how we use machine learning to push the boundaries of programmatic

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How we use Machine Learning to push the boundaries of Programmatic Nicolas Beguin

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Page 1: How we use Machine Learning to push the boundaries of Programmatic

How we use Machine Learning to push the

boundaries of Programmatic

Nicolas Beguin

Page 2: How we use Machine Learning to push the boundaries of Programmatic

P r o g r a m m a t i c c a m p a i g n s a r e u s i n g D S P ’ s a l g o r i t h m s

What happens if we create our own algorithms in addition to DSP’s ones?

We made the test on AppNexus thanks to APB

Page 3: How we use Machine Learning to push the boundaries of Programmatic

W h a t i s i t ?

APB stands for AppNexus Programmable Bidder

New AppNexus feature that allows you create your own custom algorithm to adjust your bids

Page 4: How we use Machine Learning to push the boundaries of Programmatic

W h a t i s i t ?

Strategy 1

Strategy 2

Strategy 3

Strategy 4

Without APB

$20

$15

$12

$30

Page 5: How we use Machine Learning to push the boundaries of Programmatic

W h a t i s i t ?

Strategy 1

Strategy 2

Strategy 3

Strategy 4

Without APB

$20

$15

$12

$30

With APB

$18 at 2pm

$14 at 4pm

$25 at 7pm

Strategy 1

Page 6: How we use Machine Learning to push the boundaries of Programmatic

E x a m p l e o f a d e c i s i o n t r e e

recency < 3h recency > 3h

day = monday = fri

day = tue

$10 $20 $15

day = thu

day = tue

creative size > 800

creative size = 800

$12

$35 $5

Page 7: How we use Machine Learning to push the boundaries of Programmatic

E x a m p l e o f a d e c i s i o n t r e e

Written in Bonsai

Handled by MFG Data Scientists

Uploaded to console via the API

Up to 300.000 lines!!

Page 8: How we use Machine Learning to push the boundaries of Programmatic

H o w a t r e e i s b u i l t

Step 1: CVR or CTR estimator what is the probability of a conversion or a click?

Page 9: How we use Machine Learning to push the boundaries of Programmatic

H o w a t r e e i s b u i l t

Step 1: CVR or CTR estimator what is the probability of a conversion or a click?

Step 2: Bid price optimization knowing the probability, how much should we bid?

Page 10: How we use Machine Learning to push the boundaries of Programmatic

H o w a t r e e i s b u i l t

Step 1: CVR or CTR estimator what is the probability of a conversion or a click?

Step 2: Bid price optimisation knowing the probability, how much should we bid?

Step 3: Transform into a tree once the model is known the tree can be created

Page 11: How we use Machine Learning to push the boundaries of Programmatic

I n s i g h t s f o r t h e m o d e l

Page 12: How we use Machine Learning to push the boundaries of Programmatic

W h a t w e d i d – Te s t 1 : S i m p l i f i c a t i o n

Combining individual strategies into one APB line:

Recency Desktop Bid Price

0-3 hours $9.17

3-12 hours $7.87

12-48 hours $10.47

2-7 days (48-168 hours) $6.57

7-15 days (168hours-360 hours) $9.17

Pixels Funnel Position Classification Bid Price

New Cars Models Upper $ 3.50

New Cars Models Offers Upper $ 4.50

New Cars Model BookTestDrive Middle $ 4.50

Dealer Locator Lower $ 6.10

Brochure Request Lower $ 7.10

Test Drive Conversion Lower $ 8.50

Page 13: How we use Machine Learning to push the boundaries of Programmatic

E n s u r i n g a f a i r t e s t - A / B C o n t r o l l e d G r o u p

Equal daily budget allocation to allow for fair test

Page 14: How we use Machine Learning to push the boundaries of Programmatic

W h a t w e d i d – Te s t 2 : G r a n u l a r i t y

Realised that pacing controls are sacrificed with current setup

Paired each individual non-APB strategy with a APB counterpart with far more granular optimisations and varying bidsExample: one APB line for every normal strategy

Results:

Steady performance ~25% reduction in CPA for APB lines overall and over 3 month period

Page 15: How we use Machine Learning to push the boundaries of Programmatic

C a s e S t u d y : K i a Ta c t i c a l

Budget: £30,000

Date: Oct 2016 – Dec 2016

KPI: £9 CPA

Retargeting campaign running on Mediamath and Appnexus

Appnexus split into APB and non-APB strategies:▪ Non APB: Dealer locator 0-12h, dealer locator 12h-7d, Models, and

Test drive request

▪ APB: Retargeting these pixels with optimal bid price

Page 16: How we use Machine Learning to push the boundaries of Programmatic

£- £1.000,00 £2.000,00 £3.000,00 £4.000,00 £5.000,00 £6.000,00 £7.000,00 £8.000,00

£- £2,00 £4,00 £6,00 £8,00

£10,00 £12,00 £14,00 £16,00

CPA Vs Gross Cost WoW

Gross Cost Non-APB CPA APB CPA

0,0%

20,0%

40,0%

60,0%

80,0%

100,0%

120,0%

-

50

100

150

200

250

10/10/2016 -10/16/2016

10/17/2016 -10/23/2016

10/24/2016 -10/30/2016

10/31/2016 -11/06/2016

11/07/2016 -11/13/2016

Conversions Volume Vs RR WoW

Non-APB Conversions APB Conversions Non-APB RR APB RR

£- £1.000,00 £2.000,00 £3.000,00 £4.000,00 £5.000,00 £6.000,00 £7.000,00 £8.000,00

£4,40 £4,60 £4,80 £5,00 £5,20 £5,40 £5,60 £5,80 £6,00

10/10/2016-

10/16/2016

10/17/2016-

10/23/2016

10/24/2016-

10/30/2016

10/31/2016-

11/06/2016

11/07/2016-

11/13/2016

CPM Vs Gross Cost WoW

Gross Cost Non APB CPM APB CPM

• APB drove a lower CPA WoW• Similar trend in conversion volume

and response rate • Non-APB had a lower CPM WoW

Page 17: How we use Machine Learning to push the boundaries of Programmatic

C a m p a i g n r e s u l t s

£-

£1,00

£2,00

£3,00

£4,00

£5,00

£6,00

£7,00

£8,00

£9,00

£-

£1.000,00

£2.000,00

£3.000,00

£4.000,00

£5.000,00

£6.000,00

£7.000,00

£8.000,00

10/10/2016 -10/16/2016

10/17/2016 -10/23/2016

10/24/2016 -10/30/2016

10/31/2016 -11/06/2016

11/07/2016 -11/13/2016

Retargeting CPA Vs Gross Cost WoW

Gross Cost Gross CPM Retargeting CPA

After Introduction of the APB line:

• Retargeting CPA decreased by 39%

• 13% increase in Response Rate

• 1,5x more conversions after introduction of APB

Page 18: How we use Machine Learning to push the boundaries of Programmatic

T H A N K Y O U