![Brm project report [meru cab]](https://cdn.slidesharecdn.com/ss_thumbnails/brmprojectreportmerucab-150513125342-lva1-app6891-thumbnail.jpg?width=560&fit=bounds)
More Related Content
Similar to Marketing Analytics Final Project (20)
Recently uploaded (20)
Marketing Analytics Final Project
- 1. TLC YellowTaxi By:Alex Blom, James Kimball, MoniqueAtkinson, ZhiyunQi, andAlexander Stein
- 2. Business Understanding Business Understanding: TLC's board wants a better understandingof the customers they are serving during large holidayslikeNewYear's Day.TLC would liketo predict which customers tip versus those who do not tip to increaseemployeemotivation.This is because we hypothesize that larger groups tip better, especiallyonholidayssuchas NewYear's Day. However, the current consensusamong the drivers is that large groups are lessprofitableand more annoying. Business Question: What customer segment is more likelyto tip taxi drivers? Success Matrix: We will determine this model a successif we discover a unique trend between the group size and tippingamount.
- 3. Data Understanding The data was collected and obtained from the New York City Taxi & Limousine Commission. This data set is information exclusively gained from interactions during the month of January 2019. The Data Dictionary includes categorical, text, nominal, and quantitative data. The fields we will be using within our model include: Ride Length (quantitative) Passenger Count (quantitative) Fare Amount (quantitative) Tip Amount (quantitative) Tip form (categorical)
- 4. Data Preparation We began by deleting two columns where no taxi driver filled out the requested information. We may come back another time to discover any significance of the data absence. We deleted entries that were the outside the month of January We condensed the data to business on 1/1/2019 We calculated ride length by changing the data type toTime format and subtracted the pick-up and drop-off times In a new sheet we put the tpep_pickup_datetime, tpep_dropoff_datetime, Ride_Length passenger_count trip_distance payment_type fare_amount tip_amount columns to run with the model
- 5. Modeling We chose to use the CustomerSegmentation Model. We wanted to group different customers and compare the segments to ride length, trip fare, and trip amount. The variables we considered were the pickup time, drop-off time, passenger count, trip distance, fare amount, payment type, and tip amount. We calculated ride length by doing a transformation on the pickup and drop-off times. We also considered location variable, but the location was based off of area codes fromTLC,not general zip codes or Bureaus.SinceTLCis focused on group size and tip amount, we did not use location for the final variable. We assessed the results through segmentations featured on the following slides as created by Solver. We assessed the validity of the data results by converting the data into a box and whiskers plot so we could visually see statistical outliers.
- 6. Modeling
- 7. Modeling
- 8. Modeling This graph showTip Amount per cluster. Segment 2 shows many outliers that are important to investigate. This shows that single riders who ride more than 30 min have the highest spread of tips. This is our first clue that distance, and not party number, may be more useful in determining tip amount.
- 9. Modeling This graph shows the fare amount per cluster. The graph shows that taxi drivers in segments four and five have negative tipping amounts. This is most likely due to mistaken entries from the taxi drivers. The mistakes may derive from these segments being less than 10 min rides.
- 10. Modeling This graph shows the trip distance per cluster. It is again showing that segment two has the longest ride mileage.
- 11. Modeling This graph shows the number of riders per cluster. While segment two had the largest tipping spread in the earlier graph, it tends to have a very low amount of riders that disproves our earlier hypothesis. The segments with the most riders are segment 1 and 4.
- 12. Model Evaluation We found through our 5 customer segmentations that the distance of the trip bears no correlation to passenger(s) tipping on the ride. However, we discovered two levers that changed if the rider(s) tipped: the amount of time they spent in the taxi and payment type. Our model shows that if the rider(s) spent, on average, at least 10 minutes in the taxi, then the customer was likely to tip.The other lever that changed if the rider(s) tipped at all was payment type. The group tipped if credit card was used and did not tip if cash was used. We believe this to be a consequence of the data being self-reported. Therefore, cash tips may not be reported and may be pocketed by the driver. Group size, trip distance, and the amount of the fare did not change the tip amount in a way that we could see.
- 13. Recommendation We recommendthat we dismissthis modeland create a new model thatfocuseson thedistanceof thecab ride instead of thegroup sizeto see any possible correlationof tipping patterns.While we still believe there is a way to increase driver motivation,thismodel helped us determine which variables may be morebeneficial to focuson. A possiblefix to our model is to include moresegmentations to identify more trends in oursegmentation.