A SaaS (“Software as a Service”) business refers to a software distribution model where a company provides software applications to customers over the internet (or through the cloud) on a subscription basis. Instead of purchasing and installing software on their own servers or computers, customers can access and use the software through a web browser or dedicated application.
In a SaaS business model, the software is centrally hosted and maintained by the SaaS provider. Customers typically pay a recurring fee, often on a monthly or annual basis, to access and use the software. This fee covers not only the software itself but also the infrastructure, updates, customer support, and other services provided by the SaaS company.
SaaS businesses offer a wide range of applications and services, catering to various industries and needs, such as customer relationship management (CRM), project management, human resources management, accounting, collaboration tools, and more. The SaaS model offers several advantages, including scalability, cost-effectiveness, easy accessibility, and regular updates without requiring customer intervention.
Examples of well-known SaaS businesses include Salesforce (CRM), Dropbox (cloud storage and file sharing), Slack (team communication and collaboration), and Zendesk (customer support and ticketing).
A startup can be defined as a human institution designed to create a new product or service under conditions of extreme uncertainty. Anyone creating a new product or business under such conditions is an entrepreneur whether he or she knows it or not and whether working in a government agency, for a venture-backed financial investors, or elsewhere.
We often lose sight of the fact that a startup is not just about a product, a technological breakthrough, or even a brilliant idea. A startup is greater than the sum of its parts; it is an acutely human enterprise.
The fact that a startup’s product or service is a new innovation is also an essential part of the definition and a tricky part too. In the broadest sense a startup encompasses any source of value for the people who become customers. Anything those customers experience from their interaction with a company should be considered pa rt of that company’s product.
It’s also important that the word innovation be understood broadly. Startups use many kinds of innovation: novel scientific discoveries, repurposing an existing technology for a new use, devising a new business model that unlocks value that was hidden, or simply bringing a product or service to a new location or a previously underserved set of customers. In all these cases, innovation is at the heart of the company’s success.
There is one more significant part of this definition: the context in which the innovation happens. Most businesses large and small alike-are excluded from this context.
Startups are designed to confront situations of extreme uncertainty. To open up a new business that is an exact clone of an existing business all the way down to the business model, pricing, target customer is easy by comparison as it’s about replication of a product and the surrounding ecosystem. In a startup, little is known at the outset, which is why it is such a complicated undertaking.
Ah, laziness – that all-too-familiar label we slap on anyone who prefers a Netflix binge over a stack of paperwork or who seems to treat deadlines like vague suggestions rather than hard stops. We’ve all heard it, and let’s be honest, we’ve probably used it. But what if I told you that laziness is a myth, an illusion masking deeper issues?
For over a decade, I’ve led teams in various business environments, observing countless employees grappling with deadlines, procrastinating on tasks, and missing critical project milestones. From promising candidates missing job application deadlines to team members agonising over a single project for months, the pattern is clear and familiar. But here’s the kicker – I don’t believe laziness is ever at fault. In fact, I don’t believe laziness exists at all.
As someone deeply interested in psychology, social anthropology, and sociology (admittedly an unusual thing for a business-person and entrepreneur), I delve into the situational and contextual factors driving human behaviour. If you want to understand why someone behaves a certain way, look beyond their personality or intelligence. Instead, consider their environment, the social norms, and the situational constraints they face. When an employee misses deadlines or struggles with tasks, the question to ask is: what are the barriers holding them back? What needs are unmet? What invisible hurdles are they facing?
Psychological research supports this perspective. A seminal study by Mihaly Csikszentmihalyi on “flow” states found that individuals are most productive and engaged when they are in environments that support their autonomy, competence, and relatedness. These findings suggest that when employees appear disengaged or lazy, it is often because these fundamental needs are not being met.
If the fundamental goal of entrepreneurship is to engage in organization-building under conditions of extreme uncertainty, its most vital function is learning. We must learn the truth about which elements of our strategy are working to realize our vision and which don’t make any sense. We must learn what customers really want, not what they say they want or what we think they should want. We must discover whether we are on a path that will lead to growing a sustainable business. In the Lean Startup model, we are rehabilitating learning with a concept called “validated learning”, or a rigorous method for demonstrating progress when one is embedded in the soil of extreme uncertainty in which startups grow. Validated learning is the process of demonstrating empirically that a team has discovered valuable truths a bout a startup’s present and future business prospects. It is more concrete, more accurate, and faster than market forecasting or classical business planning. It is the principal antidote to the lethal problem of achieving failure: successfully executing a plan that leads nowhere.
"Validated learning is a rigorous method for demonstrating progress when one is embedded in the soil of extreme uncertainty in which startups grow. Validated learning is the process of demonstrating empirically that a team has discovered valuable truths about a startup's present and future business prospects."
Startups have to commit, ahead of time, that regardless of what comes out of testing of the MVP, they will not give up their hopes. Successful entrepreneurs do not give up at the first sign of trouble, nor do they persevere the plane right into the ground. Instead, they possess a unique combination of perseverance and flexibility. The MVP is just the first step on a journey of learning. Down that road-after many iterations startups may learn that some element of their products or strategy are flawed and decide it is time to make a change, which is called a pivot, a different method for achieving their vision.
Startups are especially at risk when outside stakeholders and investors (especially corporate CFOs for internal projects) have a crisis of confidence.
In traditional management, a manager who promises to deliver something and fails to do so is in trouble. There are only two possible explanations: a failure of execution or a failure to plan appropriately. Both are equally inexcusable. Entrepreneurial managers face a difficult problem: because the plans and projections they make are full of uncertainty, how can they claim success when they inevitably fail to deliver what they promised?
The solution to this issue resides at the heart of the Lean Startup model. We all need a disciplined, systematic approach to figuring out if we’re making progress and discovering if we’re actually achieving validated learning. This system is called “innovation accounting” and it is a powerful alternative to traditional accounting designed specifically for startups.
At the beginning, a startup is little more than a model on a piece of paper. The financials in the business plan include projections of how many customers the company expects to attract, how much it will spend, and how much revenue and profit that will lead to. It’s an ideal that’s usually far from where the startup is in its early days.
A startup’s task is to rigorously measure where it is right now, confronting the hard truths that assessment reveals, and then devise experiments to learn how to move the real numbers closer to the ideal reflected in the business plan.
Employees and entrepreneurs tend to be optimistic by nature. They want to keep believing in their ideas even when the writing is on the wall. This is why the myth of perseverance is so dangerous.
Unfortunately, we don’t hear stories about the countless nameless others who persevered too long, leading their companies to failure.
People are accustomed to thinking of accounting as dry and boring, a necessary evil used primarily to prepare financial reports and survive audits, but that is because accounting is something that has become taken for granted.
Standard accounting is not helpful in evaluating entrepreneurs. Startups are too unpredictable for forecasts and milestones to be accurate.
Lost milestones are built the same way: hit a certain product milestone, maybe talk to a few customers, and see if the numbers go up. Unfortunately, this is not a good indicator of whether a startup is making progress. How do we know that the changes we’ve made are related to the results we’re seeing? More importantly, how do we know that we are drawing the right lessons from those changes?
To answer these kinds of questions, startups have a strong need for a new kind of accounting geared specifically to disruptive innovation. That’s what innovation accounting is.
"Innovation accounting and it is powerful alternative to traditional accounting designed specifically for startups. People are accustomed to thinking of accounting as dry and boring, a necessary evil used primarily to prepare financial reports and survive audits, but that is because accounting is something that has become taken for granted. Standard accounting is not helpful in evaluating entrepreneurs. Startups are too unpredictable for forecasts and milestones to be accurate."
Innovation accounting begins by turning the leap-of-faith assumptions into a quantitative financial model. Every business plan has some kind of model associated with it, even if it’s written on the back of a napkin. That model provides assumptions about what the business will look like at a successful point in the future.
Innovation accounting works in three steps: first, use a minimum viable product to establish real data on where the company is right now. Without a clear-eyed picture of your current status-no matter how far from the goal you may be, you cannot begin to track your progress.
Second, startups must attempt to tune the engine from the baseline toward the ideal. This may take many attempts. After the startup has made all the micro changes and product optimizations it can to move its baseline toward the ideal, the company reaches a decision point. That is the third and final third step: pivot or persevere.
Then, in the final step, the company pivots, and it starts the process all over again.
To establish the baseline, which is the first of the three milestones, a startup might create a complete prototype of its product and offer to sell it to real customers through its main marketing channel. This single MVP would test most of the startup’s assumptions and establish baseline metrics for each assumption simultaneously. Alternatively, a startup might prefer to build separate MVPs that are aimed at getting feedback on one assumption at a time.
Before building the prototype, the company might perform a smoke test with its marketing materials. This is an old direct marketing technique in which customers are provided the opportunity to pre-order a product that has not yet been built. A smoke test measures only one thing: whether customers are interested in trying a product.
These MVPs provide the first example of a learning milestone. An MVP allows a startup to fill in real baseline data in its growth model-conversion rates, sign-up and trial rates, customer lifetime value, and so on-and this is valuable as the foundation for learning about customers and their reactions to a product.
When one is choosing among the many assumptions in a business plan, it makes sense to test the riskiest assumptions first.
Once the baseline has been established, the startup can work toward the second learning milestone: tuning the engine.
Every product development, marketing, or other initiative that a startup undertakes should be targeted at improving one of the drivers of its growth model. For example, a company might spend time improving the design of its product to make it easier for new customers to use. This presupposes that the activation rate of new customers is a driver of growth and that its baseline is lower than the company would like.
Over time, a team that is learning its way toward a sustainable business will see the numbers in its model rise from the horrible baseline established by the MVP and converge to something like the ideal one established in the business plan. A startup that fails to do so will see that ideal recede ever farther into the distance.
"When this is done right, even the most powerful reality distortion field won't be able to cover up this simple fact: if we're not moving the drivers of our business model, we're not making progress. That becomes a sure sign that it's time to pivot, or the third and final milestone."
Cohort analysis (examples below) is one of the most important tools of startup analytics. Although it sounds complex, it is based on a simple premise. Instead of looking at cumulative totals or gross numbers such as total revenue and total number of customers, one looks at the performance of each group of customers that comes into contact with the product independently. Each group is called a cohort. In the example below, there are four cohorts of customers, defined by how they interact with product/company (i.e. in-store, offline, online, phone) and what type of interaction is most suitable for each stage of engagement (i.e. print, events, SEO, merchandising).
This technique is useful in many types of business because every company depends for its survival on sequences of customer behaviour called flows. Customer flows govern the interaction of customers with the company’s products. They allow us to understand a business quantitatively and have much more predictive power than do traditional gross metrics.
This is the pattern: poor quantitative results force us to declare failure and create the motivation, context, and space for more qualitative research. These investigations produce new ideas-new hypotheses-to be tested, leading to a possible pivot. Each pivot unlocks new opportunities for further experimentation, and the cycle repeats. Each time we repeat this simple rhythm: establish the baseline, tune the engine, and make a decision to pivot or persevere.
A split-test experiment is one in which different versions of a product are offered to customers at the same time. By observing the changes in behaviour between the two groups, one can make inferences about the impact of the different variations. Direct mail advertisers pioneered this technique. If you wanted to test a catalogue design, you could send a new version of it to SO percent of the customers and send the standard old catalogue to the other SO percent. To assure a scientific result, both catalogues would contain identical products; the only difference would be the changes to the design. To understand if the new design was effective, all you would have to do was keep track of the sales figures for both groups of customers. (This technique is sometimes called A/B testing after the practice of assigning letter names to each variation.) Although split testing is often thought of as a marketing-specific (or even a direct marketing-specific) practice, Lean Startups incorporate it directly into product development.
In cohort analysis, it’s critical to track metrics which reveal how successful or unsuccessful a product, campaign, or engagement type is. The image below shows examples of metrics that are useful over a certain time horizon.
Lean startups are about innovating. It is a field-tested philosophy that provides a recipe to minimize failure and increase the chances of success. It’s a powerful approach for learning about customers and their problems and about providing them with solutions of great value. The concept borrows from production practices, such as lean manufacturing and Kanban, which focus on execution and adaption as strategies to achieve innovation. The Lean Startup approach fosters both companies that are more capital efficient and those that leverage human creativity more effectively. This approach enables an organization to shift directions with agility, altering plans inch by inch.
Kanban is a powerful method that supports visualization of work. It is the best choice for startup environments, characterized by constant adjustments and quick feedback loops. While using Kanban you should focus on: visualizing the work-in-progress (using a “Kanban board”), keeping the workflow smooth and learning from mistakes to reduce the cycle time. It is straightforward to get started. You can use a white board with and sticky notes or online solutions if your team is more spread-out.
Startups face many challenges. Kanban is a perfect solution for such companies. It allows them to learn and to pull more value out of a process.
Teams using Kanban typically make surprising discoveries almost immediately about their workflow – insights that can dramatically improve the pace of development. Such improvement is important to any business, but in a startup it can be a matter of life or death. Therefore, with Kanban, startups are often prone to continuous improvement. Kanban streamlines flow, limiting work-in-progress so that value is delivered speedily and regularly, thus making the whole system much more capital-efficient.
Kanban Tool is a visual project management tool for Lean Startups. It supports real-time team collaboration and allows teams to get work done faster. It provides advanced online Kanban boards with a wide range of customizable features, including power-ups. The Kanban Tool also offers insightful analytics and metrics for planning, monitoring and improving project performance that allows users to visualize key results with breakdown charts, cumulative flow charts or lead & cycle time and then export data to Excel or CSV.
Engineers, designers, and marketers are all skilled at optimization. For example, direct marketers are experienced in split testing value propositions by sending a different offer to two similar groups of customers so that they can measure differences in the response rates of the two groups. Engineers, of course, are skilled at improving a product’s performance, just as designers are talented at making products easier to use.
Startup has to measure progress against a high bar: evidence that a sustainable business can be built around its products or services.
Learning milestones prevent this negative spiral by emphasizing a more likely possibility: the company is executing with discipline a plan that does not make sense. The innovation accounting framework makes it clear when the company is stuck and needs to change direction.
As we’ll see soon, this is a common danger. Companies of any size that have a working engine of growth can come to rely on the wrong kind of metrics to guide their actions. This is what tempts managers to resort to the usual bag of success.
Theatre tricks: last-minute ad buys, channel stuffing. And whizz bang demos, in a desperate attempt to make the gross numbers look better. Energy invested in success theatre is energy that could have been used to help build a sustainable business. Such traditional numbers used to judge startups are in reality nothing more than “vanity metrics,” and innovation accounting requires us to avoid the temptation to use them.
Innovation accounting will network if a startup is being misled by these kinds of vanity metrics: gross number of customers and so on. The alternative is the kind of metrics we use to judge our business and our learning milestones, the so-called actionable metrics.
For innovation accounting to work, the metrics used to facilitate the validated learning must actionable, accessible, and auditable (or triple A).
Actionable metrics
For a report to be considered actionable, it must demonstrate clear cause and effect. By contrast, vanity metrics fail this criterion. Take the number of hits to a company website. Let’s say we have 40,000 hits this month-a new record. What do we need to do to get more hits? Well, that depends. Where are the new hits coming from? Is it from 40,000 new customers or from someone with an extremely active web browser? Are the hits the result of a new marketing campaign or PR push? What is a hit, anyway? Does each page in the browser count as one hit, or do all the embedded images and multimedia content count as well? Those who have sat in a meeting debating the units of measurement in a report will recognize this problem.
Vanity metrics wreak havoc because they prey on a weakness of the human mind. In my experience, when the numbers go up, people think the improvement was caused by their actions.
Actionable metrics are the antidote to this issue. When cause and effect are clearly understood, people are better able to learn from their actions. Human beings are innately talented learners when given a clear and objective assessment.
Accessible metrics
All too many reports are not understood by the employees and managers who are supposed to use them to guide their decision-making.
There is an antidote to this misuse of data. First, make the reports as simple as possible so that everyone understands them. Remember the saying “Metrics are people, too.” The easiest way to make reports comprehensible is to use tangible, concrete units. What is a website hit? Nobody is really sure, but everyone knows what a person visiting the website is: one can practically picture those people sitting at their computers. This is why cohort-based reports are the gold standard of learning metrics: they turn complex actions into people-based reports. Each cohort analysis says: among the people who used our product in this period, here’s how many of them exhibited each of the behaviours we care about. For example, those visiting a website may be downloading the product, logging into the product from one’s computer, engaging in a chat with other customers, and upgrading to the paid version of the product. In other words, the report deals with people and their actions. Which are far more useful than piles of data points such as the total number of person-to-person conversations.
Auditable metrics
Startups need to be able to test the data by hand, in the messy real world, by talking to customers. This is the only way to be able to check if the reports contain true facts. Managers need the ability to spot check the data with real customers. It also offers a second benefit: systems that provide this level of auditability give managers and entrepreneurs the opportunity to gain insights into why customers are behaving the way the data indicates.
Second, those building reports must make sure the mechanisms that generate the reports are not too complex. Whenever possible, reports should be drawn directly from the master data, rather than from an intermediate system, which reduces opportunities for error. It’s not surprising that every time a team has one of its judgments or assumptions overturned as a result of a technical problem with the data, its confidence, morale, and discipline are undermined.
Only 5 percent of entrepreneurship is the big idea, the business model, the whiteboard strategizing, and the splitting up of the spoils. The other 95 percent is the gritty work that is measured by innovation accounting: product prioritization decisions, deciding which customers to target or listen to, and having the courage to subject a grand vision to constant testing and feedback.
One decision stands out above all others as the most diff cult, the most time-consuming, and the biggest source of waste for most startups. Startups must face this fundamental test: deciding when to pivot and when to persevere.
"Only 5 percent of entrepreneurship is the big idea, the business model, the whiteboard strategizing, and the splitting up of the spoils. The other 95 percent is the gritty work that is measured by innovation accounting: product prioritization decisions, deciding which customers to target or listen to, and having the courage to subject a grand vision to constant testing and and feedback."
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