GOAL
In detail, the task was to:
- Identify drivers and triggers within the overall funnel logic for the purchase of an Audi vehicle
- Take internal and external data points into account to explain the connection between KPIs and each role within the overall framework
- Show the effect of KPIs within a customer journey from a time perspective
CHALLENGE
The challenges from a data-sourcing perspective were:
- Collecting all relevant internal data (based on briefings for the Audi data science team) and providing relevant external data, including online search behavior and pre-processed buzz data
- Bringing structured and unstructured data into a standardized format for further analysis
- Identifying a format to investigate online and offline KPIs, with a concentration on time-series analyses
- Covering spurious correlations based on non-stationary time series, attribution problems due to highly correlated factors (multicollinearity), and time differences between the company’s actions and effects on touchpoints and sales
- Using statistical methods, machine learning, and probability theory methodologies
- Enabling an in-depth investigation of customer actions across the entire customer journey
SOLUTION
Based on time-series analysis, an exemplary customer journey was created that included:
- Four major customer touchpoint KPI groups
- A clustering into the phases: awareness, information/consideration, and purchase, which reflect the different stages of customer involvement
- Detailed insights into the KPI impact over time
- A framework which evaluates the impact of performance indicators that clearly affect the company’s business, and mirrors the (marketing) value chain from input to output to outcome