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Appier raises $80 million for AI that improves marketing decisions

November 26, 2019 Big Data

Getting a customer onto a company’s website to check out its products is challenging enough in itself, but getting them to complete a transaction is a whole different ball game. Last year, 75% of all ecommerce orders were abandoned before finalizing the purchase — reasons include an overly complicated checkout process, limited payment options, hidden costs, registration friction, security concerns, and more.

This is one of the problems that Taiwan-based Appier is looking to help online retailers solve. Its AI-powered platform tracks customers’ activities across a website to improve the chances of them completing a transaction.

Today, Appier announced it had raised $ 80 million in a series D round of funding from Insignia Venture Partners, HOPU-Arm Innovation Fund, TGVest Capital, Temasek’s Pavilion Capital, JAFCO Investment, and UMC Capital. This takes the company’s total funding to around $ 160 million, following its 2017 series C round of funding which saw big names including SoftBank join the fray.

Tracking

Founded in 2012, Appier uses machine learning to crunch myriad data points in real time, such as the cursor position of a mouse, how the customer taps or swipes a screen, the amount of scrolling, and more — this is then used to determine their purchase intent. In tandem, Appier can also A/B test different campaigns to determine which ones are more effective in converting a casual window-shopper into a customer, which may include customized promotional offers. This is part of a product offering it calls AiDeal, which recently launched as a result of Appier’s acquisition of Japan-based Emin.

Appier raises $80 million for AI that improves marketing decisions

Above: AiDeal allows companies to A/B test different promotions.

It’s worth noting here that the platform isn’t exclusively about getting people to buy physical goods once they’re on a website. It can also be used to proactively target people who already have an app installed on their phone. For example, a video-streaming company that offers some free shows could use Appier’s AiQua platform to test and issue push notifications or in-app messages to drive subscription signups. Similar to AiDeal, Appier’s AiQua product was the result of its acquisition of an Indian startup called QGraph last year.

Elsewhere, Appier has long offered a product it calls CrossX Advertising, which can be used by retailers to, say, deliver better-targeted ad exposures to those most likely to convert — Audi, for example, used the platform to target test-drive ads at people aged 30 and over who had previously searched online for luxury cars.

In-house

Appier said that it develops its machine learning algorithms entirely in-house rather than using an “off-the-shelf” solution, and its models are trained through ingesting data from websites, apps, customer relationship management (CRM) software, and so on, which helps improve the machine learning model over time.

“The real-world environment — unlike that of a lab — is dynamic and diverse and ‘off-the-shelf’ algorithms don’t always cope with it well,” Appier CEO and cofounder Chih-Han Yu told VentureBeat. “Our clients need to be able to use our solutions to manage many different and fast-moving scenarios — different KPIs, varying data sources, etc. This means that our scientists spend a lot of time making sure our deep learning solutions can deliver optimal performance in any situation that our clients might face.”

With another $ 80 million in the bank, Appier said that it will push ahead with global market expansion and target its technology at new industries “beyond digital marketing.”

“Our latest investment brings with it new shareholders whose growth-stage experience will help us to scale faster towards our ultimate goal of revolutionizing the way enterprises adopt and leverage AI to grow, remain competitive, and manage continuous business transformation,” Yu added.

One example Yu provided was products to help companies automate the process of building AI models, enabling them to bolster their data science capabilities without having to hire a “complete data science team,” he said.

Data decisions

Appier’s methods of tracking customers on digital properties isn’t entirely a unique approach, with the likes of Contentsquare adopting similar techniques to tell companies why their customers may be abandoning their carts before completing a purchase.

Moreover, another thing Appier and Contentsquare have in common is that they’re both tapping a growing demand for platforms that crunch large amounts of data to improve decision making — this spans far beyond retail and marketing, and into areas such as insurance and even cities’ infrastructure projects.

“Appier is riding a strong long-term trend for enterprises leveraging data to make smarter decisions,” added TGVest Capital chairman DC Cheng. “Thanks to its unique use of AI technology in the digital marketing space, Appier has been a category leader since its inception and has the opportunity to expand into new corporate functions where data-based decisions are made.”

Including its Taiwan headquarters, Appier claims 400 employees across 14 offices in 12 markets in Asia Pacific (APAC), and Yu said the company is currently looking to expand to new markets, though it wouldn’t confirm whether one of those would be the U.S.

“We are planning to look beyond our current markets and explore opportunities in other parts of the world,” Yu said. “We look forward to sharing more news on this in the coming months.”

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Big Data – VentureBeat

Convoy raises $400 million at $2.75 billion valuation to make freight trucking more efficient

November 13, 2019 Big Data

On-demand trucking and freight marketplace Convoy has raised $ 400 million in a series D round of funding co-led by Generation Investment Management and T. Rowe Price Associates, with participation from Alphabet’s CapitalG, Lone Pine Capital, Baillie Gifford, Fidelity Management and Research, and Durable Capital Partners.

The Seattle-based company has raised a chunky $ 668 million since its inception in 2015 and now claims a post-money valuation of $ 2.75 billion. Its latest cash injection follows a $ 185 million series C round led by CapitalG last September, which elevated it to the much-coveted “unicorn” club with a valuation north of $ 1 billion. Other illustrious previous investors include Salesforce CEO Marc Benioff, Dropbox CEO Drew Houston, Instagram cofounder Kevin Systrom, Jeff Bezos’ Bezos Expeditions, Y Combinator, Greylock Partners, and — because why not — Bono and The Edge from U2.

By way of a quick recap, Convoy connects shippers with carriers, matching supply with demand to ensure trucks aren’t traveling long distances with empty trailers. It’s all about optimizing and streamlining cargo loads — like Uber for trucks. In fact, Uber itself offers a similar service called Uber Freight, which it recently committed another $ 200 million to as part of a major expansion.

Countless other trucking startups have raked in big venture capital (VC) bucks — San Francisco-based KeepTruckin recently secured $ 149 million in funding from a host of big-name investors, including Alphabet’s other investment arm GV, while Next Trucking closed a $ 97 million investment.

The reason for this road freight tech boom is simple — the trucking industry generated around $ 800 billion in revenue in the U.S. alone last year, according to recent figures from the American Trucking Associations — a number that represents more than 80% of the country’s total freight bill.

“We built this company from the beginning with a focus on creating a more efficient model for connecting shippers and truckers,” explained Convoy CEO and cofounder Dan Lewis. “Trucking has historically been a zero-sum game, born from a marketplace where when one side wins, the other side loses. The magic of applying technology to this decades-old industry is that we can create a true win-win for both sides, lowering the total cost to shippers with better service while removing the hassle of wasted time and miles for truck drivers, allowing them to earn more.”

Automation

Machine learning (ML) plays a pivotal role in Convoy’s pitch — rather than relying on humans to match freight loads with trucks, the company announced earlier this year that 100% of the process is now automated in some of its biggest markets. This means that when a driver searches for a new load through the Convo app, they will be paired with the most suitable options. Moreover, Convoy’s ML smarts improve with each load as it learns from carrier behavior.

Additionally, ML plays a big role in pricing, though both parties must agree on a rate once they’ve been matched. Based on the load, route, and so on, Convoy automatically suggests a price, though carriers can counter this with a bid if they think the rate is too low.

Convoy raises $400 million at $2.75 billion valuation to make freight trucking more efficient

Above: Convoy’s carrier app

Convoy’s new gargantuan funding round will further the company’s goal of removing inefficiencies and automating processes that have historically consumed a lot of resources. This includes the recently launched Convoy Go service, a “drop-and-hook” marketplace that matches preloaded trailers with Convoy’s transport network infrastructure, reducing wait times for collecting a new load by up to two-thirds.

It’s a similar story elsewhere in the truck-tech sphere, with Uber Freight leaning heavily on machine learning as part of its offering, while upstarts such as Locus are pushing to automate logistics with supply chain platforms that help shippers optimize route planning.

Green machine

Another benefit of Convoy’s platform is helping shippers and carriers avoid “empty miles,” which can take a toll on the environment.

“Building a more efficient digital freight network also means we can dramatically reduce carbon emissions associated with empty miles, which is good for the planet,” Lewis said.

This has been a key theme to emerge across the technology industry, with the likes of Apple and Google increasingly touting their green credentials. On-demand transport giant Uber is pushing for more electric vehicles in some of its key locations, and it recently committed to powering each of its offices with renewable energy by 2025.

According to the U.S. Environmental Protection Agency (EPA), mid-weight and heavy-duty trucks accounted for 23% of all transport-based greenhouse gas emissions in 2017. And an American Transport Research Institute (ATRI) report on the operational costs of trucking found that of the 9.4 billion miles traversed annually, 20.7% involved empty loads, thought other reports — including research carried out by Convoy itself — place this figure at closer to a third.

Whatever the true figure, Convoy’s “empty mile” solution is clearly appealing to investors with commitments to both profit and ethics.

“Generation’s investment in Convoy is grounded in many years of research into the future of logistics,” said Generation Investment Management partner Joy Tuffield. “Through its use of data science, Convoy is driving the next evolution in efficiency across the industry. This is an exceptional opportunity to achieve a reduction in carbon emissions while simultaneously increasing earnings for truck drivers and increasing service quality for shippers.”

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Big Data – VentureBeat

Aurora Insight raises $18 million for sensors and AI that monitor radio frequency spectrum in near-real time

October 22, 2019 Big Data

Keeping tabs on the ever-changing global connectivity map isn’t easy, particularly for the businesses spending billions to secure wireless spectrum. It’s this gap in strategic intelligence that motivated the founding of Aurora Insight, a Washington, D.C. startup using a sensor network and machine-learned digital signals to continuously sample the full radio spectrum.

Aurora Insight has achieved a measure of success in the three years since it emerged from stealth, attracting unnamed clients across four continents. In anticipation of accelerated growth, the company today revealed that it’s raised fresh venture capital following a series A earlier this year led by Alsop Louie Partners and True Ventures. (True Ventures initially invested in 2017.) The round — which also saw participation from Tippet Venture Partners, Revolution’s Rise of the Rest Seed Fund, Promus Ventures, Alumni Ventures Group, ValueStream Ventures, and Intellectus Partners — brings its total raised to $ 18 million.

“The boundaries of wireless are being redrawn,” said cofounder and CEO Brian Mengwasser. “It’s not just about phones anymore. Buildings have become base stations, factories operate their own LTE and 5G networks, and connected cars have five integrated receivers for different networks. Our customers don’t have the time or money to deploy fleets of trucks and spectrum analyzers to determine if their wireless solutions are going to work. We enable more companies to design-to-reality and get more out of limited spectrum, which is part of everyone’s technology stack now.”

At a high level, Aurora Insight’s product suite taps a combination of maps, near-real-time radio spectrum data, and analytics to help clients identify gaps in service. Its products are designed to help network operators and carriers deploy and optimize their spectrum, and to anticipate rollouts in the course of planning for expansion.

Alsop Louie Partners and Aurora board member Gilman Louie asserts that conventional ad hoc spectrum measurement methods are “outdated” and provide only snapshots of connectivity. By contrast, Aurora Insight claims its approach reveals different patterns of spectrum occupancy (i.e., where operators have excess spectrum) and detect unused spectrum available for unlicensed use, as well as drill down to extract carrier and band information for individual cells (land areas served by at least one fixed-location transceiver) or clusters of cells.

“The reality of … techniques employed by … networks means it’s both more difficult and more important to quantify the radio spectrum,” he added. “Having the accurate and near-real-time feedback on the radio spectrum that Aurora’s technology offers could be the difference between building a … network right the first time, or having to build it twice.”

True Ventures partner and fellow Aurora Insight board member Rohit Sharma added, “Wireless spectrum is one of the most critical and valuable parts of the communications ecosystem worldwide. To date, it’s been a massive challenge to accurately measure and dynamically monitor the wireless spectrum in a way that enables the best use of this scarce commodity. Aurora’s proprietary approach gives businesses a unique way to analyze, predict, and rapidly enable the next-generation of wireless-enabled applications.”

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Big Data – VentureBeat

Healx raises $56 million to combat rare diseases with AI

October 16, 2019 Big Data

Healx, a company that said it’s using artificial intelligence (AI) to discover new drug treatments for rare diseases, has raised $ 56 million in a series B round of funding led by Atomico, with participation from Intel Capital, Balderton Capital, Global Brain, Btov Partners, Amadeus Capital Partners, and Cambridge Innovation Capital’s Jonathan Milner.

Founded out of Cambridge in 2014, Healx’s core AI Platform — Healnet — applies a range of machine learning techniques to public and proprietary data sources covering literature, clinical trials, patents, drug targets, chemical structures, symptoms, and more. Part of this involves using natural language processing (NLP) to extract insights and knowledge from all the published sources around specific diseases.

The culmination of all this data, Guilliams said, results in a knowledge graph of rare diseases which could help pharmacologists or biologists unearth effective new treatments that would otherwise be much more difficult to spot.

“We use a variety of machine learning algorithms to solve the many tasks necessary to predict drug treatments and translate them in the clinic effectively,” Healx CEO and cofounder Dr. Tim Guilliams told VentureBeat. “We use deep learning AI algorithms that predict novel connections between, for instance, drugs and disease. This is known as knowledge base completion, and relies upon our comprehensive knowledge graph of rare disease data.”

Above: Founders with the Healx team in Cambridge, U.K.

Rare diseases

While rare diseases are generally defined as diseases that impact a small percentage of people, there are up to 8,000 such diseases globally, according to the World Health Organization (WHO), affecting 400 million people globally. And the vast majority — 95% — still don’t have a FDA-approved treatment.

It’s worth stressing here that Healx isn’t setting out to develop completely new drugs, rather, it’s about getting the most value out of drugs that have already been approved, through combining them in new ways.

“We don’t develop new chemistry and molecules, we maximize the value for the existing drugs — combining them in a clever way to improve their therapeutic effect when needed,” Guilliams continued.

Prior to now, Healx had raised around $ 12 million, the bulk of which came via a $ 10 million series A round 15 months ago, and with another $ 56 million in the bank, it said that it plans to build a clinical-stage portfolio for rare diseases such as Fragile X Syndrome (FXS), a known genetic cause of autism. To help, Healx is launching a global accelerator program that will strive to find clinic-ready treatments of rare diseases within two years, a significant reduction on the decade or more it can typically take using traditional drug discovery processes.

“The current, expensive, trial-and-error-based model of drug discovery hasn’t changed in a century,” added Atomico principal and former surgeon Irina Haivas. “And it especially fails rare disease patients. 50 percent of these patients are children, many living with highly debilitating symptoms. Healx has shown that doesn’t have to be the case, by combining AI with world-class pharmacological expertise and putting patients first.”

The Rare Treatment Accelerator, as Healx’s new program is called, is intended to serve as an easy way for Healx to collaborate with clinicians and patient groups that represent people with rare diseases, and will involve reviewing shared data and resources.

“To date, it’s been families and patient groups who have had to become experts in the diseases affecting their loved ones and have often been the ones driving forward the efforts into finding new treatments,” Guilliams said. “With our unique combination of in-house R&D, industry collaborations and now the Rare Treatment Accelerator, we look forward to supporting these groups in their mission.

The AI treatment

AI and machine learning are infiltrating pretty every industry, and the drug discovery realm is no different. In the past few months alone Utah-based Recursion Pharmaceuticals took its total funding to $ 230 million with a fresh $ 121 million raise; London’s BenevolentAI secured $ 90 million, taking its total inbound investment to nearly $ 300 million; and Maryland-based Insilico Medicine locked in a further $ 37 million. As it happens, earlier this year Insilico Medicine partnered with A2A Pharmaceuticals to launch Consortium.AI, a joint venture designed to treat rare diseases with AI.

While all these companies share a common goal, vis-à-vis they’re using machines to speed up drug discovery and development, Healx is carving its niche by “maximizing the value of already-approved drugs,” rather than developing new molecules, while simultaneously focusing on rare genetic diseases.

Looking further down the line, Healx said that it’s targeting 100 rare diseases treatments by 2025.

“Because we start from safe, existing drugs, we can quickly and cheaply translate them in the clinic, which is key to Healx’s mission,” Guilliams said. “The trials for our Fragile X treatments are just the start of the impact we believe our technology is capable of having on drug discovery.”

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Big Data – VentureBeat

AI Medical Service raises $42.9 million to detect cancerous lesions from endoscope footage

October 5, 2019 Big Data

AI Medical Service, a Tokyo-based provider of AI-powered endoscopic screening products, today announced that it’s raised $ 42.9 million in a series B funding round led by Globis Capital Partners, with participation from World Innovation Lab (WIL), IGV’s Sony Innovation Fund, and undisclosed existing and previous investors. The fresh funds come on the heels of the startup’s $ 9 million funding round in August 2018, and they bring AI Medical Service’s total raised to $ 57 million.

CEO Dr. Tomohiro Tada said the influx of cash will be used to promote clinical trials and further AI Medical Service’s development pipeline, and to expand its team as the company invests in equipment and overseas growth. Specifically, it’ll target Asian nations where stomach cancer is particularly prevalent, like Singapore, Thailand, and Indonesia, and in the distant future the U.S. and Canada.

“Our company was founded on the desire to solve issues with endoscopies in the clinics. In January 2018, we published the world’s first article on AI in gastric cancer … and have since published a number of scientific articles on topics including AI in esophageal cancer, colorectal cancer, and capsule endoscopy AI,” said Tada. “In order to commercialize and deliver these products to society as soon as possible, we will make effective use of the procured funds. As our founding philosophy states, we plan to contribute to endoscopic medical treatment around the world.”

Tada, who worked as an endoscopist for 23 years, founded AI Medical Service in 2017 with the stated mission of developing systems to support endoscopic digestive exams. The average Japanese endoscopist checks more than 3,000 medical images a day, and AI Medical Service’s team conceived its tools as a way to cut down on that workload while improving the accuracy of colon and stomach cancer diagnoses — two of the three leading causes of cancer-related deaths in Japan. Troublingly, as many as a quarter of precancerous lesions are overlooked.

To reduce this, AI Medical Service’s product taps AI to analyze video feeds from endoscopes — long camera-equipped tubes that feed down the esophagus and small intestine — in real time, spotting bleeding and polyps to help doctors identify which might be cancerous. In independent tests, its models (which haven’t yet been submitted for regulatory approval) achieved 92% sensitivity in recognizing stomach cancer lesions from videos. And while they currently only evaluate still images post-procedure, AI Medical Service plans to deploy them on GPU-powered devices capable of receiving and processing live video simultaneously.

”Endoscopic treatment is a medical field that needs to be promoted in society because it leads to the improvement of diagnostic accuracy of diseases. However, the industry is facing key hurdles, including long hours required for analysis and a shortage of human resources,” said WIL general partner Masa Matsumoto. “AI Medical Service, which offers endoscopic AI to solve these problems, partners with many leading medical institutions in Japan and possesses the largest number of endoscopic images and thesis data. I believe AI Medical Service will be one of the world-leading companies, given the high barriers to entry into the market, in which there are few players and Japan has an advantage, and the company’s focus on the global market since its inception.”

Globis Capital Partners director Satoshi Fukushima added: “We foresee an irreversible trend of doctors diagnosing cancer in collaboration with AI in the near future. Supported by the world’s leading medical institutions and specialists in the field and led by experienced management, the endoscopy AI developed by AIM has huge potential to help endoscopists and patients globally.”

AI Medical Service isn’t the only startup applying computer vision to solve challenging problems in health care. There’s Aidoc, which in April raised $ 27 million in April for its AI head, chest, abdomen, and spine tests. Zebra Medical Vision spent three years developing an end-to-end computer vision system capable of diagnosing breast cancer and predicting cardiovascular disease. Paige.ai nabbed $ 25 million in February 2018 for its AI-powered cancer detection tools. And then there’s MedyMatch, a startup applying AI to medical imaging scans from stroke victims.

Also in the mix are heavy hitters like Google’s DeepMind, Google AI, and Arterys, all of which are leveraging AI to detect the presence of diseases like cancer or degenerative eye conditions. For its part, DeepMind recently partnered with the U.K.’s National Health Service to develop an algorithm that could search for early signs of blindness. And Google detailed in May an algorithm that picks out lung cancer from screening tests better than human radiologists with an average of eight years experience.

It’s a lucrative segment. According to Markets and Markets, both the cloud and on-premises computer vision in health care market could be worth $ 2.5 billion by 2027, up from $ 210 million in 2018.

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Big Data – VentureBeat

Waycare raises $7.25 million to improve city traffic using AI and big data

October 3, 2019 Big Data

Big data and machine learning have emerged as fundamental tools for companies and cities looking to unlock insights into people and places, enabled by the myriad connected devices that now permeate society.

Companies such as PredictHQ leverage data from global events to help companies like Uber and airlines forecast demand, while fellow San Francisco startup Streetlight Data aggregates mobile app data to help cities measure the flow of traffic.

In a similar vein, Israeli startup Waycare has been setting out to help city planners make better-informed traffic-management decisions. It does so by tapping historical and real-time data inputs from connected cars, telematics, road-side cameras, fleet management platforms, public transit services, construction projects, and even weather forecasts, to build a more complete picture of what’s happening on a city’s thoroughfares. Individually, these various data sets have limited use in terms of helping municipalities make planning decisions, but the sum — as the saying goes — is far greater than its parts.

When combined and applied with machine intelligence, it becomes possible to gain a deeper insight into what’s happening at street level across a city — is one particular road more congested than usual, and if so, what’s causing it? That is what Waycare is setting out to help cities determine.

Waycare raises $7.25 million to improve city traffic using AI and big data

Above: Waycare: what it looks like (annotations)

From the city’s perspective, Waycare’s interface shows a live map of incidents in a specific area, with connected cameras enabling the user to switch to a live view of a scene if a crash or other event is detected. This could expedite response times for emergency vehicles, for example, while also enabling better communication between various agencies that can see what’s happening in real time.

Above: Waycare platform view with multiple data sources

All this data can also be used to inform key decisions at the city level. Correlations can be drawn between traffic light changes and congestion, for example, and neighborhoods that are more prone to erratic driving might be flagged for additional speed cameras.

Growth

Founded out of Israel in 2016, Waycare also has an office in Los Angeles, where it has managed most of its state rollouts across the U.S. These include Southern Nevada, where it launched in 2017, followed by Florida and Ohio, among others. To help expand its platform into more markets globally, Waycare today announced that it has raised $ 7.25 million in a series A round of funding led by SJF Ventures, with participation from Next Gear Ventures, Spider Capital, Innogy, Goldbell, Zymestic Solutions, UpWest, and Janom.

“Over the past decade, we’ve seen a transformation not only in the amount of data coming from various mobility modes, such as connected vehicles, but also in the advancement of artificial intelligence technologies to interpret and learn from data,” said SJF Ventures partner Dan Geballe. “Waycare is a prime example of how AI can be deployed in the public sector to better leverage data and existing infrastructure to improve outcomes — in this case reducing traffic, injuries, and deaths on the roads.”

Waycare has secured a number of notable partnerships since its inception, including with fellow Israeli company Waze, which is owned by Google. The duo entered a data-sharing pact last year, giving Waycare access to Waze’s crowdsourced navigation data while allowing Waze to access data from Waycare, such as road hazards, disruptions, or other unforeseen incidents. Waycare has also previously teamed up with Ticketmaster, which contributes data such as expected crowd numbers for major events that can have a big impact on traffic.

“Transportation agencies across the world are grappling with the burden of improving traffic safety and congestion that affects their city’s residents, while at the same time meeting the demands of rapid changes in the mobility sector,” added Waycare CEO Noam Maital. “Waycare is fortunate to be at the crossroads of serving the public sector while partnering with the wider mobility ecosystem to help cities and states build the next generation of transportation operating systems.”

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Big Data – VentureBeat

Recurly raises $19.5 million to boost subscription revenue with machine learning

September 27, 2019 Big Data

Subscriptions are big business. McKinsey & Company estimates that 15% of online shoppers have signed up for one or more product subscriptions within the past several years, and it pegs the growth rate of the recurring ecommerce market from 2013 to 2018 at over 100% a year. A separate report by cloud-based subscription management platform Zuora, meanwhile, found that the subscription economy ticked up more than 300% between 2012 and 2018.

Perhaps it’s no wonder that startups like San Francisco-based Recurly are raking in the venture capital dough. The developer behind the recurring billing service of the same name today announced that it’s raised $ 19.5 million in funding led by F-Prime Capital, with participation from Polaris Partners, Greycroft, and Silicon Valley Bank. The fresh capital brings its total raised to nearly $ 40 million following previous raises totaling $ 19.6 million, and CEO Dan Burkhart said it’ll fuel product research and development in addition to the expansion of Recurly’s sales and marketing divisions.

As part of this latest round, Recurly brought two new executives onto its team. Former platform and architecture engineer Tony Allen steps in as chief technology officer, and Shane Oren joins as SVP of sales after stints at NetSuite and Nice Satmetrix.

“The subscription commerce market is undergoing a dramatic shift as consumer purchasing behaviors are quickly evolving to expect flexible, pay-as-you-go models from their favorite brands,” said Burkhart in a statement. “We are excited about the role that our company plays in helping these high growth brands to innovate with subscriptions in a way that delights their customers while delivering breakout results against their competitors.”

For the uninitiated, Recurly taps machine learning algorithms trained on hundreds of transactions to improve billing continually, driving a claimed monthly revenue boost of 12% on average. From within its cloud dashboard, clients can create plans with a variety of billing models (e.g., fixed, seat-based, hybrid, and usage-based) and frequencies, and extend to subscribers the ability to purchase single or multiple plans and combine them with one-time products or services.

Recurly automatically prorates billing changes that result from subscriber upgrades, along with those arising from downgrades, refunds, or service credits. Moreover, its automated tools generate invoices and provide data supporting monthly close processes, all while automatically delivering emails to subscribers regarding charges and changes related to their subscriptions (including expired payment methods).

Recurly supports over a dozen payment gateways including Amazon Pay, Stripe, PayPal, Chase, Braintree, and CyberSource, and it calculates and collects sales tax, VAT, or GST (along with compliance documentation) for any charge in the U.S., Canada, Europe, Australia, New Zealand, Israel, and South Africa. It additionally supports things like discounted price promotions and special offers, plus gift subscriptions that subscribers can purchase for friends and family.

Where security is concerned, Recurly ships with several permissions groups preconfigured. Only administrators have the ability to manage user roles and permissions or allow read-only access to databases. And on the customer-facing side of the equation, Recurly’s fraud mitigation models automatically identify and address potentially illicit transactions, minimizing chargebacks and preventing card-not-present, account takeover, and account creation fraud.

Recurly’s analytics suite provides an overview of subscriber, plan, and revenue data, along with KPIs and short- and long-tail trends. The date ranges, intervals, and even currency can be customized, as well as the prominence of metrics like net billings, subscriber retention, monthly recurring revenue, recover revenue, and average revenue per customer.

Helpfully, this and other data collected by Recurly can be scheduled to export automatically into third-party business systems.

Speaking of integrations, Recurly plays nicely with a range of customer management platforms and accounting suites, including those from Salesforce, Netsuite, Intuit’s Quickbooks, Xero, Avalara, Kount, Vertex, and Okta. As for services that aren’t natively supported, it offers a robust API and full-featured developer hub.

Recurly raises $19.5 million to boost subscription revenue with machine learning

Recurly says that since its launch in 2010, it’s deployed subscription billing for thousands of companies across 42 countries, including AMC Networks, AccuWeather, AllTrails, Asana, Asana, BarkBox, CBS Interactive, Canary, Cinemark, FabFitFun, Insightly, JW Player, Loot Crate, Pipedrive, Quizlet, Showtime, Signpost, Sittercity, Sling TV, Sprout Social, and Twitch. To date, the company claims it’s recovered over $ 450 million in revenue and facilitated billions of credit card transactions.

Recurly competes to a degree with the publicly traded Zuora, Chargebee, and ReCharge, which offer comparable subscription management solutions. But F-Prime Capital senior VP Shervin Ghaemmaghami argues that Recurly’s momentum can’t be beat.

“Recurly has provided the billing automation platform behind the success of thousands of major subscription success stories,” said Ghaemmaghami. “We see a tremendous opportunity in building upon this leadership to power the success of the rapidly growing subscription commerce market.”

Recurly currently has 175 employees across offices in San Francisco, Boulder, and New Orleans.

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Big Data – VentureBeat

TigerGraph raises $32 million for graph database tools

September 25, 2019 Big Data

TigerGraph, a Redwood City, California-based software developer providing a suite of enterprise graph database tools, today announced that that it’s secured $ 32 million in series B funding led by private equity firm SIG. The infusion of capital comes after a $ 31 million series A in September 2017 and nearly doubles the startup’s haul to $ 60 million, as it continues to attract marquee clients like Zillow, Intuit, Amgen, Wish, Kickdynamic, and China Mobile.

“Today’s vast amount of data, together with increasingly powerful processing capabilities enabled by the cloud, means it is now possible to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries,” said CEO and founder Yu Xu, a former IBM, Teradata, and Twitter engineer and systems architect who founded TigerGraph in 2011. “The funding will fuel a new wave of growth and expansion for TigerGraph to make deep link analysis accessible to virtually every organization in the world and help users unleash the power of interconnected data.”

Graph databases and graph-oriented databases leverage graph structures for semantic queries, with nodes, edges, and properties that store and represent data. They’re a type of non-relational technology that depicts the relationships connecting various entities (like two people in a social network, for instance) and that can analyze interconnected data.

TigerGraph says its cloud-hosted and pay-as-you-go service — which is now generally available — simplifies graph management and configuration organization-wide, even for departments lacking the technical prowess to produce graph databases from scratch. To this end, its three-step graph-generating tool ostensibly gets apps up and running within minutes to hours. Plus, TigerGraph delivers a dozen starter kits addressing use cases like fraud detection, personalized real-time recommendation, computation, explainable AI, machine learning, and supply chain analysis.

TigerGraph’s eponymous TigerGraph Cloud scales up to tens of terabytes, 100 billion vertices, and 600 billion edges on the high end. It can support with a single machine more than 100,000 real-time deep link analytics queries and 50GB to 150GB of data per second. On a cluster of 20 commodity machines, it’s capable of streaming over 2 billion daily events in real time.

TigerGraph’s SQL-like graph query language enables ad-hoc data exploration and analysis, while its architecture makes use of compression to minimize memory overhead. Graphs are structured such that vertices and edges act as parallel storage and computation units, each of which can hold any amount of arbitrary information. This allows TigerGraph Cloud to run multiple engines hosting graphs with different partitioning algorithms, queries to which a front-end server can automatically route based on type.

“At Kickdynamic we know that compelling, individualized experiences are the most effective way to create customer loyalty and drive revenue,” said Kickdynamic chief product officer Gabriele Corti. “Having tried various other solutions, we found that TigerGraph offered the best combination of performance and advanced, real-time, analytical capabilities. TigerGraph’s scalable graph database will enhance our platform and enable us to continue to achieve our vision of delivering advanced personalization in email.”

Markets and Markets anticipates the graph database market will reach $ 2.4 billion by 2023 from $ 821.8 million in 2018, and analysts at Gartner expect that enterprise graph processing and graph databases will grow 100% annually through 2022. Startups like Neo4j, MongoDB, Cambridge Semantics, DataStax, and others have risen to meet the need, in addition to incumbents like Microsoft and Oracle. Even Amazon threw its hat in the ring in November 2017 with the launch of Neptune, a fully managed graph database powered by its Amazon Web Services division.

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Big Data – VentureBeat

Acronis raises $147 million for tools that back up, protect, and restore data

September 18, 2019 Big Data

Data recovery juggernaut Acronis today announced it’s nabbed $ 147 million in an investment round led by Goldman Sachs at a valuation of over $ 1 billion, following on the heels of angel, debt, and later-stage raises from March 2014 to May 2016. The capital infusion brings Singapore- and Switzerland-based Acronis’ total raised to well over $ 150 million, which founder and CEO Serguei Beloussov said will be used to expand its engineering team, build additional data centers, grow its North America business, and pursue acquisitions.

It’s been a banner year for Acronis, which reported 20% business growth in 2018 and which is on track to notch over 30% growth (and over 100% on the Cyber Cloud side of the business) by 2020. It recently launched the Acronis Cyber Platform, a holistic cloud-hosted solution that enables third-parties to customize, extend, and integrate its products. And in April, Acronis announced a partnership with Acronis SCS, an independent U.S. sister company dedicated to providing secure backup, data protection, and cyber protection services to federal, state and local government, education, and nonprofit organizations.

“We are excited about Goldman Sachs‘ investment,” said Beloussov. “The investment round led by Goldman Sachs will help us to fast-track the product development through acquisitions of companies and additional resources, and accelerate the growth.”

Acronis raises $147 million for tools that back up, protect, and restore data

Above: Acronis True Image.

Image Credit: Acronis

Acronis traces its origins to Parallels corporate parent SWsoft, where Beloussov, Ilya Zubarev, Stanislav Protassov, and Max Tsyplyaev founded it as a separate division in 2001. Acronis spun out in 2003, after which it pivoted focus from disk partitioning and boot loader utilities to backup and disaster recovery software based on disk imaging technology. Over the next decade or so, it acquired three firms — BackupAgent (which specialized in cloud backup), nScaled (disaster recovery), and GroupLogic (enterprise data transformation and storage) — and it launched a global partner program, prior to which it founded an R&D wing in Acronis Labs.

Acronis’ bread-and-butter segments are backup, disaster recovery, secure file access, sync and share, and partitioning, with clients ranging from single-license home users to large enterprises. True Image, the backup software by which Acronis is perhaps best known, uses virtualization and machine learning techniques to mirror images, clone disks, provide blockchain data notarization, and scan for malware in copied files. Acronis claims that last year, its AI-powered defenses defeated more than 400,000 attacks including cryptomining, in which a system’s resources are quietly diverted to generate cryptocurrency tokens.

As for the Acronis Cloud platform, it encompasses backups, disaster recovery, and file synchronization with a selected set of cloud apps curated by service providers. Plan subscribers can perform local and offsite cloud backup of disks, servers, data, or entire mobile devices to data centers compliant with certifications, audits, and standards including SOC-2, the Health Insurance Portability and Accountability Act (HIPPA), and the Payment Card Industry Data Security Standard (PCI DSS).

Acronis Backup and Acronis Backup Advanced, two additional products in Acronis’ disk-based backup and recovery suite, actively protect files from unauthorized modification and encryption while minimizing process disruption to a few seconds. Through a centralized management dashboard, admins can back up to virtually any kind of storage (across over 20 platforms) and convert backups into a set of virtual machine files ready to run on various hypervisors.

Above: One of Acronis’ U.S. offices.

Image Credit: Acronis

Aside from backup software, Acronis also provides Disk Director, a shareware app that partitions machines and allows them to run multiple operating systems. It’s able to format drives with system file in a range of storage formats, including FAT16, FAT32, NTFS, Ext3, and SWAP, and it can create a single, logical volume from the unallocated space across up to 32 physical disks.

Disk Director joins Acronis Snap Deploy, which creates a standard machine configuration that can be deployed across up to hundreds of live Windows computers simultaneously. Acronis Files Advanced secures access to files that are synced between devices. And Acronis Access Connect enables Mac users can connect to and mount directories on a Windows file server just as native Apple Filing Protocol volumes.

Acronis occupies a data backup and recovery market anticipated to be worth $ 11.59 billion by 2022, according to Markets and Markets. It competes to a degree with San Francisco-based Rubrik, which has raised $ 553 million in venture capital to date for its live data access and recovery offerings; Clumio, which raked in $ 51 million for its cloud-hosted backup and recovery tools; and Cohesity, which bills itself as the industry’s first hyperconverged secondary storage for backup, development, file services, and analytics. That’s not to mention data recovery juggernaut Veeam, which now serves 80% of the Fortune 500 and 58% of the Global 5000.

But vice president of Goldman Sachs Growth Holger Staude asserts that Acronis has rivals beat when it comes to install base and availability. The company counts more than 5 million consumers and 500,000 businesses among its client roster, including 80% of the Fortune 1000 companies. Moreover, Acronis’ products are available through 50,000 partners and service providers in over 150 countries in more than 30 languages.

“We are excited to invest in Acronis at this stage of rapid growth,” said Goldman Sachs Growth vice president Holger Staude. “The traditional backup and data protection market is changing due to an innovative solution delivered efficiently by Acronis Cyber Protection through a vast channel of service providers.”

Acronis has more than 1,300 employees in 18 countries, the bulk of whom are based in Singapore, Bulgaria, and Arizona. Acronis Labs is based in the U.S. and Singapore, and Acronis has data centers internationally including in France, Singapore, Japan, and Germany.

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Big Data – VentureBeat

Panoramic raises $35 million to unify and model marketing data

September 13, 2019 Big Data

Several years ago, a trio of entrepreneurs, Peter Muzzonigro, Johnny Wong, and Bryan Baum — the last of whom sold his first two companies (Blue Vision Labs and Represent.com) for over $ 100 million each — set out to tackle an enduring dilemma: how best to unify disparate marketing toolsets and analytics data without disrupting existing workflows. After spending the better part of months developing a solution — Panoramic — and quietly launching it in 2018, they embarked on an ambitious customer acquisition strategy, which netted them the business of brands including Sweetgreen, Sony Home Entertainment, TruConnect, MGM, Blumhouse Productions, and Cha Cha Matcha. Now, with fresh funding in the bank from growth equity investors, they’re gearing up for growth globally.

Panoramic today emerged from stealth with $ 35 million in funding from TPG Growth’s Affinity Group and others, which Muzzonigro said will be used to grow existing markets (chiefly entertainment, ecommerce, technology, quick-service restaurants, and digital media agencies), deepen brand and agency partnerships, and invest in AI-powered solutions designed for marketing analytics. “Marketers were expected to map and model their own data — effectively needing to understand how to code to understand the value of their data,” said Muzzonigro. “Panoramic solves that problem by providing marketers with a specially built platform that requires no coding and allows time-to-value to occur in days or weeks versus months.”

Panoramic’s approach combines automation with the domain knowledge of in-house data scientists and analysts, enabling marketers to build customized and holistic dashboards for data analysis, benchmarking, and more. Its cloud-hosted tools ingest and map marketing data into useful insights, using an API to tie in systems already in active use. Its collaborative chat feature eliminates the need to manually export, attach, and send reports to various groups and threads, and its conversational AI surfaces key statistics and trends likely to be of value.

Team members and managers can set and measure goals within Panoramic, or leverage its recommended goals and objectives instead. Furthermore, they can track goals from any of the dozens of third-party apps and services with which Panoramic integrates, including AppNexus, Google Analytics, Marketo, HubSpot, Salesforce Sales Cloud, Tune, and Zendesk.

Panoramic occupies a digital marketing software segment that’s anticipated to be worth $ 105.28 billion by 2025. The competition is fierce — incumbents like Adobe, IBM, and Salesforce offer robust data modeling and analytics solutions, as do startups like ConDati (which raised $ 4.75 million in May), People.ai ($ 30 million in October), and 6sense ($ 27 million in April). But according to clients like 20th Century Fox, the extensibility of Panoramic’s offering helps it stand out in a crowded field.

“The Panoramic platform knew exactly what information my team and I needed even before we did. It literally found the needle in the haystack and brought it to our attention, enabling us to shift the labor of building reports to thinking through and solving our marketing problems before there were any negative impacts on our campaign performance,” said Julie Rieger, former chief data strategist at Panoramic client 20th Century Fox. “We tried other tools but they couldn’t get us anywhere near the value that Panoramic delivered. We even attempted building our own solution in-house, but quickly realized we’d need an entire team to maintain a platform of this magnitude.”

Panoramic is headquartered in Los Angeles, with global offices in the U.S. and Europe.

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Big Data – VentureBeat

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