Cross-channel attribution

Xiaolong (Jonathan) Zhang Manager of emerging analytics and innovation

Presented by:

Hairong Crigler VP of Marketing Analytics

Let the data do the work

Cross-channel landscape: Empowered consumers

52% of laptop owners also own

a smartphone

23% of laptop owners also own

a tablet

31% of smartphone owners also own a tablet

13% own a laptop, smartphone and tablet

Source: PEW RESEARCH CENTER’S PROJECT FOR EXCELLENCE IN JOURNALISM 2012 STATE OF THE NEWS MEDIA

Cross-channel landscape: Digital + Mobile = TV in 2016

Source: eMarketer

US % spend share by channel

Attribution defined Allocate sales across multiple channels on the purchase path that customers choose

Path to purchase

Catalog

Catalog

Email Email Email

Email Search

Purchase (online or

brick and mortar)

Customer 1

Customer 2

Campaign data

Time

Customer data

DM AWARENESS

DM ACQUISITION

EMAIL RETENTION 1 EMAIL RETENTION 2

RETAIL AWARENESS

MOBILE ACQ. 1 MOBILE ACQ. 2

FACEBOOK AWARENESS

45%

25%

15%

5%

10%

Data-driven attribution Cross-channel marketing data meets customer data

PID Res Age 1 $12 20 2 $25 30 3 $45 55 . . . n $99 35 . . .

Attribution

Mass Media Not

cutting my budget

Challenges to data-driven attribution

Disintegrated data…..

Digital

New kid on the block

Print

Always did it this

way

Direct

We are the work

horse How do I assign credit where credit is due?

CMO

PID Period 0 Period 1 Period 2 Period 3 Period 4 Period 5 Sales

1 $20

2 $10

3 $30

4 $40

Rule-based ad hoc attribution solutions: Last touch

Channel Attribution Formula Attributed % 20+10+40=70 70% 30 30%

PID Period 0 Period 1 Period 2 Period 3 Period 4 Period 5 Sales

1 $20

2 $10

3 $30

4 $40

Rule-based ad hoc attribution solutions: First touch

Channel Attribution Formula Attributed % 40 40% 20+10+30=60 60%

PID Period 0 Period 1 Period 2 Period 3 Period 4 Period 5 Sales

1 $20

2 $10

3 $30

4 $40

Rule-based ad hoc attribution solutions: Equal weights

Channel Attribution Formula Attributed % (2/3)*20 +(1/2)*10+40=58.3 58.3% (1/3)*20+(1/2)*10+30=41.7 41.7%

1/3

1/2

1

Rule-based ad hoc attribution solutions

Ideal data

Channel 1

Channel 2

Channel n

The unrealistic assumptions

Channels are identical and independent …

Touches within a channel are identical and independent

Data-driven attribution

Customers touched by one

channel Responses

One channel with a match key from each touch: 100% attribution

$10

Customers touched by channel 1

Responses

Customers touched by channel 2

Multi-channel with a match key from each channel

$4

$6 $5

Direct match back

Sales by channel Channel 1 Channel 2 Alone 4 6 Both 5 5 Total 9 11 Two Attribution Results A 6= 4 + (4/10)*5 9= 6 + (6/10)*5 B 6.25= 4 + (9/20)*5 8.75= 6 + (11/20)*5

Data-driven attribution in reality Indirect match attribution

Promotional Universe (with ID)

Response Universe

(with resp. code)

Match Processing

Reporting Database

Advanced algorithm to best match records using • Name & address • Cookies • Email • Phone number • Client customized PID Business rules are applied to assign response across channels

Not all channels are created equal

Not all touches leave lasting impressions

Data-driven attribution in reality Fractional allocation key levers

Data-driven attribution in reality Fractional attribution

Initial channel weight Catalog 40% Email 40% Search 20% Total 100%

Final channel attribution Catalog 30% Email 55% Search 15% Total 100%

Fractional attribution algorithm

Final channel % = f (Key Levers)

See Appendix B for a numerical illustration

Data-driven attribution in reality Modeling for attribution

Sales = 1000 + 10*TS1 + 20*TS2 + 2*TS1*TS2

PID Period 0

Period 1

Period 2

Period 3

Period 4

Period 5

TS1 TS2 Sales

1 1 5 $20

2 2 3 $10

3 3 0 $30

4 0 4 $40

Channel 1 Channel 2 TS: Touch stock

Putting it all together

TPA

Fractional allocation

Inferred attribution

Direct attribution

Experian Marketing Services’ Attribution Platform

TPA: Touch Point Attributor TPA: Touch Point Attributor

Appendix A

Appendix B: Fractional attribution example

Channels

Initial Attribution

(C)

Confidence Adjusted Recency (D=A*B)

Channel Attribution Weight

(100*C*D)

Final Attribution

Search 20% 0.6*0.5=30% 6 6/38 =16% Catalog 40% 0.8*0.5=40% 16 16/38 =42% Email 40% 0.5*0.8=40% 16 16/38 =42% Total 100% 38 100%

Channels

Match Confidence

(A)

Recency Boost Week 1

(B) Week 2

Week 3

Week 4

Search

(cookie match) 60% 50% 40% 10% 0% Catalog

(name & address match) 80% 50% 25% 20% 5% Email

(email address match) 50% 80% 20% 0% 0%

Data input for a typical matched response

One iteration of attribution update algorithm: Attribution at week 1