Challenges:
  • High expensive churn is normal but the cause is not clear
  • Rising Cost-of-Acquisition plus flat to down Long Term Value
  • Short history, limited sub base to automated modeling doesn’t work
Solution:
  • ML Toolkit and ML Interpretability Services used to generate a churn prediction model
  • Churn cohorts generated weekly identifying high 30 day churn propensity
  • Reasons for churn identified, making intelligence actionable

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Challenges
  • Key promo has been used on and off for years – but impact on revenue and margin unclear
  • Multiple factors at the store level impact sales so its hard to assess promo effectiveness
  • Marketing spend varies week over week which is an added variable
Fokal Data Services
  • Data Cultivation
  • Insight Extraction and Visualization
  • Massively Parallel Processing
OUTCOMES
  • Integrated client data with external cultivated data (census, BLS, reviews)
  • Delivered 360 viz to review KPIs
  • Provided additional demographic and behavioral insights
Fokal ML Services
  • Auto Feature Engineering
  • ML Toolkit
  • ML Interpretability Services
OUTCOMES
  • Assessed promo impact on multiple variables – revenue, profit, returns
  • Built models with 3rd party data overlay to understand impact of demographics on promo
  • Store segmentation with key variabes to show where the promo is effective - allowing targeted promos