WCAI Research opportunitywcai.wharton.upenn.edu/wp-content/uploads/Bazaarvoice_Final... · Director of Product Management, Bazaarvoice Chris Kramer ... –Supply Side and Demand Side

KNOWLEDGE FOR ACTION

He Said, She Bought: Analyzing the Impact of

User-Generated Reviews on Purchase Behavior

Data at a Glance:Observation Window: February & March 2015

Data are collected from a single UK-based big-

box online retailer and come from two

categories: technology and home & garden

• Product Information & Pageviews• text descriptions as they appear on

the product page

• Ratings & Reviews• aggregate and individual ratings, full text of

individual reviews

• Transactions• purchase channel & links to pages viewed

WEBINAR

WILL BEGIN

SHORTLY

Submit your proposal by Monday, May 11, 2015. Learn more at http://wcai.wharton.upenn.edu/research

Presentation recording and slide deck

will be made available on the WCAI

website by Monday, April 27th

A WCAI Research Opportunity sponsored by Bazaarvoice


A WCAI Research Opportunity

sponsored by Bazaarvoice

April 24, 2015

He Said, She Bought: Analyzing

the Impact of User-Generated

Reviews on Purchase Behavior


Introductions

Melissa Hartz

Assistant Research Director, WCAI

Colleen O’Neill

Executive Director, WCAI

Elea Feit

Senior Fellow, WCAI

2

Conrad Wredberg

Director of Product Management,

Bazaarvoice

Chris Kramer

Senior Manager, Analytics,

Bazaarvoice

KNOWLEDGE FOR ACTION TRANSFORMING BUSINESS THROUGH CUSTOMER DATA 4



Data Background

6

Ray views different models of flat-screen televisions on the retailer’s website.

A

B


We See What Customers See…

7

Because users are tracked by cookies, we can see the full dimensions and details of each product

Ray views during his visits.



8

Because users are tracked by cookies, we can see the full dimensions and details of each product

Ray views during his visits.



9

product

If Ray scrolls down further on the page, we can see the full-text reviews, questions, and answers

he is exposed to and interacts with on the product page.

March 15, 2015

This product exceeds all my expectations.This is the second LG receiver we own and when we decided to change up to a flat screen it was obvious which manufacturer to use and our purchase has not disappointed.

Pros: Performance, Easy to Use, QualityYes, I would recommend this product.

Overall: 5/5

Quality: 5/5

Features: 4/5

LydiaFemaleLondon, UK

March 04, 2015

Good LookingExcellent TV, great picture and sound. Just wish it had a touch button to turn on and off

Yes, I would recommend this product.

Overall: 4/5

Quality: 5/5

Features: 4/5

TimothyMaleNorth York



10

If Ray scrolls down further on the page, we can see the full-text reviews and questions he was

exposed to, as well as any answers he chose to view (answers must be expanded to see the text).

March 18, 2015

Are the support legs on the TV movable?

AlanMaleSouth Shields

Q

A

JanFemaleAge: 45-54

March 18, 2015

No, the legs cannot be moved.

Hide --

KNOWLEDGE FOR ACTION 11

…and What They Buy

Finally, we can see when Ray ultimately purchases a television from the retailer’s website.

$249.99


Data Structure

Product

Pageviews

Leader

Ratings &

Reviews

Transactions

Observation Window: February & March 2015


Data Structure

Product

Pageviews

Leader

Ratings &

Reviews

Transactions



Product Pageviews

14

The dataset contains details for approximately 34,000 unique products in two

categories: technology and home & garden.

Product pageview data include:

- product name and ID

- full description of the product

- date/time the user viewed the

product page

- number of questions and reviews

- percent of reviewers who would

recommend the product at the time

it was viewed by one user


Pageviews by Hour, Two Days

15

, , , ,


Pageviews by Minute by Product, Two Days

, , , ,


Sneak Peek - Product Pageviews

17

Note: There are ~40 columns in the pageviews table. This is not an exhaustive list of columns.


Data Structure

Product

Pageviews

Leader

Ratings &

Reviews

Transactions



Data Structure

Product

Pageviews

Leader

Ratings &

Reviews

Transactions



Ratings & Reviews

20

In addition to numeric ratings, the dataset also contains questions & answers and full-text reviews.

Additionally, we have some sense of the user’s experience with the review through features, such

as flags designating if the content was visible in the browser for at least five seconds, or if the user

opened a picture or video attached to a review.

Ratings & reviews data include:

- date and time the review was published

- date and time the review was displayed to the user

- numeric rating*

- full text of review as it appears on the website*

- whether or not the review has ever been edited

- number of comments on the review*

- whether or not the review is featured*

*information is as of the time the content was displayed to the given user.


Review Impressions by Hour, Mobile vs. Non-Mobile

21

, , , ,


Review Impressions by Hour by Content Type

22

, , , ,


500 Words Used Most Frequently in Reviews


Interaction with Ratings & Reviews

24

March 15, 2015



Overall: 5/5

Quality: 5/5

Features: 4/5


Click to view pictures attached to this review (2).


Interaction with Ratings & Reviews

25

March 15, 2015



Overall: 5/5

Quality: 5/5

Features: 4/5


Click to view pictures attached to this review (2).


Interactions by Minute for Two Days

26

Number of Interactions

Interaction Type


Sneak Peek - Reviews

27


Data Structure

Product

Pageviews

Leader

Ratings &

Reviews

Transactions



Data Structure

Product

Pageviews

Leader

Ratings &

Reviews

Transactions



Transactions

30

The dataset contains approximately 1 million individual orders. The median

order consists of a single item and costs ~$30.

Transaction data include:

- timestamp of the transaction

- identifier for the order

- flag for mobile/non-mobile transaction

- browser used to make the purchase

- price of individual basket items

- total purchase amount for the order


Transactions Over One Day, Mobile vs. Non-Mobile

31


Transactions Over One Day by Number of Items in Order

32


Sneak Peek - Transaction

33


Data Structure

Product

Pageviews

Leader

Ratings &

Reviews

Transactions



Possible Research Areas from Bazaarvoice

35

• How do customers explore

products before coming to a

purchase decision? How do

reviews affect this?

• What characteristics of reviews

influence path to purchase?

• How does volume or valence of

reviews impact purchase behavior?

• Does the volume or valence of

reviews for one product have a

“halo” effect on reviews and/or

purchases for other products from

the same manufacturer?

• Which reviews should be featured

in order to maximize conversion

and/or attract the most valuable

customers?


Possible Research Areas from Bazaarvoice

36

• How do customers explore

products before coming to a

purchase decision? How do

reviews affect this?

• What characteristics of reviews

influence path to purchase?

• How does volume or valence of

reviews impact purchase behavior?

• Does the volume or valence of

reviews for one product have a

“halo” effect on reviews and/or

purchases for other products from

the same manufacturer?

• Which reviews should be featured

in order to maximize conversion

and/or attract the most valuable

customers?

Something not on this list…


Proposal Format

37

Proposals should be in PDF format, no more than 2,000 words, and include the

following information:

• Title

• Author(s) name, title, affiliation and e-mail address: Please designate a corresponding

author.

• Summary information: a single “slide” that visually summarizes team & project.

• Abstract

• Introduction: Describe expected contribution(s), covering both the academic and practical

aspects. Please keep it concise, and cite relevant work as necessary to explain your academic

contribution. There is no need to include a lengthy literature review.

• Detailed project proposal: Please include enough detail that we can assess the feasibility &

merit of the proposed approach. For example, modeling projects should include at least a sketch

of the model. In addition, include a rough estimate of how long the project will take. Also include

the business relevance of your research and the impact for the sponsor as well.

• Data Needs: Bulleted list of data required or requested for analysis not explicitly mentioned

during this webinar. While we can’t guarantee the inclusion of these items, we are happy to

investigate the availability.

• Biographies: Include up to a paragraph-long biography highlighting what each team member

will contribute to the project.


Proposal Submission Process

38

• Read more about the proposal format at

http://www.wharton.upenn.edu/wcai/Proposals.cfm

• Submit proposals at http://www.wharton.upenn.edu/wcai/proposalform.cfm

no later than Monday, May 11, 2015, 12 noon US Eastern.

– Proposals will only be accepted online.

– A single PDF combining the written portion and the single slide.

• Proposals will be evaluated both on academic contribution and potential to

significantly improve the research sponsor’s marketing practice by the

proposal review committee:

Elea Feit (Drexel University), Eric Bradlow (WCAI),

Pete Fader (WCAI), and representatives from Bazaarvoice.

• Contact [email protected], if you have questions prior to

submitting your proposal.

http://www.wharton.upenn.edu/wcai/Proposals.cfm

http://www.wharton.upenn.edu/wcai/proposalform.cfm

mailto:[email protected]


A Few Operational Notes

This webinar is intended for researchers & scholars. It should not be published

or presented without permission from WCAI.

• Questions on program: [email protected]

• Press contacts: [email protected]

To get access to the data, research teams should submit a proposal to WCAI

and the sponsor for approval.

Please note that the data sponsor cannot be named in any publication or

presentation.

39




Other WCAI Opportunities for Researchers

40

If you registered for this webinar, you will receive regular announcements

regarding:

• Research Opportunities like this one:

– Supply Side and Demand Side Data for a Fast Food Chain

– 2-3 more projects in the Fall

Also find us at:

• SSRN Research Paper series:

http://www.ssrn.com/link/Wharton-Cust-Analytics-Initiative-RES.html

• Announcements: http://wcai.wharton.upenn.edu

http://www.ssrn.com/link/Wharton-Cust-Analytics-Initiative-RES.html

http://wcai.wharton.upenn.edu/


Documents

WCAI Research opportunitywcai.wharton.upenn.edu/wp-content/uploads/Bazaarvoice_Final... · Director of Product Management, Bazaarvoice Chris Kramer ... –Supply Side and Demand Side