Mobile Marketing: The Persuasive Impact of Real-Time Reviews
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1 Marketing: The Persuasive Impact of Real-Time Reviews Sam Ransbotham (Boston College) Nick Lurie (University of Connecticut)
2 idealism realism 2
3 Modern equivalent of fly in soup? 3
4 What is changing? Social Media connections influence Ubiquitous Computing physical temporal? Modern Consumers? connecting to firms 4
5 computing and user reviews 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? 5
6 Tension Physical Time 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) Temporal Reviews 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) 6
7 Restaurant reviews An experience good Widely used Nice literature base Interesting / Resonates Clean comparison 7
8 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 Complete Data (desktop only, mobile only, desktop + mobile) Focal Subset (desktop + mobile) Total Users 125,146 68,491 61,155 4,499 4,499 Restaurants 144,227 89,309 89,221 23,959 18,569 Reviews 299, , ,304 27,994 20,616 8
9 Reviews over time Number of Reviews Month 9
10 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 Quite addictive 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 class s, spouse, peer-reviews, Dimension This Review All Reviews Self-references (%) Social words (%) Positive emotions (%) Negative emotions (%) Cognitive wording (%) Swearing (%) Big words (%)
11 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 11
12 How do mobile and desktop reviews differ? Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
13 How do mobile and desktop reviews differ? Length (word count) Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
14 How do mobile and desktop reviews differ? Complexity (ARI) Word count Reading complexity Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
15 How do mobile and desktop reviews differ? Past tense wording (%) Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
16 How do mobile and desktop reviews differ? Social wording (%) Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
17 How do mobile and desktop reviews differ? Positive emotion (%) Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
18 How do mobile and desktop reviews differ? Negative emotion (%) Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
19 How do mobile and desktop reviews differ? Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
20 How do mobile and desktop reviews differ? Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
21 How do mobile and desktop reviews differ? Cognitive processes (%) Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
22 How do mobile and desktop reviews differ? Perceptive processes (%) Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
23 How do mobile and desktop reviews differ? Word count Reading complexity (ARI) Past tense Social processes Positive emotion Negative emotion Anger Swear words Cognitive processes Perceptive processes
24 What do mobile reviews recommend? 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 review 5000 iterations; *** p < Model A0 Model A1 Model A2 Model B2-0.28*** -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) Cognitive (std) -0.08*** -0.08*** -0.13*** Perceptive (std) 0.01** 0.01*** 0.07*** User fixed effects included reviews more likely negative than by non-mobile reviews 24
25 Distribution of influence Density Density Number of Likes per Review Number of Likes per Review 25
26 How influential are mobile reviews? 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 2 Score: dislike 0.27*** 0.18*** 0.20*** Score: like Score: really like 0.26*** 0.24*** 0.22*** -0.44*** -0.27*** Word count (x1000) 4.27*** 3.73*** Complexity (ARI x 100) * Past (std) Social (std) 0.03** 0.02* Positive emotions (std) -0.04*** Negative emotions (std) Anger (std) -0.03* -0.03* Swearing (std) Cognitive (std) * Perceptive (std) -0.04*** -0.04** 26
27 How do mobile reviews differ in Content 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 Influence 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 27
28 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 28
29 Scenario-based experiment Imagine that you are picking a restaurant for tonight. 2 x 2 design: mobile versus desktop; positive versus negative 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." Ratings of: credibility, valence, similarity, influence, timing 29
30 Preliminary experimental results Usefulness of Reviews Credibility of Reviews Usefulness Negative 4.87 Credibility Positive Valence Negative Positive Valence 30
31 How does mobile affect relationships? Focal Firm Other Firms Practical Implications of our Research Firm-to-Customer Communication Knowing which reviews are likely to influence Customer-to-Firm Communication prospective customers Observing and Firm- may Customer merit response Interaction Responding to and profiting from an increasingly mobile customer base Monitoring Inter-Customer Interaction Encouraging or discouraging real-time reviews Observing Firm- Customer Interaction through incentive or infrastructural mechanisms Focal Customer Inter-Customer Interaction Other Customers see Gallaugher & Ransbotham, Social Media and Customer Dialog Management at Starbucks, MISQE, 2010
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