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5 things you need to win your first customer
A startup is a beautiful thing. It’s the tangible outcome of an idea birthed in a garage or on the back of a napkin. But ask any founder what really proves their startup has taken off, and they will almost instantly say it’s when they win their first customer.
That’s easier said than done, though, because winning that first customer will take a lot more than an Ivy-educated founder and/or a celebrity investor pool.
To begin with, you’ll have to craft a strong ideal customer profile to know your customer’s pain points, while developing a competitive SWOT analysis to scope out alternatives your customers can go to.
Your target customer will pick a solution that will help them achieve their goals. In other words, your goals should align with your customer’s goals.
You’ll also need to create a shortlist of influencers who have your customer’s trust, identify their decision-makers who make the call to buy (or not), and create a mapped list of goals that align your customer’s goals to yours.
Understanding and executing on these things can guarantee you that first customer win, provided you do them well and with sincerity. Your investors will also see the fruits of your labor and be comforted knowing their dollars are at good work.
Let’s see how:
1. Craft the ideal customer profile (ICP)
The ICP is a great framework for figuring out who your target customer is, how big they are, where they operate, and why they exist. As you write up your ICP, you will soon see the pain points you assumed about them start to become more real.
To create an ICP, you will need to have a strong articulation of the problem you are trying to solve and the customers that experience this problem the most. This will be your baseline hypothesis. Then, as you develop your ICP, keep testing your baseline hypothesis to weed out inaccurate assumptions.
Getting crystal clear here will set you up with the proper launchpad. No shortcuts.
Here’s how to get started:
- Develop an ICP (Ideal Customer Profile) framework.
- Identify three target customers that fit your defined ICP.
- Write a problem statement for each identified target customer.
- Prioritize the problem statement that resonates with your product the most.
- Lock on the target customer of the prioritized problem statement.
Practice use case:
You are the co-founder at an upcoming SaaS startup focused on simplifying the shopping experience in car showrooms so buyers enjoy the process. What would your ICP look like?
2. Develop the SWOT
The SWOT framework cannot be overrated. This is a great structure to articulate who your competitors are and how you show up against them. Note that your competitors can be direct or indirect (as an alternative), and it’s important to categorize these buckets correctly.
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EverAfter closes $13M to help companies ride off into the sunset with their customers
EverAfter secured $13 million in seed funding to continue developing its no-code customer-facing tool that streamlines onboarding and retention and enables business-to-business clients to embed personalized customer portals within any product.
The Tel Aviv-based company was founded in 2020 by Noa Danon and Tal Shemesh. CEO Danon, who comes from a project management background, said they saw a disconnect between the user and product experience.
The company’s name, EverAfter, comes from the concept that in SaaS companies, someone has to be in charge of the “EverAfter,” with customers, even as the relationship changes, Danon told TechCrunch.
Via its no-code platform, customer success teams are able to build a website in weeks using drop-and-drag widgets like training materials, timelines, task management and meeting summaries, and then configure what each user sees. Then there is a snippet of code that is embedded into the product.
EverAfter also integrates with existing customer relationship management, project management and service ticket tools, while also updating Salesforce and HubSpot directly through an interface.
“It’s like the customer owns a piece of real estate inside the product,” Danon said.
TLV Partners and Vertex Ventures co-led the round and were joined by angel investors Benny Shneider, Zohar Gilon and Amit Gilon.
Yanai Oron, general partner at Vertex Ventures, said he is seeing best-in-breed companies try to solve customer churn or improve the relationship process on their own and failing, which speaks to the complexity of the problem.
Startups in this space are coming online and raising money, but with EverAfter, they are differentiating themselves by not only putting a dashboard on their product, but launching with the capabilities to manage thousands of customers using the product, he added.
“I’ve been tracking the customer success space over the past few years, and it is a growing field with the least sophisticated tools,” Oron said. “During COVID, companies realized it was easier to retain customers rather than get new ones. We are all used to more self-service and wanting to get the answer ourselves, and customers are the same. Companies also started to be more at ease in letting customers develop things on their own and leave R&D departments to do other things.”
Clients include Taboola, AppsFlyer and Verbit, with Verbit reporting its company’s customer success managers save 10 hours a week managing ongoing customer communication by using EverAfter, Danon added. This comes as CallMiner reports that unplanned customer churn costs companies $35.3 billion in the U.S. alone.
EverAfter offers both customer success and partner management software and clients can choose a high-touch service or kits and templates for self-service.
The new funding will enable the company to focus on integration and expansion into additional use cases. Since being founded, EverAfter has grown to 20 employees and 30 customers. The founders also want to utilize the data they are collecting on what works and doesn’t work for each customer.
“There are so many interesting things that happen between companies and customers, from onboarding to business reviews, and we are going to expand on those,” Danon said. “We want to be the first thing companies put inside their product to figure out the relationship between customers and customer success teams and managers.”
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Fin names former Twilio exec Evan Cummack as CEO, raises $20M
Work insights platform Fin raised $20 million in Series A funding and brought in Evan Cummack, a former Twilio executive, as its new chief executive officer.
The San Francisco-based company captures employee workflow data from across applications and turns it into productivity insights to improve the way enterprise teams work and remain engaged.
Fin was founded in 2015 by Andrew Kortina, co-founder of Venmo, and Facebook’s former VP of product and Slow Ventures partner Sam Lessin. Initially, the company was doing voice assistant technology — think Alexa but powered by humans and machine learning — and then workplace analytics software in 2020. You can read more about Fin’s origins at the link below.
The new round was led by Coatue, with participation from First Round Capital, Accel and Kleiner Perkins. The original team was talented, but small, so the new funding will build out sales, marketing and engineering teams, Cummack said.
“At that point, the right thing was to raise money, so at the end of last year, the company raised a $20 million Series A, and it was also decided to find a leadership team that knows how to build an enterprise,” Cummack told TechCrunch. “The company had completely pivoted and removed ‘Analytics’ from our name because it was not encompassing what we do.”
Fin’s software measures productivity and provides insights on ways managers can optimize processes, coach their employees and see how teams are actually using technology to get their work done. At the same time, employees are able to manage their workflow and highlight areas where there may be bottlenecks. All combined, it leads to better operations and customer experiences, Cummack said.
Graphic showing how work is really done. Image Credits: Fin
Fin’s view is that as more automation occurs, the company is looking at a “renaissance of human work.” There will be more jobs and more types of jobs, but people will be able to do them more effectively and the work will be more fulfilling, he added.
Particularly with the use of technology, he notes that in the era before cloud computing, there was a small number of software vendors. Now with the average tech company using over 130 SaaS apps, it allows for a lot of entrepreneurs and adoption of best-in-breed apps so that a viable company can start with a handful of people and leverage those apps to gain big customers.
“It’s different for enterprise customers, though, to understand that investment and what they are spending their money on as they use tools to get their jobs done,” Cummack added. “There is massive pressure to improve the customer experience and move quickly. Now with many people working from home, Fin enables you to look at all 130 apps as if they are one and how they are being used.”
As a result, Fin’s customers are seeing metrics like 16% increase in team utilization and engagement, a 25% decrease in support ticket handle time and a 71% increase in policy compliance. Meanwhile, the company itself is doubling and tripling its customers and revenue each year.
Now with leadership and people in place, Cummack said the company is positioned to scale, though it already had a huge head start in terms of a meaningful business.
Arielle Zuckerberg, partner at Coatue, said via email that she was part of a previous firm that invested in Fin’s seed round to build a virtual assistant. She was also a customer of Fin Assistant until it was discontinued.
When she heard the company was pivoting to enterprise, she “was excited because I thought it was a natural outgrowth of the previous business, had a lot of potential and I was already familiar with management and thought highly of them.”
She believed the “brains” of the company always revolved around understanding and measuring what assistants were doing to complete a task as a way to create opportunities for improvement or automation. The pivot to agent-facing tools made sense to Zuckerberg, but it wasn’t until the global pandemic that it clicked.
“Service teams were forced to go remote overnight, and companies had little to no visibility into what people were doing working from home,” she added. “In this remote environment, we thought that Fin’s product was incredibly well-suited to address the challenges of managing a growing remote support team, and that over time, their unique data set of how people use various apps and tools to complete tasks can help business leaders improve the future of work for their team members. We believe that contact center agents going remote was inevitable even before COVID, but COVID was a huge accelerant and created a compelling ‘why now’ moment for Fin’s solution.”
Going forward, Coatue sees Fin as “a process mining company that is focused on service teams.” By initially focusing on customer support and contact center use case — a business large enough to support a scaled, standalone business — rather than joining competitors in going after Fortune 500 companies where implementation cycles are long and there is slow time-to-value, Zuckerberg said Fin is better able to “address the unique challenges of managing a growing remote support team with a near-immediate time-to-value.”
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Insight Partners leads $30M round into Metabase, developing enterprise business intelligence tools
Open-source business intelligence company Metabase announced Thursday a $30 million Series B round led by Insight Partners.
Existing investors Expa and NEA joined in on the round, which gives the San Francisco-based company a total of $42.5 million in funding since it was founded in 2015. Metabase previously raised $8 million in Series A funding back in 2019, led by NEA.
Metabase was developed within venture studio Expa and spun out as an easy way for people to interact with data sets, co-founder and CEO Sameer Al-Sakran told TechCrunch.
“When someone wants access to data, they may not know what to measure or how to use it, all they know is they have the data,” Al-Sakran said. “We provide a self-service access layer where they can ask a question, Metabase scans the data and they can use the results to build models, create a dashboard and even slice the data in ways they choose without having an analyst build out the database.”
He notes that not much has changed in the business intelligence realm since Tableau came out more than 15 years ago, and that computers can do more for the end user, particularly to understand what the user is going to do. Increasingly, open source is the way software and information wants to be consumed, especially for the person that just wants to pull the data themselves, he added.
George Mathew, managing director of Insight Partners, believes we are seeing the third generation of business intelligence tools emerging following centralized enterprise architectures like SAP, then self-service tools like Tableau and Looker and now companies like Metabase that can get users to discovery and insights quickly.
“The third generation is here and they are leading the charge to insights and value,” Mathew added. “In addition, the world has moved to the cloud, and BI tools need to move there, too. This generation of open source is a better and greater example of all three of those.”
To date, Metabase has been downloaded 98 million times and used by more than 30,000 companies across 200 countries. The company pursued another round of funding after building out a commercial offering, Metabase Enterprise, that is doing well, Al-Sakran said.
The new funding round enables the company to build out a sales team and continue with product development on both Metabase Enterprise and Metabase Cloud. Due to Metabase often being someone’s first business intelligence tool, he is also doubling down on resources to help educate customers on how to ask questions and learn from their data.
“Open source has changed from floppy disks to projects on the cloud, and we think end users have the right to see what they are running,” Al-Sakran said. “We are continuing to create new features and improve performance and overall experience in efforts to create the BI system of the future.
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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.
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Coralogix logs $55M for its new take on production analytics, now valued at $300M-$400M
Data may be the new oil, but it’s only valuable if you make good use of it. Today, a startup that has built a new kind of production analytics platform for developers, security engineers, and data scientists to track and better understand how data is moving around their networks is announcing a round of funding that underscores the demand for their technology.
Coralogix, which provides stateful streaming services to engineering teams, has picked up $55 million in a Series C round of funding.
The round was led by Greenfield Partners, with Red Dot Capital Partners, StageOne Ventures, Eyal Ofer’s O.G. Tech, Janvest Capital Partners, Maor Investments and 2B Angels also participating.
This Series C is coming about 10 months after the company’s Series B of $25 million, and from what we understand, Coralogix’s valuation is now in the range of $300 million to $400 million, a big jump for the startup, coming on the back of it growing 250% since this time last year, racking up some 2,000 paying customers, with some small teams paying as little as $100/year and large enterprises paying $1.5 million/year.
Previously, Coralogix — founded in Tel Aviv and with an HQ also in San Francisco — had also raised a round of $10 million.
Coralogix got its start as a platform aimed at quality assurance support for R&D and engineering teams. The focus here is on log analytics and metrics for platform engineers, and this still forms a big part of its business today. Added to that, in recent years, Coralogix’s tools are being applied to cloud security services, contributing to a company’s threat intelligence by providing a way to observe data for any inconsistencies that typically might point to a breach or another incident. (It integrates with Alien Vault and others for this purpose.)
The third area that is just picking up now and will be developed further — one of the uses of this investment, in fact — will be to develop how Coralogix is used for business intelligence. This is a particularly interesting area because it plays into how Coralogix is built, to provide analytics on data before it is indexed.
“It’s about high-volume, but low-value data,” Ariel Assaraf, Coralogix’s CEO, said in an interview. “Customers don’t want to store the data [or index it] but want to view it live and visualize it. We are starting to see a use case where business information and our analytics come together for sentiment analysis and other areas.”
There are dozens of strong companies providing tools these days to cover log analytics and data observability, underscoring the general growth and importance of DevOps these days. They include companies like DataDog, Sumo Logic and Splunk.
However, Assaraf believes that what sets his company apart is its approach: Essentially, it has devised a way of observing and analyzing data streams before they get indexed, giving engineers more flexibility to query the data in different ways and glean more insights, faster. The other issue with indexing, he said, is that it impacts latency, which also has a big impact on overall costs for an organization.
For many of Coralogix’s competitors, turning around the nature of the business to focus not first on indexing would be akin to completely rebuilding the business, hard to do at their scale (although this is what Coralogix did when it pivoted as a small company several years ago, which is when Assaraf took on the role of CEO). One company he believes might be more of a direct rival is Confluent.
“I think we will see Confluent getting into observability very soon because they have the streaming capabilities,” he said, “but not the tools we have.” Another potential competitor looming on the horizon: Salesforce, and its potential move into that area, underscores the shifting sands of what is powering enterprise IT investment decisions today.
Salesforce already has Heroku, Slack and Tableau, three major tools developers use for tracking and working with data, Assaraf pointed out, and there were strong rumors of it trying to buy DataDog, “so we definitely see where they are going. For sure, they understand the way things are changing. All the budgets when Salesforce first started were in marketing and sales. Now you sell to IT. Salesforce understands that shift to developers, and so that is where they are going.”
It makes for a very interesting landscape and future for companies like Coralogix, one that investors believe the startup will continue to shape as it has up to now.
“The dramatic shift in digital transformation is generating an explosion of data, which until now has forced enterprises to decide between cost and coverage,” said Shay Grinfeld, managing partner at Greenfield Partners. “Coralogix’s real-time streaming analytics pipeline employs proprietary algorithms to break this tradeoff and generate significant cost savings. Coralogix has built a customer roster that comprises some of the largest and most innovative companies in the world. We’re thrilled to partner with Ariel and the Coralogix team on their journey to reinvent the future of data observability.”
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Edge Delta raises $15M Series A to take on Splunk
Seattle-based Edge Delta, a startup that is building a modern distributed monitoring stack that is competing directly with industry heavyweights like Splunk, New Relic and Datadog, today announced that it has raised a $15 million Series A funding round led by Menlo Ventures and Tim Tully, the former CTO of Splunk. Previous investors MaC Venture Capital and Amity Ventures also participated in this round, which brings the company’s total funding to date to $18 million.
“Our thesis is that there’s no way that enterprises today can continue to analyze all their data in real time,” said Edge Delta co-founder and CEO Ozan Unlu, who has worked in the observability space for about 15 years already (including at Microsoft and Sumo Logic). “The way that it was traditionally done with these primitive, centralized models — there’s just too much data. It worked 10 years ago, but gigabytes turned into terabytes and now terabytes are turning into petabytes. That whole model is breaking down.”
Image Credits: Edge Delta
He acknowledges that traditional big data warehousing works quite well for business intelligence and analytics use cases. But that’s not real-time and also involves moving a lot of data from where it’s generated to a centralized warehouse. The promise of Edge Delta is that it can offer all of the capabilities of this centralized model by allowing enterprises to start to analyze their logs, metrics, traces and other telemetry right at the source. This, in turn, also allows them to get visibility into all of the data that’s generated there, instead of many of today’s systems, which only provide insights into a small slice of this information.
While competing services tend to have agents that run on a customer’s machine, but typically only compress the data, encrypt it and then send it on to its final destination, Edge Delta’s agent starts analyzing the data right at the local level. With that, if you want to, for example, graph error rates from your Kubernetes cluster, you wouldn’t have to gather all of this data and send it off to your data warehouse where it has to be indexed before it can be analyzed and graphed.
With Edge Delta, you could instead have every single node draw its own graph, which Edge Delta can then combine later on. With this, Edge Delta argues, its agent is able to offer significant performance benefits, often by orders of magnitude. This also allows businesses to run their machine learning models at the edge, as well.
Image Credits: Edge Delta
“What I saw before I was leaving Splunk was that people were sort of being choosy about where they put workloads for a variety of reasons, including cost control,” said Menlo Ventures’ Tim Tully, who joined the firm only a couple of months ago. “So this idea that you can move some of the compute down to the edge and lower latency and do machine learning at the edge in a distributed way was incredibly fascinating to me.”
Edge Delta is able to offer a significantly cheaper service, in large part because it doesn’t have to run a lot of compute and manage huge storage pools itself since a lot of that is handled at the edge. And while the customers obviously still incur some overhead to provision this compute power, it’s still significantly less than what they would be paying for a comparable service. The company argues that it typically sees about a 90 percent improvement in total cost of ownership compared to traditional centralized services.
Image Credits: Edge Delta
Edge Delta charges based on volume and it is not shy to compare its prices with Splunk’s and does so right on its pricing calculator. Indeed, in talking to Tully and Unlu, Splunk was clearly on everybody’s mind.
“There’s kind of this concept of unbundling of Splunk,” Unlu said. “You have Snowflake and the data warehouse solutions coming in from one side, and they’re saying, ‘hey, if you don’t care about real time, go use us.’ And then we’re the other half of the equation, which is: actually there’s a lot of real-time operational use cases and this model is actually better for those massive stream processing datasets that you required to analyze in real time.”
But despite this competition, Edge Delta can still integrate with Splunk and similar services. Users can still take their data, ingest it through Edge Delta and then pass it on to the likes of Sumo Logic, Splunk, AWS’s S3 and other solutions.
Image Credits: Edge Delta
“If you follow the trajectory of Splunk, we had this whole idea of building this business around IoT and Splunk at the Edge — and we never really quite got there,” Tully said. “I think what we’re winding up seeing collectively is the edge actually means something a little bit different. […] The advances in distributed computing and sophistication of hardware at the edge allows these types of problems to be solved at a lower cost and lower latency.”
The Edge Delta team plans to use the new funding to expand its team and support all of the new customers that have shown interest in the product. For that, it is building out its go-to-market and marketing teams, as well as its customer success and support teams.
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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.”
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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.
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Tellius announces $8M Series A to build ML-fueled business data query tool
Getting actionable business information into the hands of users who need it has always been a challenge. If you have to wait for experts to help you find the answers, chances are you’re going to be too late. Enter Tellius, an early-stage startup building a solution to help business users find the information they need when they need it.
Today the company announced an $8 million Series A led by Sands Capital Ventures, with participation from Grotech. Today’s investment brings the total raised to $17 million, according to the company.
CEO and founder Ajay Khanna says the company is attempting to marry two technologies that have traditionally lived in silos: business intelligence and artificial intelligence. He believes that bringing them together can lead to greater wisdom and help close the insight gap.
“Tellius is an AI-driven decision intelligence platform, and what we do is we combine machine learning — AI-driven automation — with a Google-like natural language interface, so combining the left brain and the right brain to enable business teams to get insights on the data,” Khanna told me.
The idea is to let the machine learning teams and the business analysts continue to do their thing, but provide an application where business users can put all of that to work. “We believe that to go from data to decisions, you need to know not only what happened, but why things change and how you can improve your company,” he said.
The product takes aim at three employee groups. The first is the business user, who can simply query the data with a natural language question to get results. The second is a data analyst, who can get more granular by choosing a specific model to base the query on, and finally a data scientist who can enhance the query with Python or Spark code.
It connects to various data sources, including Salesforce and Google Analytics, data lakes like Snowflake, csv files to take advantage of Excel data or cloud storage tools like Amazon S3. It comes in two versions: one that the customer can connect to the cloud infrastructure provider of choice, and one which they run as a service and manage for the customers.
Khanna says that as companies struggled to change the way they do business during the pandemic, they needed the kind of insights his company provides, and business grew 300% last year as a result.
The startup launched in 2016 after Khanna sold a previous company, which allowed him to bootstrap while in stealth. They spent a couple of years building the product and brought the first version of Tellius to market in Q3 2018. That’s when they took a $7.5 million seed round.
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