vision 2014: next generation mobile offers powered by optimization

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Next-generation mobile offers — powered by optimization

Cherian Abraham Experian

Tom O'Neill Experian

#vision2014

2 © 2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.

Mobile offers

landscape

Are offers the key to elusive

monetization in mobile?


Banking: Mobile has all the growth


Delivery mechanisms are evolving


Retail environment is evolving


Customer’s polygamous relationship to devices


What does “wallet share” mean in a digital landscape?

Can we solve “wallet share” without the context of data and analytics?

What’s at stake?

Incremental growth in mobile Wallet share

Sources: Financial Times, Inside Network, BCG interviews and analysis 1 Percentage of companies pursuing the business opportunity (based on 100 companies observed) 2 Most revenues derived from deals/offers and advertising, followed by merchant acquiring; less than five percent is likely to accrue to issuers

Deals and offers will constitute a significant revenue pool

Potential revenue pools, 2015

High

($10– >$20 billion/year)

Medium

($5–$10 billion/year)

Low

(<$1–$5 billion/year)

Low

(<5%–0%)

Medium

(10%–25%)

HIgh

(25%– >45%)

Competitive

intensity1

Deals / offers and advertising2

Unbanked (prepaid / mobile money)

International transfers Micromerchant

acquiring

Domestic transfers

Virtual currency


Mis-alignment of merchant and bank objectives

Customer targeting – beyond transactional data

Offer relevance

Optimal profitability

Automation without sacrificing relevance

Building a feedback loop

Proximity. Propensity. Profitability.

Mobile offers Current challenges


Mobile offers Current challenges

0

0.5

1

1.5

2

2.5

3

3.5

4

2009 2010 2011 2012 2013

NCH 2013 Coupon Stats show redemption rates are struggling

+23.1% +3.1% +6.1%

-17.1% -3.4%


Opportunities

Shifting focus from interchange to marketing revenue

Online to offline attribution for merchants – influence future spend

Threats

Slipping loyalty in a staged wallet environment

Opportunity loss from irrelevant and un-optimized offers

Recommendations

Look beyond transactional data for insights

Build a roadmap for transactional marketing – starting with data quality, merchant value, customer relevance

Offer optimization drives optimal profitability

Transactional marketing and data analytics


Optimization

Tom O’Neill


Arbitrate across local and

national offers, from the same

or different merchants, and

across same or similar offers

1

Marketswitch® Offer Management optimization

Experian’s Marketswitch® Offer Management optimization allows client to:


Understand customer

value for each potential

offering before targeting

2




Look across any number

of factors related to your

specific business metrics

3




Assess offers from multiple providers, offer

profitability, customer propensity across all

your customers to choose those that they

are most likely to redeem, and those that

are most likely to net the most profit or

other business objective

4




Deploy optimized

decisioning through both

batch and real-time

channels

5




Data Warehousing Customer history, analytics and scoring

Decisioning Execution

$2 Off Next

Starbucks

$10 Off Next

Home Depot

$5 Off Next

Applebee’s

Online placement

In-app

Email

Text

Continuous feedback

Where optimization fits


Operational constraints Customer contact limits

Vendor / merchant targets

Other business or operating constraints

Business objectives Maximize profitability

Maximize redemption rate

Marketswitch®

Optimization

Marketswitch® Offer Management platform Example

Customer data Redemption rate

Transaction information

Customer history

Profitability

Channel availability

Offer/other data Offering definitions

Channel targets/limits

Offer eligibility

Promotion revenue share

Activation timelines

Vendor partner terms

Deliver optimal incentive offerings to

customers through real-time touch -points

while satisfying customer and partner

requirements:

Customer 1

Customer 3

Customer 2

$5 Off Next Meal

Purchased at

Applebee’s

Customer 4

$2 Off at Starbucks

with Purchase of $10

or More

$10 Off at Home

Depot with Purchase

of $100 or More

No Offer


Objective: Maximize probable NPV

Constraints: 1 - Each customer can be offered only one offer

2 - Each offer can be presented to only one customer

Simplified marketing optimization example

Offer $5 Off at

Applebees

$2 Off at

Starbucks

$10 Off at

Home Depot

Customer

101

Take Up 10% 4% 12%

NPV $2.5 $0.4 $2.4

Customer

102

Take Up 9% 7% 6%

NPV $2.25 $0.7 $1.2

Customer

103

Take Up 5% 8% 10%

NPV $1.25 $0.8 $2.0

Each of the

three

customers has

been scored

with a

likelihood of

sale and NPV

Challenge: Consider three customers, each of whom can be allocated one of three offers

Solution using scoring and

ranked selection techniques –

one customer at a time: $4.50

Solution using scoring

techniques and portfolio

optimization: $5.45

= 20% improvement


Approach: By determining the solution for a non-linear utility function that optimally weights each decision option, we get the global optimum through the calculation of each record individually

Simplified marketing optimization example

Offer $5 Off at

Applebees

$2 Off at

Starbucks

$10 Off at

Home Depot

Customer

101

Take Up 10% 4% 12%

NPV 2.5 + 0 =

$2.5

.4 + 1.5 =

$1.9

2.4 + .2 =

$2.6

Customer

102

Take Up 9% 7% 6%

NPV 2.25 + 0 =

$2.25

.7 + 1.5 =

$2.2

1.2 + .2 =

$1.4

Customer

103

Take Up 5% 8% 10%

NPV 1.25 + 0 =

$1.25

.8 + 1.5 =

$2.3

2 + .2 = $2.2

Adjust each

Decision Option

by the

following:

$5 Off

Applebees: +

$0

$2 Off

Starbucks: +

$1.5

$10 Off Home

Depot: + $0.2

Scoring each record with the derived utility factor maximizes our total NPV while meeting our

defined constraints


Why does approach matter? The ability to deploy in real-time is critical in digital

Marketswitch® makes a single pass through the dataset and scores each record individually

Decisions are output to a flat file or table

Batch deployment Customers D1 D2 D3 D4 D5 D6 D7 D8 D9

1 0 0 0 1 0 0 0 0 0

2 0 0 0 0 0 1 0 0 0

3 0 0 1 0 0 0 0 0 0

4 1 0 0 0 0 0 0 0 0

5 0 0 0 1 0 0 0 0 0

6 0 0 0 0 0 0 0 1 0

7 0 0 0 0 0 1 0 0 0

8 0 1 0 0 0 0 0 0 0

9 0 0 0 0 0 0 0 0 1


Marketswitch® real-time deployment

Marketswitch® scores each record as that visitor contact occurs

Makes the same exact decisions as the batch roll-out given identical populations

Real-time deployment Customers D1 D2 D3 D4 D5 D6 D7 D8 D9

2 1

5 1

7 1


What was the

business case? Q:

Need was to deliver contextual

marketing, such as digital

redemption offers, to client’s credit

card customers through

interactive and real-time channels,

based on their transaction history

and purchasing needs



Why was

Marketswitch®

chosen?

Q:


Produced value recognized across

decisioning areas

Proved ability to incorporate any

number of factors and requirements

Provides ability to deploy optimized

strategies within a real-time

decisioning environment


What was the

approach used? Q:

Phased approach enabling client

to establish and broaden the

overall platform from optimizing

offers within a single vendor

partner in scheduled batch

processes to optimizing offers

across multiple vendor partners

and real-time



Offer optimization Offer redemption

Business Objectives • Maximize Profitability

• Maximize Redemption Rate

Operational Constraints • Customer contact limits

• Vendor / Merchant targets

• Other business or operating constraints

Customer Data • Redemption rate

• Revenue

• Customer history / eligibility

Offer/Other Data • Offering definitions

• Offer costs

• Promotion revenue share

• Publisher partner terms

Example environment How does it work?

Marketswitch®

Optimization

Engine

Offer development

$5 Off Applebee’s

$2 Off at Starbucks

$10 Off Lowe’s

Expected

Performance

• Models

• Financials

• Assumptions

$3 Off Friendly’s

$10 Off Target

$10 Off Lowe’s

Expected

Performance

• Models

• Financials

• Assumptions

$1 Off Starbuck’s

$10 Off Wal-Mart

$10 Off Lowe’s

Expected

Performance

• Models

• Financials

• Assumptions

Lowe’s

$10

off!

Marketswitch®

Real-Time

Optimization

Engine

Starbucks

$2

off!

Customer

A

Customer

B

Real-time

customer /

behaviour /

interaction

data

Interface to

real-time

customer

interaction

application


Offer delivery Offer decision Purchase activity

Real-time

customer /

behaviour /

interaction

data

Marketswitch®

Real-Time

Optimization

Engine

Customer A makes a

hardware purchase at Sears

• Purchase type

• Purchase amount

• Purchase time

• Merchant Information

Interface to

real-time

customer

interaction

application

Customer perspective How does it work?

Lowe’s

$10

off!

Interface to

real-time

customer

interaction

application


Driving better ROI in

mobile offers


Focus shifts to personalized offers and customer loyalty

Future revenue will be driven by shared objectives

Decisions are data driven and quantifiable

Competing “what-if” strategies will be measured against each other

Optimization maximizes business goals in constrained environments

Customer offers determined in true real-time at point of interaction

Drive insights from current data and make it actionable for the mobile channel

Mobile offers Looking ahead


For additional information, please contact:

[email protected] | @cherian_abraham

[email protected]

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