Supercharging Business Decision with AI: Insights, Optimize and Personalize

Uber在食品配送、骑乘共享和货运方面运营多实体市场,实时平衡每个垂直市场的供需。Uber的系统和服务执行近乎实时的分析,以预测市场需求,并将其转化为激励我们的驾驶员合作伙伴和/或为我们的车手提供促销。技术服务为我们的运营团队创造每周的财务目标,使其保持在预算范围内,同时满足最大订单数量。
我们的财务数据栈是使用我们的核心消息传递框架、数据转换管道以及跨越各种云的实时数据计算基础设施构建的。业务智能服务在数据的速度和准确性之间执行一个持续的平衡操作,而这仅仅是因为需要具有适当的粒度级别,以便对全球运营团队的收入和预算支出进行适当的可视性。离线和在线数据处理的结合能力也有助于运行机器学习模型,及时识别金融欺诈和安全事件。
在本文中,我们将介绍由业务需求定义的数据处理原则。我们还涵盖负责制定数百万财务决策的技术基础设施。最后,我们将与您分享我们在寻找技术答案方面的业务需求。

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1.Supercharging Business Decisions with AI: Ins ights , Optimize and Pers onalize

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3.How many Drivers do we need in December 2019 in San Francisco?

4.How far do we plan? Tactic Strategic 4-6 12-18 weeks months

5.How do we know how many people are going to take Uber? Trip Forecasting Historical Trips Historical Trips data for a city Long-Term Forecasts Acquisition Spend Upto 52 Weeks Marketing (acquiring new Time Series Model riders/drivers) Used for year long budget planning Events Big events in the city

6. Time-Series Forecas ting Algorithms Reference https://www.datascience.com/blog/time-series-forecasting-machine-learning-differences

7.Forecasting - Predictive Model Cohort Rider Retention Trips/Active Driver First Trips Trips Rate User Eater Month of joining

8. Forecasting - Bayes ian Model It’s a probabilis tic graphical model that repres ents a s et of variables and their conditional dependencies via a graph (DAG). For example, a Bayes ian network could repres ent the probabilis tic relations hips between dis eas es and s ymptoms . Given s ymptoms , the network can be us ed to compute the probabilities of the pres ence of various dis eas es . Reference https://en.wikipedia.org/wiki/Bayesian_network#/media/File:SimpleBayesNetNodes.svg

9.Forecasting - Black Box Model Cohort Ensemble Bayesian Black Box Output Classical Backtesting

10.Forecasting Models - Neural Net Trip transactions Cost Trip Product Curve Mix Model Model Model Marketing spending Cost Trips Trip Fare Curve Net-Inflow Cost Model Model Model Ensemble Model Model Model GB Driver/Rider signup Service Net-Inflow Cost Retention Trip Fee Model Model Model Model Revenue Model User behavior Incentive Trip UFP Model Model Up/Down Holiday/events Model Planning-as-a-Service Finance Modeling and Computation Platform

11.Number of trips Number of Drivers needed But how do you balance the Market with Drivers & Riders

12.Optimization

13. Optimization Process Financial Planning Tool: Previous slides Bi-annual Output: monthly/weekly spend budget, by lever/PU Continuous On-demand Cross Lever Budgeting Budget Setting Regional Growth Leads Tool: Cross Lever Optimizer (CLOe) Finance Teams: Strategic + Regional Finance, Perf Marketing, Central Ops Ops Output: updated weekly spend budget, by lever/PU Incentive Budget Paid Budget Referrals Budget Incentive Spend Paid Spend Referral Spend Tools: Finplan Tools: Mixed media model Tools: Web referrals Output: Weekly EDI/ERI/UFP Output: Weekly marketing Output: City referral structures Spenders spend by city channel spend by city Ops Marketplace Marketing

14.Trip Forecasting & Optimization Historical Trips Long-Term Forecasts Historical Trips data for a city Upto 52 Weeks Time Series Model Acquisition Spend Used for year long budget Marketing (acquiring new planning riders/drivers) Driver Incentives Short-Term Rolling (~ $1Bn) Forecasts 1-12 Weeks Rider Promotions Personalized Model (~ $500m) Adjust budgets in spend levers to achieve trip targets - Represents a spending lever

15.Scenario Planning Scenario Generation Deviation from Forecast Weekly/monthly Incentive planning Which subset of users should we focus integrated with trip forecasting on to meet goal? Combining insights like these can help Uber adjust its budget in the short term.

16.Optimization Model Overview User Level Model Historical FTs Incentive spend Paid + Organic Driver Pe r trip me trics Re fe rral First time Driver RR & TPA fore cast (Fare ) Drive r (FTD) by channe l Trips = FT x RR x TPA Drive r FT model (base d on channe l cost curve s) Trips production GB function Trips = FT x RR x TPA Paid + Organic First time Ride r (FTR) by channe l Legend Re fe rral Rider RR & TPA Model signal/input Cost curve Lever Promo Historical FTs YYY Incentive spend Rider FT model (base d on channe l cost curve s) Rider 16 CLOe helps to optimize growth spend across marketing, referrals and incentives.

17.Trip Model Detail (LSTM) y1 y2 yn y1 y2 yn λ π λ π λ π λ π λ π λ π FC FC FC FC FC FC LSTM 2 LSTM 2 LSTM 2 LSTM 2 LSTM 2 LSTM 2 I_t+1 I_t+2 I_t+n I_t’+1 I_t’+2 I_t’+n LSTM 1 LSTM 1 F_t F_t’ training n = 12 predict n = 12

18.LifeTime Value

19.LTV is an estimate of LifeTime contribution of each user in order to drive efficiency in marketing, incentive spend and as a KPI to inform product improvements

20.Model Overview Model Overview: ● We use Gradient Boosting Trees Mode l which consolidate s pre dictions of hundre ds of inde pe nde ntly traine d tre e s. Its an ite rative Mode l and Pre diction syste m. Figure shows the use r le ve l GB Mode l pre diction. ● We are using the Gamma-Gamma BG/NBD mode l to pre dict the ne xt 2-ye ars of rolling gross bookings for e ach use r. Eng Platform: Apache Spark Ecosystem ● PySpark platform to proce ss Pe tabyte s of data ● Combine s Que ry, data frame s and machine le arning Machine Graph ● Ability to acce ss data across Ube r’s data store s: Hive , Spark Streaming Learning Analytics HDFS, Cassandra, and S3. SQL (MLlib) (GraphX) Spark Core API R Python Scala Java

21.Platform

22.Finance Intelligence at Uber Planning Forecasting Budgeting Optimization Lifetime Value Scenario Management Service Analytics Model Orchestrator S Metrics Computation Management APIs Security Model Computation Service Forecasting Models Optimization Models LTV Models Finance Data Warehouse Data Pipelines Metrics Store Dashboards

23.Data Platform Overview

24.Financial Data Store (FDS)

25.Future

26.Looking forward to.. ● Uber Freight ● Uber Health ● Drones (Food Delivery) ● Uber Elevate (Air Transportation) ● Autonomous Vehicles ● Facilitate better Transportation

27.Proprietary and confidential © 2018 Uber Technologies, Inc. All rights reserved. No part of this docume nt may be re produce d or utilize d in any form or by any me ans, e le ctronic or me chanical, including photocopying, re cording, or by any information storage or re trie val syste ms, without pe rmission in writing from Ube r. This docume nt is inte nde d only for the use of the individual or e ntity to whom it is addre sse d and contains information that is privile ge d, confide ntial or othe rwise e xe mpt from disclosure unde r applicable law. All re cipie nts of this docume nt are notifie d that the information containe d he re in include s proprie tary and confide ntial information of Ube r, and re cipie nt may not make use of, disse minate , or in any way disclose this docume nt or any of the e nclose d information to any pe rson othe r than e mploye e s of addre sse e to the e xte nt ne ce ssary for consultations with authorize d pe rsonne l of Ube r.

28.Business Facts ● X Ride sharing cities, YUberEats cities ● $zz bn gross bookings (excluding Uber Eats) ● ??M+ active riders, ?M+ active drivers ● ??M+ trips/day Goal: Enable intelligent models to make data-driven financial decisions faster and more accurately.