CASE STUDY

Big Data Exchange (BDEX) Customer Revisit Likelihood Scoring Algorithm Leveraging Location Data

·

Objectives

  • Design an algorithm to predict customer revisit likelihood using location data.
  • Implement and refine multiple iterations to optimize accuracy and reliability.
  • Validate the model’s effectiveness through rigorous testing.
  • Explore potential monetization opportunities.

Outcome

Although the algorithm demonstrated potential in predicting revisit likelihood, the project was ultimately not effectively monetized. Key takeaways include:

  • The importance of aligning data science projects with clear business use cases.
  • The necessity of early-stage validation with potential customers or end-users.
  • The value of iterative refinement in algorithm development, even if commercialization is uncertain.

Approach

  1. Data Collection & Preparation
    • Utilized location observation data to track customer movement patterns.
    • Cleaned and structured the data using Alteryx to ensure consistency and usability.
  2. Algorithm Development
    • Designed and implemented a scoring model to quantify revisit probability.
    • Conducted 150 iterations, adjusting variables, weightings, and methodologies to improve prediction accuracy.
    • Leveraged Alteryx’s advanced analytics capabilities for data transformation and scoring refinement.
  3. Testing & Validation
    • Applied the model to historical datasets to assess performance.
    • Conducted A/B testing to compare different scoring methodologies.
    • Evaluated results against real-world visit patterns.

Challenges & Insights

  • Data Complexity: Location observation data required extensive preprocessing to ensure accuracy.
  • Iteration Fatigue: Despite 150 refinements, a universally effective model remained elusive.
  • Monetization Hurdles: The project faced difficulties in commercializing the algorithm, as it lacked direct market applicability in its final form.

Future Recommendations

  • Partner with retailers to fine-tune the algorithm based on real-world applications.
  • Explore integration with loyalty programs or targeted marketing campaigns.
  • Conduct feasibility studies on alternative revenue models for location-based a