20130711 - Customer Journey - Universität Passau - Jan Schumann

Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau

Using Online User Journey Data for Conversion Prediction and Attribution Modeling

Prof. Dr. Jan Hendrik Schumann Lehrstuhl für Marketing und Innovation

Universität Passau

5. Werbeplanung.at SummitWien, 11. Juli 2013


Advertisers employ various channels to reach consumersover the Internet

Search engines Social MediaAffiliate

networks

Price comparisons

Display/content ads

Newsletter

Typical online channels for consumer communication

• 75% of advertisers

use 5 or more

channels

• 90% of advertisers

use 3 or more

channels


Online user journeys are diverse and can comprise multiple points of contact on different channels

Search(SEO)Search

(SEO)

Display

Search (SEA)

Blogs

SocialNetworks

Newsletter

Onlineshop 1

Onlineshop 2

Onlineshop 1

Pricecomparison

Sites

Onlineauctions

Search(SEO)

Search (SEA)

GroupBuyingPortals

Blogs

ForumsReview

VideoPortals

Affiliate

Pricecomparison

Sites Newsletter

SocialNetworks

Micromedia

Micromedia

Display

Customer journey

Conversion


Despite major technological developments advertisers often still struggle with fundamental questions

Typical questions of advertisers

To what extent does it pay off to reach consumers on multiple

channels?

How should marketing budgets be optimally

allocated?

How can I use information about the prior user journey to predict

conversion probabilities?


We used cookie-level data to analyze drivers of conversion probabilities in multichannel campaigns

Conversion probabilities

of individual users

User history

Did user purchase before?

Intensity

Number of clicks

Duration

Channels

Number of involved channels

Channel switching

Informational Navigational

Navigational Informational

1

2

3

4

5

1,664,673

user journeys from

fashion online shop

User journey characteristics

Study 1


We argue that channel switching behavior is a good proxy for users‘ purchase decision processes

Navigate to a specific website

Classifying online advertising channels by primary user intent(based on research on user intention in information retrieval scenarios)

Find informationon a specific topic

Search engines

Newsletter

Affiliate networks

Display/content ads

Study 1


User history, number of channels involved and channel switching are strong predictors of conversion probability

+2200%

User history

Did user purchase before?

Intensity

Number of Clicks

Duration

Channels

Number of involved channels

Channel switching

Inf. Nav.

Nav. Inf.

1

2

3

4

5

+108%

+600%

-15%

+3%

-0.2%

no yes

+ 1 Click

+ 1 Hour

+ 1 channel

no yes

no yes

User journey characteristic Change

Impact onconversion prob.

Study 1


The results provide three key insights

1.Target recent customers

2.Try to reach individual users on multiple channels

3.Use user journey information for your budget

allocation

(e.g., RTB)

Study 1


To address the attribution problem we propose a complex statistical model for budget allocation

Search(SEO)Search

(SEO)

Display

Search (SEA)

Blogs

SocialNetworks

Newsletter

Onlineshop 1

Onlineshop 2

Onlineshop 1

Pricecomparison

Sites

Onlineauctions

Search(SEO)

Search (SEA)

GroupBuyingPortals

Blogs

ForumsReview

VideoPortals

Affiliate

Pricecomparison

Sites Newsletter

SocialNetworks

Micromedia

Micromedia

Display

Marketers employ various online channels such as SEA or Display in their promotional mix

Little is known on how to attribute credit to exposures along the user journey

Today, marketers often rely on simple heuristics like "last click wins"

Advertisers’ questions

• Which framework can be applied to ascertain the correct value contribution?

• How should marketing budgets be optimally allocated?

Our contribution

• Comprehensive analysis framework based on first- and higher-order Markov graphs

• Implementation and practical impact in a real life system

Study 2

Customer journey

Conversion


Four real-life clickstream datasets are used to test and validate our graph-based Markov framework

Data characteristics

Descriptives

DS 1 DS 2 DS 3 DS 4

• Data collection in cooperation with intelliAd, a German multi-channel tracking provider

• 4 real-life clickstream data sets from 3 industries

• Individual-level cookie data including converting and non-converting journeys

Industry Travel Fashion retail

Fashion retail

Luggage retail

Number of different channels

8 8 8 8

Number of clicks

1,478,359

926,995

1,125,979

615,111

Number of journeys

600,978 622,593

862,112 405,339

Thereof with length ≥ 2

206,519 87,578 142,039 105,031

Averagejourney length1

2.46(8.860)

1.49(3.142)

1.31(1.238)

1.52(4.587)

Number of conversions

9,860 22,040 16,200 8,115

Journey conversion rate

1.64% 3.54% 1.88% 2.00%

1) Standard deviation in parentheses

Study 2


The new framework provides an improved measurement of online channel contribution

Simple heuristic “last click” vs. novel Markov framework

SEO16.8

14.5

SEA19.3

18.3

Type In30.8

44.3

Referrer4.0

1.5

Display5.0

2.5

Affiliate11.3

8.9

Newsletter12.7

9.8

Retargeting----

PriceComparison 0.2

0.1

High con-tribution

Low con-tribution

3.52.5

2.21.3

----

4.45.9

----

12.69.5

55.553.5

19.224.6

2.72.8

Online retail “apparel”1 Online retail “luggage / equipment”2

- 22%

4%

32%

--

--

68%

43%

- 26%

- 3%

- 31%

6%

16%

30%

26%

101%

159%

65%

--

1) Minimum journey length: 2; avg. journey length: 4.48 (7.73); journey conversion rate: 0.1862) Minimum journey length: 2; avg. journey length: 3.00 (8.85); journey conversion rate: 0.047

Markov modelLast click wins … Relative change, percent

Value contribution by channel1

Percent Study 2


This framework makes relevant contributions to multichannel online marketing

1.Complex statistical models can lead to much fairer results

than simple heuristics

2.Channel attribution is a moving target and needs to be

constantly monitored

3.Framework is easy to interpret and understand for

practicioners (IntelliAd Attribution-Analyzer)

4.Framework is highly versatile and can be applied for various

purposes (attribution, RTB…)

Study 2


Next frontiers in our research

1. Include more information (impressions, social media, multiple devices, offline channels, offline behavior...)

2. Include additional financial measures such as costs, revenues and CLV

3.Set up large-scale field experiments with randomized exposure


Thank you very much for your attention!

20130711 - Customer Journey - Universität Passau - Jan Schumann

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