— Using time series forecasting for predicting Freska’s customer churn

Customer case: predicting churn for Freska

  • Address information: Postcode, City, Country
  • Booking details: Worker, Duration, Frequency, Canceled, if the cleaning place hasdogs, hascats, hasotherpets, PetsInfo, cleaning area, notes
  • Satisfaction metrics: RatingOverall, RatingBehavior, RatingQuality, RatingOnTime, ApprovedByCustomer
  • Marketing information: SalesAgents, SalesAgency
  • Number of months from first booking, total booking hours this month
  • A field that is manually entered telling if the customer churned this month (True, False)
"from": "trainingData",
"where": {
"totalHoursThisMonth": "5",
"bookingHoursThisMonth": "2 3",
"churnStatusThisMonth": true,
"totalHours1MonthAgo": "2",
"bookingHours1MonthAgo": "1 1",
"churnStatus1MonthAgo": false,
"predict": "churnStatusNextMonth"

Next steps



-- decision automation in the cloud. #ML for #nocode and #rpa operators.

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