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Show Me the Money! An Analysis of Project Updatesduring Crowdfunding Campaigns

Anbang Xu1, Xiao Yang

2, Huaming Rao

3, Wai-Tat Fu

1, Shih-Wen Huang

4, Brian P. Bailey

1

1University of Illinois, Urbana, IL, USA {xu26, wfu, bpbailey}@illinois.edu

2

Tsinghua University, Beijing, China y-x10@mails.tsinghua.edu.cn3Nanjing University of Science and Technology, Nanjing, China huaming.rao@gmail.com4

University of Washington, Seattle, WA, USA wenhuang@cs.washington.edu

ABSTRACT

Hundreds of thousands of crowdfunding campaigns havebeen launched, but more than half of them have failed. Tobetter understand the factors affecting campaign outcomes,this paper targets the content and usage patterns of projectupdatescommunications intended to keep potential fundersaware of a campaigns progress. We analyzed the contentand usage patterns of a large corpus of project updates onKickstarter, one of the largest crowdfunding platforms.

Using semantic analysis techniques, we derived a taxonomyof the types of project updates created during campaigns,and found discrepancies between the design intent of aproject update and the various uses in practice (e.g. socialpromotion). The analysis also showed that specific uses ofupdates had stronger associations with campaign successthan the projects description. Design implications wereformulated from the results to help designers better supportvarious uses of updates in crowdfunding campaigns.

Author Keywords

Crowdfunding; updates; crowdsourcing.

ACM Classification Keywords

H.5.3 [Information Interfaces and Presentation]: Group and

Organization Interfaces - Web-based interaction.

INTRODUCTION

Crowdfunding offers a new paradigm for entrepreneurs toinitiate, expand, or advertise their business ideas [10, 25].While the concept of crowdfunding is still nascent [4], ithas already shown immense promise. At the time of thiswriting, for example, Kickstarter, the largest onlinecrowdfunding platform, has successfully funded 48,393campaigns. These campaigns have generated 782 millionUS dollars from more than 4.7 million people [36]. Manycampaigns have succeeded in reaching their funding goals,however, more than half of the campaigns have failed [36].

A key challenge is therefore to understand why somecampaigns succeed while others fail. Prior work has

highlighted the relation between the project representationand the outcome of a campaign [12, 23, 35]. The resultssuggest that project creators should focus on improving theproject representation, which mainly includes the video andtextual description on the project page (see Figure 2a). Forexample, the top rule for success suggested by Kickstarter

is to create a video for the project page [35].Besides careful preparation of a projects representation,creating updates is also an important part of managing acampaign [13]. The form of a project update is similar to ablog post (see Figure 2b) and the design intent is to keepbackers (funders) informed of a project's progress [34].

Updates are critical to the success of a campaign. Forexample, we sampled 8,529 campaigns from Kickstarterand found that the chance of success of a project without anupdate was only 32.6%. In contrast, as shown in Figure 1,the chance of success with updates is 58.7% (2= 285.18,

p< .001). This suggests that updates may be as important asthe creation of the project representation in determining theoutcome of a campaign. However, prior work has notexamined the types of updates created in a campaign, thedistribution of updates across categories or time, or howdifferent types of updates relate to the campaign outcomes.

In this paper, to better understand the nature of projectupdates, we analyzed how creators use updates duringcrowdfunding campaigns and how these updates relate tothe success of the campaigns. Our main contributions are:

Figure 1. The success rates of campaigns with updates and

campaigns without updates.

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copies

bear this notice and the full citation on the first page. To copy otherwise,or republish, to post on servers or to redistribute to lists, requires priorspecific permission and/or a fee.CHI14, April 26 May 1 2014, Toronto, ON, Canada.Copyright 2014 ACM 978-1-4503-2473-1/14/04$15.00.

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An empirically-based taxonomy of the types of projectupdates created during a campaign. We identified seventypes of themes in the updates and found differencesbetween the stated design intent of updates and howproject creators actually use project updates in practice.

We report how the different types of updates relate tothe outcomes of the campaigns. Results revealed therelative importance of the update representation (i.e. itscontent) compared to the project representation (e.g., thepresence of videos and images and description length).

Design implications for crowdfunding tools to bettersupport the practice of creating and using updates duringa campaign, thereby improving their effectiveness.

RELATED WORKThe factors that lead to successful fundraising have been ofgreat interest to researchers [5, 19]. Recent studies havehighlighted the importance of the project representation andhave found that many attributes related to the project page(see Figure 2a) can influence the success of a campaign.For instance, Kickstarter suggests that the number one ruleof success of a campaign is to have a video on the projectpage to communicate the overall project ideas [35]. Priorwork confirms that the outcome of a campaign is related tothe presence of a video [12] and the quality of the video[23]. Prior work has also revealed that the success of acampaign is related to the textual content of the project

page such as the length and readability of the content [12],and certain phrases used in the content [22].

In addition to the project representation, Kickstarter alsoprovides the ability to create project updates. The intent ofan update is to keep backers (funders) informed of aproject's progress [34]. However, creators are free toprovide any information about the project through anupdate. Researchers have included project updates in theirregression analysis of campaign outcomes [18, 23]. The

number of updates was found to be positively related to the

success of a campaign and support the recommendation thatcreators provide frequent updates [35]. However, becauseprior work has treated updates as a single variable, it is notknown what types of updates are created, which ones occurmost often or when they occur during a campaign, or howthe different types of updates relate to campaign success.

The initial settings of a campaign such as the amount of thefunding goal and the duration of the campaign can alsopredict its success. For example, Muller et al. [25] foundthat the quantitative differentiator between successful andunsuccessful campaigns is that successful campaigns have asmaller amount of funding goals than unsuccessfulcampaigns. It has also been found that the duration of acampaign is negatively related to its success [23]. Thesocial network of project creators is often the initial fundingsource of many campaigns and plays an important role indetermining success [1, 23]. For example, Mollick foundthat the number of Facebook friends of creators ispositively correlated with the success of campaigns.

Qualitative studies have been conducted to understand thesuccess of crowdfunding campaigns and the motivation andbarriers to participation [11, 13, 14]. Hui et al. [13]conducted interviews with project creators and participantobservation to understand the work needed for the creators.They found that successful creators made large efforts in

reaching out to personal on- and off-line networks for fundsduring the campaign. Yet, it is not clear how the degree ofthis effort influences the outcome of the campaign.

Our research contributes to this corpus of prior work bystudying a crowdfunding site from a unique perspective the use of project updates. To the best of our knowledge,we are the first to report the types of updates created duringcrowdfunding campaigns and to report the relationshipbetween the different update types and campaign success.

(a) Project page of a campaign (b) One of the project updates of the camapign

Figure 2. (a) The project page is the main page of a campaign on Kickstarter.com. The campaign shown is raising funds for an

iPhone application that can help people find free beaches in Malibu. This campaign has four updates so far. If a user (e.g. funder)

clicks on the update tab, s/he will see the updates displayed in reverse chronological order. (b) An update of the campaign posted

in the middle of the campaign. Project creators in this update introduced two new rewards (poster and a beach bag) to attract

additional funders. In this update, project creators also encourage people to promote the campaign in a social network.

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RESEARCH QUESTIONS AND DATA SET

Our study was designed to understand the practice of usingupdates in crowdfunding campaigns and centered on twokey research questions:

RQ1: What are the usage patterns of updates? What typesof updates occur during campaigns, how often do theyoccur, and when does each type of update occur?

RQ2: How are the types of updates associated with thesuccess of a campaign? How important is the representationof updates compared with the representation of the mainproject in explaining the success of a campaign?

A quantitative analysis was conducted for our study. Fordata collection, we used the site thekickbackmachine.comwhich lists the project IDs of Kickstarter projects in reversechronological order. Using a custom extractor, we firstcollected the listed project IDs from thekickbackmachineand then collected the corresponding content fromKickstarter. In total, we collected publicly available datafrom 8,529 campaigns on Kickstarter. The campaigns

started between March 19, 2013 and May 17, 2013. Themean duration of a campaign in the data set was 32.1 (days)with a standard deviation of 10.2. For each campaign, wecollected the updates that were posted before the outcomeof the campaign was determined (successful orunsuccessful). A majority of the campaigns (58.6%) had atleast one update. Project creators can choose to provide aprivate project update that can only be viewed by itsfunders. We found 3,098 private updates that we could notcollect, and we only collected the updates that can beaccessed by the public (the potential funders). Thiscollectively provided us with a corpus of 21,234 updates.The data set includes the content of each campaign and thecontent and timestamp of each update.

ANALYZING UPDATES (RQ1)

To identify the types of themes in the project updates, weapplied Latent Dirichlet Allocation (LDA), an unsupervisedgenerative method that is often used to discover hiddenthemes in documents and the words associated with eachtheme. The method enables analysis of large amounts ofunlabeled documents by clustering words that frequentlyco-occur. Several steps were performed to process the data.

Step 1: Sample the data. The updates were not distributedevenly across the projects. For example, some projects hadmany updates while others had only a few. If we directlyapplied topic modeling on the updates, the identified

themes and the words associated with a theme might be toospecific to the content of some of the projects. To avoidthis, we randomly selected three updates from a project orall the updates from a project if it had fewer than three.

Step 2: Clean the data. We converted the text to lowercaseand removed the punctuation characters such as ();:.

Step 3:Create Bag of words. We adopted a bigram bagof words model, a common approach in computational

linguistics [21]. That is, we used single words (unigrams),and two word phrases (bigrams) to represent the text.

We first attempted to perform LDA on the preprocessedtext by treating each update as a document. However, wefound that the resulting themes had too much overlap (i.e.many themes shared the same words), making theinterpretation of the themes difficult. This is a known

limitation of standard LDA. An alternative that has beenapplied in the text mining community is to decompose thedocument into finer granularities such as the sentence levelin order to detect more specific topics and reduce theoverlap [15]1. Thus, we applied LDA at the sentence-level,meaning that each sentence was treated as a document.

Step 4: Decompose the updates into sentences. Weseparated all text into sentences based on terminalpunctuations (., ?, and !). We excluded sentences thathad fewer than three unigrams (15% of the sentences wereremoved); as these sentences were too short for successfulmodel training and were usually not too meaningful. Byapplying LDA at the sentence-level, we found the themes

easier to interpret because there was less overlap.

Researchers typically examine the output of differentthemes in order to decide the number of unique themes[32]. Following this data-driven approach, two expertsfamiliar with crowdfunding reviewed the outcomes fromthe LDA models. The experts started by fixing a largenumber of themes (30 in this case), and reduced the numberif they could find duplicates (e.g. themes described by thesame set of words). The experts were able to finalize sevenunique themes from the LDA results and category labelswere assigned to the themes and any disagreements wereresolved by discussion.

We then created a dictionary based on the results of LDAand used the dictionary to assign themes to the updates.Specifically, we constructed dictionaries to represent theidentified themes by selecting the top 60 words (unigramsand bigrams) that were most strongly associated with eachtheme according to the LDA model. We also excluded afew words that are related to project categories or locations(e.g. game, music, and New York). This dictionary-basedapproach was applied to the entire data set. We classified anupdate to belong to a theme if it contained at least twounigrams or bigrams from the corresponding dictionary. Bythis definition, a majority of the updates belonged to only asingle theme.

To verify the reliability of the produced taxonomy, werecruited two people to code a sample of the updates basedon the taxonomy. First, the coders received training inwhich they were introduced to the categories, definitions,

1 Sentence-level LDA works well when most of the sentencescontain only a single theme. This was the case for our data set.