analytics
Auto Added by WPeMatico
5 ways AI can help mitigate the global shipping crisis
With the fourth quarter now upon us, every industry faces a challenge in managing a holiday production calendar that will deliver the goods. The key for startups looking to defend the quarter from disruptions is to adopt a proactive, data-driven approach to inventory management.
Here are five methods we’ve been counseling clients to adopt:
- Use data and analytics to identify and map out the inventory being affected by the global shipping crisis. If you don’t have the data about what is on a ship transporting your materials, then use this crisis as an opportunity to justify prioritizing supply chain digital transformation with data, IoT and advanced analytics (e.g., machine learning and simulation). You need to know the location of your goods all times if you are going to successfully gauge what impact a shortage will have on your operation.
Ultimately, AI will help startups understand how myriad disruptions affect their supply chain so they can better respond with a Plan B when the unthinkable happens.
- If you don’t have the data readily available, then you need to partner with a vendor and use a secure environment to share second-party data to deliver AI-driven actionable insights on the business impact on all parties involved, from startup to retailer to the consumer.
- Simulate and forecast the impact of these supply-side issues on the demand side. Conduct scenario planning exercises and inform critical business decisions. If this ability is not in place, an emergency like a pandemic, civil unrest or an uncontrollable rate hike will wreak havoc on your business plan. Use this situation as an opportunity to put a disaster management program in place to prepare for the potential risks.
Powered by WPeMatico
FullStory raises $103M at a $1.8B valuation to combat rage clicks on websites and apps
Even with all the years of work that have been put into improving how screen-based interfaces work, our experiences with websites, mobile apps and any other interactive service you might use still often come up short: we can’t find what we want, we’re bombarded with exactly what we don’t need or the flow is just buggy in one way or another.
Now, FullStory, one of the startups that’s built a platform to identify when all of the above happens and provide suggestions to publishers for fixing it — it’s obsessed enough with the issue that it went so far as to trademark the phrase “Rage Clicks”, the focus of its mission — is announcing a big round of funding, a sign of its success and ambitions to do more.
The Atlanta-based company has closed a Series D round of $103 million, an oversubscribed round that actually was still growing between me interviewing the company and publishing this story (when we talked last week the figure was $100 million). Permira’s growth fund — which has previously invested in other customer experience startups like Klarna and Nexthink — is leading this round, with previous investors Kleiner Perkins, GV, Stripes, Dell Technologies Capital, Salesforce Ventures and Glynn Capital also participating.
FullStory, which has raised close to $170 million to date, has confirmed that the investment values the company at $1.8 billion.
Scott Voigt, FullStory’s founder and CEO, tells me that FullStory currently has some 3,100 paying customers on its books across verticals like retail, SaaS, finance and travel (customers include Peloton, the Financial Times, VMware and JetBlue), which collectively are on course to rack up more than 15 billion user sessions this year — working out to 1 trillion interactions involving clicks, navigations, highlights, scrolls and frustration signals. It says that annual recurring revenue has to date risen by more than 70% year-on-year.
The plan now will be to continue investing in R&D to bring more real-time intelligence into its products, “and pass those insights on to customers,” and also to “move more aggressively into Europe and Asia Pacific,” he added.
FullStory competes with others like Glassbox and Decibel, although it also claims its tools have more presence on websites than its three biggest competitors combined.
Working across different divisions like product, customer success and marketing, and engineering, FullStory uses machine learning algorithms to analyze how people navigate websites and other digital interfaces.
If approved as part of the “consent gate” you might encounter because of, say, GDPR regulations, it then tracks things like when people are clicking in areas excessively over a short period of time because of delays (the so-called “rage clicks”); or when a click leads nowhere because of, for example, a blip in a piece of JavaScript; or when a person is just scrolling or moving their mouse or cursor or finger in a frustrated (fast) way — again with little or no subsequent activity (or activity from the customer ceasing altogether) resulting from it. It doesn’t use — nor does it have plans to — use eye tracking, or anything like sentiment analysis around data that customers put into, say, customer response windows.
FullStory then packages up the insights that it does collect into data streams that can be used with various visualization tools (having Salesforce as a strategic backer is interesting in this regard, given that it owns Tableau), or spreadsheets, or whatever a customer chooses to put them into. While it doesn’t offer direct remediation (perhaps an area it could tackle in the future), it does offer suggestions for alternative actions to fix whatever problems are arising.
Image Credits: FullStory (opens in a new window)
Part of what has given FullStory a big boost in recent times (this round is by far the biggest fundraise the company has ever done) is the fact that, in today’s world, digital business has become the centerpiece of all business. Because of COVID-19 and the need for social distancing that have taken away some of the traffic of in-person experiences like going to stores, organizations that have natively or built experiences online are seeing unprecedented amounts of traffic; and they are now joined by organizations that have shifted into digital experiences simply to stay in business.
All of that has contributed to a huge amount of content online, and a big shift in mindset to making it better (and in the most urgent of cases, even more basically, simply usable), and that has resulted in the stars aligning for companies like FullStory.
“The category was so nascent to begin with that we had to explain the concept to customers,” Voigt told me of the company’s early days, where selling meant selling would-be customers on to the very idea of digital experience insights. “But digital experience, in the wake of COVID-19, suddenly mattered more than it ever has before, and the continued amount of inbound interest has been afterburner for us.” He noted that demand is increasing among mid-market and enterprise organizations, and something that has also helped FullStory grow is the general movement of talent in the industry.
“Our customers tend to take their tools with them when they change their jobs,” he said. Those tools include FullStory’s analytics.
The evolution of bringing more AI into the world of basically structuring what might otherwise be unstructured data has been a big boost to the world of analytics, and investors are interested in FullStory because of how it’s taken that trend and grown its business on top of it.
“We are very excited to partner with the FullStory team as they continue to expand and build a truly extraordinary technology brand that improves the digital experience for all stakeholders,” said Alex Melamud, who led the transaction on behalf of Permira Growth, in a statement.
“Traditional analytics have been upended by AI- and ML-enabled approaches that can instantly uncover nuanced patterns and anomalies in customer behavior,” said Bruce Chizen, a senior advisor at Permira, in a statement. “Leveraging both structured and unstructured data, FullStory has rapidly established itself as the market and technology leader in DXI and is now the fastest-growing company in the category and the de facto system of record for all digital experience data.” Chizen is joining the FullStory Board with this round.
Powered by WPeMatico
Firebolt raises $127M more for its new approach to cheaper and more efficient Big Data analytics
Snowflake changed the conversation for many companies when it comes to the potentials of data warehousing. Now one of the startups that’s hoping to disrupt the disruptor is announcing a big round of funding to expand its own business.
Firebolt, which has built a new kind of cloud data warehouse that promises much more efficient, and cheaper, analytics around whatever is stored within it, is announcing a major Series B of $127 million on the heels of huge demand for its services.
The company, which only came out of stealth mode in December, is not disclosing its valuation with this round, which brings the total raised by the Israeli company to $164 million. New backers Dawn Capital and K5 Global are in this round, alongside previous backers Zeev Ventures, TLV Partners, Bessemer Venture Partners and Angular Ventures.
Nor is it disclosing many details about its customers at the moment. CEO and co-founder Eldad Farkash told me in an interview that most of them are U.S.-based, and that the numbers have grown from the dozen or so that were using Firebolt when it was still in stealth mode (it worked quietly for a couple of years building its product and onboarding customers before finally launching six months ago). They are all migrating from existing data warehousing solutions like Snowflake or BigQuery. In other words, its customers are already cloud-native, Big Data companies: it’s not trying to proselytize on the basic concept but work with those who are already in a specific place as a business.
“If you’re not using Snowflake or BigQuery already, we prefer you come back to us later,” he said. Judging by the size and quick succession of the round, that focus is paying off.
The challenge that Firebolt set out to tackle is that while data warehousing has become a key way for enterprises to analyze, update and manage their big data stores — after all, your data is only as good as the tools you have to parse it and keep it secure — typically data warehousing solutions are not efficient, and they can cost a lot of money to maintain.
The challenge was seen firsthand by the three founders of Firebolt, Farkash (CEO), Saar Bitner (COO) and Ariel Yaroshevich (CTO) when they were at a previous company, the business intelligence powerhouse Sisense, where respectively they were one of its co-founders and two members of its founding team. At Sisense, the company continually came up against an issue: When you are dealing in terabytes of data, cloud data warehouses were straining to deliver good performance to power their analytics and other tools, and the only way to potentially continue to mitigate that was by piling on more cloud capacity. And that started to become very expensive.
Firebolt set out to fix that by taking a different approach, rearchitecting the concept. As Farkash sees it, while data warehousing has indeed been a big breakthrough in Big Data, it has started to feel like a dated solution as data troves have grown.
“Data warehouses are solving yesterday’s problem, which was, ‘How do I migrate to the cloud and deal with scale?’” he told me back in December. Google’s BigQuery, Amazon’s RedShift and Snowflake are fitting answers for that issue, he believes, but “we see Firebolt as the new entrant in that space, with a new take on design on technology. We change the discussion from one of scale to one of speed and efficiency.”
The startup claims that its performance is up to 182 times faster than that of other data warehouses with a SQL-based system that works on academic research that had yet to be applied anywhere, around how to handle data in a lighter way, using new techniques in compression and how data is parsed. Data lakes in turn can be connected with a wider data ecosystem, and what it translates to is a much smaller requirement for cloud capacity. And lower costs.
Fast forward to today, and the company says the concept is gaining a lot of traction with engineers and developers in industries like business intelligence, customer-facing services that need to parse a lot of information to serve information to users in real time and back-end data applications. That is proving out what investors suspected would be a shift before the startup even launched, stealthily or otherwise.
“I’ve been an investor at Firebolt since their Series A round and before they had any paying customers,” said Oren Zeev of Zeev Ventures. “What had me invest in Firebolt is mostly the team. A group of highly experienced executives mostly from the big data space who understand the market very well, and the pain organizations are experiencing. In addition, after speaking to a few of my portfolio companies and Firebolt’s initial design partners, it was clear that Firebolt is solving a major pain, so all in all, it was a fairly easy decision. The market in which Firebolt operates is huge if you consider the valuations of Snowflake and Databricks. Even more importantly, it is growing rapidly as the migration from on-premise data warehouse platforms to the cloud is gaining momentum, and as more and more companies rely on data for their operations and are building data applications.”
Powered by WPeMatico
Enterprise AI platform Dataiku launches managed service for smaller companies
Dataiku is going downstream with a new product today called Dataiku Online. As the name suggests, Dataiku Online is a fully managed version of Dataiku. It lets you take advantage of the data science platform without going through a complicated setup process that involves a system administrator and your own infrastructure.
If you’re not familiar with Dataiku, the platform lets you turn raw data into advanced analytics, run some data visualization tasks, create data-backed dashboards and train machine learning models. In particular, Dataiku can be used by data scientists, but also business analysts and less technical people.
The company has been mostly focused on big enterprise clients. Right now, Dataiku has more than 400 customers, such as Unilever, Schlumberger, GE, BNP Paribas, Cisco, Merck and NXP Semiconductors.
There are two ways to use Dataiku. You can install the software solution on your own, on-premise servers. You can also run it on a cloud instance. With Dataiku Online, the startup offers a third option and takes care of setup and infrastructure for you.
“Customers using Dataiku Online get all the same features that our on-premises and cloud instances provide, so everything from data preparation and visualization to advanced data analytics and machine learning capabilities,” co-founder and CEO Florian Douetteau said. “We’re really focused on getting startups and SMBs on the platform — there’s a perception that small or early-stage companies don’t have the resources or technical expertise to get value from AI projects, but that’s simply not true. Even small teams that lack data scientists or specialty ML engineers can use our platform to do a lot of the technical heavy lifting, so they can focus on actually operationalizing AI in their business.”
Customers using Dataiku Online can take advantage of Dataiku’s pre-built connectors. For instance, you can connect your Dataiku instance with a cloud data warehouse, such as Snowflake Data Cloud, Amazon Redshift and Google BigQuery. You can also connect to a SQL database (MySQL, PostgreSQL…), or you can just run it on CSV files stored on Amazon S3.
And if you’re just getting started and you have to work on data ingestion, Dataiku works well with popular data ingestion services. “A typical stack for our Dataiku Online Customers involves leveraging data ingestion tools like FiveTran, Stitch or Alooma, that sync to a cloud data warehouse like Google BigQuery, Amazon Redshift or Snowflake. Dataiku fits nicely within their modern data stacks,” Douetteau said.
Dataiku Online is a nice offering to get started with Dataiku. High-growth startups might start with Dataiku Online as they tend to be short on staff and want to be up and running as quickly as possible. But as you become bigger, you could imagine switching to a cloud or on-premise installation of Dataiku. Employees can keep using the same platform as the company scales.
Powered by WPeMatico
Productivity startup Time is Ltd. raises $5.6M to be the ‘Google Analytics for company time’
Productivity analytics startup Time is Ltd. wants to be the Google Analytics for company time. Or perhaps a sort of “Apple Screen Time” for companies. Whatever the case, the founders reckon that if you can map how time is spent in a company, enormous productivity gains can be unlocked and money better spent.
It’s now raised a $5.6 million late-seed funding round led by Mike Chalfen, of London-based Chalfen Ventures, with participation from Illuminate Financial Management and existing investor Accel. Acequia Capital and former Seal Software chairman Paul Sallaberry are also contributing to the new round, as is former Seal board member Clark Golestani. Furthermore, Ulf Zetterberg, founder and former CEO of contract discovery and analytics company Seal Software, is joining as president and co-founder.
The venture is the latest from serial entrepreneur Jan Rezab, better known for founding SocialBakers, which was acquired last year.
We are all familiar with inefficient meetings, pestering notifications chat, video conferencing tools and the deluge of emails. Time is Ltd. says it plans to address this by acquiring insights and data platforms such as Microsoft 365, Google Workspace, Zoom, Webex, MS Teams, Slack and more. The data and insights gathered would then help managers to understand and take a new approach to measure productivity, engagement and collaboration, the startup says.
The startup says it has now gathered 400 indicators that companies can choose from. For example, a task set by The Wall Street Journal for Time is Ltd. found the average response time for Slack users versus email was 16.3 minutes, comparing to emails which was 72 minutes.
Chalfen commented: “Measuring hybrid and distributed work patterns is critical for every business. Time Is Ltd.’s platform makes such measurement easily available and actionable for so many different types of organizations that I believe it could make work better for every business in the world.”
Rezab said: “The opportunity to analyze these kinds of collaboration and communication data in a privacy-compliant way alongside existing business metrics is the future of understanding the heartbeat of every company — I believe in 10 years time we will be looking at how we could have ignored insights from these platforms.”
Tomas Cupr, founder and Group CEO of Rohlik Group, the European leader of e-grocery, said: “Alongside our traditional BI approaches using performance data, we use Time is Ltd. to help improve the way we collaborate in our teams and improve the way we work both internally and with our vendors — data that Time is Ltd. provides is a must-have for business leaders.”
Powered by WPeMatico
June makes product analytics more accessible
Meet June, a new startup that wants to make it easier to create analytics dashboards and generate reports even if you’re not a product analytics expert. June is built on top of your Segment data. Like many no-code startups, it uses templates and a graphical interface so that non-technical profiles can start using it.
“What we do today is instant analytics and that’s why we’re building it on top of Segment,” co-founder and CEO Enzo Avigo told me. “It lets you access data much more quickly.”
Segment acts as the data collection and data repository for your analytics. After that, you can start playing with your data in June. Eventually, June plans to diversify its data sources.
“Our long-term vision is to become the Airtable of analytics,” Avigo said.
If you’re familiar with Airtable, June may look familiar. The company has built a template library to help you get started. For instance, June helps you track user retention, active users, your acquisition funnel, engagement, feature usage, etc.
Image Credits: June
Once you pick a template, you can start building a report by matching data sources with templates. June automatically generates charts, sorts your user base into cohorts and shows you important metrics. You can create goals so that you receive alerts in Slack whenever something good or bad is happening.
Advanced users can also use June so that everyone in the team is using the same tool. They can create custom SQL queries and build a template based on those queries.
The company raised a seed round of $1.85 million led by Point Nine. Y Combinator, Speedinvest, Kima Ventures, eFounders and Base Case also participated, as well as several business angels.
Prior to June, the startup’s two co-founders worked for Intercom. They noticed that the analytics tool was too hard to use for many people. They didn’t rely on analytics to make educated decisions.
There are hundreds of companies using June every week and that number is growing by 10% per week. Right now, the product is free but the company plans to charge based on usage.
Image Credits: June
Powered by WPeMatico
This crypto monitoring startup — ‘We’re bomb-sniffing dogs’ — just raised Series A funding
Solidus Labs, a company that says its surveillance and risk-monitoring software can detect manipulation across cryptocurrency trading platforms, is today announcing $20 million in Series A funding. It’s pretty great timing, given the various signals coming from the U.S. government just last week that it’s intent on improving its crypto monitoring efforts — such as the U.S. Treasury’s call for stricter cryptocurrency compliance with the IRS.
Of course, Solidus didn’t spring into existence last week. Rather, Solidus was founded in 2017 by several former Goldman Sachs employees who worked on the firm’s electronic trading desk for equities. At the time, Bitcoin was only becoming buzzier, but while the engineers anticipated different use cases for the cryptocurrency, they also recognized that a lack of compliance tools would be a barrier to its adoption by bigger financial institutions, so they left to build some.
Fast forward and Solidus today employs 30 people, has raised $23.75 million, and is in the process of doubling its head count to address growing demand. On Friday, we talked with Solidus’s New York-based co-founder and CEO Asaf Meir — one of those former Goldman engineers — about the company’s new round, which was led by Equity Partners, with participation from Hanaco Ventures, Avon Ventures, 645 Ventures, the cryptocurrencies derivative exchange FTX, and a sprinkling of government officials, including former CFTC chair Chris Giancarlo and former SEC commissioner Troy Paredes. We also talked about the kinds of crypto crimes that are on the rise. Excerpts from that chat follow, edited lightly for length.
TC: Who are your customers?
AM: We work with exchanges, broker dealers, OTC desks, liquidity providers and regulators — anyone who is exposed to the risk of buying and selling cryptocurrencies, crypto assets or digital assets, whatever you want to call them.
TC: What are you promising to uncover for them?
AM: What we detect, largely speaking, is volume and price manipulation, and that has to do with wash trading, spoofing, layering, pump and dumps and an additional growing library of crypto-native alerts that truly only exist in our unique market.
We had a 400% increase in inbound demand over 2020 driven largely by two factors, I think. One is regulatory scrutiny. Globally, regulators have gone off to market participants, letting them know that they have to ask for permission, not forgiveness. The second reason — which I like better — is the drastic institutional increase in appetite toward exposure for this asset class. Every institution, the first question they ask any executing platform is: ‘What are your risk mitigation tools? How do you make sure there is market integrity?’
TC: We talked a couple of months ago, and you mentioned having a growing pipeline of customers, like the trading platform Bittrex in Seattle. Is demand coming primarily from the U.S.?
AM: We have demand in Asia and in Europe, as well, so we will be opening offices there, too.
TC: Is your former employer Goldman a customer?
AM: I can’t comment on that, but I would say there isn’t a bank right now that isn’t thinking about how they’re going to get exposure to crypto assets, and in order to do that in a safe, compliant and robust way, they have to employ crypto-specific solutions.
Right now, there’s the new frontier — the clients we’re currently working with, which are these crypto-pure exchanges, broker dealers, liquidity providers and even traditional financial institutions that are coming into crypto and opening a crypto operation or a crypto desk. Then there’s the new new frontier; your NFTs, stablecoins, indexes, lending platforms, decentralized protocols and God knows what [else] all of a sudden reaching out to us, telling us they want to do the right thing, to ensure the users on their platform are well-protected, and that trading activities are audited, and [to enlist us] to prevent any manipulation.
TC: How does your subscription service work and who is building the tech?
AM: We consume private data from our clients — all their training data — and we then put it in our detection models, which we ultimately surface through insights and alerts on our dashboard, which they have access to.
As for who is building it, we have a lot of fintech engineers who are coming from Goldman and Morgan Stanley and Citi and bringing that traditional knowledge of large trading systems at scale; we also have incredible data scientists out of Israel whose expertise is in anomaly detection, which they are applying to financial crime, working with us.
TC: What do these crimes look like?
AM: When we started out, there was much more wholesale manipulation happening whether through wash trading or pump and dumps — things that are more easy to perform. What we’re seeing today are extremely sophisticated manipulation schemes where bad actors are able to exploit different executing platforms. We’re quite literally surfacing new alerts that if you were to use a legacy, rule-based system you wouldn’t be able to [surface] because you’re not really sure what you’re looking for. We oftentimes have an alert that we haven’t named yet; we just know that this type of behavior is considered manipulative in nature and that our client should be looking into it.
TC: Can you elaborate a bit more about these new anomalies?
AM: I’m conflicted about how much can we share of our clients’ private data. But one thing we’re seeing is [a surge in] account extraction attacks, which is when through different ways, bad actors are able to gain access to an account’s funds and are able in a sophisticated way to trade out of the exchange or broker dealer or custodian. That’s happening in different social engineering-related ways, but we’re able, through account deviation and account profiling, to alert the exchange or broker dealer or financial institution we’re working with to avoid that.
We’re about detection and prevention, not about tracing [what went wrong and where] after the fact. And we can do that regardless of knowing even personal identifiable information about that account. It’s not about the name or the IP address; it’s all about the attributes of trading. In fact, if we have an exchange in Hong Kong that’s experiencing a pump and dump on a certain coin pair, we can preemptively warn the rest of our client base so they can take steps to prepare and protect themselves.
TC: On the prevention front, could you also stop that activity on the Hong Kong exchange? Are you empowered by your clients to step in if you detect something anomalous?
AM: We’re bomb-sniffing dogs, so we’re not coming to disable the bot. We know how to take the data and point out manipulation, but it’s then up to the financial institution to handle the case.
Pictured above: Seated left to right is CTO Praveen Kumar and CEO Asaf Meir. Standing is COO Chen Arad.
Powered by WPeMatico
Analytics as a service: Why more enterprises should consider outsourcing
With an increasing number of enterprise systems, growing teams, a rising proliferation of the web and multiple digital initiatives, companies of all sizes are creating loads of data every day. This data contains excellent business insights and immense opportunities, but it has become impossible for companies to derive actionable insights from this data consistently due to its sheer volume.
According to Verified Market Research, the analytics-as-a-service (AaaS) market is expected to grow to $101.29 billion by 2026. Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights. Through AaaS, managed services providers (MSPs) can help organizations get started on their analytics journey immediately without extravagant capital investment.
MSPs can take ownership of the company’s immediate data analytics needs, resolve ongoing challenges and integrate new data sources to manage dashboard visualizations, reporting and predictive modeling — enabling companies to make data-driven decisions every day.
AaaS could come bundled with multiple business-intelligence-related services. Primarily, the service includes (1) services for data warehouses; (2) services for visualizations and reports; and (3) services for predictive analytics, artificial intelligence (AI) and machine learning (ML). When a company partners with an MSP for analytics as a service, organizations are able to tap into business intelligence easily, instantly and at a lower cost of ownership than doing it in-house. This empowers the enterprise to focus on delivering better customer experiences, be unencumbered with decision-making and build data-driven strategies.
Organizations that have not started on their analytics journey or are spending scarce data engineer resources to resolve issues with analytics implementations are not identifying actionable data insights.
In today’s world, where customers value experiences over transactions, AaaS helps businesses dig deeper into their psyche and tap insights to build long-term winning strategies. It also enables enterprises to forecast and predict business trends by looking at their data and allows employees at every level to make informed decisions.
Powered by WPeMatico
With the right tools, predicting startup revenue is possible
For a long time, “revenue” seemed to be a taboo word in the startup world. Fortunately, things have changed with the rise of SaaS and alternative funding sources such as revenue-based investing VCs. Still, revenue modeling remains a challenge for founders. How do you predict earnings when you are still figuring it out?
The answer is twofold: You need to make your revenue predictable, repeatable and scalable in the first place, plus make use of tools that will help you create projections based on your data. Here, we’ll suggest some ways you can get more visibility into your revenue, find the data that really matter and figure out how to put a process in place to make forecasts about it.
You need to make your revenue predictable, repeatable and scalable in the first place, plus make use of tools that will help you create projections based on your data.
Base projections on repeatable, scalable results
Aaron Ross is a co-author of “Predictable Revenue,” a book based on his experience of creating a process and team that helped grow Salesforce’s revenue by more than $100 million. “Predictable” is the key word here: “You want growth that doesn’t require guessing, hope and frantic last-minute deal-hustling every quarter- and year-end,” he says.
This makes recurring revenue particularly desirable, though it is by no means the be-all-end-all of predictable revenue. On one hand, there is always the risk that recurring revenue won’t last, as customers may churn and organic growth runs out of gas. On the other, there is a broader picture for predictable revenue that goes beyond subscription-based models.
Ross and his co-author, Marylou Tyler, outline three steps to predictable revenue: predictable lead generation, a dedicated sales development team and consistent sales systems. They wrote an entire book about it, so it would be hard to sum it up here. So what’s the takeaway? You shouldn’t base your projections on processes and results that aren’t repeatable and scalable.
Cross the hot coals
In their early days, startups usually grow via word of mouth, luck and sheer hustle. The problem is that it likely won’t lead to sustainable growth; as the saying goes, what got you here won’t get you there. In between, there is typically a phase of uncertainty and missed results that Ross refers to as “the hot coals.”
Before the hot coals, predicting revenue is vain at best, and oftentimes impossible. I, for one, remember being at a loss when an old-school investor asked me for five-year profit-and-loss projections when my now-defunct startup was nowhere near a stable money-making path. Not all seed investors expect this, so there was obviously a mismatch here, but the challenge is still the same for most founders: How do you bridge the gap between traditional projections and the reality of a startup?
Powered by WPeMatico
Orca Security raises $210M Series C at a unicorn valuation
Orca Security, an Israeli cybersecurity startup that offers an agent-less security platform for protecting cloud-based assets, today announced that it has raised a $210 million Series C round at a $1.2 billion valuation. The round was led by Alphabet’s independent growth fund CapitalG and Redpoint Ventures. Existing investors GGV Capital, ICONIQ Growth and angel syndicate Silicon Valley CISO Investment also participated. YL Ventures, which led Orca’s seed round and participated in previous rounds, is not participating in this round — and it’s worth noting that the firm recently sold its stake in Axonius after that company reached unicorn status.
If all of this sounds familiar, that may be because Orca only raised its $55 million Series B round in December, after it announced its $20.5 million Series A round in May. That’s a lot of funding rounds in a short amount of time, but something we’ve been seeing more often in the last year or so.
Orca Security co-founders Gil Geron (left) and Avi Shua (right). Image Credits: Orca Security
As Orca co-founder and CEO Avi Shua told me, the company is seeing impressive growth and it — and its investors — want to capitalize on this. The company ended last year beating its own forecast from a few months before, which he noted was already aggressive, by more than 50%. Its current slate of customers includes Robinhood, Databricks, Unity, Live Oak Bank, Lemonade and BeyondTrust.
“We are growing at an unprecedented speed,” Shua said. “We were 20-something people last year. We are now closer to a hundred and we are going to double that by the end of the year. And yes, we’re using this funding to accelerate on every front, from dramatically increasing the product organization to add more capabilities to our platform, for post-breach capabilities, for identity access management and many other areas. And, of course, to increase our go-to-market activities.”
Shua argues that most current cloud security tools don’t really work in this new environment. Many, because they are driven by metadata, can only detect a small fraction of the risks, and agent-based solutions may take months to deploy and still not cover a business’ entire cloud estate. The promise of Orca Security is that it can not only cover a company’s entire range of cloud assets but that it is also able to help security teams prioritize the risks they need to focus on. It does so by using what the company calls its “SideScanning” technology, which allows it to map out a company’s entire cloud environment and file systems.
“Almost all tools are essentially just looking at discrete risk trees and not the forest. The risk is not just about how pickable the lock is, it’s also where the lock resides and what’s inside the box. But most tools just look at the issues themselves and prioritize the most pickable lock, ignoring the business impact and exposure — and we change that.”
It’s no secret that there isn’t a lot of love lost between Orca and some of its competitors. Last year, Palo Alto Networks sent Orca Security a sternly worded letter (PDF) to stop it from comparing the two services. Shua was not amused at the time and decided to fight it. “I completely believe there is space in the markets for many vendors, and they’ve created a lot of great products. But I think the thing that simply cannot be overlooked, is a large company that simply tries to silence competition. This is something that I believe is counterproductive to the industry. It tries to harm competition, it’s illegal, it’s unconstitutional. You can’t use lawyers to take your competitors out of the media.”
Currently, though, it doesn’t look like Orca needs to worry too much about the competition. As GGV Capital managing partner Glenn Solomon told me, as the company continues to grow and bring in new customers — and learn from the data it pulls in from them — it is also able to improve its technology.
“Because of the novel technology that Avi and [Orca Security co-founder and CPO] Gil [Geron] have developed — and that Orca is now based on — they see so much. They’re just discovering more and more ways and have more and more plans to continue to expand the value that Orca is going to provide to customers. They sit in a very good spot to be able to continue to leverage information that they have and help DevOps teams and security teams really execute on good hygiene in every imaginable way going forward. I’m super excited about that future.”
As for this funding round, Shua noted that he found CapitalG to be a “huge believer” in this space and an investor that is looking to invest into the company for the long run (and not just trying to make a quick buck). The fact that CapitalG is associated with Alphabet was obviously also a draw.
“Being associated with Alphabet, which is one of the three major cloud providers, allowed us to strengthen the relationship, which is definitely a benefit for Orca,” he said. “During the evaluation, they essentially put Orca in front of the security leadership at Google. Definitely, they’ve done their own very deep due diligence as part of that.”
Early Stage is the premier ‘how-to’ event for startup entrepreneurs and investors. You’ll hear first-hand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company-building: Fundraising, recruiting, sales, product market fit, PR, marketing and brand building. Each session also has audience participation built-in – there’s ample time included for audience questions and discussion. Use code “TCARTICLE” at checkout to get 20 percent off tickets right here.
Powered by WPeMatico