now playing: ai, ml and finance in real life

NOW PLAYING:

AI, ML AND FINANCE IN REAL LIFE

October 22, 2019

Agenda#1: Churn Prediction—

The AI & FP&A Combo

#2: Transforming the Expense Process

Panel Discussion

Churn Prediction:

The AI & FP&A Combo

Part 1:

AI & CPM: Looking Into the Future

➢ About ServiceMaster & Terminix

➢ About Jedox

➢ Challenge: Churn Prediction

➢ Roadmap of Digitalization

➢ What We Learned

About Us

➢ A leading cloud EPM solution

➢ More than 2,500 customers in 140 countries

➢ 250+ certified business partners globally

➢ Delivers:

➢ Self-service budgeting

➢ Unified planning and forecasting

➢ Reporting & analytics & dashboards

➢ Seamless data integration

➢ Recognized as a leader by independent

analyst firms

➢ Home & commercial pest control

services

➢ Subsidiary of ServiceMaster

➢ Fortune 1000 company

➢ Based in Memphis, Tennessee

➢ Largest brand

➢ ~ 80 % of ServiceMaster revenue

➢ More than $5 billion in sales

➢ Significant impact on recurring revenue & growth

➢ Churn rate is a key driver of customer retention

➢ Implement effective product and pricing strategies

➢ Influence on Financial Planning:

➢ Customer care costs

➢ Sales commissions (new vs. renewal)

➢ Sales & marketing costs

➢ The accuracy of churn is critical for accurate FP&A

Churn Prediction for FP&A

➢ Multiple product offerings

➢ Diverse geographic footprint

(Seasonal & weather effects)

➢ Diverse demographic markets

➢ Massive amounts of data & variety of customer and deal

characteristics

➢ Recording, analyzing and predicting customer churn in timely

manner becomes quite difficult

Challenge: Churn Prediction

The Approach:

Digitalization of FP&A – RoadmapArea Foundation Automation Transformation Digitalization

Data Entry Excel Bottom-Up

+Integration

+Top-Down +AI

Business

Logic

Aggregations Allocations

Driver-Based

+Simulations

Process Workflow +Alerts +Prescriptive

UI Excel, PPT Excel & Web +Mobile

Roadmap of Digitalization at Service Master

Area Foundation Automation Transformation Digitalization

Year 2016 2017 2017-18 2019

Business • Manual work

• Excel-based

• Data extracts

from multiple

sources

• Limited version

control

• Move to the

EPM AI tool

• Data integration

with source

systems

• Centralized data

source

• Reporting

• Business Modeling

(Drivers)

• Faster decision-

making

• Web UI

• Unified planning

• Improved analytics

• AI

• Continuous

improvements

• Maintenance &

learning

• Ongoing training

Organization HQ – Corp FP&A FP&A

& BI and IT

Expand to subsidiaries Functional owners

➢ Solid FP&A is in place for budgeting and forecasting

➢ System already connected to data sources

➢ Internal teams (BI & FPA) well trained on

➢ Data integration

➢ Report creation

➢ System maintenance

➢ AI capabilities came “Out of the Box” with the FP&A platform

➢ Time saving (System selection, learning curve)

Leverage Current FP&A Solution

Mission Statement

Analyze and predict customer churn using data from

ServiceMaster’s FP&A platform and in-house data warehouse.

Provide insights about:

➢ Churn drivers (reasons)

➢ Prediction of customer churn (agreement-level)

AI Working Cycle

Data

Collection

Evaluate

Drivers

Analyze

Results

Expansion

Sample data from a few branches (10%)

~10 features (out of 30) that are relevant to identify “churn pattern”

97.6% Accuracy to predict Churn Yes/No

Full data set

First Step: Churn Prediction

Learning & Configuration

Drivers

Expansion

Data Input

Testing & Model Results

Second Step: Churn Prediction

Run model on all branches

Data from ALL branches

~10 features (same as the first stage)

Accuracy: 95.3% Churn=“Yes” - 98.99% Churn=“No”

Actionable list of high-risk customers (time-based)

Drivers

Expansion

Data Input


Third Step: Churn Prediction

"Near-future" churn

Additional data source; ~3 months after the first snapshot

8 features to predict “next quarter churn” (out of 40)

Accuracy: 70-85% (not enough “trained periods” and data changes)

“Focus list” – For preemptive actions by other departments

Drivers

Expansion

Data Input


Next Steps

Corrective or proactive actions➢ Share information with the branches

➢ Measure success

Improve model to support increased prediction accuracy ➢ Frequent data loads (i.e. every 3 months)

➢ Add additional data sources → more features (focus on data that

changes over time)

➢ Share information with the branches

➢ Measure success

Challenges

➢ Define an accurate business question

➢ Skillset

➢ Collaboration with other departments

➢ Training & maintaining the model

The impact to the organization

➢ Better financial forecast → planning

➢ Create an inside team to transform business activities

Change to business and capabilities

➢ Preliminary results shows improvement in retention (goal: 5% decrease in churn)

What did we learn from the AI process?

➢ Find a small use case with discernable business impact (Forecast, driver analysis, data cleansing)

➢ Prepare known data (Additional data can be added later)

➢ Look for an available technology - Using a known, trusted, in-house tool saves implementation, selection time and resources

➢ Create a prototype

➢ Remember, AI is a learning process more than a project

Back at the office:

Summary & Recommendations

Part 2:

Transforming the Expense Process

Naveen Singh

CEO, Center

Rahim Shakoor

Controller, Docker

Transforming the Expense Process

Agenda:

• Problem

• Business Process

• Impact

• Back in the Office

State of Docker 2017: Challenges

60% Y-o-Y

employee growth

Robust travel

budget

Minimal policy

& analytics

The Status Quo Trajectory

2016 2017 2018 2019

Cumulative Change Relative to 2016

Processing Time

Expense Software Fees

Travel Costs

What takes so much time?

• Tracking down expenses

• Checking for coding

• Manual auditing

• Processing accruals

Docker Roadmap

Identify current

workflow issues

2017 2018 2019 2020

“Automation stops at

the back office.”

The Evolution of Expense Management

Foundation Digitization Automation Transformation

Spreadsheet with

physical receipts

Expense report +

receipt capture

Approval routing &

tracking

Excel Mobile phones Cloud

Better

outcomes

AI/ML

Docker Roadmap

Start initial

pilot

2017 2018 2019 2020

Respond to

user feedback

Predict expense type to increase accuracy

Data Input Card transactions

Model Flow of data from transactions to GL

Train/Test Employee use + finance review

Results: reduced review time

Expand Data Set Deploy broadly

Maintenance Periodic re-training and testing

Before:

Roadmap

2017 2018 2019 2020

Focus on

easy wins

Initial Results

2016 2017 2018 2019

Cumulative Change Relative to 2016

Processing Time

Expense Software Fees

Travel Costs

What’s Next?

Pay Process Audit Report Optimize

Real-time data AI and ML Analytics

Streamlined workflow

Better

decisions=

Highlighting Insights

Key Takeaways

• Next generation technology bring

processing costs down significantly

• AI and ML automate and analyze real-time

data to drive better outcomes

Back In The

Office:

• Get started on the journey from foundation to transformation

• Consider how to shift from a system of record approach to a system of intelligence

• Measure and understand your current process

• Research how your current technology providers are innovating around AI to streamline processes

Panel

Discussion Jamie Cousin

Liran Edelist

Rahim Shakoor,

Naveen Singh

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