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KLM - Emerce PBACross channel attribution past the last click

How to redistribute performance “weight” over media?

Frank Pon

December 2nd

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KLM Promotions Team Goals

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Steer customers via most (cost) effective e-acquisition channel

Social mediaMeta Search E-mail BannersAffiliates Search engines

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… but more importantly, approach/philosophy …

• Goal: pure conversions, sales, revenue

• Primarily tickets

• Central approach, (mostly) central execution and budget

• Close cooperation with local establishments and budgets

• Central expertise centres for advice and execution

• Optimisation across channels & countries

• High impact: central Affiliation activities in limited countries, E-mail 70 countries, etc

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KLM Promotions Team Organization

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KLM Promotions Team working together

K L M P ro m o tio n s T e am"E -com m e rce u m fe ld "

E m ily H a rd yM a na g e r S e arch E n g in e M a rke ting

A rn o N ijla ndM a n a ge r D isp la y M arke ting

E m ilie H a rd ickM a n a ge r E -m a il M arke ting

A rja n G roo tve ldM a n ag e r M e ta S e arch M a rke ting

T e re sa B arte ldsM a na g e r A ff ilia t ion

O rch id e a K ad a ruskanC a m pa ig n M a na g er

N ie ls Jo n kerJu n io r C am p a ig n M a na g er

F ra nk P onS e n io r P ro m otio n s M a na g er

B o b L an ge rakW e b A n a lyst

F ra n s P o ld e rva a rtW e b A n a lyst

T ja llin g S m itD ire c to r E -a cq u is it ion

Jo o s t W ilb rinkE -d eve lop m en t m a n ag e r E -acq u is it ion

S a n dra M a asE -d eve lop m en t m a n ag e r E -acq u is it ion

R o b Z w e rinkD ire c to r E -de ve lop m e nt

C h ris S pra n g e rsS e n io r E -sa le s M an a g er

?D ire c to r E -sa les

G u id o va n T ilE -co nve rge n ce

M a rtijn van d er Z eeV ice P re s ide n t E -co m m e rce

A ir F ra nce K LM

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KLM Promotions Team Results

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Acquisition results: shareFor first 6 months 2010 on average 66% of all KLM bookings is attributable

to one of the acquisition channels

Source: DART, KLM only, apr – aug 10, fully de-duplicated on last click basis Top 13NOTE: Actual acquisition share is expected to be considerably higher: 1. Only structural acquisition activities are included: bookings resulting from campaigns (e.g. world Deals) are not included2. Tracking problems with email since EBT7 (May 09) -> share likely higher.

Channel share

Natural bookings (34.0%) 35,580

Affiliation (3.8%) 4,000

Metasearch (4.8%) 5,012

E- mail (11.7%) 12,296

Display (19.6%) 20,538

Search (26.0%) 27,271

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E-acquisition share growth

*Top 13 results

E-acquisitie share

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Apr/13

May/13

Jun/13

Jul/13

Aug/13

Sep/13

Oct/13

Nov/13

Dec/13

Jan/14

Feb/14

Mar/14

Apr/14

May/14

Jun/14

Jul/14

Aug/14

maand

sh

are Totaal

Lineair (Totaal)

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But how to get from

?

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The «last touch» model, default attribution rules

The last exposure before conversion gets the full credit, with clicks taking priority over impressions

In other words, no matter how many different ads a consumer has previously been exposed to, the last click before purchase is attributed 100% of the revenue

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Analysis of pre-booking behavior (30 days)

Technique DART Floodlight Tag# Reported bookings 1 (last click counted)Payment Last click (where applicable* )Future budget allocation

Budget according to weight in decision making process -> attribution model

*Display view (14 days) unless click

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Dig Deeper

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Considering touchpoints prior to last touch?

% of touchpoints in the last 14 days prior to conversion /visitignored in the “last touch” approach

1st elements in 2009 showed a potential double digit gain in ROI through reallocation of budgets across channels and countries

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Towards a KLM proprietary attribution model

Overall objectives

Defining methodological guidelines, implementation rules

• to attribute more “correctly” conversion credit to all channels analyzing cross-channel tracking data,

• eventually, to create the framework for an optimization of holistic online strategy including investment allocation across channels

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Adding up an attribution model to include all ROI drivers

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Modeling test period, input variables selection

KLM data base2010 Feb 01-Apr 15th 10+ millions cookies

Sampling 176,461 cookieswith a tagged activity on KLM web site @ each step of the EBT

Clustering the digital “journeys”Operational segments according to the outcomeAnalysis to determine key criteria to measure “assisting power” for each channel

Statistical modeling of individual paths and overall –aggregated time series to quantify contributions to response from each online channel, their interactions

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We need more “mad” scientist!

To break the rules & learn…

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Each cookie described on a 6-dimension scale

Channel (s)consumer was exposed to

/ interacted with

Depth of on-site interaction measured as EBT max level reached

Frequencyor number of contacts of each typeprior to on-site activity

Recencyor number of contacts per period of time prior to on-site activity

Interaction typee.g. click vs. exposure for display ad, keyword

entered for Search…

on-site visits number of visits, max EBT

level reach at each of them

Engagementwith KLM

Contacts, interactions with online ad vehicles

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Key factors in the “online consumer path”Multi-channel interactions at individual level

In the 9 markets, 39% of purchases with Search as last touchpoint had an exposure to a Display ad in the 14 days prior to purchasing

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Key factors in the “online consumer path”Multi-channel interactions with a recency effect

Purchase with Search as last touchpoint with previous display exposure

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Key factors in the “online consumer path”Multi-channel interactions with a frequency effect

Purchase Search as last touchpoint with previous display exposure Distribution- nb of Display contacts prior to purchase

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What the heck does this tell us?

What ingredients are we missing?

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Key factors in the “online consumer path”Different interaction types

In the 9 markets, 77% of purchases with Search as last touchpoint are done through “navigational” keywords – Ad Group KLM

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Key performance measurement factorsNumber of visits on the web site

Cross-analyzing number of days with an on-site visit and eventual purchasing – Example of UK data

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Visit without purchase(95,6%)

Eventual purchase(4,4%)

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EBT7- Pre-purchase steps

1

23

4

56

7

8

Interactio

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dep

th 1

Interactio

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epth

2

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Re-allocation algorithm: inputs & KPIs

Navigational Search

Searchother

keywords

Affiliation

Meta-search

Last touchpoint

Other interaction in the last 7 days

Assisting drivers

Display in the last 14 days

Display in the last 7 days

Display in the last

2 days

DisplayClick

DisplayExposure

KLM onsite performance measurement •Number of visits (in # of days)

•“Depth” of each

•Eventual purchase

Exposure to KLM display ads# of contacts (grouped)

Interaction with KLM

e-mailing

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What did we learn … earn!

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Re-assigning credit to online tactics

Based on online path patterns: weighted according to contribution to customers response (visits, multiple visits, purchases) – not to be generalized as fitted to BY0910-Q4

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Potential gain in incremental bookings via re-allocation across channels and countries +5 to +8%

Based upon 2010, Feb. 01-Apr. 15 only

Potential gain at same “offer attractiveness”

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Key take-outs!

1.) Although online marketing is perceived as sexy, innovative and creative, some of the real value is in good old boring analysis

2.) Don’t limit yourself to existing industry standards and models, dig deeper!

3.) Don’t stop being sexy and explore “single channel” options like BT to the fullest in parallel

..... Model = “ a simplified version of the truth”

.... so keep on testing & experimenting

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