Data-Driven Strategies for Effective Marketing Campaigns
Overview
The Company: A well-established retail food company selling products from five major categories through three sales channels: physical stores, catalogs, and the company's website.
Objective: To understand our customer behavior and to develop predictive models aimed at maximizing the profit of the upcoming direct marketing campaign. This involves implementing customer segmentation and analyzing data to identify customers most likely to respond positively to the campaign offer.
Dataset: To achieve this goal, a pilot campaign was conducted involving existing customers. These customers were randomly selected and contacted via phone, email, and survey to discuss the possibility of acquiring a gadget. Over the subsequent months, customers who made a purchase in response to the offer were accurately labeled.
Conclusion: With all the findings we have on customer behavior, it's evident that household composition emerges as the single most significant factor influencing average spending. In summary, households without children, constituting just 28% of our customer base, spend 250% more than those with children. These customers are three times more likely to positively respond to our marketing. This underscores that household composition is the primary driver of customer spending within our company, fundamentally shaping our marketing strategies. Moreover, our work has resulted in models that accurately pinpoint customers with a high likelihood of responding positively to marketing campaigns. This allows the marketing team to focus their efforts and resources on these customers, enhancing efficiency and effectiveness across various marketing functions.
This overview captures the essence of the project, but there's much more to explore in the detailed notebook. There, you'll find rich visualizations, in-depth analyses, and a comprehensive understanding of each step in the process. The well-organized table of contents allows you to easily navigate to specific sections that interest you, from data preprocessing to key insights and recommendations.
