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CUSTOMER JOURNEY Der Weg des Kunden zum Produkt umspannt Touchpoints in Online, Mobile, In-Store, Kundencenter, sozialen Netzen, Werbung im TV, Print, auf Plakaten, im Radio – einfach gesagt: Er sieht alles und das überall. Wie man dem Konsumenten trotzdem ein sinnvolles Bild der Marke gibt und ein unverwechselbares Angebot schafft. Wir begleiten den Konsumenten auf seinem Weg zum Kauf. Prof. Dr. Jan Hendrik Schumann ist Inhaber des Lehrstuhls für Marketing und Innovation an der Universität Passau. Seine Forschungsschwerpunkte liegen in den Bereichen Onlinemarketing, Technologie und Innovation, Wertorientiertes Kundenbeziehungsmanagement und Internationales Marketing. Seine Forschung im Bereich Online-Marketing beschäftigt sich Prof. Dr. Schuman besonders intensiv mit dem Thema Customer Journey. Ziel der Forschung ist neben der Entwicklung eines besseren Verständnisses für die Such- und Entscheidungsprozesse im Internet auch die Entwicklung praktischer Tools zur Optimierung von Conversions und Budgetallokationen. Kooperationspartner aus der Praxis sind unter anderem die IntelliAd Media GmbH, die ValueClick Deutschland GmbH sowie Plan.Net. Die Forschungsarbeiten von Prof. Dr. Schuman werden vom deutschen Bundesministerium für Bildung und Forschung gefördert und wurden mehrfach international ausgezeichnet – zuletzt erhielt er einen Research Grant on Innovations in Advertising Effectiveness Measurement der Wharton Customer Analytics Initiative.
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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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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?
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
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
Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau
Thank you very much for your attention!