Enhancing Marketing Intelligence Through Dashboard Automation and Purchase Intent Analytics: A Business Analytics Case Study

Marketing teams in consumer goods companies rely heavily on external market data, but turning that data into information that can be used in everyday decision-making remains challenging. This thesis addresses this issue through two levels of work carried out during a six-month internship at De’Longhi Group. The first part of the work is operational. Raw market data that arrives from two external retail tracking providers, GfK and Circana, was integrated into a unified data pipeline built on Google BigQuery and made accessible through three Looker Studio dashboards serving different audiences within the organisation. The resulting system reduced the need for manual extraction and formatting and allowed different teams to access the relevant data directly through the dashboards. The second part is analytical. A panel of 709 Global Web Index audience segments tracked across eight quarters in Germany was used to investigate which consumer characteristics and market conditions are associated with higher coffee machine purchase intent. A fixed effects regression combining segment-level intent data with quarterly retail market controls from GfK shows that behavioural and attitudinal segments are consistently stronger indicators of purchase intent than demographic ones, and that quarters with higher internet sales share tend to coincide with elevated intent across segments while higher average market prices tend to suppress it. The thesis concludes by discussing how these analytical results could be incorporated into the existing dashboard environment and updated as new data becomes available.