Despite the widespread application of analytics dashboards and data-driven KPIs across the C-suite, most senior marketing teams are utilizing data primarily for backward-looking analysis to measure performance rather than building analytics dashboards that drive future initiatives and planning. For that reason, the majority of brands actually underutilize data that they already have at their fingertips.
Without diminishing the importance of using data KPIs to evaluate past marketing performance, there’s often vast untapped value to be found in re-engineering some of your data points and what you can learn from them for better understanding your target audience and optimizing a campaign that speaks more effectively to their needs. By doing this, we’ve found marketing programs can not only become more effective in conversion of new customers, but also result in customers that have higher engagement with the brand and higher lifetime value.
When we have broad audiences, it’s hard to deliver campaigns that are relevant to everyone. Therefore, one of the first steps to achieve better results in campaigns is to better know your target customer and segment the audience in a meaningful and actionable way, so you’re able to serve the direct needs of each small audience cluster.
An effective way to perform audience segmentation and targeting is through a combination of design thinking methods and data science. The framework enables you to have a comprehensive understanding of different consumer profiles with different behaviors and needs, so as to convey the right message to the right audience and ensure that products and services are clearly communicated to meet their needs and help them achieve their jobs to be done.
data science enables the analysis of large volumes of data, with the use of sophisticated statistical techniques, which allow for finding patterns among consumers. Thus, demographic characteristics, geographic information, product use, and behavioral characteristics, for example, can be used to analyze and segment consumers. design thinking processes, on the other hand, allow us to analyze consumers in depth and, thus, identify the most relevant factors to segment them according to their needs as well as to create personas. When both are combined, it is possible to identify the patterns of similarity and dissimilarity, considering the most important factors, in addition to understanding the relevant characteristics that differentiate and describe them, which supports the creation of campaigns that resonate better with them.
This design-driven data science framework is normally based on in-depth qualitative interviews with consumers to understand customer profiles and needs more deeply: on the analysis of large volumes of customer data for generating insights about customers behaviors, preferences and profiles; on advanced analytics techniques and machine learning (ML) to cluster customers and perform statistical analysis; and on the use of frameworks such as jobs to be done to capture consumers’ needs. This process is iterative and provides a means for testing hypotheses generated on the qualitative interview. Also, as some of the consumer insights generated from data analysis are based on correlations, which does not imply causation, data insights can also suggest some points to be explored more deeply on the qualitative interviews.
Marketers are always under a strict budget. Therefore, it’s very important to optimize spending so as to derive maximum ROI from their allocated budgets in campaigns. Data analysis and machine learning can be great tools to improve customer acquisition processes and reduce its costs.
