Failures to be celebrated: an analysis of major pivots of software startups

1 Introduction

• Intermediate failures can provide validated learning that helps startups test assumptions and avoid fatal business failure.The study focuses on these intermediate failures, not the final failure that ends a startup.Source: §1 · p. 3
• A pivot is a strategic change to a business concept, product, or elements of a business model.Examples include Flickr shifting from a game to photo sharing and Twitter shifting from podcasting to microblogging.Source: §1 · p. 3
• The study addresses a research gap on what triggers software startup pivots and which types startups undertake.It is presented as an early effort to lay foundations for future research.Source: §1 · pp. 3–4
• The researchers analyzed online secondary data about major pivots in 49 software startups, then identified and grouped their triggers and pivot types.The cases included companies such as YouTube, Flickr, Pinterest, and Twitter.Source: §1 · p. 4

2.1 Software Startups and Lean Startup

• Lean Startup uses Build-Measure-Learn loops and minimum viable products to test customer response and validate business-model hypotheses before deciding whether to pivot or persevere.Software startups also face resource constraints, time pressure, uncertain conditions, and dynamic markets.Source: §2.1 · p. 4

2.2 Failure in Software Startups

• Intermediate project failures can support learning and pivots, whereas a startup’s final failure may terminate the business.Prior work on software startup failures was scarce, and the authors note a lack of research on factors triggering pivots.Source: §2.2 · p. 5

2.3 Pivots in Software Startups

• For this study, a pivot significantly changes one or more, but not all, of a startup’s product, team, business model, or growth engine.Changing all these elements at once is treated as starting a different business, not pivoting.Source: §2.3 · p. 5
• Prior research offered pivot examples and categories, but lacked empirical support, explanations for why pivots occur, or clear categorization procedures.The authors used Ries’s pivot types as a starting point while noting their need for systematic validation.Source: §2.3 · pp. 5–6
• The reviewed pivot types include zoom-in, zoom-out, customer-segment, customer-need, platform, business-architecture, value-capture, growth-engine, channel, and technology pivots.The passages describe changes such as turning a product feature into the whole product or switching the technology used to deliver the same solution.Source: §2.3 · pp. 6–7
• Other reviewed categories include platform, business-architecture, value-capture, growth-engine, and channel pivots; a social pivot was also proposed, with limited supporting argument and examples.The categories describe shifts in platform, business model, monetization, growth strategy, customer access, or social factors.Source: §2.3 · pp. 6–7

3 Research Approach

• The exploratory study used secondary data because research on software startup pivots was nascent and the authors sought an initial understanding to guide future inquiry.They used a case survey to aggregate characteristics across heterogeneous cases, following an adapted systematic-review protocol.Source: §3 · p. 7

3.1 Data Collection Steps

• The first two authors separately evaluated 783 accessible URLs, discussing 39 disagreements (5%) to consensus; screening yielded 138 webpages and 101 potential cases, with 49 retained for analysis after quality assurance.Source: §3.1 · pp. 9–10
• The search strategy could miss pivot accounts that did not use the term “pivot,” although the authors considered the term commonly used in startup communities.The search used “startup” rather than “software startup” because online sources may describe these firms as Internet or tech startups.Source: §3.1 · p. 9

3.2 Data Analysis Steps

• Researchers qualitatively coded case explanations for pivot types and triggering factors, using open coding for triggers and an initial pivot-type category set.The two coders resolved disagreements by discussion and consensus; analysis yielded 10 pivot types and 14 triggering factors.Source: §3.2 · p. 11
• Pivot types were grouped by product, market, financial, and team dimensions, while triggering factors were grouped as external or internal.External factors were defined as beyond a startup’s control; internal factors stemmed from its own decisions or activities.Source: §3.2 · p. 12

4.1 Description of the 49 Pivoted Software Startups

• Most cases were based in the United States (37), with four in Canada and two in Israel; two companies’ locations were unavailable.The remaining four were in the United Kingdom, Australia, New Zealand, and India.Source: §4.1 · p. 12
• Social networks, e-commerce, and finance and business were the main domains, and most products were market-driven and Internet-based.The cited domain shares were 30.61%, 24.44%, and 12.24%, respectively.Source: §4.1 · p. 12
• Twenty-four cases were founded or released their first products between 2010 and 2015, while 13 launched products during 2005–2009.Seven first released products between 1998 and 2004; dates were unavailable for five cases.Source: §4.1 · p. 12
• 49 startups contributed 55 pivot instances, meaning some cases contained more than one pivot example.Source: §4.1 · p. 13

• The overview lists major pivot-triggering factors identified from the 49 cases.Source: §4.2 · p. 13
• Triggering factors are grouped as external or internal.Source: §4.2 · p. 13
• A single pivot instance may be triggered by more than one factor.Source: §4.2 · p. 13

• The types are organized as product, market, and other; no financial or team-related pivots were identified.Each pivot instance is classified under one pivot type only.Source: §4.3 · p. 13
• Market pivots account for 45.40% of 55 instances, product pivots 31%, and new types 23.60%, including market zoom-in, complete, and side-project pivots.Source: §4.3 · p. 13
• The following case descriptions detail pivot types and their corresponding triggers, including cases with multiple pivot instances.Source: §4.3 · p. 13

4.4.1 Zoom-In Pivot

• Flickr, Slack, and Voylla made product zoom-in pivots after users appreciated one feature rather than the whole product.Flickr pivoted from Game Neverending, an online multiplayer game that failed to attract customers, to a photo-sharing application after its photo-sharing tool became the game’s most popular aspect.Source: §4.4.1 · p. 13
• Negative customer reaction was the leading trigger (15), followed by inability to compete and technology challenges (5 each); investor or partner influence and feature appreciation occurred 4 times each.Technology challenges included limitations of existing technologies and the emergence of disruptive technologies.Source: §4.4.1 · p. 18
• Other triggers included unexpected product use (4), wrong timing and positive response from an unforeseen segment (3 each), and legal issues or a more successful side project (1 each).Unexpected use meant customers used a product in an unanticipated way; a side project trigger arose when customers lacked interest in the main project but were interested in the side project.Source: §4.4.1 · p. 18
• Zoom-in pivots narrowed a product or market, often when internal work exposed a stronger customer pain point or a feature attracted more use.Voylla narrowed its offerings at the content level, unlike software-feature zoom-ins such as Flickr and Slack. Pinterest pivoted from shopping app Tote after users favored sharing item lists and mobile payments were not yet sophisticated.Source: §4.4.1 · pp. 18–21

4.4.2 Technology Pivot

• Technology challenges drove Wix from a Flash-based website builder to HTML5 and Android from a camera operating system to a handset-focused mobile platform.Wix faced Flash performance problems on smartphones; Android faced a shrinking camera market alongside the emergence of smartphone technology.Source: §4.4.2 · p. 21

4.4.3 Platform Pivot

• All three sampled platform pivots moved from product to platform, with internal needs driving appMobi and StyleZen and a Facebook technology change driving SymphonyCommerce.StyleZen turned tools developed to market its shopping site into the Ahalogy Pinterest-optimization platform; SymphonyCommerce pivoted after Facebook's Open Graph changes compromised its viral growth.Source: §4.4.3 · pp. 21–22

4.4.4 Zoom-Out Pivot

• Zoom-out pivots broadened offerings when narrow services proved unscalable or the original business model required intensive investment.Elto expanded beyond small website tweaks to offer more services, while Carbon added services such as laundry pickup and became a delivery concierge after its parking model required venture-subsidized spending.Source: §4.4.4 · pp. 22–23

4.5.1 Customer Need Pivot

• Jammber pivoted toward payments and document filing after musicians’ paperwork emerged as a pain point, while ChartBeat and YouTube also pivoted amid weak or unexpected customer response.ChartBeat’s users chatted instead of using its intended browsing feature; YouTube moved from video dating to online video sharing.Source: §4.5.1 · p. 23
• Other customer-need pivots followed investor input, flawed monetization, emergent use, unscalability, or competition as startups redirected toward newly recognized demands.Examples include NowInStore’s move to marketing content, NextBigSound’s shift to music analytics, Yelp’s move to reviews, and Signpost’s response to a stronger competitor.Source: §4.5.1 · pp. 23–24

4.5.2 Customer Segment Pivot

• Customer-segment pivots shifted offerings from consumers to business or government users after price sensitivity, competition, weak traction, or newly revealed demand.Eden moved toward enterprise customers, Groupize toward group meetings, Keas toward workplace wellness, and Socrata toward government open data.Source: §4.5.2 · pp. 24–25

4.5.3 Channel Pivot

• Site59 changed its distribution channel to B2B2C after low customer acquisition, reaching end customers through airlines and travel portals.The initial minivacation-package idea had not gone viral as expected.Source: §4.5.3 · p. 25

4.5.4 Zoom-In Pivot

• Market zoom-in narrows a broad target market to a specific segment; Ignighter focused its dating site on India after finding promising user growth there.The founders examined demographic data and selected India over other countries.Source: §4.5.4 · pp. 25–26

4.6.1 Complete Pivot

• A complete pivot changes one or more of a startup’s product, target market, or financing when its initial idea is outplayed; the entrepreneurial team carries prior learning forward.Twitter moved from podcasting to messaging after Apple iTunes entered the space, while Streamline changed direction following mentor advice.Source: §4.6.1 · p. 26
• Complete pivots arose from negative customer response, legal trouble, weak traction or revenue prospects, and solving an internal problem.Examples include Seesmic’s shift after weak video engagement, Curios.me’s move after legal issues, Fab.com’s move into home goods, and Woot’s discovery of an online-shopping model.Source: §4.6.1 · pp. 26–27

4.6.2 Side Project Pivot

• A side-project pivot occurs when a parallel project, potentially serving a different market, overtakes the main project; Groupon grew from The Point’s group-buying side project.The Point had little traction and no clear revenue model, while its group-buying project attracted more users.Source: §4.6.2 · p. 27
• Life On Air shifted its focus fully to Meerkat after roughly 15,000 people used the live-streaming app in three days.Meerkat was a side project distinct from the company’s main app, Air.Source: §4.6.2 · p. 27

4.7 Multiple Pivots

• Six startups had two documented pivot instances, and the examples show pivots occurring together or at separate stages.Instagram combined a product zoom-in with a technology pivot, while RetentionScience made two separate complete pivots.Source: §4.7 · pp. 27, 29
• Instagram narrowed Burbn to photo sharing, then moved from an HTML5 browser app to an iOS app because of latency.The users particularly valued photo sharing, and the existing technology's limitations prompted the technology pivot.Source: §4.7 · pp. 27–28
• Handmake Me shifted from professional creatives to hobbyists, then replaced its reverse marketplace with a conventional marketplace after buyers made no requests.The conventional marketplace included customized buyer options and quality assurance; the founders reported that buying, selling, and revenue followed.Source: §4.7 · p. 28
• MishGuru changed direction after finding its 3D horseshoe concept was not scalable, while PayPal's shift to online payments was associated with timing and its X.com merger and eBay partnership.MishGuru's pivot involved a new challenge discovered while exploring collaborative video making; Workible's founders cited market research and mobile demand before shifting toward an instant candidate-matching platform.Source: §4.7 · pp. 28–29

5 Discussion

• Validated learning from failure is common to the studied pivot cases, and without learning from failures startups risk eventual permanent failure.The authors connect that learning to correcting startup direction under uncertain and chaotic conditions.Source: §5 · p. 29

5.1 Reflection on Pivot Types

• Customer need pivots were most common, customer segment pivots were the second most common market-related type, and product pivots included zoom-in, zoom-out, and technology changes.The study also identifies complete, side project, and market zoom-in pivots; team- and finance-related pivot cases were absent, and market zoom-in appeared only once.Source: §5.1 · pp. 29–31
• The authors describe side project and market zoom-in pivots as new types, but say market zoom-in's robustness remains untested after one observed instance.They found no cases of pivots involving business architecture, value capture, engine of growth, or social aspects, and call for broader case collections to examine them.Source: §5.1 · pp. 31–32

5.2 Reflection on Triggering Factors

• Most identified triggers were external, and negative customer reaction was the most common trigger; a flawed business model was a prominent internal trigger.Competition, technology challenges, and investor or mentor suggestions were also discussed as triggers; the authors caution that trigger lists are not exhaustive and types and triggers lack a one-to-one relationship.Source: §5.2 · pp. 32–33

5.3.1 Threats to Construct Validity

• Searching only for the term “pivot” may have missed cases described instead as changing direction or strategy.The authors note that those broader terms do not necessarily capture the specific meaning of pivot used in the study.Source: §5.3.1 · p. 33

5.3.2 Threats to Internal Validity

• Secondary accounts varied in quality and detail, although the researchers triangulated sources where possible and used researcher triangulation and a case-review protocol.Some cases relied on news writers without direct startup experience, and case detail varied across startups.Source: §5.3.2 · pp. 33–34

5.3.3 Threats to External Validity

• The online sample may not generalize broadly: cases were selected from ranked Google results, many published examples may favor successful pivots, and 37 of 49 startups were from the United States.The authors also note missing business domains and uneven case counts across pivot types and triggering factors; their aim was theoretical rather than statistical generalizability.Source: §5.3.3 · pp. 34–35

6 Conclusions and Future Work

• The case survey of 49 software startups offers an initial classification of pivot types and factors that triggered pivots.The authors position it as foundational knowledge for studying startup pivots.Source: §6 · p. 35
• Customer need was the most common pivot type, zoom-in and technology were major product pivots, and negative customer reaction and flawed business models were the leading triggers.The study introduces market zoom-in, complete, and side project pivots, and identifies a knowledge gap concerning team- and finance-related pivots.Source: §6 · p. 35
• Startup teams can use the pivot types and triggers to inform decisions, track customer reaction and product usage, and treat failure as an opportunity for validated learning.The authors say feature-usage analytics may be useful when considering product zoom-in pivots.Source: §6 · p. 35
• The identified types and triggers provide a conceptual basis for future pivot research and hypotheses about their relationships.The authors call for primary-data validation, longitudinal study, and further investigation of pivot processes, consequences, and trigger-to-pivot relationships.Source: §6 · pp. 35–36