AspirationalX

Jira: AXMC-233

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

    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