Become a Data-driven Marketer

000s. Marketing teams are particularly impacted by this shift, given their role in handling digital channels and adapting to new digital workflows. Adele highlighted that data literacy covers a range of skills, including data analysis, programming, and machine learning. With every industry generating more data than ever before, marketers must learn to measure impact accurately. Data literacy enables marketers to attribute the success of campaigns more accurately, optimize budgets, and make more frequent data-driven decisions. Moreover, the automation of workflows through machine learning and AI is largely untapped, with about 70% of marketing teams not fully utilizing these technologies. Adele emphasized that data literacy is the foundation for creating hyper-personalized customer experiences, which are increasingly expected by consumers today.

Data Science Use Cases for Enhanced Marketing Strategies

Adele presented ten data science use cases that can be immediately applied by marketing teams to enhance their strategies. These include creating a single source of truth for data, leveraging advanced data visualizations for storytelling, democratizing data through dashboards, and automating customer segmentation. By integrating APIs and employing programming skills, marketers can consolidate data from disparate tools, reducing discrepancies and confusion. Data storytelling, particularly through tools like Tableau, allows marketers to convey insights effectively to leadership. Furthermore, democratizing data with dashboards can significantly reduce the time to insight, as evidenced by PepsiCo's 90% reduction in analysis time. Automated segmentation, a more advanced use case, allows marketers to develop intuitive customer segments based on behavior and demographics, leading to more personalized campaigns.

Data Science Training for Marketers

To close the gap between current data practices and advanced data-driven marketing, Adele suggested several upskilling strategies. The first step is understanding your objectives and aligning your upskilling efforts with them. Making learning a part of the job is vital; managers should celebrate learning as a driver of results and create an environment that encourages experimentation and psychological safety. Adele also recommended experimenting with different learning modalities, such as internal data science academies or hackathons, to find what works best for the team. She stressed the importance of learning by doing, tackling easy-to-reach projects, and iterating on solutions to gradually build data proficiency.

Working Together: Marketing and Data Teams

Adele emphasized the importance of collaboration between marketing and data teams to achieve data-driven success. While marketers should be data-savvy, the heavy lifting of data analysis and dashboard reporting often falls under the responsibility of BI and data teams. However, Adele argued that marketing teams can and should take ownership of certain data science use cases, especially those that involve democratizing data access and proving the value of data-driven initiatives. By developing proof of concept projects, marketers can demonstrate the potential ROI of data-driven strategies, encouraging greater support and collaboration from data teams. This approach not only promotes innovation but also ensures that marketing teams remain agile and responsive to changing data environments.