Patterns in Online Shopping Behavior(and possible links to call centers)

Peter S. FaderProfessor of Marketing

The Wharton School, University of Pennsylvaniafader@wharton.upenn.edu

www.petefader.com

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

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?

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

HHID DATE TIME ACTIVE DOMAIN URL

10145 3/17/98 9:47:02 PM 2 www.amazon.com /exec/obidos/isbn=0060972777/kcweba/0728/0973004/501562:10145 3/17/98 9:49:36 PM 48 www.amazon.com /exec/obidos/isbn=0859652424/kcweba/0728/0973004/501562

10145 3/18/98 5:33:59 PM 120 www.amazon.com /exec/obidos/quicksearch/query/nscp0873199410145 3/18/98 5:35:59 PM 19 www.amazon.com /exec/obidos/isbn=0671024868/7024/1486849/08491310145 3/18/98 5:36:18 PM 8 www.amazon.com /exec/obidos/shopping/basket/7024/1486849/08491310145 3/18/98 5:36:26 PM 9 www.amazon.com /exec/obidos/subst/home/home.html/7024/1486849/084913?:10145 3/18/98 5:47:02 PM 57 www.amazon.com /exec/obidos/order/form/page2/7024/1486849/08491310145 3/18/98 5:47:59 PM 30 www.amazon.com /exec/obidos/confirm/order/7024/1486849/084913

10145 3/20/98 8:55:35 PM 26 www.amazon.com /exec/obidos/subst/home/home.html/7024/1486849/08491310145 3/20/98 8:56:01 PM 21 www.amazon.com/exec/obidos/recommendations/past/purchases/7024/1486849/084913:

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?

Frequency of site visits

0%

10%20%

30%

40%

50%60%

70%

1 2 3 4 5 6 7 8 9 10+

Number of Visits

% o

f H

ouse

hol

ds Bookstore

CD Store

Visit dynamics: aggregate pattern

• Visits/visitor ratio suggests that consumers are visiting the site more frequently over time

BOOKSTORE CD STORE

Months 1-4 Months 5-8 Months 1-4 Months 5-8

Total Numberof Visits

5402 5899 1729 1890

Number ofUnique Visitors

2693 2717 988 920

Visits / Visitor 2.01 2.17 1.75 2.05

How has visiting behavior evolved at Amazon?

Mean evolution is close to 1.0 (E[cij] = 0.998)

• All shoppers have an initial visit rate (

• After each visit, the rate is bumped up or down by an “updating multiplier,” (c)

0%

10%

20%

30%

40%

50%

60%

0.02 0.06 0.1 0.14 0.18 0.22 0.26 0.3 0.34 0.38

Rate of Visit ()

% o

f Hou

seho

lds

r = 0.324 = 16.857

Distribution of Initial Visiting Rates

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.05 0.25 0.45 0.65 0.85 1.05 1.25 1.45 1.65 1.85

Updating Multiplier (c)

f(c)

s = 2.299 = 2.304

Distribution of Updating Multiplier

slowing down

speedingup

Forecasting validation

0

2000

4000

6000

8000

10000

1 4 7 10 13 16 19 22 25 28 31 34

Week

Rep

eat

Vis

its

Actual Static Model Evolving Visit Model

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)

Bookstore Conversion Rates

Conversion Rate Decreasing Frequency Increasing Frequency

Infrequent Visitors 11.1% 10.9% 11.3%

Frequent Visitors 16.6% 14.6% 18.6%

CD Store Conversion Rates

Conversion Rate Decreasing Frequency Increasing Frequency

Infrequent Visitors 4.8% 3.8% 5.7%

Frequent Visitors 5.6% 4.0% 7.6%

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 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?

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

Associations across browsing patterns

Site Btime

Site Btime

Site AtimePerson 1

Site AtimePerson 2

• Need to account for

– Co-incidence (similarity in arrival times)

– Overall rate propensities (similarity in latent rates)

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

Actual vs. Predicted % of Previous Non-Visitors

0

5

10

15

20

25

30

35

Amazon Barnes & Noble

% o

f P

revi

ou

s N

on

-Vis

itors

Actual Proposed Model Independent Model(s)

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

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

Conversion rates across visits

P P

t1 t2 t3 t4 t5 t6

?

P P

t1 t2 t3 t4

?

P P

t1 t2 t3 t4 t5 t6

?P

Conceptual model

What contributes to the Net Effect of Visits (Vij)?

• Directed-Buyer: consistently large baseline effects on purchasing

• Searcher: variable effects of visits that accumulate

• Browser: variable visit effects that do not accumulate

)( Threshold

Purchasing

)( PurchaseLast Since

Visits ofEffect Net

)( PurchaseLast Since

Visits ofEffect Net

ijij

ijij

TV

Vp

What is the probability that person i will buy at visit j (pij)?

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 rates 29.3%

Beta binomial model 33.0%

Logistic regression 33.0%

Logistic regression (2 seg) 28.0%

Proposed conversion model 37.0%

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?

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

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.

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

4. Have them spend more at each transaction

• 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.

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?