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Audi has been developing a KPI tracking system and data lake to save all external and internal data related to their brand. However, the volume of data does not reflect its importance. Therefore, TD Reply was asked to quantify and qualify important KPIs from a customer perspective.
PERIOD: 2016


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


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


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