Certain keywords might see your Kickstarter campaign take a hit. According to a study led by researchers from the Singapore Management University, HEC Paris, the University of Technology Sydney and INSEAD, a product that claims to be both useful and a novelty tends to suffer in raising funds.
In the research paper titled “Does the Crowd Support Innovation? Innovation Claims and Success on Kickstarter”, the study analysed 50,310 Kickstarter projects. Results show that while the total amount pledged is boosted when a product is said to be useful (or novel), claiming that it is both reduces the total amount pledged by 26%.
“Prior research has shown that products that are novel and useful typically succeed in the marketplace,” said study co-author Amitava Chattopadhyay, Professor of Marketing and the GlaxoSmithKline Chaired Professor of Corporate Innovation at INSEAD. “But when projects make both claims, backers either assume a product’s benefits are inflated, that it carries a high risk of failure or that it divides the crowd between believers and sceptics, making it hard for backers to pick a side.”
“The higher level of uncertainty in the crowdfunding context drives backers to choose modest innovations and shy away from more extreme innovations,” said Cathy Yang, Assistant Professor of Marketing at HEC Paris.
“This is deeply disappointing as the premise of crowd funding is to support creativity and innovation”, said Anirban Mukherjee, Assistant Professor of Marketing at Singapore Management University. “Entrepreneurs therefore might be advised to frame a project as only novel or only useful, rather than both”, Dr Ping Xiao of the University of Technology Sydney (UTS) added.
The research drew data from all the projects listed on Kickstarter since its launch in 2009. To focus on the crowd’s appetite for innovation, they eliminated arts-related projects as these tend to be evaluated mostly on the basis of their artistic value. They then kept all the U.S-based projects that fell in the nine largest remaining Kickstarter product categories. Machine-learning tools were used to extract from the final dataset, a list of descriptors from the text, lead image and video of each project. The number of occurrences of the word “novel”or “useful” and its synonyms were compared with the individual projects’ funding results.