MAF Presentation - Loyalty 2018-10-06 MAF Website

Loyalty Analytics

MAF Fall 2018 MeetingSeptember 21, 2018

PwC

Loyalty IntroductionBreakage / Redemption Rates

37

Modeling Breakage 10Attrition Risk 17Customer Lifetime Value 21Other Analytics 24

Agenda

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Loyalty Introduction

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Earning and redemption cycle gives rise to future redemptions on points already earned

Parallels with traditional insurance reserving

Loyalty Introduction

HospitalityHospitalityRetailRetail

AirlineAirline GamingGaming

Unpaid Claim

Reserve

Unpaid Claim

ReserveLoyalty LiabilityLoyalty Liability

3.8 billion loyalty program memberships in US in 2016*

Loyalty programs span many industries

* 2017 Colloquy Loyalty Census

PwC

Re-design loyalty programs to ensure that the finances are sound

Revise policy language / forms

Valuing Points

Currency Actuarial Pricing

Other Parallels Between Loyalty Programs & Insurance

Program Design Underwriting &

Policy Form Updates

Determine or revise the pricing of points for sale to external parties (e.g., credit card companies or points.com)

Ratemaking

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• The redemption rate % is generally the harder input to estimate with accuracy

• Traditional triangular methods often used

• Redemption CPP has a shorter tail and changes manifest more rapidly

Loyalty Liability Estimates

Estimated Redemption

Rate%

# of Points Outstanding

Redemption Cost per Point

$

Rewards Liability

$

Future Points to be Redeemed

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Breakage / Redemption Rates

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Earn & Burn

Customer earns benefits while

interacting with company

Customer redeems earned

benefits

Benefits remain unused

Customer halts interactions

and/or expiration

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Usage & Breakage Rates

Usage rate – Percentage of points earned/outstanding which will ultimately be redeemed

Breakage rate – Percentage of points earned/outstanding which will not ultimately be redeemed

100%

12%

88%

31%

69%

48%

52%

68%

32%

Unused

Redeemed

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Modeling Breakage

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A Simple SolutionA

ctua

l / P

redi

cted

U

ltim

ate

Usa

ge R

ate

Predictor

GLM fitted to historically observed usage rates

~

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A Simple SolutionA

ctua

l / P

redi

cted

U

ltim

ate

Usa

ge R

ate

Predictor

GLM fitted to historically observed usage rates

~ Future usage predicted using fitted

coefficients

∗

x*

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A (Not So) Simple Solution

GLM fitted to predict 12m usage

GLM fitted to predict

24m usage

GLM fitted to predict

36m usage

GLM fitted to predict

ultimate usage………

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A (Not So) Simple Solution

…

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A (Not So) Simple Solution

0 0.5 1 1.5 2 2.50%

10%

20%

30%

40%

50%

60%

70%

80%

Years

Red

empt

ion

Rat

e

Ult

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0 0.5 1 1.5 2 2.50%

10%

20%

30%

40%

50%

60%

70%

80%

Years

Red

empt

ion

Rat

e

Ult

A (Not So) Simple Solution

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Attrition Risk

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Attrition Risk Scores

Attrition Risk – Likelihood that customer will stop interacting with the company by some future date

- Insurance – Easy to define an attrition event

- Loyalty – Need to define an objective measure

Attrition Risk Score – Measure of likelihood of attrition, possibly banded

Considerations- Future period of time

- Significant reduction in activity

- Earn & Burn

Find sub-population characteristics with a high correlation with attrition risk

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Dynamic Attrition Risk Scoring

0%

20%

40%

60%

80%

100%

1‐Jan‐17 1‐Feb‐17 1‐Mar‐17 1‐Apr‐17 1‐May‐17 1‐Jun‐17

AttritionRiskScore

EarlyWarning MemberA MemberB

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Customer Lifetime Value

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Customer Lifetime Value

The value a customer brings to a loyalty program, expressed as a lifetime dollar value.

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Components of Value

CLV

Activity period

Revenue generated

Cost of Acquisition

Cost of Retention

Cost of capital

Time value of money

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Other Analytics

Cost per Point New Business Conversion

Tier Movements Customer Segmentation

Advanced Analytics

PwC

Loyalty Analytics: Measuring Loyalty Program Performance Whitepaper

Accessed via: http://pwc.to/2tu6y1C

1. Changing the loyalty industry

2. Identifying loyalty program goals

3. Developing analytic targets

4. Loyalty models and metrics

5. Taking action

PwC

Questions?

Jean-François Greeff – Manager, PwC Actuarial Servicesjean.francois.greeff@pwc.com

Mark Doucette – Director, PwC Actuarial Servicesmark.r.doucette@pwc.com