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Patterns in Online Shopping Behavior (and possible links to call centers) Peter S. Fader Professor of Marketing The Wharton School, University of Pennsylvania.

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Presentation on theme: "Patterns in Online Shopping Behavior (and possible links to call centers) Peter S. Fader Professor of Marketing The Wharton School, University of Pennsylvania."— Presentation transcript:

1 Patterns in Online Shopping Behavior (and possible links to call centers) Peter S. Fader Professor of Marketing The Wharton School, University of Pennsylvania

2

3 The basic business model for e-commerce is no different than for many traditional goods & services 1.Generate/understand store traffic 2.Convert visits into purchases 3.Have customers buy repeatedly over time 4.Have them spend more at each transaction

4 1. Generate/understand traffic Changes in visit patterns over time (Moe and Fader 2003) –How does behavior evolve as the visitor gains experience with the site? –What can visiting patterns tell us about purchasing propensities? Modeling browsing behavior at multiple websites (Park and Fader 2003) –What can we learn about our customer behavior at one site from observing their behavior at other sites?

5 Data description Focus of the traffic and conversion studies: –Media Metrix’s PC Meter panel of 20,000 web users –Two sites (Amazon.com and CDNOW, from 3/98 – 10/98) –Aggregated to household level and daily level Define purchases as any visits with “confirm-order” in URL HHIDDATETIMEACTIVEDOMAINURL /17/989:47:02 PM2www.amazon.com/exec/obidos/isbn= /kcweba/0728/ / : /17/989:49:36 PM48www.amazon.com/exec/obidos/isbn= /kcweba/0728/ / /18/985:33:59 PM120www.amazon.com/exec/obidos/quicksearch/query/nscp /18/985:35:59 PM19www.amazon.com/exec/obidos/isbn= /7024/ / /18/985:36:18 PM8www.amazon.com/exec/obidos/shopping/basket/7024/ / /18/985:36:26 PM9www.amazon.com/exec/obidos/subst/home/home.html/7024/ /084913? : /18/985:47:02 PM57www.amazon.com/exec/obidos/order/form/page2/7024/ / /18/985:47:59 PM30www.amazon.com/exec/obidos/confirm/order/7024/ / /20/988:55:35 PM26www.amazon.com/exec/obidos/subst/home/home.html/7024/ / /20/988:56:01 PM21www.amazon.com/exec/obidos/recommendations/past/purchases/7024/ / :

6 1.Generate/understand traffic Changes in visit patterns over time (Moe and Fader 2003) –How does behavior evolve as the visitor gains experience with the site? –What can visiting patterns tell us about purchasing propensities?

7 Frequency of site visits

8 Visit dynamics: aggregate pattern Visits/visitor ratio suggests that consumers are visiting the site more frequently over time

9 How has visiting behavior evolved at Amazon? Mean evolution is close to 1.0 (E[c ij ] = 0.998) All shoppers have an initial visit rate (  After each visit, the rate is bumped up or down by an “updating multiplier,” (c) slowing down speeding up

10 Forecasting validation

11 Visit frequency and purchasing propensity Mall shopping research: there is a relationship between visit frequency and purchasing propensity (Celsi and Olson 1988, Janiszewski 1998, Jarboe and McDaniel 1987, Roy 1994)

12 1. Generate/understand traffic Changes in visit patterns over time (Moe and Fader 2003) –How does behavior evolve as the visitor gains experience with the store site? –What can visiting patterns tell us about purchasing propensities? Modeling browsing behavior at multiple websites (Park and Fader 2003) –What can we learn about our customer behavior at one site from observing their behavior at other sites?

13 Modeling browsing behavior at multiple websites What is the nature of the associations among visit patterns across sites? How much does combining information improve our view of future visit behavior? Site B ? time Site A ? time

14 Associations across browsing patterns Site B time Site B time Site A time Person 1 Site A time Person 2 Need to account for –Co-incidence (similarity in arrival times) –Overall rate propensities (similarity in latent rates)

15 Model results Fit models over the first 4 months of data Forecast the number of previous non-visitors who first visit during the second 4 months

16 The basic business model for e-commerce is no different than for many traditional goods & services 1.Generate/understand store traffic 2.Convert visits into purchases 3.Have customers buy repeatedly over time 4.Have them spend more at each transaction

17 2. Convert visits into purchases Across visits (Moe and Fader 2003) –How do conversion rates change from visit to visit? –Role of store visits: browsing, searching, or directed buying Within a visit (work in progress) –What will the shopper do after the current page? Buy? Exit? –Response to “interstitial” promotions

18 Conversion rates across visits P P t 1 t 2 t 3 t 4 t 5 t 6 ? P P t 1 t 2 t 3 t 4 ? P P t 1 t 2 t 3 t 4 t 5 t 6 ? P

19 Conceptual model What contributes to the Net Effect of Visits (V ij )? Directed-Buyer: consistently large baseline effects on purchasing Searcher: variable effects of visits that accumulate Browser: variable visit effects that do not accumulate What is the probability that person i will buy at visit j (p ij )?

20 Ranking likely buyers Rank customers based on four months of observed data using different models Observe actual purchasing behavior in next visit Top 10% according to…Actual Conversion Rate Historical conversion rates29.3% Beta binomial model33.0% Logistic regression33.0% Logistic regression (2 seg)28.0% Proposed conversion model37.0%

21 Conversion rates within a given visit What drives the probability of purchase? –Baseline for this visit (from V-P model) –Page-to-page effects: >Types of page being viewed >Duration of page views >Number of different products and categories viewed What drives the probability of exit?

22 The basic business model for e-commerce is no different than for many traditional goods & services 1.Generate/understand store traffic 2.Convert visits into purchases 3.Have customers buy repeatedly over time 4.Have them spend more at each transaction

23 3. Have customers buy repeatedly over time Observations about transaction dynamics (Fader, Hardie, and Huang 2003): –Individual-level repeat buying behavior appears to be quite random at the start, but settles down towards a “steady state” over time –So it’s hard to gauge someone’s long-term repeat buying tendencies from their early behavior at a given site –But the aggregate pattern of “steady state” behaviors can be described relatively early and very accurately  It is possible to make strong statements about the lifetime value of a given group of customers, but individual-level descriptions are not very trustworthy.

24 The basic business model for e-commerce is no different than for traditional goods & services 1.Generate store traffic 2.Convert visits into purchases 3.Have customers buy repeatedly over time 4.Have them spend more at each transaction

25 It is very important to separate out transactions from purchase quantities CDNOW forecasting model (Fader and Hardie 2001) –Number of albums purchased per transaction is governed by a “coin-flipping” process –Everyone has a unique “coin,” (i.e., tendency to buy multiple albums) –This process does not appear to vary over time among repeat buyers: almost all dynamics are due to transaction effects  Purchase quantity models are straightforward and reliable when transaction dynamics are handled separately.

26 Moving from online shopping to call centers… Do you know when your customer(s) will contact you next? Do you know what they will be trying to accomplish? Are you prepared to handle their requests? Do you have a sense of what’s going to add (or extract) value during their next contact?


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