Audi – Competitor Analysis

The idea for this tool came up during an innovation day together with the Advanced Analytics team of Automotive BI. The goal of this day was to share knowledge between the two teams and join forces to come up with analytical solutions to improve marketing and sales within automotive. 

During the brainstorm, many questions came up that revolved around the sales funnel and the competitive landscape, like: How interested are customers in our models and those of our competitors? How do our lease prices compare to those of competitors? Can we predict how well a model will sell based on its price position? Or based on other indicators like Google search and social media? What is the effect of price adjustments on sales figures? What is the effect of marketing actions on sales figures?

The Competitor Analysis aims to answer all of these questions by creating insights into the different factors that may indicate how well a model will sell. The first factor is media attention, which we included by scraping news articles, reviews and blog posts from Google News. The second factor is marketing, for which we used MediaCom data about media spend across different channels (television, internet, magazines, out of home and radio). The third factor is online search behaviour, for which we used Google Trends data. Finally we included lease prices, which we scraped from leasing websites and brand websites.

By combining all of this external data with our internal sales KPIs (website visits, configurations, quotes, orders and registrations) we are able to observe trends, identify correlations and notice deviations. For example, we may see that a radio commercial or viral news article generates a lot of traffic to our brand website. Or we may notice that a competitor has dropped its lease price for a specific model, which sparks the interest of potential customers.In the coming months, Audi will start using the tool to evaluate the sales performance of their different models and make decisions about pricing and marketing accordingly. In the meantime we are also onboarding CUPRA, with the final goal to scale up to all brands.

  • Kevin Haver

    Lead Data Scientist
  • Ellen Mik

    Senior Data Scientist