Mobile Marketing: The Persuasive Impact of Real-Time Reviews

© Sam Ransbotham

Mobile Marketing: The Persuasive Impact of

Real-Time Reviews

Sam Ransbotham (Boston College)

Nick Lurie (University of Connecticut)

© Sam Ransbotham 2

idealism

realism

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Modern equivalent of fly in soup?

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What is changing?

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Ubiquitous Computingphysicaltemporal

Social Mediaconnectionsinfluence

Modern Consumers?connecting to firms

?

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Comparing reviews written using mobile devices with those written on traditional desktop computers…

How do they differ in content?

How do they differ in influence?

What mechanisms drive differences?

Mobile computing and user reviews

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Tension

PhysicalTime pressure and tradeoffs between physical and cognitive effort on mobile devices drive important differences in content, potentially affecting the usefulness of the content (Lurie et al. 2009)

TemporalReviews written during or immediately after service experiences should be less extreme and less biased than retrospective reviews. Yet, real-time reviews may be impulsive and lack reflection, limiting their usefulness (Novemsky and Ratner 2003)

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Restaurant reviews

• An experience good

• Widely used

• Nice literature base

• Interesting / Resonates

• Clean comparison

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Data

• 299,798 restaurant reviews– Users can read and write restaurant reviews; little governance

• Data Collection– My typical: 114-node Linux cluster, extensive download/parse

– For each: user, restaurant, date, title, text, mobile/desktop, recommend, “likes”

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Complete Data(desktop only, mobile only,

desktop + mobile)Focal Subset

(desktop + mobile)

Total Desktop Mobile Desktop Mobile

Users 125,146 68,491 61,155 4,499 4,499

Restaurants 144,227 89,309 89,221 23,959 18,569

Reviews 299,798 163,494 136,304 27,994 20,616

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Reviews over time

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200808 200810 200812 200902 200904 200906 200908

MobileDesktop

Month

Num

ber

of R

evie

ws

010

0020

0030

0040

0050

0060

00

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Text analysis

• Linguistic Inquiry and Word Count with 2007 dictionaries– Originally developed by Pennebaker, Booth, Francis (UT-Austin)

– Used in text analysis research, often with transcription

• Based on word usage– Typically categorizes ~86% of words used

– Example: Negative emotion scale (affective) uses 499 words (e.g. “hurt”, “ugly”, “nasty”)

– Example: Amazon

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“Knowing what your customers are worth is the secret to focusing your time and money where it makes the most difference. You can't be all things to all people, so you need to learn to find out who really matters to your success. Fader makes it clear with great ideas and a readable style.” - Andy Sernovitz

Dimension This Review All ReviewsSelf-references (%) 0.00 1.15Social words (%) 14.81 4.49Positive emotions (%) 1.85 4.36Negative emotions (%) 0.00 0.64Cognitive wording (%) 12.96 7.05Swearing (%) 0.00 0.00Big words (%) 12.96 30.77

Quite addictive…class emails, spouse, peer-reviews,…

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Differences in content

Processed the full text of 299,798 reviews

• Major categories– Linguistic Processes 26 measures

– Psychological Processes 32 measures

– Personal Concerns 7 measures

– Spoken Categories 3 measures

• 68 total measures calculated– Many highly correlated, nested

– Focus on 11 distinct measures

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How do mobile and desktop reviews differ?

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev

Word count 70.20 50.00 70.16 32.62 24.00 32.35Reading complexity (ARI) 21.65 17.33 14.71 23.08 18.35 17.29Past tense 3.51 2.22 4.15 3.34 0.00 4.81Social processes 5.63 5.10 4.70 5.02 4.00 5.75Positive emotion 6.90 5.71 5.92 10.21 8.00 9.73Negative emotion 1.00 0.00 2.28 1.31 0.00 4.08Anger 0.22 0.00 1.04 0.34 0.00 2.54Swear words 0.05 0.00 0.48 0.11 0.00 1.83Cognitive processes 14.42 14.44 6.17 13.22 13.33 8.06Perceptive processes 2.60 1.92 3.63 2.76 0.00 4.84

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


Desktop Mobile

050

100

150

Length (word count)

Desktop Mobile

050

100

150

Length (word count)

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev

Word count 70.20 50.00 70.16 32.62 24.00 32.35Reading complexity 21.65 17.33 14.71 23.08 18.35 17.29Past tense 3.51 2.22 4.15 3.34 0.00 4.81Social processes 5.63 5.10 4.70 5.02 4.00 5.75Positive emotion 6.90 5.71 5.92 10.21 8.00 9.73Negative emotion 1.00 0.00 2.28 1.31 0.00 4.08Anger 0.22 0.00 1.04 0.34 0.00 2.54Swear words 0.05 0.00 0.48 0.11 0.00 1.83Cognitive processes 14.42 14.44 6.17 13.22 13.33 8.06Perceptive processes 2.60 1.92 3.63 2.76 0.00 4.84

Desktop Mobile

1015

2025

3035

40

Complexity (ARI)

Desktop Mobile

1015

2025

3035

40

Complexity (ARI)

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


Desktop Mobile

05

1015

Past tense wording (%)

Desktop Mobile

05

1015

Past tense wording (%)

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


Desktop Mobile

05

1015

20

Social wording (%)

Desktop Mobile

05

1015

20

Social wording (%)

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


Desktop Mobile

05

1015

2025

Positive emotion (%)

Desktop Mobile

05

1015

2025

Positive emotion (%)

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


Desktop Mobile

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Negative emotion (%)

Desktop Mobile

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Negative emotion (%)

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


Desktop Mobile

05

1015

2025

30

Cognitive processes (%)

Desktop Mobile

05

1015

2025

30

Cognitive processes (%)

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


Desktop Mobile

02

46

810

Perceptive processes (%)

Desktop Mobile

02

46

810

Perceptive processes (%)

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Desktop Mobile

Mean Median StdDev

Mean Median StdDev


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What do mobile reviews recommend?

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Model A0 Model A1 Model A2 Model B2Mobile -0.28*** -0.39*** -0.40***

Word count (x1000) 2.12*** 1.21*** 0.68**

Complexity (ARI x 100) 0.90*** -0.94*** -1.56***

Past (std) -0.31*** -0.30*** -0.43***

Social (std) 0.06*** 0.05*** 0.10***

Positive emotions (std) 0.36*** 0.39*** 0.42***

Negative emotions (std) -0.56*** -0.55*** -0.63***

Anger (std) 0.05*** 0.05*** 0.20***

Swearing (std) -0.01 -0.01 0.01

Cognitive (std) -0.08*** -0.08*** -0.13***

Perceptive (std) 0.01** 0.01*** 0.07***

User fixed effects included

Ordered logistic regression (Bayesian) on the rating (dislike, neutral, like, really like)48,610 observations of 4,499 users with at least one mobile and one desktop review5000 iterations; *** p < 0.001

Mobile reviews more likely negative than by non-mobile reviews

© Sam Ransbotham

Distribution of influence

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Desktop

Number of Likes per Review

Den

sity

0 2 4 6 8 10

0.0

0.5

1.0

1.5

2.0

Mobile

Number of Likes per Review

Den

sity

0 2 4 6 8 10

0.0

0.5

1.0

1.5

2.0

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How influential are mobile reviews?

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Negative binomial regression on the number of users who like the review. 48,610 observations of 4,499 users with at least one mobile and one desktop review(additional controls for age, time, intercept)

Users are less influenced by mobile reviews than by desktop reviews

Model 0 Model 1 Model 2Score: dislike 0.27*** 0.18*** 0.20***

Score: like -0.02 -0.02 -0.03

Score: really like 0.26*** 0.24*** 0.22***

Mobile -0.44*** -0.27***

Word count (x1000) 4.27*** 3.73***

Complexity (ARI x 100) -0.13 -0.17*

Past (std) 0.01 0.01

Social (std) 0.03** 0.02*

Positive emotions (std) -0.04*** -0.02

Negative emotions (std) 0.02 0.02

Anger (std) -0.03* -0.03*

Swearing (std) -0.01 -0.01

Cognitive (std) -0.02 -0.02*

Perceptive (std) -0.04*** -0.04**

© Sam Ransbotham

How do mobile reviews differ in…

Content Influence

Differences • Shorter, no less complex• More positive emotion• Closer to real-time• Surprisingly not negative,

angry• Less cognitive (slightly), more

affective• More likely to be negative

• Less likely to influence users • (even after controlling for all things

“mobile”—i.e., shorter length, etc.)

Why? • Physical• Temporal

• Expect bias? • Heuristic for content differences?• Ongoing research to better

understand mechanisms

Next steps? • Additional data collection• Alternative models: user

heterogeneity

• Additional data collection• Alternative models: user

heterogeneity, predictive

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What mechanisms drive differences?

• Review characteristics (page placement?)

• Endogenous choice of real-time (only if extreme?)

• Use of “likes” as measure of influence (same as behavior?)

Next step (experiment)

• Controlled mapping of “mobile” to “real-time”

• Measure influence

• Both scenarios and field

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Scenario-based experiment

“Imagine that you are picking a restaurant for tonight.”

2 x 2 design: mobile versus desktop; positive versus negative

Ratings of: credibility, valence, similarity, influence, timing

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Mobile Review

One great experience sure can make one addicted for a lifetime. I have eaten here only once and will definitely be back. Yes I would like to go back even knowing that there are many other great places in the city.

The best way I can sum up the food is inspired , and in such a competitive market this inspiration is worth a second trip. I was at Joe's with a group of four and every single dish we ordered was special. But the food alone is not what gets this place the outstanding five stars. What does then? Well when you mix great food with an incredibly attentive waiter you end up with a remarkably wonderful experience. Our waiter was extremely friendly, quick, and did not make any mistakes with the orders.

The icing on the cake was when we got to sample the house desserts. The waiter knew that it was our first time here and he took extra steps in making sure we had a wonderful experience. For the first time in my life I went to the manager and praised the service . I told him about our experience and what a good job our waiter did. The manager was also helpful and asked if there is anything he could do to further improve my experience. All in all it was one of the best experiences I have had at any restaurant."

© Sam Ransbotham

Preliminary experimental results

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Negative Positive

DesktopMobile

Usefulness of Reviews

Valence

Use

fuln

ess

02

46

8

6.59

4.87

7.27

7.67

Negative Positive

DesktopMobile

Usefulness of Reviews

Valence

Use

fuln

ess

02

46

8

6.59

4.87

7.27

7.67

Negative Positive

DesktopMobile

Credibility of Reviews

Valence

Cre

dibi

lity

02

46

8

6.00

5.27

6.606.87

Negative Positive

DesktopMobile

Credibility of Reviews

Valence

Cre

dibi

lity

02

46

8

6.00

5.27

6.606.87

© Sam Ransbotham

How does mobile affect relationships?

Firm-to-Customer Communication

Customer-to-Firm Communication

Inter-Customer Interaction

Monitoring Inter-Customer Interaction

Observing Firm- Customer Interaction

Observing Firm- Customer Interaction

Focal Customer

Focal Firm

Other Customers

Other Firms

see Gallaugher & Ransbotham, “Social Media and Customer Dialog Management at Starbucks”, MISQE, 2010

Practical Implications of our Mobile Research

• Knowing which reviews are likely to influence prospective customers and may merit response

• Responding to and profiting from an increasingly mobile customer base

• Encouraging or discouraging real-time reviews through incentive or infrastructural mechanisms

Documents

Mobile Marketing: The Persuasive Impact of Real-Time Reviews