Big Data – What’s the Big Deal?and How Can It Be Used for

Risk Decision Making?

Paychex Risk AnalyticsProviding the Power of Knowing

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History of Paychex

1971

1983

Market Cap$15 Billion

Service RevenuesOver $2 Billion

A leading national provider of payroll, human resource, and

employee benefit services, providing American businesses

the freedom to succeed.

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What’s the Buzz?

In 2009, Google processed 24 petabytes of data each day

1 = 1.25 terabytes

24 petabytes = 20K

Or think of it this way:

So

This is enough text to fill up 2 billion filing cabinet drawers daily.

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To the Moon and Back… Literally

2.5 quintillion (18 zeros) bytes of data generated in 2012

Or the equivalent of 24 trillion minutes of music

In 2012 alone, 2.5 quintillion (that’s 18 zeros) bytes of data was generated.

Over One Year…

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How is this happening?

100,000 Tweets

204 million e-mails

2 million Google searches

685 thousand Facebook shares

48 hours YouTube videos

571 new websites

Data Created Every

Minute

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Where You See Predictive Modeling

Ever wonder how Amazon can make recommendations to you?

Or how your credit score works?

Or how your bank or credit card company can flag certain transactions as being potentially fraudulent?

Many use the collected data and statistical analyses to take the “guess-work” out of guessing.

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What happened?

How many, how often, where?

Where exactly is the problem?

What actions are needed?

Why is this happening?

What if these trends continue?

What will happen next?

What’s the best that can happen?

Optimization

PredictiveModeling

Forecasting

StatisticalAnalysis

AlertsQuery

DrilldownAd hocReports

Std.Reports

Proactive Decision Making

Big Data Intelligence Competitive Advantage

Reactive Decision Making

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Connection to ERM

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SAM/TIMPredicts number of units/inherent opportunity per zip code

Predictive Models

Models inDevelopment

Financial Risk

SaRAHEmployee retention model

Scores past due invoices based on probability of turning 60 days past due.

MARIA

MARCOLogistic model identifies which accounts are most likely to liquidate within 90 days. MARCO 2.0 Retool Deployed

Operations Risk

PEGModel used to gauge the price sensitivity of each client. Used for discount decision making.

PAMCore client retention model geared towards controllable losses. PAM 2.0 deployed

Core life time value model predicts both revenue and retention. OLIVER 3.0 deployed; used for retention campaigns

OLIVER

MATTMMS client retention model,targeting controllable losses.

More Strategic

Risk

Strategic Risk

CPA ReferralWill identify which CPAs are most likely to refer a potential payroll client.

PATRICKIdentifies Core clients mostlikely to purchase 401(k). 50% improvement in dial:appt ratio

STEPHIdentifies clients most likely to purchase H&B. ‘A’ clients 16x more likely to purchase

Portfolio Credit ModelIdentifies clients most likely to return within the next 3 months, for ‘controllable’ reasons.

Identifies overall opportunity per territory

MMS Territory

Paychex Model Portfolio

PST1000Core cross-sell model aimed to identify clients most likely to purchase PST1000.

WC Cross-SellIdentifies clients most likely to lead to an approved quote.

Usage:

Field OpsHRS SalesMarketingNational Sales SupportRisk ManagementSalesHuman Resources

NSS Target/3B 2.0Prioritize NSS call queues by likelihood for booking an appointment.

Client ReferralIdentifies current clients most likely to produce a referral lead.

ASO RetentionWill predict which ASO clients are likely to churn.ASO Upsell

Core up-sell model predicting clients best suited for ASO.

PAULCore up-sell model predicting clients best suited for PEO.

Will identify current clients most likely to purchase TLO; will augment PST1000 model

TLO Upsell

Management/PayrollSpecialist TurnoverWill predict which managers/payroll specialists are likely to churn.

Determine which product a new Core client is most likely to purchase next.

Next Best Offer

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Model Development

Sample set snapshot of client base; gather MANY variables

Evaluate client behavior at some time interval after snapshot

Model allows us to predict future client behavior based on how sample set behaved

Today

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Operations Risk Models

Predictive Models

Operations Risk

PEGModel used to gauge the price sensitivity of each client. Used for discount decision making.

PAMCore client retention model geared towards controllable losses. PAM 2.0 deployed

Core life time value model predicts both revenue and retention. OLIVER 3.0 deployed; used for retention campaigns

OLIVER

MATTMMS client retention model,targeting controllable losses.

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OLIVER Validation

F

D

C

B

A

0% 10% 20% 30% 40% 50% 60%

OLIVER Loss Rates

Higher expected loss rate in critical ‘D’ and

‘F’ buckets

Lower expected loss rate identification in ‘A’ and ‘B’ buckets

AVERAGE LOSS RATE

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Operations Objective - Control Activities

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Putting this intelligence in decision-maker hands

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Even more info — Retention Tracking System

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Where We Can Go

Likely to Stay Likely to Leave

What we’re doing now

With Increased Usage and Data Input

“Lost Causes”

“Sure Things”

Not only will we be able to provide analysis on

the most effective strategies, we’ll also be

able to identify the target clients better to optimize

efforts.

PERSUADABLES

“Sleeping Dogs”

Likely to StayLikely to Leave

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Is This Working to Reduce Operations Risk?

Strategy Applied No Strategy Applied

6.7% 25.2%

In Region 3, an analysis of the their FY13 Retention program shows that clients in the RTS that had a strategy applied had a much lower loss rate than clients without a

strategy applied.

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PEG Validation

1

2

3

4

5

0 1 2 3 4 5 6 7

PEG Loss Rates

Loss Rate (%)

PEG

Score

[Least

Sensit

ive t

o M

ost

Sensit

ive]

Higher expected loss rate in the most sensitive

buckets [4 & 5]

AVERAGE LOSS RATE AFTER PRICE

INCREASE

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Discounted Client Mix Regions that followed the Discount Model

recommendations are discounting the clients identified as being the most likely to leave.

Regions that did not follow the Discount Model recommendations

were discounting more evenly across all buckets, including clients most

likely to stay with Paychex.

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Discounting Validation

Statistically significant difference between the

loss rates of the clusters most likely to

leave versus the clusters most likely to

stay.

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SAM/TIMPredicts number of units/inherent opportunity per zip code

Predictive Models

SaRAHEmployee retention model

Strategic Risk

Identifies overall opportunity per territory

MMS Territory

NSS Target/3B 2.0Prioritize NSS call queues by likelihood for booking an appointment.

Client ReferralIdentifies current clients most likely to produce a referral lead.

Strategic Risk Models

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Territory Identification & Mapping - TIM

Top unassigned zipcodes for state zt48_d00

Rank for Variable nobs 0 1 2 3 4 5 6

A purple color indicates a high

opportunity for that unassigned zip code. Deeper colors signify

stronger opportunities.

A dark green area represents zip codes

currently assigned to a rep.

White areas represent unassigned zip codes that do not fall within the top 2,000 areas of

opportunity.

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Territory Expansion

Top unassigned zipcodes for state zt28_d00

Rank for Variable nobs 0 1 2 3 4 5 6

Rich zip codes just outside current

footprint represent prime opportunities to

expand existing territories.

Clusters of rich zip codes represent optimal areas to

establish new territories.

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Predictive Models

More Strategic

Risk

PATRICKIdentifies Core clients mostlikely to purchase 401(k). 50% improvement in dial:appt ratio

STEPHIdentifies clients most likely to purchase H&B. ‘A’ clients 16x more likely to purchase

PST1000Core cross-sell model aimed to identify clients most likely to purchase PST1000.

WC Cross-SellIdentifies clients most likely to lead to an approved quote.

ASO UpsellCore up-sell model predicting clients best suited for ASO.

PAULCore up-sell model predicting clients best suited for PEO.

Determine which product a new Core client is most likely to purchase next.

Next Best Offer

More Strategic Risk Models

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PATRICK Revenue Contributions

Jun-11 Sep-11 Dec-11 Mar-12 Jun-12 Sep-12 Dec-12 Mar-13 $-

$1,000,000

$2,000,000

$3,000,000

$4,000,000

$5,000,000

$6,000,000

$7,000,000

PATRICK Revenue Growth(FY12-FY13 YTD) Over $6 million in

incremental revenue.

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Predictive Models

Financial Risk

Scores past due invoices based on probability of turning 60 days past due.

MARIA

MARCOLogistic model identifies which accounts are most likely to liquidate within 90 days. MARCO 2.0 Retool Deployed

Portfolio Credit ModelIdentifies clients most likely to return within the next 3 months, for ‘controllable’ reasons.

Financial Risk Models

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A collections representative may spend up to 90 days

working a client that won’t result in payment

Financial Risk Problem

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Model for Accounts Receivable Collections Optimization

MARCO provides a daily collectability score for open AR referrals and the probability of payment with less than 9 calls.

Intelligent and

automated isolation of

accounts most likely

to pay.

Work Smarter

Financial Risk Solution - MARCO

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Optimizing the Cycle

60 days

THIRD PARTY

COLLECTIONSMARCO

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Optimizing the Cycle

We can bypass 90 days of collections efforts for 30% of clients, worth 10% of recoverable

revenue

Clients in the first several buckets are much more likely to meet their obligations

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Conclusion

Let’s not be left behind.

Paychex Risk Analytics

Frank Fiorille

ffiorille@paychex.com

585-336-7310

Analytics is Changing the World…