LOS ANGELES--(BUSINESS WIRE)--Bottlenose, the first application for Trendfluence™ discovery in social streams, today announced that the company has completed a $3.6 million Series A round of venture capital financing.
The round was led by ff Venture Capital, with participation from Lerer Ventures, Transmedia, Advancit, as well as other leading funds and angel investors. The Series A financing will fund new hires in engineering, sales and marketing to scale operations for the formal entry of Bottlenose into the enterprise market this autumn.
"We are excited to have the opportunity to lead the A round, as we believe that Nova and the Bottlenose team are building a truly compelling and disruptive business," said John Frankel, Partner, ff Venture Capital. "After all, we traditionally partner with companies that are changing the way people behave, and we look forward to supporting Bottlenose with all of our internal resources as the team continues to flourish and thrive."
An early, free alpha version of Bottlenose, released in 2012, spurred interest and demand from nearly 100,000 professional marketers seeking real-time solutions for mapping trends in social networks, in a way that allowed them to see through the fog of social media. Early enterprise partners helped shape Bottlenose for enterprise use, resulting in today’s robust system for revealing Trendfluence in firehose levels of data.
Several brands and agencies—including Pepsi, FleishmanHillard, Razorfish, and DigitasLBi—leverage Bottlenose Enterprise for tracking live and emerging trends and events, directing advertising and marketing initiatives, engaging customer communities and gathering industry intelligence.
The New Science of Trendfluence™ Makes Social Listening Actionable
Bottlenose has developed a new technology for isolating Trendfluence from the noise of social streams. Trendfluence enables Bottlenose customers to proactively identify, anticipate and instigate the trends that drive their businesses.
Bottlenose applies big data cloud computing and analytics to continuously data-mine streams from social networks and enterprise data sources, to detect, visualize and monitor trends as they develop and move in real-time. As trends take shape in real-time, Bottlenose applies proprietary natural language and statistical techniques (16 pending patents) to calculate and visualize the live attention and sentiment around them.
With hundreds of millions of messages, topics, people and links analyzed to-date, and billions more being added on an ongoing basis, Bottlenose is constantly sensing the unfolding live conversation across major social networks, isolating the topics, people, issues, and content that have gathering speed, influence and shove.
Queries Return a 360 Degree View of Vital Trends Reflecting the Emotion of Your Market
The ability to detect real-time trends enables marketers to understand the emotional energy of the crowd and how that is affecting their businesses and brands, right now. It also helps enterprises discover and monitor the “unknown unknowns” on the horizon that may grow into threats, issues, or opportunities—up to hours, days, or even weeks before they are noticed by others.
Bottlenose customers gain an unprecedented ability to find and focus on the trends that matter, as or before they materialize, to inform their real-time tactics and strategies.
Major brand Fortune 500 customers are using Bottlenose to:
- Detect emerging threats and opportunities
- Inform advertising keyword buying strategies
- Direct real-time content creation and curation
- Visualize and track activity around live events
- Monitor and predict brand health and crisis management outcomes
- Conduct real-time market and opinion research
- Extract customer insights and competitive intelligence
- Cross-correlate social activity with business outcomes like stock prices, engagement, and sales
“We are thankful to have the support of forward-thinking investors and enterprise customers who share our vision and understand the growing importance of real-time discovery analytics applied to massive data streams. We’ve seen significant traction from Fortune 500s since the enterprise version went beta in January, both in volume of inbound, and deal size,” said Nova Spivack, CEO and cofounder of Bottlenose. “Social networks have created an environment where rumors, breaking news stories, and customer sentiment can spike and spread globally in minutes. Big brands are now in an arms race to proactively detect and respond to these emerging issues in real-time, instead of after the fact.”
Previously available as a free, trial application, Bottlenose is in limited release on a subscription basis to enterprise customers. General Availability of Bottlenose is slated for autumn.
For inquires related to sales, case studies, or product offerings please visit: http://bottlenose.com/pro.
Bottlenose is the first application for Trendfluence discovery in social and business data streams. Bottlenose provides an enterprise-grade dashboard for discovering, monitoring and acting on influential trends, beginning with social media communications affecting brands.
Bottlenose was founded in 2010 by serial entrepreneur, Nova Spivack, and Web technologist, Dominiek ter Heide. Bottlenose has offices in Los Angeles, California, New York City, and Amsterdam, the Netherlands.
Learn more about Bottlenose here: http://bottlenose.com/.
About ff Venture Capital:
ff Venture Capital is an institutional venture capital investor in seed-stage companies. Since 1999, our Partners have made over 180 investments in over 72 companies. Our exits include Cornerstone OnDemand (IPO, CSOD) and Quigo Technologies (sold to AOL for a reported $340m). ffVC has twenty employees based in New York and New Jersey and extensive resources dedicated to portfolio acceleration, including strategy consulting, recruiting assistance, in-house accounting services, communications and PR strategy, engineering assistance, a pool of preferred service providers and an executive portfolio community.
To learn more about ff Venture Capital, visit: http://www.ffvc.com