Analyzing the spillover roles of user-generated reviews on purchases: Evidence from Clickstream data

1. Analyzing the Spillover Roles of User-
Generated Reviews on Purchases:
Evidence from Clickstream Data
Young Kwark (Univ. of Florida)
Gene Moo Lee * (Univ. Texas at Arlington)
Paul A. Pavlou (Temple University)
Liangfei Qiu (Univ. of Florida)
March 5, 2016
Winter Conference on Business Intelligence
Snowbird, Utah
* Presenter

2. WCBI, Utah, March 2016
Product reviews and sales
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1. Online product reviews affect (or predict) product sales [Archak
et al. 2011; Clemons et al. 2006; Forman et al. 2008; Goes et al. 2014]
• Review information reduce product uncertainty [Dimoka et al.
2012; Hong & Pavlou 2014]
• Sellers’ strategic respond to reviews [Chen and Xie 2005 and
2008; Kwark et al. 2014]
2. Related products can also affect focal product sales [Shocker et
al. 2004; Wdel & Zhang 2004]
• Substitutes and complements
• Related products’ review may have spillover effects

3. WCBI, Utah, March 2016
Research questions
1. (Spillover effects) How do online product reviews of other but
related products in a consumer’s consideration set affect the
probability of the consumer purchasing a focal product?
2. (Moderation) How do following factors moderate spillover effects?
• Substitutable vs. complementary products
• Same vs. different brands
• Channel media (mobile vs. PC)
3. How do we measure complements and substitutes?
3

4. WCBI, Utah, March 2016
Literature review
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• Online product reviews
• Effect of reviews on focal product’s aggregate sales
• Spillover effects of related products
• Promotion spillover; Word-of-Mouth
• Research gaps:
• Spillover effect of reviews on
• Individual level purchase decision

5. WCBI, Utah, March 2016
Main hypotheses: spillover effects
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• Review ratings has a positive effect on demand [Chevalier &
Mazylin 2006; Clemons et a. 2006; Duan et al. 2008]
• Two products A and B are:
1. Substitutes, if A & B can be interchanged
2. Complements, if A & B are purchased together for a specific need
[H1a] Avg. ratings of high similarity products (substitutes) have a
negative role on the purchase of the focal product
[H1b] Avg. ratings of low similarity products (complements) have a
positive role on the purchase of the focal product

6. WCBI, Utah, March 2016
Related products and brands (1)
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[H2a] Avg. ratings of high similarity products (substitutes) in a different
brand with a focal product have a negative role on the purchase of the
focal product.
[H2b] Avg. ratings of high similarity products (substitutes) in the same
brand with a focal product have a negative role on the purchase of the
focal product.
[H3a] Avg. ratings of low similarity products (complements) in a different
brand with a focal product have a positive role on the purchase of the
focal product
[H3b] Avg. ratings of low similarity products (complements) in the same
brand with a focal product have a positive role on the purchase of the
focal product

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Related products and brands (2)
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• Same brand substitutes have two opposing effects: (1)
Substitutable effects; (2) perception transfer within brand
[H2c] Negative effect of avg. ratings of high similarity products
(substitutes) on the purchase of the focal product is larger in
different brands than in the same brand with the focal product.
• Same brand complements have multiple positive effects: (1)
synergetic effect; (2) perception transfer within brand
[H3c] Positive effect of avg. ratings of low similarity products
(complements) on the purchase of the focal product is larger in
the same brand than in different brands with the focal product

8. WCBI, Utah, March 2016
Channel moderation
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• Effect of reviews may be reduced in mobile due to:
• Higher search cost [Ghose et al. 2012], Reduced learning
experiences [Maniar et al. 2008], Visual design [Luca 2006]
[H4a] Avg. ratings of high similarity products (substitutes) have a
stronger negative effect on the focal product purchase of mobile
users than of PC users.
[H4b] Avg. ratings of low similarity products (complements) have a
weaker positive effect on the focal product purchase of mobile users
than of PC users
[H4c] Moderating effect of channel media is stronger in the substitute
products than in the complement products.

9. WCBI, Utah, March 2016
Clickstream data
• A 2-month user-level data from a UK box retailer
• Two product categories: Home/Garden and Technology
• Sessions with multiple products: 43% (home), 34% (tech)
• Average session duration: 57 mins (home), 25 mins (tech)
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Related products: substitutes and complements
• Our approach = consumer co-visit + product similarity
• Definition based on consumer perceptions from marketing
[Walters 1991]
1. Consumer co-visits two products => related products
2. High similar (>80%) related products => Substitutes
3. Low similarity (<20%) related products => Complements
• We validate this with the economic definition based on price
elasticity [Mas-Colell et al. 1995]
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11. WCBI, Utah, March 2016
Topic models for product similarity
• Apply topic model (Latent Dirichlet Allocation) on product text
descriptions [Blei et al. 2003, Shi et al. 2015]
1. Input: product descriptions (27,714 home / 7,492 tech)
2. Outputs
1. 50 topics (topic = a set of related keywords)
2. 50-dimensional topic vector for each product
• Product similarity of products A and B
= cosine similarity between two topic vectors (range = [0, 1])
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Stats of Home/Garden data
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• Purchase probability: 4.1% (in 493K “prod x user” observations)
• Channel: mobile: 35%, iOS devices: 26%
• rate_subs = avg. ratings of substitute products accessed in the same session
• Same definition for rate_comp, rate_{subs, comp}_{samebrand, diffbrand}

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Econometric Model
• Consumer i / Product j / User session t
• DV: purchase = 1, not purchase = 0
• a_i: consumer fixed effect
• b_1, b_2: direct impacts of focal products’ avg. rating and rate count
• b_3: spillover effect of substitute products’ avg. rating
• b_4: spillover effect of complementary products’ avg. rating
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Empirical results
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Results: Spillover effects
1. Purchase probability: 4.1%
2. Rating of focal product: +0.25% (6.2% increase)
3. [H1a] Ratings of substitutes: -0.28% (6.9% decrease)
4. [H1b] Ratings of complements: +0.57% (13.9% increase)
H1a and H1b are supported
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Substitutes and brand effects
1. Compare spillover effects in same vs. different brands
2. Magnitude of negative spillover of substitute products’ ratings
1. Home: different brands (-0.39%) > same brand (-0.02%)
2. Tech: different brands (-0.29%) > same brand (-0.10%)
H2a (different brands) and H2b (same brand) are supported
H2c (different brands > same brand) is supported
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18. WCBI, Utah, March 2016
Complements and brand effects
1. Compare spillover effects in same vs. different brands
2. Magnitude of positive spillover of complement products’
ratings
1. Home: same brand (0.23%) < different brands (0.46%)
2. Tech: same brand (0.61%) > different brands (0.39%)
H3a (different brands) and H3b (same brand) are supported
H3c (same brand > different brands) is not supported
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19. WCBI, Utah, March 2016
Measure validation with price elasticity
• Economic definition based on price elasticity
• A and B are substitutes: if A’s price increases, then B’s demand increases
• A and B are complements: if A’s price decreases, then B’s demand increases
• Run our regression with price (focal, substitute, complement)
• But prices are not exogenously given
• Two-stage least square (2SLS) with instrument variable
• IV: # of products from the brand [Chung et al. 2013]
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Measure validity with IV estimation
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• Price elasticity: price_subs (+) and price_comp (-) on the
purchase of focal products

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Summary
1. Spillover effects of related products’ ratings on
focal product purchase in individual level
2. Identification of substitute and complement
products with text mining (validated with price
elasticity)
3. Moderating effects of mobile device
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22. Thank you!
Young Kwark: young.kwark@warrington.ufl.edu
Gene Moo Lee: gene.lee@uta.edu
Paul Pavlou: pavlou@temple.edu
Liangfei Qiu: liangfei.qiu@warrington.ufl.edu

23. WCBI, Utah, March 2016
Literature: Online product reviews
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• Online product reviews => Focal product aggregate sales [Archak et al. 2011; Clemons
et al. 2006; Forman et al. 2008; Goes et al. 2014]
• Predictive power or influence
• Information role of reviews to reduce product uncertainty [Dimoka et al. 2012; Hong &
Pavlou 2014]
• Sellers’ strategic respond to reviews [Chen and Xie 2005 and 2008; Kwark et al. 2014]
• Review characteristics (negative emotion) and product types ==> review helpfulness
[Hong et al. 2012; Yin et al. 20124]
• Promotional marketing/recommendation and reviews [Lu et al. 2013; Jabr & Zheng
2014; Lee & Kartik 2015]
• Herding behavior in reviews [Duan et al. 2009]
• Factors affecting review behavior [Gao et al. 2015; Goes et al. 2014]

24. WCBI, Utah, March 2016
Literature: Substitutes and complements
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• Effects of related products
• Spillover effects across products in marketing [Anderson & Simester 2013]
• Market basket or product bundle [Russell & Peterson 2000]
• Related products’ marketing effects [Shocker et al. 2004; Wdel & Zhang 2004]
• Definition of substitutes and complements:
• Economics: substitutes (negative price elasticity) and complements (purchased together;
positive price elasticity) [Mass-Colell et al. 1995]
• Marketing: consumer perceptions, subst./comp.-in-use, similar product-usage patterns
• Continuum of substitutes and complements [Walters 1991]
• Higher similarity in product usage: substitute
• Products with co-usage: complements

25. WCBI, Utah, March 2016
Literature: Spillover role of Word-of-Mouth
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• Spillover: when a message influences beliefs related
to attributes not contained in the message
• WOM spillover for marketing decision
• Exclusivity [Peres and Van den Bulte 2014], market
entrant [Libai et al. 2009], rivals ad [Anderson &
Simester 2013], product recalls [Borah & Tellis 2015]
• The effects are measured indirectly (survey, etc.)

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Hypothesis development: H2
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• Substitutes can be replaced by each other [Srivastava et al. 1984]
• Customer perception on brands with similar products are correlated
[Roehm & Tybout 2006; Peres & Van den Bulte 2014; Anderson & Simester 2013]
• Thus, spillover roles of substitutes’ reviews will be negative regardless
of their brands
[H2a] Avg. ratings of high similarity products (substitutes) in a different
brand with a focal product have a negative role on the purchase of the
focal product.
[H2b] Avg. ratings of high similarity products (substitutes) in the same
brand with a focal product have a negative role on the purchase of the
focal product.

27. WCBI, Utah, March 2016
Hypothesis development: H3
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• Synergetic nature between complement products
• Promotion of a product stimulates sales of a complement
[Mulhern 1989; Walters 1991]
• Thus, spillover roles of complements’ reviews will be positive
regardless of their brands
[H3a] Avg. ratings of low similarity products (complements) in a
different brand with a focal product have a positive role on the
purchase of the focal product
[H3b] Avg. ratings of low similarity products (complements) in the
same brand with a focal product have a positive role on the
purchase of the focal product

28. Business topic model
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Per-word
topic
assignment
Observed
product
description
Product
topics
Per-product
topics
distribution
Topic
parameter
Proportions
parameter
K: # topics
D: # products
N: # words

29. WCBI, Utah, March 2016
Stats of Technology Data
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• Purchase probability: 3.7% / Mobile: 28% / iOS devices: 20%
• rate_subs = avg. of ratings of substitute products that are accessed in the same
user session
• Same definition for rate_comp, rate_{subs, comp}_{samebrand, diffbrand}

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Moderating Role: Mobile and iOS
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Robustness Check I
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Robustness Check II
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