According to McKinsey, between March and August 2020, one in five consumers switched brands and seven in 10 consumers experimented with new digital shopping channels.

Digitalization in the retail sector has made a leap forward 10 years in a matter of months. As a result, the flow of data has grown exponentially, and brands are faced not so much with the need to arm themselves with data-driven marketing tools and methodologies, but with the need to update outdated data modeling that is no longer able to capture changes with the required level of detail and speed.

Data-driven marketing uses models trained to recognize and infer consumer behavior. In the post-pandemic “new normal,” the same behavior has become harder to read and categorize; they have become even more elusive, susceptible to deviations from patterns that have taken on a repetitive form. Faced with a situation where historical data and patterns cannot serve as a basis for accurate predictive analysis, many marketers have chosen the path already chosen: they returned to mass communications and advertising.

Data-driven marketing offers a very different perspective: By improving existing tools—algorithms that become more powerful and flexible as they are trained on selected datasets—companies can develop more precise strategies to drive meaningful customer acquisition even in the face of unpredictable events. To keep up with changing needs and expectations and anticipate changes in customer behavior, brands must commit to innovating the way they manage data, from capturing new types of information to retraining algorithms.

Companies can no longer do without data-driven marketing because today it is the only approach that can evolve in the context of changes in habits and consumption patterns (sometimes preceding and causing the same changes).

The more marketers understand the customer journey, the more likely they are to develop the right messages and meet consumers at the right time and place they prefer.