Supercharging Business Decisions with AI: Ins ights , Optimize and

Personalize

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

How far do we plan?

Tactic

4-6weeks

Strategic

12-18 months

How do we know how many people are going to take Uber?

Acquisition SpendMarketing (acquiring new

riders/drivers)

Historical TripsHistorical Trips data for a city

Long-Term Forecasts Upto 52 Weeks

Used for year long budget planning

Time Series Model

EventsBig events in the city

Trip Forecasting

Time-Series Forecas ting Algorithms

Reference https://www.datascience.com/blog/time-series-forecasting-machine-learning-differences

Forecasting - Predictive ModelCohort

Rider

Driver

Eater

Month of joining

First Trips Retention Rate

Trips/Active User Trips

Forecasting - Bayes ian Model

It’s a probabilis tic graphical model that represents a set ofvariables and their conditional dependencies via a graph(DAG). For example, a Bayes ian network could represent theprobabilis tic relationships between diseases and symptoms.Given symptoms , the network can be used to compute theprobabilities of the presence of various diseases .

Reference https://en.wikipedia.org/wiki/Bayesian_network#/media/File:SimpleBayesNetNodes.svg

Forecasting - Black Box ModelCohort

Bayesian

Classical

Black Box Output

Backtesting

Ensemble

Cost Curve Model

Cost Curve Model

Retention Model

Incentive Model

Trip Model

Trip Model

Trip Model

Trip Model

EnsembleModel

Fare Model

Product Mix

Model

Service Fee

Model

UFP Up/Down

Model

Net-InflowModel

Net-Inflow Model

Cost Model

Cost Model

Finance Modeling and Computation Platform

Planning-as-a-Service

Trips

GB

Revenue

Trip transactions

Marketing spending

Driver/Rider signup

User behavior

Holiday/events

Forecasting Models - Neural Net

Number of trips Number of Drivers needed

But how do you balance the Market with Drivers & Riders

Optimization

Referral SpendTools: Web referrals

Output: City referral structures

Incentive SpendTools: Finplan

Output: Weekly EDI/ERI/UFP spend by city

Financial PlanningTool: Previous slides

Output: monthly/weekly spend budget, by lever/PU

Budget SettingRegional Growth Leads

FinanceOps

SpendersOps

MarketplaceMarketing

Paid SpendTools: Mixed media modelOutput: Weekly marketing

channel spend by city

Bi-annual

Continuous

Incentive Budget Paid Budget Referrals Budget

On-demand Cross Lever BudgetingTool: Cross Lever Optimizer (CLOe)

Teams: Strategic + Regional Finance, Perf Marketing, Central OpsOutput: updated weekly spend budget, by lever/PU

Optimization Process

Acquisition SpendMarketing (acquiring new

riders/drivers)

Historical TripsHistorical Trips data for a city

Long-Term Forecasts Upto 52 Weeks

Used for year long budget planning

Rider Promotions(~ $500m)

Driver Incentives(~ $1Bn)

Short-Term Rolling Forecasts1-12 Weeks

Adjust budgets in spend levers to achieve trip targets

Time Series Model

- Represents a spending lever

Personalized Model

Trip Forecasting & Optimization

Scenario GenerationWeekly/monthly Incentive planning integrated with trip forecasting

Deviation from ForecastWhich subset of users should we focus on to meet goal?

Combining insights like these can help Uber adjust its budget in the short term.

Scenario Planning

16Rider FT model (based on channe l cost curves)

Drive r FT model (based on channe l cost curves)

Paid + Organic

Refe rral First time Drive r (FTD) by channe l

Paid + Organic

Refe rral

First time Rider (FTR) by

channe l

Trips production

function

Per trip metrics forecast (Fare )

GB

Legend

YYY

Model signal/input Cost curve Lever

Promo

Historical FTs

User Level ModelIncentive spend

Driver

Trips = FT x RR x TPA

Incentive spend Rider

Trips = FT x RR x TPA

Historical FTsRider RR & TPA

Driver RR & TPA

CLOe helps to optimize growth spend across marketing, referrals and incentives.

Optimization Model Overview

λ π

LSTM 1

y1

LSTM 2

λ π

FC

λ π

y2 yn

FC FC

LSTM 2 LSTM 2

F_t

I_t+1 I_t+2 I_t+n

λ π

LSTM 1

y1

LSTM 2

λ π

FC

λ π

y2 yn

FC FC

LSTM 2 LSTM 2

F_t’

I_t’+1 I_t’+2 I_t’+n

n = 12 n = 12training predict

Trip Model Detail (LSTM)

LifeTime Value

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

Spark SQL

Apache Spark Ecosystem

StreamingMachine Learning (MLlib)

Graph Analytics (GraphX)

Spark Core APIR Python Scala Java

Eng Platform:● PySpark platform to process Pe tabyte s of data● Combines Query, data frames and machine learning● Ability to access data across Uber’s data store s: Hive ,

HDFS, Cassandra, and S3.

Model Overview:● We use Gradient Boosting Trees Mode l which

consolidate s predictions of hundreds of independently trained tree s. Its an ite rative Mode l and Prediction system. Figure shows the use r leve l GB Mode l prediction.

● We are using the Gamma-Gamma BG/NBD mode l to predict the next 2-years of rolling gross bookings for each use r.

Model Overview

Platform

Forecasting Budgeting Lifetime ValueOptimization

S

AnalyticsSecurity

Model Orchestrator Metrics Computation Management APIs

Forecasting Models Optimization Models

Scenario Management Service

LTV Models

Model Computation Service

Data Pipelines Metrics Store Dashboards

Finance Data Warehouse

Planning

Finance Intelligence at Uber

Data Platform Overview

Financial Data Store (FDS)

Future

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

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Business Facts

● X Ride sharing cities, Y UberEats 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.