The Language that Gets People to Give:Phrases that Predict Success on

Kickstarter

Tanushree Mitra & Eric Gilbert

What makes some projects

succeed while others fail ?

Predictive Features of success and failure ?

Statistical Model

QUANTITATIVE APPROACH

Dependent Variable:Project outcome (Funded or

Not)

Independent Variables Predictive Features

Statistical Model

QUANTITATIVE APPROACH

Dependent Variable:Project outcome (Funded or

Not)

Independent Variables Predictive Features(?)

Video Present

Goal

Duration

Facebook Connected

Pitch

Category

DATA

45,815K Kickstarter projectsall projects as of June 2012

51.53% funded

48.47% not funded

45K Kickstarter project URLs

Fetch project end date

Project reached end date?

Scrape project description

Lowercase text

Remove stop words

[uni,bi,tri]-grams

phrase frequency > 50?

phrase in all 13 categories?

Scrape control variables

Penalized Logistic Regression

45K Kickstarter project URLs

Scrape project pitch

Lowercase text

Remove stop words

[uni,bi,tri]-grams

phrase frequency > 50?

phrase in all 13 categories?

Scrape control variables

Statistical Model

Project reached end date?

Fetch project end date

45K Kickstarter project URLs

Fetch project end date

Lowercase text

Remove stop words

[uni,bi,tri]-grams

phrase frequency > 50?

phrase in all 13 categories?

Scrape control variables

Statistical Model

Scrape project pitch

Project reached end date?

45K Kickstarter project URLs

Fetch project end date

[uni,bi,tri]-grams

phrase frequency > 50?

phrase in all 13 categories?

Scrape control variables

Statistical Model

Project reached end date?

Scrape project pitch

Lowercase text

Remove stop words

45K Kickstarter project URLs

Fetch project end date

Project reached end date?

Scrape project pitch

Lowercase text

Remove stop words

phrase frequency > 50?

phrase in all 13 categories?

Scrape control variables

Statistical Model

[uni,bi,tri]-grams

45K Kickstarter project URLs

Fetch project end date

Project reached end date?

Scrape project pitch

Lowercase text

Remove stop words

Scrape control variables

Statistical Model

[uni,bi,tri]-grams

phrase frequency > 50?

phrase in all 13 categories?

Pitch

~20K unigrams, bigrams, trigrams

45K Kickstarter project URLs

Fetch project end date

Project reached end date?

Scrape project pitch

Lowercase text

Remove stop words

phrase frequency > 50?

phrase in all 13 categories?

Statistical Model

[uni,bi,tri]-grams

Scrape control variables

59 control variables

Video Present

Goal

Duration

Facebook Connected

Category

45K Kickstarter project URLs

Fetch project end date

Project reached end date?

Scrape project pitch

Lowercase text

Remove stop words

phrase frequency > 50?

phrase in all 13 categories?

[uni,bi,tri]-grams

59 control variables

Statistical Model

Scrape control variables

Statistical Model

STATISTICAL TECHNIQUE

Dependent Variable:Project outcome (Funded or

Not)

Independent Variables Phrases (20K) + Controls (59)

Penalized Logistic Regression

STATISTICAL TECHNIQUE

Dependent Variable:Project outcome (Funded or

Not)

Independent Variables Phrases (20K) + Controls (59)

Friedman et al. 2010

Baseline Model

Controls Only Model

Phrases + ControlsModel

Results: MODEL FITS

Explanatory Power | Error40.8 % |

17.03%

Explanatory Power | Error58.56 % |

2.24%

| Error |

48.47 %

(NF) Predictors

not been able ( β = − 4.07 )

“I have not been able to finish the film because none of my editors will see the project through to the end.”

(NF) Predictors

later i ( β = − 3.04 )

hope to get ( β = − 2.39 )

“I can’t take size orders and possibly hope to get them all made in time

for christmas.”

(NF) Predictors

even a dollar( β = − 3.10 )

Wattenberg & Viegas, 2008

(F) Predictors

mention your ( β = 2.69 )

also receive ( β = 1.83 )

add $40 and you will also receive two vip tickets to the premiere

screening.

(F) Predictors

next step is ( β = 1.07 )

Recording is pretty much done, next step is production.

(F) Predictors

cats ( β = 2.64 )

A closer look at predictive phrases

UNDERSTANDING CONTEXT

Principles of Persuasion

Cialdini, R. B. 1993

1. Reciprocity2. Scarcity3. Authority4. Social Proof5. Social

Identity6. Liking

Principles of Persuasion

Cialdini, R. B. 1993

1. Reciprocity2. Scarcity3. Authority4. Social Proof5. Social

Identity6. Liking

Principles of Persuasion

Cialdini, R. B. 1993

RECIPROCITY

Brehm & Cole 1966, Goranson & Berkowitz, 1966, Ciladini 2001

we’ll mention your (β = 2.69) name in the sleeve of our

full length album

RECIPROCITY

I will thank you on my website, send you

good karma and (β = 2.04) ..

RECIPROCITY

SCARCITY

Ciladini 2001, Ciladini & Goldstein 2004

also, you will be given the chance (β = 2.69) to purchase our small batch

pieces before the public domain

SCARCITY

AUTHORITY

Ciladini 2001, Ciladini & Goldstein 2004

the project will be (β = 18.48)produced by

dove award winning producer

AUTHORITY

SOCIAL PROOF

Ciladini 2001

[name] has pledged (β = 5.42) some money..… so, you can see that i already have people willing to support my

art.

SOCIAL PROOF

Language is a reliable signal of success of crowd-funded

projectsSo are some controls….

CONTROLSGraphic Design β = 1.35Video Present β = 0.60 Facebook Connected β = 0.13

CONTROLSIllustration β = -2.55Journalism β = -1.12Project Duration β = -0.01

… …

IMPLICATIONS

IMPLICATIONS

http://www.cc.gatech.edu/~tmitra3/data/KS.predicts

Also at: http://b.gatech.edu/1mf1C6E

The Language that Gets People to Give:Phrases that Predict Success on

Kickstarter Tanushree Mitra & Eric Gilbert @tanmit | @eegilbert

DATA: http://b.gatech.edu/1mf1C6E

Scan presence of KS phrases in

Google 1T

54K Kickstarter phrasesNon-zero β weights

Google 1T Corpus phrases

GENERAL PHRASES:- 494 positive predictors- 453 negative predictors

χ2 test between phrase frequencies

+ Bonferroni Correction

Search for phrases with significantly higher

difference+

Membership higher in Ggle 1T

Fancy Stats. Huh!

next step isin the

upcomingto announce

….

provide usneed one

….