marketing strategy on data?

Customer data analysis is the foundation of data-driven marketing.

In the digital world we live in, data is the main source of corporate information, especially in digital marketing. 

Today, marketing without data is not marketing.

Without data, marketing is reduced to biased opinions and preconceived and prejudiced ideas of a group of people. In this line, without data, marketing actions and strategies are like fishing and hoping that a fish will bite. By chance or because we believe there are many fish in that area.

When a company starts developing a data-driven marketing strategy, it usually focuses on collecting as much data as possible to build a solid foundation of information. In the first stage towards knowledge and data-informed decision making, this can be overwhelming.

However, those organizations whose data strategies are in a mature phase and already have a solid base of knowledge, collecting massive amounts of data is not the best idea.

What marketing data is useful?

Differentiating between useful data and those that do not provide any value is one of the most complex steps in developing a Smart Data Marketing strategy. To do this, we must first have a clear understanding of our brand objectives and performance indicators that we will use to measure them.

To build a Smart Data Marketing strategy, there are a series of fundamental cornerstones that we must rely on:

Companies not used to working with data, analyzing it, and in an early stage of digitalization may feel overwhelmed in the planning and conceptualization phases of a data strategy. In these cases, the best option is to seek experts who guide the way and lay the foundation for future utilization of marketing data and informed decision-making.

If you don't know where to start, at Kale we have developed a guide with the 10 key marketing and sales KPIs that any company should know. In addition, we explain their purpose, how to apply them, and how to calculate them:

Download guide

Kale Tip: Use these KPIs to build your Smart Data Marketing plan and start defining your marketing goals.



Data is the foundation for informed decision-making, including in the areas of marketing, sales, and customer engagement strategies.

To make better marketing decisions and achieve better results, our Smart Data Marketing strategy should be focused on making better decisions. In this sense, the focus should be on leveraging the data we already have, strategic planning, and analyzing useful data.

Rather than accumulating large amounts of data, the key lies in asking the right questions and collecting the data we need to answer them.