Apr
30
2021
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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.

Apr
27
2021
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What can the OKR software sector tell us about startup growth more generally?

In the never-ending stream of venture capital funding rounds, from time to time, a group of startups working on the same problem will raise money nearly in unison. So it was with OKR-focused startups toward the start of 2020.

How were so many OKR-focused tech upstarts able to raise capital at the same time? And was there really space in the market for so many different startups building software to help other companies manage their goal-setting? OKRs, or “objectives and key results,” a corporate planning method, are no longer a niche concept. But surely, over time, there would be M&A in the group, right?

During our first look into the cohort, we concluded that it felt likely that there was “some consolidation” ahead for the group “when growth becomes more difficult.” At the time, however, it was clear that many founders and investors expected the OKR software market to have material depth.

They were right, and we were wrong. A year later, in early 2021, we asked the same group how their previous year had gone. Nearly every single company had a killer year, with many players growing by well over 100%.


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OKR company Ally.io grew 3.3x in 2020, for example, while its competitor Gtmhub grew by 3x over the same time period. More capital followed. Ally.io raised $50 million in a Series C in the first quarter, while Gtmhub put together a $30 million Series B during the same period.

They won’t be the final startups in the OKR cohort to raise this year. We know this because we reached out to the group again this week, this time probing their Q1 performance, and, critically, asking the startups to discuss their level of optimism regarding the rest of 2021.

As before, the group’s recent results are strong, at least when compared to their own planning. But notably, the collection of competing companies is more optimistic than before about the rest of the year than they were before Q1 2021. Things are heating up for the OKR startup world.

A takeaway from our work today is that our prior notes about how impressively deep the software market is proving to be may have been too modest. And frankly, that’s super-good news for startups and investors alike. So much for SaaS-fatigue.

In a sense, we should not be surprised that OKR startups are doing well or that the startup software market is so large. You’d imagine that the historic pace of venture capital investment that we’ve seen so far in 2021 in Europe and the United States was based on results, or evidence that there was lots more room for software-focused startups to grow.

Interestingly, while these companies look similar to outsiders, they are each betting on strategies and differentiators that could help them win in their selected portion of the OKR space. Which also means that the sector may not be as crowded as it seems.

Don’t take our word for it. Let’s hear from Gtmhub COO Seth Elliott, Workboard CEO and co-founder Deidre Paknad, Koan CEO and co-founder Matt Tucker, Ally.io CEO and co-founder Vetri Vellore, and Perdoo CEO and founder Henrik-Jan van der Pol about just what the software market looks like to them.

We’ll start with how the startups performed in Q1 2021, dig into how they feel about the rest of the year, and then talk about how differentiation among the cohort could be helping them not step on each other’s toes.

Rapid growth

WorkBoard is having a strong start to 2021. Paknad’s company, which raised in both March of 2019 and January of 2020, told The Exchange that it hired 82 people in the first three months of 2021, and that it plans on doing it again in the current quarter. WorkBoard is “investing heavily,” Paknad said via DM, and “made [its] Q1 targets.”

Apr
21
2021
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As UiPath closes above its final private valuation, CFO Ashim Gupta discusses his company’s path to market

After an upward revision, UiPath priced its IPO last night at $56 per share, a few dollars above its raised target range. The above-range price meant that the unicorn put more capital into its books through its public offering.

For a company in a market as competitive as robotic process automation (RPA), the funds are welcome. In fact, RPA has been top of mind for startups and established companies alike over the last year or so. In that time frame, enterprise stalwarts like SAP, Microsoft, IBM and ServiceNow have been buying smaller RPA startups and building their own, all in an effort to muscle into an increasingly lucrative market.

In June 2019, Gartner reported that RPA was the fastest-growing area in enterprise software, and while the growth has slowed down since, the sector is still attracting attention. UIPath, which Gartner found was the market leader, has been riding that wave, and today’s capital influx should help the company maintain its market position.

It’s worth noting that when the company had its last private funding round in February, it brought home $750 million at an impressive valuation of $35 billion. But as TechCrunch noted over the course of its pivot to the public markets, that round valued the company above its final IPO price. As a result, this week’s $56-per-share public offer wound up being something of a modest down-round IPO to UiPath’s final private valuation.

Then, a broader set of public traders got hold of its stock and bid its shares higher. The former unicorn’s shares closed their first day’s trading at precisely $69, above the per-share price at which the company closed its final private round.

So despite a somewhat circuitous route, UiPath closed its first day as a public company worth more than it was in its Series F round — when it sold 12,043,202 shares at $62.27576 apiece, per SEC filings. More simply, UiPath closed today worth more per-share than it was in February.

How you might value the company, whether you prefer a simple or fully diluted share count, is somewhat immaterial at this juncture. UiPath had a good day.

While it’s hard to know what the company might do with the proceeds, chances are it will continue to try to expand its platform beyond pure RPA, which could become market-limited over time as companies look at other, more modern approaches to automation. By adding additional automation capabilities — organically or via acquisitions — the company can begin covering broader parts of its market.

TechCrunch spoke with UiPath CFO Ashim Gupta today, curious about the company’s choice of a traditional IPO, its general avoidance of adjusted metrics in its SEC filings, and the IPO market’s current temperature. The final question was on our minds, as some companies have pulled their public listings in the wake of a market described as “challenging.”

Why did UiPath not direct list after its huge February raise?

Apr
16
2021
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Data scientists: Bring the narrative to the forefront

By 2025, 463 exabytes of data will be created each day, according to some estimates. (For perspective, one exabyte of storage could hold 50,000 years of DVD-quality video.) It’s now easier than ever to translate physical and digital actions into data, and businesses of all types have raced to amass as much data as possible in order to gain a competitive edge.

However, in our collective infatuation with data (and obtaining more of it), what’s often overlooked is the role that storytelling plays in extracting real value from data.

The reality is that data by itself is insufficient to really influence human behavior. Whether the goal is to improve a business’ bottom line or convince people to stay home amid a pandemic, it’s the narrative that compels action, rather than the numbers alone. As more data is collected and analyzed, communication and storytelling will become even more integral in the data science discipline because of their role in separating the signal from the noise.

Data alone doesn’t spur innovation — rather, it’s data-driven storytelling that helps uncover hidden trends, powers personalization, and streamlines processes.

Yet this can be an area where data scientists struggle. In Anaconda’s 2020 State of Data Science survey of more than 2,300 data scientists, nearly a quarter of respondents said that their data science or machine learning (ML) teams lacked communication skills. This may be one reason why roughly 40% of respondents said they were able to effectively demonstrate business impact “only sometimes” or “almost never.”

The best data practitioners must be as skilled in storytelling as they are in coding and deploying models — and yes, this extends beyond creating visualizations to accompany reports. Here are some recommendations for how data scientists can situate their results within larger contextual narratives.

Make the abstract more tangible

Ever-growing datasets help machine learning models better understand the scope of a problem space, but more data does not necessarily help with human comprehension. Even for the most left-brain of thinkers, it’s not in our nature to understand large abstract numbers or things like marginal improvements in accuracy. This is why it’s important to include points of reference in your storytelling that make data tangible.

For example, throughout the pandemic, we’ve been bombarded with countless statistics around case counts, death rates, positivity rates, and more. While all of this data is important, tools like interactive maps and conversations around reproduction numbers are more effective than massive data dumps in terms of providing context, conveying risk, and, consequently, helping change behaviors as needed. In working with numbers, data practitioners have a responsibility to provide the necessary structure so that the data can be understood by the intended audience.

Apr
02
2021
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RPA market surges as investors, vendors capitalize on pandemic-driven tech shift

When UIPath filed its S-1 last week, it was a watershed moment for the robotic process automation (RPA) market. The company, which first appeared on our radar for a $30 million Series A in 2017, has so far raised an astonishing $2 billion while still private. In February, it was valued at $35 billion when it raised $750 million in its latest round.

RPA and process automation came to the fore during the pandemic as companies took steps to digitally transform. When employees couldn’t be in the same office together, it became crucial to cobble together more automated workflows that required fewer people in the loop.

RPA has enabled executives to provide a level of workflow automation that essentially buys them time to update systems to more modern approaches while reducing the large number of mundane manual tasks that are part of every industry’s workflow.

When UIPath raised money in 2017, RPA was not well known in enterprise software circles even though it had already been around for several years. The category was gaining in popularity by that point because it addressed automation in a legacy context. That meant companies with deep legacy technology — practically everyone not born in the cloud — could automate across older platforms without ripping and replacing, an expensive and risky undertaking that most CEOs would rather not take.

RPA has enabled executives to provide a level of workflow automation, a taste of the modern. It essentially buys them time to update systems to more modern approaches while reducing the large number of mundane manual tasks that are part of just about every industry’s workflow.

While some people point to RPA as job-elimination software, it also provides a way to liberate people from some of the most mind-numbing and mundane chores in the organization. The argument goes that this frees up employees for higher level tasks.

As an example, RPA could take advantage of older workflow technologies like OCR (optical character recognition) to read a number from a form, enter the data in a spreadsheet, generate an invoice, send it for printing and mailing, and generate a Slack message to the accounting department that the task has been completed.

We’re going to take a deep dive into RPA and the larger process automation space — explore the market size and dynamics, look at the key players and the biggest investors, and finally, try to chart out where this market might go in the future.

Meet the vendors

UIPath is clearly an RPA star with a significant market share lead of 27.1%, according to IDC. Automation Anywhere is in second place with 19.4%, and Blue Prism is third with 10.3%, based on data from IDC’s July 2020 report, the last time the firm reported on the market.

Two other players with significant market share worth mentioning are WorkFusion with 6.8%, and NTT with 5%.

Mar
19
2021
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It’s time to abandon business intelligence tools

Organizations spend ungodly amounts of money — millions of dollars — on business intelligence (BI) tools. Yet, adoption rates are still below 30%. Why is this the case? Because BI has failed businesses.

Logi Analytics’ 2021 State of Analytics: Why Users Demand Better survey showed that knowledge workers spend more than five hours a day in analytics, and more than 99% consider analytics very to extremely valuable when making critical decisions. Unfortunately, many are dissatisfied with their current tools due to the loss of productivity, multiple “sources of truth,” and the lack of integration with their current tools and systems.

A gap exists between the functionalities provided by current BI and data discovery tools and what users want and need.

Throughout my career, I’ve spoken with many executives who wonder why BI continues to fail them, especially when data discovery tools like Qlik and Tableau have gained such momentum. The reality is, these tools are great for a very limited set of use cases among a limited audience of users — and the adoption rates reflect that reality.

Data discovery applications allow analysts to link with data sources and perform self-service analysis, but still come with major pitfalls. Lack of self-service customization, the inability to integrate into workflows with other applications, and an overall lack of flexibility seriously impacts the ability for most users (who aren’t data analysts) to derive meaningful information from these tools.

BI platforms and data discovery applications are supposed to launch insight into action, informing decisions at every level of the organization. But many are instead left with costly investments that actually create inefficiencies, hinder workflows and exclude the vast majority of employees who could benefit from those operational insights. Now that’s what I like to call a lack of ROI.

Business leaders across a variety of industries — including “legacy” sectors like manufacturing, healthcare and financial services — are demanding better and, in my opinion, they should have gotten it long ago.

It’s time to abandon BI — at least as we currently know it.

Here’s what I’ve learned over the years about why traditional BI platforms and newer tools like data discovery applications fail and what I’ve gathered from companies that moved away from them.

The inefficiency breakdown is killing your company

Traditional BI platforms and data discovery applications require users to exit their workflow to attempt data collection. And, as you can guess, stalling teams in the middle of their workflow creates massive inefficiencies. Instead of having the data you need to make a decision readily available to you, instead, you have to exit the application, enter another application, secure the data and then reenter the original application.

According to the 2021 State of Analytics report, 99% of knowledge workers had to spend additional time searching for information they couldn’t easily locate in their analytics solution.

Feb
22
2021
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Winning enterprise sales teams know how to persuade the Chief Objection Officer

Many enterprise software startups at some point have faced the invisible wall. For months, your sales team has done everything right. They’ve met with a prospect several times, provided them with demos, free trials, documentation and references, and perhaps even signed a provisional contract.

The stars are all aligned and then, suddenly, the deal falls apart. Someone has put the kibosh on the entire project. Who is this deal-blocker and what can software companies do to identify, support and convince this person to move forward with a contract?

I call this person the Chief Objection Officer.

Who is this deal-blocker and what can software companies do to identify, support and convince this person to move forward with a contract?

Most software companies spend a lot of time and effort identifying their potential buyers and champions within an organization. They build personas and do targeted marketing to these individuals and then fine-tune their products to meet their needs. These targets may be VPs of engineering, data leaders, CTOs, CISOs, CMOs or anyone else with decision-making authority. But what most software companies neglect to do during this exploratory phase is to identify the person who may block the entire deal.

This person is the anti-champion with the power to scuttle a potential partnership. Like your potential deal-makers, these deal-breakers can have any title with decision-making power. Chief Objection Officers aren’t simply potential buyers who end up deciding your product is not the right fit, but are instead blockers-in-chief who can make departmentwide or companywide decisions. Thus, it’s critical for software companies to identify the Chief Objection Officers that might block deals and, then, address their concerns.

So how do you identify the Chief Objection Officer? The trick is to figure out the main pain points that arise for companies when considering deploying your solution, and then walk backward to figure out which person these challenges impact the most. Here are some common pain points that your potential customers may face when considering your product.

Change is hard. Never underestimate the power of the status quo. Does implementing your product in one part of an organization, such as IT, force another department, such as HR, to change how they do their daily jobs?

Think about which leaders will be most reluctant to make changes; these Chief Objection Officers will likely not be your buyers, but instead the heads of departments most impacted by the implementation of your software. For example, a marketing team may love the ad targeting platform they use and thus a CMO will balk at new database software that would limit or change the way customer segment data is collected. Or field sales would object to new security infrastructure software that makes it harder for them to access the company network from their phones. The head of the department that will bear the brunt of change will often be a Chief Objection Officer.

Is someone’s job on the line?

Another common pain point when deploying a new software solution is that one or more jobs may become obsolete once it’s up and running. Perhaps your software streamlines and outsources most of a company’s accounts payable processes. Maybe your SaaS solution will replace an on-premise homegrown one that a team of developers has built and nurtured for years.

Feb
18
2021
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Why do SaaS companies with usage-based pricing grow faster?

Today we know of HubSpot — the maker of marketing, sales and service software products — as a preeminent public company with a market cap above $17 billion. But HubSpot wasn’t always on the IPO trajectory.

For its first five years in business, HubSpot offered three subscription packages ranging in price from $3,000 to $18,000 per year. The company struggled with poor churn and anemic expansion revenue. Net revenue retention was near 70%, a far cry from the 100%+ that most SaaS companies aim to achieve.

Something needed to change. So in 2011, they introduced usage-based pricing. As customers used the software to generate more leads, they would proportionally increase their spend with HubSpot.  This pricing change allowed HubSpot to share in the success of its customers.

In a usage-based model, expansion “just happens” as customers are successful.

By the time HubSpot went public in 2014, net revenue retention had jumped to nearly 100% — all without hurting the company’s ability to acquire new customers.

HubSpot isn’t an outlier. Public SaaS companies that have adopted usage-based pricing grow faster because they’re better at landing new customers, growing with them and keeping them as customers.

Image Credits: Kyle Poyar

Widen the top of the funnel

In a usage-based model, a company doesn’t get paid until after the customer has adopted the product. From the customer’s perspective, this means that there’s no risk to try before they buy. Products like Snowflake and Google Cloud Platform take this a step further and even offer $300+ in free usage credits for new developers to test drive their products.

Many of these free users won’t become profitable — and that’s okay. Like a VC firm, usage-based companies are making a portfolio of bets. Some of those will pay off spectacularly — and the company will directly share in that success.

Top-performing companies open up the top of the funnel by making it free to sign up for their products. They invest in a frictionless customer onboarding experience and high-quality support so that new users get hooked on the platform. As more new users become active, there’s a stronger foundation for future customer growth.

Jan
22
2021
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Drupal’s journey from dorm-room project to billion-dollar exit

Twenty years ago Drupal and Acquia founder Dries Buytaert was a college student at the University of Antwerp. He wanted to put his burgeoning programming skills to work by building a communications tool for his dorm. That simple idea evolved over time into the open-source Drupal web content management system, and eventually a commercial company called Acquia built on top of it.

Buytaert would later raise over $180 million and exit in 2019 when the company was acquired by Vista Equity Partners for $1 billion, but it took 18 years of hard work to reach that point.

When Drupal came along in the early 2000s, it wasn’t the only open-source option, but it was part of a major movement toward giving companies options by democratizing web content management.

Many startups are built on open source today, but back in the early 2000s, there were only a few trail blazers and none that had taken the path that Acquia took. Buytaert and his co-founders decided to reduce the complexity of configuring a Drupal installation by building a hosted cloud service.

That seems like a no-brainer now, but consider at the time in 2009, AWS was still a fledgling side project at Amazon, not the $45 billion behemoth it is today. In 2021, building a startup on top of an open-source project with a SaaS version is a proven and common strategy. Back then nobody else had done it. As it turned out, taking the path less traveled worked out well for Acquia.

Moving from dorm room to billion-dollar exit is the dream of every startup founder. Buytaert got there by being bold, working hard and thinking big. His story is compelling, but it also offers lessons for startup founders who also want to build something big.

Born in the proverbial dorm room

In the days before everyone had internet access and a phone in their pockets, Buytaert simply wanted to build a way for him and his friends to communicate in a centralized way. “I wanted to build kind of an internal message board really to communicate with the other people in the dorm, and it was literally talking about things like ‘Hey, let’s grab a drink at 8:00,’” Buytaert told me.

He also wanted to hone his programming skills. “At the same time I wanted to learn about PHP and MySQL, which at the time were emerging technologies, and so I figured I would spend a few evenings putting together a basic message board using PHP and MySQL, so that I could learn about these technologies, and then actually have something that we could use.”

The resulting product served its purpose well, but when graduation beckoned, Buytaert realized if he unplugged his PC and moved on, the community he had built would die. At that point, he decided to move the site to the public internet and named it drop.org, which was actually an accident. Originally, he meant to register dorp.org because “dorp” is Dutch for “village or small community,” but he mistakenly inverted the letters during registration.

Buytaert continued adding features to drop.org like diaries (a precursor to blogging) and RSS feeds. Eventually, he came up with the idea of open-sourcing the software that ran the site, calling it Drupal.

The birth of web content management

About the same time Buytaert was developing the basis of what would become Drupal, web content management (WCM) was a fresh market. Early websites had been fairly simple and straightforward, but they were growing more complex in the late 90s and a bunch of startups were trying to solve the problem of managing them. Buytaert likely didn’t know it, but there was an industry waiting for an open-source tool like Drupal.

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