An Automotive Giant Outlined Buyer Personas And Utilized Customer Segmentation For Effective Marketing






Analytical Dashboard

Business and function heads were able to take strategic decisions with the help of intuitive KPIs.

Successfully mapped customer life cycle to draw insights on customers switching from authorized centers to grey market for servicing, and customers who sell their bikes, to re-market new bike models to them and influence purchase decisions.

Cluster profiles helped in various marketing activities such as cross-selling, customer targeting for campaign and customer engagement, among others.

One of the world’s largest two-wheeler and three-wheeler manufacturing companies wanted to drive more effective marketing activities, built from a customer standpoint. The existing marketing efforts did not utilize specific customer personas.The absence of adequate data and attributes in defining segments meant that the business primarily relied on products for segmentation.

TheMathCompany partnered with the automotive giant to create customer segments based on characteristics, needs and usage attributes of customers, so it would help the business in understanding buyer personas better and thereby help in streamlining marketing activities.

TheMathCompany developed a customized solution that offered customer profiles with key metrics and exploratory data analysis with actionable insights in a user-friendly dashboard that could easily be used by the function and business heads. The process of gathering clustering results involved:

  • Processing multiple unstructured data sources to identify key metrics such as demographics of customers, purchase location, product usage figures, service details, and product attributes such as model, color, segment, age, price, market price sensitivity, etc.
  • Due to the large dataset, K-means was the viable option to obtain the valuable output. Hierarchical and DBScan imposed challenges in terms of time complexity and limitations of the algorithm respectively. Value of k (# of clusters) was decided based on business context and iterations as the elbow curve and silhouette methods had no significant results.
  • Customer profiles were built based on preferences and similarities/differences in comparison with the overall customer base.

Tactics around targeting existing based on the customer profiles was outlined for the 3 primary divisions that have touchpoints with the customer:

  • Sales
  • Service
  • Customer relations

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