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20130711 - Customer Journey - Universität Passau - Jan Schumann
 

20130711 - Customer Journey - Universität Passau - Jan Schumann

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CUSTOMER JOURNEY ...

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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    20130711 - Customer Journey - Universität Passau - Jan Schumann 20130711 - Customer Journey - Universität Passau - Jan Schumann Presentation Transcript

    • 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 Summit Wien, 11. Juli 2013
    • Innovation – Insights – Interaction Prof. Dr. Jan Schumann, Universität Passau Advertisers employ various channels to reach consumers over the Internet Search engines Social Media Affiliate networks Price comparisons Display/content ads Newsletter Typical online channels for consumer communication
    • 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 Social Networks Newsletter Online shop 1 Online shop 2 Online shop 1 Price comparison Sites Online auctions Search (SEO) Search (SEA) Group Buying Portals Blogs Forums Review Video Portals Affiliate Price comparison Sites Newsletter Social Networks Micro media Micro media 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 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 information on 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 on conversion 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 Social Networks Newsletter Online shop 1 Online shop 2 Online shop 1 Price comparison Sites Online auctions Search (SEO) Search (SEA) Group Buying Portals Blogs Forums Review Video Portals Affiliate Price comparison Sites Newsletter Social Networks Micro media Micro media 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 2Customer 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 Average journey 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 SEO 16.8 14.5 SEA 19.3 18.3 Type In 30.8 44.3 Referrer 4.0 1.5 Display 5.0 2.5 Affiliate 11.3 8.9 Newsletter 12.7 9.8 Retargeting -- -- Price Comparison 0.2 0.1 High con- tribution Low con- tribution 3.5 2.5 2.2 1.3 -- -- 4.4 5.9 -- -- 12.6 9.5 55.5 53.5 19.2 24.6 2.7 2.8 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: 0.186 2) 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!