Sep
13
2021
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3 keys to pricing early-stage SaaS products

I’ve met hundreds of founders over the years, and most, particularly early-stage founders, share one common go-to-market gripe: Pricing.

For enterprise software, traditional pricing methods like per-seat models are often easier to figure out for products that are hyperspecific, especially those used by people in essentially the same way, such as Zoom or Slack. However, it’s a different ballgame for startups that offer services or products that are more complex.

Most startups struggle with a per-seat model because their products, unlike Zoom and Slack, are used in a litany of ways. Salesforce, for example, employs regular seat licenses and admin licenses — customers can opt for lower pricing for solutions that have low-usage parts — while other products are priced based on negotiation as part of annual renewals.

You may have a strong champion in a CIO you’re selling to or a very friendly person handling procurement, but it won’t matter if the pricing can’t be easily explained and understood. Complicated or unclear pricing adds more friction.

Early pricing discussions should center around the buyer’s perspective and the value the product creates for them. It’s important for founders to think about the output and the outcome, and a number they can reasonably defend to customers moving forward. Of course, self-evaluation is hard, especially when you’re asking someone else to pay you for something you’ve created.

This process will take time, so here are three tips to smoothen the ride.

Pricing is a journey

Pricing is not a fixed exercise. The enterprise software business involves a lot of intangible aspects, and a software product’s perceived value, quality, and user experience can be highly variable.

The pricing journey is long and, despite what some founders might think, jumping headfirst into customer acquisition isn’t the first stop. Instead, step one is making sure you have a fully fledged product.

If you’re a late-seed or Series A company, you’re focused on landing those first 10-20 customers and racking up some wins to showcase in your investor and board deck. But when you grow your organization to the point where the CEO isn’t the only person selling, you’ll want to have your go-to-market position figured out.

Many startups fall into the trap of thinking: “We need to figure out what pricing looks like, so let’s ask 50 hypothetical customers how much they would pay for a solution like ours.” I don’t agree with this approach, because the product hasn’t been finalized yet. You haven’t figured out product-market fit or product messaging and you want to spend a lot of time and energy on pricing? Sure, revenue is important, but you should focus on finding the path to accruing revenue versus finding a strict pricing model.

Aug
26
2021
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Are B2B SaaS marketers getting it wrong?

Which terms come to mind when you think about SaaS?

“Solutions,” “cutting-edge,” “scalable” and “innovative” are just a sample of the overused jargon lurking around every corner of the techverse, with SaaS marketers the world over seemingly singing from the same hymn book.

Sadly for them, new research has proven that such jargon-heavy copy — along with unclear features and benefits — is deterring customers and cutting down conversions. Around 57% of users want to see improvements in the clarity and navigation of websites, suggesting that techspeak and unnecessarily complex UX are turning customers away at the door, according to The SaaS Engine.

That’s not to say SaaS marketers aren’t trying: Seventy percent of those surveyed have been making big adjustments to their websites, and 33% have updated their content. So how and why are they missing the mark?

They say there’s no bigger slave to fashion than someone determined to avoid it, and SaaS marketing is no different. To truly stand out, you need to do thorough competitor analysis.

There are three common blunders that most SaaS marketers make time and again when it comes to clarity and high-converting content:

  1. Not differentiating from competitors.
  2. Not humanizing “tech talk.”
  3. Not tuning their messaging to prospects’ stage of awareness at the appropriate stage of the funnel.

We’re going to unpack what the research suggests and the steps you can take to avoid these common pitfalls.

Blending into the competition

It’s a jungle out there. But while camouflage might be key to surviving in the wild, in the crowded SaaS marketplace, it’s all about standing out. Let’s be honest: How many SaaS homepages have you visited that look the same? How many times have you read about “innovative tech-driven solutions that will revolutionize your workflow”?

The research has found that of those using SaaS at work, 76% are now on more platforms or using existing ones more intensively than last year. And as always, with increased demand comes a boom in competition, so it’s never been more important to stand out. Rather than imitating the same old phrases and copy your competitors are using, it’s time to reach your audience with originality, empathy and striking clarity.

But how do you do that?

Aug
19
2021
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Companies betting on data must value people as much as AI

The Pareto principle, also known as the 80-20 rule, asserts that 80% of consequences come from 20% of causes, rendering the remainder way less impactful.

Those working with data may have heard a different rendition of the 80-20 rule: A data scientist spends 80% of their time at work cleaning up messy data as opposed to doing actual analysis or generating insights. Imagine a 30-minute drive expanded to two-and-a-half hours by traffic jams, and you’ll get the picture.

As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now.

While most data scientists spend more than 20% of their time at work on actual analysis, they still have to waste countless hours turning a trove of messy data into a tidy dataset ready for analysis. This process can include removing duplicate data, making sure all entries are formatted correctly and doing other preparatory work.

On average, this workflow stage takes up about 45% of the total time, a recent Anaconda survey found. An earlier poll by CrowdFlower put the estimate at 60%, and many other surveys cite figures in this range.

None of this is to say data preparation is not important. “Garbage in, garbage out” is a well-known rule in computer science circles, and it applies to data science, too. In the best-case scenario, the script will just return an error, warning that it cannot calculate the average spending per client, because the entry for customer #1527 is formatted as text, not as a numeral. In the worst case, the company will act on insights that have little to do with reality.

The real question to ask here is whether re-formatting the data for customer #1527 is really the best way to use the time of a well-paid expert. The average data scientist is paid between $95,000 and $120,000 per year, according to various estimates. Having the employee on such pay focus on mind-numbing, non-expert tasks is a waste both of their time and the company’s money. Besides, real-world data has a lifespan, and if a dataset for a time-sensitive project takes too long to collect and process, it can be outdated before any analysis is done.

What’s more, companies’ quests for data often include wasting the time of non-data-focused personnel, with employees asked to help fetch or produce data instead of working on their regular responsibilities. More than half of the data being collected by companies is often not used at all, suggesting that the time of everyone involved in the collection has been wasted to produce nothing but operational delay and the associated losses.

The data that has been collected, on the other hand, is often only used by a designated data science team that is too overworked to go through everything that is available.

All for data, and data for all

The issues outlined here all play into the fact that save for the data pioneers like Google and Facebook, companies are still wrapping their heads around how to re-imagine themselves for the data-driven era. Data is pulled into huge databases and data scientists are left with a lot of cleaning to do, while others, whose time was wasted on helping fetch the data, do not benefit from it too often.

The truth is, we are still early when it comes to data transformation. The success of tech giants that put data at the core of their business models set off a spark that is only starting to take off. And even though the results are mixed for now, this is a sign that companies have yet to master thinking with data.

Data holds much value, and businesses are very much aware of it, as showcased by the appetite for AI experts in non-tech companies. Companies just have to do it right, and one of the key tasks in this respect is to start focusing on people as much as we do on AIs.

Data can enhance the operations of virtually any component within the organizational structure of any business. As tempting as it may be to think of a future where there is a machine learning model for every business process, we do not need to tread that far right now. The goal for any company looking to tap data today comes down to getting it from point A to point B. Point A is the part in the workflow where data is being collected, and point B is the person who needs this data for decision-making.

Importantly, point B does not have to be a data scientist. It could be a manager trying to figure out the optimal workflow design, an engineer looking for flaws in a manufacturing process or a UI designer doing A/B testing on a specific feature. All of these people must have the data they need at hand all the time, ready to be processed for insights.

People can thrive with data just as well as models, especially if the company invests in them and makes sure to equip them with basic analysis skills. In this approach, accessibility must be the name of the game.

Skeptics may claim that big data is nothing but an overused corporate buzzword, but advanced analytics capacities can enhance the bottom line for any company as long as it comes with a clear plan and appropriate expectations. The first step is to focus on making data accessible and easy to use and not on hauling in as much data as possible.

In other words, an all-around data culture is just as important for an enterprise as the data infrastructure.

Aug
10
2021
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VCs are betting big on Kubernetes: Here are 5 reasons why

I worked at Google for six years. Internally, you have no choice — you must use Kubernetes if you are deploying microservices and containers (it’s actually not called Kubernetes inside of Google; it’s called Borg). But what was once solely an internal project at Google has since been open-sourced and has become one of the most talked about technologies in software development and operations.

For good reason. One person with a laptop can now accomplish what used to take a large team of engineers. At times, Kubernetes can feel like a superpower, but with all of the benefits of scalability and agility comes immense complexity. The truth is, very few software developers truly understand how Kubernetes works under the hood.

I like to use the analogy of a watch. From the user’s perspective, it’s very straightforward until it breaks. To actually fix a broken watch requires expertise most people simply do not have — and I promise you, Kubernetes is much more complex than your watch.

How are most teams solving this problem? The truth is, many of them aren’t. They often adopt Kubernetes as part of their digital transformation only to find out it’s much more complex than they expected. Then they have to hire more engineers and experts to manage it, which in a way defeats its purpose.

Where you see containers, you see Kubernetes to help with orchestration. According to Datadog’s most recent report about container adoption, nearly 90% of all containers are orchestrated.

All of this means there is a great opportunity for DevOps startups to come in and address the different pain points within the Kubernetes ecosystem. This technology isn’t going anywhere, so any platform or tooling that helps make it more secure, simple to use and easy to troubleshoot will be well appreciated by the software development community.

In that sense, there’s never been a better time for VCs to invest in this ecosystem. It’s my belief that Kubernetes is becoming the new Linux: 96.4% of the top million web servers’ operating systems are Linux. Similarly, Kubernetes is trending to become the de facto operating system for modern, cloud-native applications. It is already the most popular open-source project within the Cloud Native Computing Foundation (CNCF), with 91% of respondents using it — a steady increase from 78% in 2019 and 58% in 2018.

While the technology is proven and adoption is skyrocketing, there are still some fundamental challenges that will undoubtedly be solved by third-party solutions. Let’s go deeper and look at five reasons why we’ll see a surge of startups in this space.

 

Containers are the go-to method for building modern apps

Docker revolutionized how developers build and ship applications. Container technology has made it easier to move applications and workloads between clouds. It also provides as much resource isolation as a traditional hypervisor, but with considerable opportunities to improve agility, efficiency and speed.

Aug
04
2021
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Enterprise AI 2.0: The acceleration of B2B AI innovation has begun

Two decades after businesses first started deploying AI solutions, one can argue that they’ve made little progress in achieving significant gains in efficiency and profitability relative to the hype that drove initial expectations.

On the surface, recent data supports AI skeptics. Almost 90% of data science projects never make it to production; only 20% of analytics insights through 2022 will achieve business outcomes; and even companies that have developed an enterprisewide AI strategy are seeing failure rates of up to 50%.

But the past 25 years have only been the first phase in the evolution of enterprise AI — or what we might call Enterprise AI 1.0. That’s where many businesses remain today. However, companies on the leading edge of AI innovation have advanced to the next generation, which will define the coming decade of big data, analytics and automation — Enterprise AI 2.0.

The difference between these two generations of enterprise AI is not academic. For executives across the business spectrum — from healthcare and retail to media and finance — the evolution from 1.0 to 2.0 is a chance to learn and adapt from past failures, create concrete expectations for future uses and justify the rising investment in AI that we see across industries.

Two decades from now, when business leaders look back to the 2020s, the companies who achieved Enterprise AI 2.0 first will have come to be big winners in the economy, having differentiated their services, scooped up market share and positioned themselves for ongoing innovation.

Framing the digital transformations of the future as an evolution from Enterprise AI 1.0 to 2.0 provides a conceptual model for business leaders developing strategies to compete in the age of automation and advanced analytics.

Enterprise AI 1.0 (the status quo)

Starting in the mid-1990s, AI was a sector marked by speculative testing, experimental interest and exploration. These activities occurred almost exclusively in the domain of data scientists. As Gartner wrote in a recent report, these efforts were “alchemy … run by wizards whose talents will not scale in the organization.”

Jul
29
2021
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4 key areas SaaS startups must address to scale infrastructure for the enterprise

Startups and SMBs are usually the first to adopt many SaaS products. But as these customers grow in size and complexity — and as you rope in larger organizations — scaling your infrastructure for the enterprise becomes critical for success.

Below are four tips on how to advance your company’s infrastructure to support and grow with your largest customers.

Address your customers’ security and reliability needs

If you’re building SaaS, odds are you’re holding very important customer data. Regardless of what you build, that makes you a threat vector for attacks on your customers. While security is important for all customers, the stakes certainly get higher the larger they grow.

Given the stakes, it’s paramount to build infrastructure, products and processes that address your customers’ growing security and reliability needs. That includes the ethical and moral obligation you have to make sure your systems and practices meet and exceed any claim you make about security and reliability to your customers.

Here are security and reliability requirements large customers typically ask for:

Formal SLAs around uptime: If you’re building SaaS, customers expect it to be available all the time. Large customers using your software for mission-critical applications will expect to see formal SLAs in contracts committing to 99.9% uptime or higher. As you build infrastructure and product layers, you need to be confident in your uptime and be able to measure uptime on a per customer basis so you know if you’re meeting your contractual obligations.

While it’s hard to prioritize asks from your largest customers, you’ll find that their collective feedback will pull your product roadmap in a specific direction.

Real-time status of your platform: Most larger customers will expect to see your platform’s historical uptime and have real-time visibility into events and incidents as they happen. As you mature and specialize, creating this visibility for customers also drives more collaboration between your customer operations and infrastructure teams. This collaboration is valuable to invest in, as it provides insights into how customers are experiencing a particular degradation in your service and allows for you to communicate back what you found so far and what your ETA is.

Backups: As your customers grow, be prepared for expectations around backups — not just in terms of how long it takes to recover the whole application, but also around backup periodicity, location of your backups and data retention (e.g., are you holding on to the data too long?). If you’re building your backup strategy, thinking about future flexibility around backup management will help you stay ahead of these asks.

Jul
20
2021
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How we built an AI unicorn in 6 years

Today, Tractable is worth $1 billion. Our AI is used by millions of people across the world to recover faster from road accidents, and it also helps recycle as many cars as Tesla puts on the road.

And yet six years ago, Tractable was just me and Raz (Razvan Ranca, CTO), two college grads coding in a basement. Here’s how we did it, and what we learned along the way.

Build upon a fresh technological breakthrough

In 2013, I was fortunate to get into artificial intelligence (more specifically, deep learning) six months before it blew up internationally. It started when I took a course on Coursera called “Machine learning with neural networks” by Geoffrey Hinton. It was like being love struck. Back then, to me AI was science fiction, like “The Terminator.”

Narrowly focusing on a branch of applied science that was undergoing a paradigm shift which hadn’t yet reached the business world changed everything.

But an article in the tech press said the academic field was amid a resurgence. As a result of 100x larger training data sets and 100x higher compute power becoming available by reprogramming GPUs (graphics cards), a huge leap in predictive performance had been attained in image classification a year earlier. This meant computers were starting to be able to understand what’s in an image — like humans do.

The next step was getting this technology into the real world. While at university — Imperial College London — teaming up with much more skilled people, we built a plant recognition app with deep learning. We walked our professor through Hyde Park, watching him take photos of flowers with the app and laughing from joy as the AI recognized the right plant species. This had previously been impossible.

I started spending every spare moment on image classification with deep learning. Still, no one was talking about it in the news — even Imperial’s computer vision lab wasn’t yet on it! I felt like I was in on a revolutionary secret.

Looking back, narrowly focusing on a branch of applied science undergoing a breakthrough paradigm shift that hadn’t yet reached the business world changed everything.

Search for complementary co-founders who will become your best friends

I’d previously been rejected from Entrepreneur First (EF), one of the world’s best incubators, for not knowing anything about tech. Having changed that, I applied again.

The last interview was a hackathon, where I met Raz. He was doing machine learning research at Cambridge, had topped EF’s technical test, and published papers on reconstructing shredded documents and on poker bots that could detect bluffs. His bare-bones webpage read: “I seek data-driven solutions to currently intractable problems.” Now that had a ring to it (and where we’d get the name for Tractable).

That hackathon, we coded all night. The morning after, he and I knew something special was happening between us. We moved in together and would spend years side by side, 24/7, from waking up to Pantera in the morning to coding marathons at night.

But we also wouldn’t have got where we are without Adrien (Cohen, president), who joined as our third co-founder right after our seed round. Adrien had previously co-founded Lazada, an online supermarket in South East Asia like Amazon and Alibaba, which sold to Alibaba for $1.5 billion. Adrien would teach us how to build a business, inspire trust and hire world-class talent.

Find potential customers early so you can work out market fit

Tractable started at EF with a head start — a paying customer. Our first use case was … plastic pipe welds.

It was as glamorous as it sounds. Pipes that carry water and natural gas to your home are made of plastic. They’re connected by welds (melt the two plastic ends, connect them, let them cool down and solidify again as one). Image classification AI could visually check people’s weld setups to ensure good quality. Most of all, it was real-world value for breakthrough AI.

And yet in the end, they — our only paying customer — stopped working with us, just as we were raising our first round of funding. That was rough. Luckily, the number of pipe weld inspections was too small a market to interest investors, so we explored other use cases — utilities, geology, dermatology and medical imaging.

Jul
13
2021
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Build a digital ops toolbox to streamline business processes with hyperautomation

Reliance on a single technology as a lifeline is a futile battle now. When simple automation no longer does the trick, delivering end-to-end automation needs a combination of complementary technologies that can give a facelift to business processes: the digital operations toolbox.

According to a McKinsey survey, enterprises that have likely been successful with digital transformation efforts adopted sophisticated technologies such as artificial intelligence, Internet of Things or machine learning. Enterprises can achieve hyperautomation with the digital ops toolbox, the hub for your digital operations.

The hyperautomation market is burgeoning: Analysts predict that by 2025, it will reach around $860 billion.

The toolbox is a synchronous medley of intelligent business process management (iBPM), robotic process automation (RPA), process mining, low code, artificial intelligence (AI), machine learning (ML) and a rules engine. The technologies can be optimally combined to achieve the organization’s key performance indicator (KPI) through hyperautomation.

The hyperautomation market is burgeoning: Analysts predict that by 2025, it will reach around $860 billion. Let’s see why.

The purpose of a digital ops toolbox

The toolbox, the treasure chest of technologies it is, helps with three crucial aspects: process automation, orchestration and intelligence.

Process automation: A hyperautomation mindset introduces the world of “automating anything that can be,” whether that’s a process or a task. If something can be handled by bots or other technologies, it should be.

Orchestration: Hyperautomation, per se, adds an orchestration layer to simple automation. Technologies like intelligent business process management orchestrate the entire process.

Intelligence: Machines can automate repetitive tasks, but they lack the decision-making capabilities of humans. And, to achieve a perfect harmony where machines are made to “think and act,” or attain cognitive skills, we need AI. Combining AI, ML and natural language processing algorithms with analytics propels simple automation to become more cognitive. Instead of just following if-then rules, the technologies help gather insights from the data. The decision-making capabilities enable bots to make decisions.

 

Simple automation versus hyperautomation

Here’s a story of evolving from simple automation to hyperautomation with an example: an order-to-cash process.

Jul
08
2021
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Achieving digital transformation through RPA and process mining

Understanding what you will change is most important to achieve a long-lasting and successful robotic process automation transformation. There are three pillars that will be most impacted by the change: people, process and digital workers (also referred to as robots). The interaction of these three pillars executes workflows and tasks, and if integrated cohesively, determines the success of an enterprisewide digital transformation.

Robots are not coming to replace us, they are coming to take over the repetitive, mundane and monotonous tasks that we’ve never been fond of. They are here to transform the work we do by allowing us to focus on innovation and impactful work. RPA ties decisions and actions together. It is the skeletal structure of a digital process that carries information from point A to point B. However, the decision-making capability to understand and decide what comes next will be fueled by RPA’s integration with AI.

From a strategic standpoint, success measures for automating, optimizing and redesigning work should not be solely centered around metrics like decreasing fully loaded costs or FTE reduction, but should put the people at the center.

We are seeing software vendors adopt vertical technology capabilities and offer a wide range of capabilities to address the three pillars mentioned above. These include powerhouses like UiPath, which recently went public, Microsoft’s Softomotive acquisition, and Celonis, which recently became a unicorn with a $1 billion Series D round. RPA firms call it “intelligent automation,” whereas Celonis targets the execution management system. Both are aiming to be a one-stop shop for all things related to process.

We have seen investments in various product categories for each stage in the intelligent automation journey. Process and task mining for process discovery, centralized business process repositories for CoEs, executives to manage the pipeline and measure cost versus benefit, and artificial intelligence solutions for intelligent document processing.

For your transformation journey to be successful, you need to develop a deep understanding of your goals, people and the process.

Define goals and measurements of success

From a strategic standpoint, success measures for automating, optimizing and redesigning work should not be solely centered around metrics like decreasing fully loaded costs or FTE reduction, but should put the people at the center. To measure improved customer and employee experiences, give special attention to metrics like decreases in throughput time or rework rate, identify vendors that deliver late, and find missed invoice payments or determine loan requests from individuals that are more likely to be paid back late. These provide more targeted success measures for specific business units.

The returns realized with an automation program are not limited to metrics like time or cost savings. The overall performance of an automation program can be more thoroughly measured with the sum of successes of the improved CX/EX metrics in different business units. For each business process you will be redesigning, optimizing or automating, set a definitive problem statement and try to find the right solution to solve it. Do not try to fit predetermined solutions into the problems. Start with the problem and goal first.

Understand the people first

To accomplish enterprise digital transformation via RPA, executives should put people at the heart of their program. Understanding the skill sets and talents of the workforce within the company can yield better knowledge of how well each employee can contribute to the automation economy within the organization. A workforce that is continuously retrained and upskilled learns how to automate and flexibly complete tasks together with robots and is better equipped to achieve transformation at scale.

Jul
01
2021
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To guard against data loss and misuse, the cybersecurity conversation must evolve

Data breaches have become a part of life. They impact hospitals, universities, government agencies, charitable organizations and commercial enterprises. In healthcare alone, 2020 saw 640 breaches, exposing 30 million personal records, a 25% increase over 2019 that equates to roughly two breaches per day, according to the U.S. Department of Health and Human Services. On a global basis, 2.3 billion records were breached in February 2021.

It’s painfully clear that existing data loss prevention (DLP) tools are struggling to deal with the data sprawl, ubiquitous cloud services, device diversity and human behaviors that constitute our virtual world.

Conventional DLP solutions are built on a castle-and-moat framework in which data centers and cloud platforms are the castles holding sensitive data. They’re surrounded by networks, endpoint devices and human beings that serve as moats, defining the defensive security perimeters of every organization. Conventional solutions assign sensitivity ratings to individual data assets and monitor these perimeters to detect the unauthorized movement of sensitive data.

It’s painfully clear that existing data loss prevention (DLP) tools are struggling to deal with the data sprawl, ubiquitous cloud services, device diversity and human behaviors that constitute our virtual world.

Unfortunately, these historical security boundaries are becoming increasingly ambiguous and somewhat irrelevant as bots, APIs and collaboration tools become the primary conduits for sharing and exchanging data.

In reality, data loss is only half the problem confronting a modern enterprise. Corporations are routinely exposed to financial, legal and ethical risks associated with the mishandling or misuse of sensitive information within the corporation itself. The risks associated with the misuse of personally identifiable information have been widely publicized.

However, risks of similar or greater severity can result from the mishandling of intellectual property, material nonpublic information, or any type of data that was obtained through a formal agreement that placed explicit restrictions on its use.

Conventional DLP frameworks are incapable of addressing these challenges. We believe they need to be replaced by a new data misuse protection (DMP) framework that safeguards data from unauthorized or inappropriate use within a corporate environment in addition to its outright theft or inadvertent loss. DMP solutions will provide data assets with more sophisticated self-defense mechanisms instead of relying on the surveillance of traditional security perimeters.

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