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Lean Analytics: Use Data to Build a Better Startup Faster (Lean (O'Reilly))

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Lean Analytics: Use Data to Build a Better Startup Faster (Lean (O’Reilly))

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A startup is an organization formed to search for a scalable and repeatable business model.

Lean Analytics is a way of quantifying your innovation, getting you closer and closer to a continuous reality check — in other words, to reality itself.

Lean, analytical thinking is about asking the right questions, and focusing on the one key metric that will produce the change you’re after.

A good metric is comparative. Being able to compare a metric to other time periods, groups of users, or competitors helps you understand which way things are moving. “Increased conversion from last week” is more meaningful than “2% conversion.” A good metric is understandable. If people can’t remember it and discuss it, it’s much harder to turn a change in the data into a change in the culture. A good metric is a ratio or a rate. Accountants and financial analysts have several ratios they look at to understand, at a glance, the fundamental health of a company.[5] You need some, too. There are several reasons ratios tend to be the best metrics: Ratios are easier to act on. Think about driving a car. Distance travelled is informational. But speed — distance per hour — is something you can act on, because it tells you about your current state, and whether you need to go faster or slower to get to your destination on time. Ratios are inherently comparative. If you compare a daily metric to the same metric over a month, you’ll see whether you’re looking at a sudden spike or a long-term trend. In a car, speed is one metric, but speed right now over average speed this hour shows you a lot about whether you’re accelerating or slowing down. Ratios are also good for comparing factors that are somehow opposed, or for which there’s an inherent tension. In a car, this might be distance covered divided by traffic tickets. The faster you drive, the more distance you cover — but the more tickets you get. This ratio might suggest whether or not you should be breaking the speed limit.

A good metric changes the way you behave. This is by far the most important criterion for a metric: what will you do differently based on changes in the metric?

But if you want to change behavior, your metric must be tied to the behavioral change you want. If you measure something and it’s not attached to a goal, in turn changing your behavior, you’re wasting your time.

If you want to choose the right metrics, you need to keep five things in mind: Qualitative versus quantitative metrics Qualitative metrics are unstructured, anecdotal, revealing, and hard to aggregate; quantitative metrics involve numbers and statistics, and provide hard numbers but less insight. Vanity versus actionable metrics Vanity metrics might make you feel good, but they don’t change how you act. Actionable metrics change your behavior by helping you pick a course of action. Exploratory versus reporting metrics Exploratory metrics are speculative and try to find unknown insights to give you the upper hand, while reporting metrics keep you abreast of normal, managerial, day-to-day operations. Leading versus lagging metrics Leading metrics give you a predictive understanding of the future; lagging metrics explain the past. Leading metrics are better because you still have time to act on them — the horse hasn’t left the barn yet. Correlated versus causal metrics If two metrics change together, they’re correlated, but if one metric causes another metric to change, they’re causal. If you find a causal relationship between something you want (like revenue) and something you can control (like which ad you show), then you can change the future.

Quantitative data is easy to understand. It’s the numbers we track and measure — for example, sports scores and movie ratings. As soon as something is ranked, counted, or put on a scale, it’s quantified. Quantitative data is nice and scientific, and (assuming you do the math right) you can aggregate it, extrapolate it, and put it into a spreadsheet.

Qualitative data is messy, subjective, and imprecise. It’s the stuff of interviews and debates. It’s hard to quantify. You can’t measure qualitative data easily. If quantitative data answers “what” and “how much,” qualitative data answers “why.” Quantitative data abhors emotion; qualitative data marinates in it.

If you have a piece of data on which you cannot act, it’s a vanity metric. If all it does is stroke your ego, it won’t help. You want your data to inform, to guide, to improve your business model, to help you decide on a course of action.

Whenever you look at a metric, ask yourself, “What will I do differently based on this information?” If you can’t answer that question, you probably shouldn’t worry about the metric too much.

Analytics has a role to play in all four of Rumsfeld’s quadrants: It can check our facts and assumptions — such as open rates or conversion rates — to be sure we’re not kidding ourselves, and check that our business plans are accurate. It can test our intuitions, turning hypotheses into evidence. It can provide the data for our spreadsheets, waterfall charts, and board meetings. It can help us find the nugget of opportunity on which to build a business.

A leading metric (sometimes called a leading indicator) tries to predict the future.

For leading indicators to work, you need to be able to do cohort analysis and compare groups of customers over periods of time.

As a leading indicator, customer complaints also give you ammunition to dig into what’s going on, figure out why customers are complaining more, and address those issues.

As we’ve said, a real metric has to be actionable. Lagging and leading metrics can both be actionable, but leading indicators show you what will happen, reducing your cycle time and making you leaner.

Finding a correlation between two metrics is a good thing. Correlations can help you predict what will happen. But finding the cause of something means you can change it. Usually, causations aren’t simple one-to-one relationships.

You prove causality by finding a correlation, then running an experiment in which you control the other variables and measure the difference.

First, know your customer. There’s no substitute for engaging with customers and users directly. All the numbers in the world can’t explain why something is happening. Pick up the phone right now and call a customer, even one who’s disengaged.

Cohort analysis can be done for revenue, churn, viral word of mouth, support costs, or any other metric you care about.

The results can pay off dramatically: Jay Parmar, co-founder of crowdfunded ticketing site Picatic, told us that simply changing the company’s call to action from “Get started free” to “Try it out free” increased the number of people who clicked on an offer — known as the click-through rate — by 376% for a 10-day period.

Take a look at the top three to five metrics that you track religiously and review daily. Write them down. Now answer these questions about them: How many of those metrics are good metrics? How many do you use to make business decisions, and how many are just vanity metrics? Can you eliminate any that aren’t adding value? Are there others that you’re now thinking about that may be more meaningful? Cross off the bad ones and add new ones to the bottom of your list, and let’s keep going through the book.

Marc Andreesen puts it, “Markets that don’t exist don’t care how smart you are.”[

Don’t start a business you’re going to hate. Life is too short, and your weariness will show.

For each intersection between rings, he suggests a course of action: If you want to do something and are good at it, but can’t be paid to do it, learn to monetize. If you’re good at something and can be paid to do it, but don’t like doing it, learn to say no. If you like to do something and can be paid to do it, but aren’t very good at it, learn to do it well.

If you identify a real need, you won’t be the only one satisfying it, and you’ll need all the talent you can muster in order to succeed.

Never forget that you’re trying to answer three fundamental questions: Have I identified a problem worth solving? Is the solution I’m proposing the right one? Do I actually want to solve it? Or, more succinctly: should I go build this thing?

The fundamental KPI for stickiness is customer retention. Churn rates and usage frequency are other important metrics to track. Long-term stickiness often comes from the value users create for themselves as they use the service.

Virality is attractive because it compounds — if every user adds another 1.5 users, your user base will grow infinitely until you’ve saturated all users.[17

Getting paid is, in some ways, the ultimate metric for identifying a sustainable business model. If you make more money from customers than it costs you to acquire them — and you do so consistently — you’re sustainable. You don’t need money from external investors, and you’re growing shareholder equity every day.

You still need to worry about cash flow and growth rate, which are driven by how long it takes a customer to pay off. One way to measure this is time to customer breakeven — that is, how much time it will take to recoup the acquisition cost of a customer.

Setting up and managing instrumentation is fairly easy these days with tools like Geckoboard, Mixpanel, Kissmetrics, Totango, Chartbeat, and others. But don’t let your ability to track so many things distract you. Capture everything, but focus on what’s important.

A 90-day repurchase rate of 1% to 15% means you’re in acquisition mode. A 90-day repurchase rate of 15% to 30% means you’re in hybrid mode. A 90-day repurchase rate of over 30% means you’re in loyalty mode.

While it’s important to optimize revenues, don’t try to make your customers into something they’re not. “I don’t try to force my customer to do things my customer isn’t pre-inclined to do.

“The key to successful e-commerce is in increasing shopping cart size; that’s really where the money is made. I like to think of customer acquisition cost as a fixed cost, so any increase in order size is expanding your margin.”

Tools like ClickTale perform abandonment analysis within the form itself, making it easier to pinpoint bottlenecks in the conversion process where you’re losing customers.

Most SaaS providers generate revenue from a monthly (or yearly) subscription that users pay. Some charge on a consumption basis — for storage, for bandwidth, or for compute cycles — although this is largely confined to Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) cloud computing companies today. Many SaaS providers offer a tiered model of their service, where the monthly fee varies depending on some dimension of the application. This might be the number of projects in a project management tool, or the number of customers in a customer relationship management application. Finding the best mix of these tiers and prices is a constant challenge, and SaaS companies invest considerable effort in finding ways to upsell a user to higher, more lucrative tiers.

Because the incremental cost of adding another customer to a SaaS service is negligible — think of how little it costs Skype to add a new user — many SaaS providers use a freemium model of customer acquisition.[

Uptime and reliability How many complaints, problem escalations, or outages the company has.

tracks how much a customer is worth in the entirety of his relationship with the company — the customer lifetime value (CLV). CLV and CAC are the two essential metrics for a subscription business.

Customer acquisition payback is a great example of a single number that encompasses many things, since it rolls up marketing efficiency, customer revenue, cash flow, and churn rate.

Before focusing on sophisticated financial metrics, start with revenue. But don’t ignore costs, because profitability is the real key to growth. You know it’s time to scale when your paid engine is humming along nicely, which happens when the CAC is a small fraction of the CLV — a sure sign you’re getting a good return on your investment. Most SaaS businesses thrive on monthly recurring revenue — customers continue to pay month after month — which is a great foundation on which to build a business.

The ultimate metric for engagement is daily use. How many of your customers use your product on a daily basis?

Habits are hard to form — and with any new product, you’re creating new habits, which you want to do as quickly and intensely as possible.

If there’s a subsection of users who are hooked on your product — your early adopters — figure out what’s common to them, refocus on their needs, and grow from there.

If you’re building something genuinely disruptive, you need to consider the technology adoption lifecycle, from early to mainstream. Hybrid cars, Linux servers, home stereos, and microwaves were first adopted by a small segment of their markets, but took years of evangelism and millions of marketing dollars to be considered conventional.

To find ways you might improve things, segment users who do what you want from those who don’t, and identify ways in which they’re different. Do the engaged users all live in the same city? Do all users who eventually become loyal contributors learn about you from one social network? Are the users who successfully invite friends all under 30 years old? If you find a concentration of desirable behavior in one segment, you can then target it. To decide whether a change worked, test the change on a subset of your users and compare that subset’s results to others. If you put in a new reporting feature, reveal it to half of your users, and see if more of them stick around for several months. If you can’t test features in this way without fallout — the customers who didn’t get the new feature might get angry — then at the very least, compare the cohort of users who joined after the feature was added to those who came before.

Churn is the percentage of people who abandon your service over time. This can be measured weekly, monthly, quarterly, etc., but you should pick a timespan for all your metrics and stick to it in order to make comparing them easier.

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We recommend defining an inactive user as someone who hasn’t logged in within 90 days (or less). At that point, they’ve churned out; in an always-connected world, 90 days is an eternity.

If 2.5% of customers churn every month, it means that the average customer stays around for 40 months (100/2.5). This is how you can start to calculate the lifetime value of a customer (40 months × average monthly revenue per user).

The shorter the time period you measure, the less that changes during that specific period will distort things.

“People don’t do subscriptions for haircuts, hamburgers, and hiring. You have to understand your customer, who they are, how and why they buy, and how they value your product or service.”

Just because SaaS is a recurring service doesn’t mean it needs to be priced that way. If your product is ephemeral — like a transient job posting — it might be better to offer more transactional pricing. Pricing is a tricky beast. You need to test different price points qualitatively (by getting feedback from customers) and quantitatively. Don’t assume a low price is the answer; customers might not attribute enough value to your offering. And remember that everything, including price, makes up the “product” you’re offering.

While a subscription model lends itself to more predictive financial planning and less volatile revenue numbers, it doesn’t always fit the value proposition, or how customers expect to pay.

While this unfair advantage is gradually changing — revenues for less popular applications are growing overall — the facts are simple: if you want to make money, you need to be ranked highly in app stores, and getting featured helps a great deal.

Visitor demographics will become increasingly important as social platforms like Facebook introduce third-party-placed advertising based on demographic segments — you’ll get paid based on who your visitors are rather than what your site contains.

Fred Wilson calls mobile notification a game changer:[48] Notifications become the primary way I use the phone and the apps. I rarely open Twitter directly. I see that I have “10 new @mentions” and I click on the notification and go to [the] Twitter @mention tab. I see that I have “20 new checkins” and I click on the notification and go to the Foursquare friends tab. He cites three main reasons why this is such a significant shift: First, it allows me to use a lot more engagement apps on my phone. I don’t need them all on the main page. As long as I am getting notifications when there are new engagements, I don’t really care where they are on the phone. Second, I can have as many communications apps as I want. I’ve currently got SMS, Kik, Skype, Beluga, and GroupMe on my phone. I could have plenty more. I don’t need to be loyal to any one communication system, I just need to be loyal to my notification inbox. And finally, the notification screen is the new home screen. When I pull out my phone, it is the first thing I do.

Early on, a marketplace can grow its inventory by hand, using decidedly low-tech approaches. Do things that don’t scale. For some marketplaces, a per-listing or per-transaction fee, rather than a commission, works well. If you can build buyer attention, it’ll be easy to convince sellers to join you, so go where the money is. A static, curated site can be enough to prove the viability of a big-ticket, slow-turnover marketplace. Ultimately, volume of sales, and the resulting revenue, is the only metric that matters.

Josh Breinlinger, a venture capitalist at Sigma West who previously ran marketing at labor marketplace oDesk, breaks up the key marketplace metrics into three categories: buyer activity, seller activity, and transactions. “I almost always recommend modeling the buyer side as your primary focus, and then you model supply, more in the sense of total inventory,” he says. “It’s easy to find people that want to make money; it’s much harder to find people that want to spend money.”

The reality is that every startup goes through stages, beginning with problem discovery, then building something, then finding out if what was built is good enough, then spreading the word and collecting money. These stages — Empathy, Stickiness, Virality, Revenue, and Scale — closely mirror what other Lean Startup advocates advise.

Fifth, you need scale. With revenues coming in, it’s time to move from growing your business to growing your market. You need to acquire more customers from new verticals and geographies. You can invest in channels and distribution to help grow your user base, since direct interaction with individual customers is less critical — you’re past product/market fit and you’re analyzing things quantitatively.

At the outset, you’re spending your time discovering what’s important to people and being empathetic to their problems. You’re searching through listening. You’re digging for opportunity through caring about others. Right now, your job isn’t to prove you’re smart, or that you’ve found a solution. Your job is to get inside someone else’s head. That means discovering and validating a problem and then finding out whether your proposed solution to that problem is likely to work.

If you can’t find 15 people to talk to, well, imagine how hard it’s going to be to sell to them. So suck it up and get out of the office. Otherwise, you’re wasting time and money building something nobody wants.

People only get really interesting when they start to rattle the bars of their cages. — Alain de Botton,

Some ways to get a more honest answer from people are: Get them a prototype early on. Use paper prototyping, or a really simple mockup in PowerPoint, Keynote, or Balsamiq, to watch how they interact with your idea without coaching. See if they’ll pay immediately. Watch them explain it to their friends and see if they understand how to spread the message. Ask for referrals to others who might care.

Customers are people. They lead lives. They have kids, they eat too much, they don’t sleep well, they phone in sick, they get bored, they watch too much reality TV. If you’re building for some kind of idealized, economically rational buyer, you’ll fail. But if you know your customers, warts and all, and you build things that naturally fit into their lives, they’ll love you.

After all, you’ll be selling to people. You need to know how to reach them, interrupt them, and make a difference in their lives at the exact moment when they need your solution.

Surveys can be effective, assuming you’ve done enough customer development already to know what questions to ask. The challenge with surveys is finding people to answer them. Unlike the one-to-one interviews you’ve been conducting so far, here you need to automate the task and deal with the inevitable statistical noise.

Google also has a survey offering, called Google Consumer Surveys, that’s specifically designed to collect consumer information.[54] Because of the wide reach of Google’s publishing and advertising network, the company can generate results that are statistically representative of segments of the population as a whole. Google’s technique uses a “survey wall” approach, but by simplifying the survey process to individual questions requiring only a click or two, the company achieves a 23.1% response rate (compared to less than 1% for “intercept” surveys, 7–14% for phone surveys, and 15% for Internet panels).[55] However, because of the quick-response format, it’s hard to collect multiple responses and correlate them, which limits the kinds of analysis and segmentation you can do.

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Don’t just ask questions. Know how the answers to the questions will change your behavior. In other words, draw a line in the sand before you run the survey.

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Always ask the segmentation questions up front and the open-ended ones at the end. That way you know if your sample was representative of the market you’re targeting, and if people don’t finish the last questions, you still have enough quantitative responses to generate results in which you can be confident.

Before sending it out, try the survey on people who haven’t seen it. You’ll almost always find they get stuck or don’t understand something. You’re not ready to send the survey out until at least three people who haven’t seen it, and are in your target market, can complete it without questions and then explain to you what each question meant. This is no exaggeration: everyone gets surveys wrong.

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Your job isn’t to build a product; it’s to de-risk a business model. Sometimes the only way to do this is to build something, but always be on the lookout for measurable ways to quantify risk without a lot of effort.

No feature should be built without a corresponding metric on usage and engagement. These sub-metrics all bubble up to the OMTM; they’re pieces of data that, aggregated, tell a more complete story. If you can’t instrument a feature or component of your product, be very careful about adding it in — you’re introducing variables that will become harder and harder to control.

You don’t just want signs of engagement. You want proof that your product is becoming an integral part of your users’ lives, and that it’ll be hard for them to switch.

Your top priority is to build a core set of features that gets used regularly and successfully, even by a small group of initial users. Without that, you don’t have a solid enough foundation for growth.

When you know users keep coming back, it’s time to grow your user base.

Too often, features get added to a product without any quantifiable validation — which is a direct path toward scope creep and feature bloat. If you’re unable to quantify the impact of a new feature, you can’t assess its value, and you won’t really know what to do with the feature over time. If this is the case, leave it as is, iterate on it, or kill it.

Building features is easy if you plan them beforehand and truly understand why you’re doing something. It’s important to tie your high-level vision and long-term goals down to the feature level. Without that alignment, you run the risk of building features that can’t be properly tested and don’t drive the business forward.

It takes extra engineering effort to be able to turn on and off individual features, and to measure the resulting change in user behavior, but that investment pays off in reduced cycle time and better learning.

Customers may give you feedback you don’t like. Just remember that they don’t have the same mental model you do, they aren’t in your target market. They often lack training to use your product properly.

The reality is, users will always complain. That’s just the way it goes. Even if people are using your product, you have good engagement metrics, and your product is sticky, they’re still going to complain. Listen to their complaints, and try to get to the root of the issue as quickly as possible without overreacting.

A minimum viable vision (MVV) is one that captivates. It scales. It has potential. It’s audacious and compelling. As a founder, you have to hold that huge, hairy, world-changing vision in one hand, and the practical, pragmatic, seat-of-the-pants reality in the other.

When your engagement numbers are healthy and churn is relatively low, it’s time to focus on growing your user base. Don’t run out and buy ads immediately, though. First, you need to leverage the best, most convincing campaign platform you have — your current users. It’s time to go viral.

Define an active user. What percentage of your users/customers is active? Write this down. Could this be higher? What can you do to improve engagement?

There are three kinds of virality: Inherent virality is built into the product, and happens as a function of use. Artificial virality is forced, and often built into a reward system. Word-of-mouth virality is the conversations generated by satisfied users, independent of your product or service.

The number you’re after is your viral coefficient, which venture capitalist David Skok sums up nicely as “the number of new customers that each existing customer is able to successfully convert.”[

Ultimately, what we’re after is a viral coefficient above 1, because this means the product is self-sustaining. With a viral coefficient above 1, every single user is inviting at least another user, and that new user invites another user in turn. That way, after you have some initial users your product grows by itself.

While this seems relatively straightforward, finding a good leading indicator, and experimenting to determine how it affects the future of the company, is hard work. It’s also how many of today’s break-out entrepreneurs drove their growth.

Correlation is nice. But if you’ve found a leading indicator that causes a change later on, that’s a superpower, because it means you can change the future.

Growth hacking combines many of the disciplines we’ve looked at in the book: finding a business model, identifying the most important metric for your current stage, and constantly learning and optimizing that metric to create a better future for your organization.

Customer lifetime value and customer acquisition cost drive your growth, and you’ll run experiments to try to capture more loyal users for less, tweaking how you charge, when you charge, and what you charge for.

The goal in the Revenue stage is to turn your focus from proving your idea is right to proving you can make money in a scalable, consistent, self-sustaining way.

Measuring revenue is easy enough, but remember that while raw revenue might be going “up and to the right,” revenue per customer is a better indicator of actual health. It’s a ratio, after all, and there’s a lot more you can learn from it.

The entrepreneur had reasonable answers to key questions: how big can the business grow, how good can the margins get, and what kinds of barriers to entry does it have?

Startup CEOs seeking venture capital would do well to remember the penny machine. It’s a good way to ensure you’re thinking like a venture capitalist. Every time your pitch strays from the simplicity of this meeting, it’s a warning sign that you need to go back and tighten it up.

To measure the health of the machine, divide how much you changed the annual recurring revenue in the past quarter by what it cost you to do so. You need three numbers to do this calculation: Your quarterly recurring revenue for quarter x (QRR[x]) Your quarterly recurring revenue for the quarter before x (QRR[x – 1]) Your sales and marketing expense for the quarter before x (QExpSM[x – 1]) If you don’t have quarterly sales and marketing spending, you can take the annual spending and divide it by four. This also helps smooth out spikes in marketing spend or seasonal shifts, since not all the sales you get this quarter are a result of last quarter’s sales efforts — some may have benefitted from previous quarters. The formula looks like this: If the result is below 0.75, you have a problem. When you pump money into the machine, less money comes out. That’s a bad thing for this stage of your business, because it means there’s a fundamental flaw in your business model. If the result is better than 1, you’re doing well — you can fund your growth with the proceeds, funneling revenue increases back into the machine to increase sales and marketing spend.

One variant on freemium is pay-for-privacy, where the content your users create is available to everyone unless they explicitly pay to keep it to themselves.

If your users all pay, then you need to decide if you’ll have trial periods, discounts, or other incentives. Ultimately, the best revenue strategy is to make a great product: the best startups have what Steve Jobs referred to as the “insanely great,” with customers eager to give them money for what they see as true value.

When it comes to turning revenues into additional customers, the most basic rule is simple: spend less money acquiring customers than you get from them.

Balancing acquisition, revenue, and cash flow is at the core of running many business models, particularly those that rely on subscription revenue and paying to gain customers. As you play with the numbers to strike that balance, there are really four variables you work on: The money in the bank at the outset (i.e., your investment) The amount of money spent on customer acquisition each month The revenue you bring in from users The rate of churn from users

Recognize that being able to make money is an inherent assumption of most business models, but that to de-risk the model you need to test it early. Be prepared to radically change, or even shut down, parts of your company in your quest for revenue.

Instead of building new features or rebuilding from scratch, try pointing your product at a new market. We think of this as market/product fit instead of product/market fit, because you’re trying to find a market that fits your existing product.

The Reality of Freemium in SaaS.[

http://www.slideshare.net/sixteenventures/the-reality-of-freemium-in-saas

If the Revenue stage was about proving a business, the Scale stage is about proving a market.

Firms can focus on a niche market (a segmentation strategy), they can focus on being efficient (a cost strategy), or they can try to be unique (a differentiation strategy).

Scaling is good if it brings in incremental revenue, but you have to watch for a decrease in engagement, a gradual saturation of the initial market, or a rising cost of customer acquisition. Changes in churn, segmented by channels, show whether you’re growing your most important asset — your customers — or hemorrhaging attention as you scale.

This combination of agility and methodical precision is what distinguishes great startups from stalled ones.

When you’re scaling, you know your product and your market. Your metrics are now focused on the health of your ecosystem, and your ability to enter new markets.

You need to understand if you’re focused on efficiency or differentiation. Trying to do both as a way of scaling is difficult. If you’re efficiency-focused, you’re trying to reduce costs; if you’re differentiation-focused, you’re increasing margins.

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Success is not final, failure is not fatal: it is the courage to continue that counts. — Sir Winston Churchill

All companies have cancellations (or churn), and it’s one of the most critical metrics to track and understand — not only is it essential for calculating metrics like customer lifetime value, but it’s also an early warning signal that something is going wrong or that a competing solution has emerged.

Having a cancellation number isn’t enough; you need to understand why people are abandoning your product or service. Jason did just that by calling customers who cancelled.

It’s easy to get stuck on one specific metric that looks bad and invest considerable time and money trying to improve it. Until you know where you stand against competitors and industry averages, you’re blind. Having benchmarks helps you decide whether to keep working on a specific metric or move on to the next challenge.

As we’ve seen, you’re doing well when you spend less than a third of your customer revenue acquiring new customers. For bigger-ticket applications (with a CLV of over $50K) things are less bleak, with most companies spending between 0.2% and 2% of CLV on acquisition.

Startups, Paul says, go through three distinct growth phases: slow, where the organization is searching for a product and market to tackle; fast, where it has figured out how to make and sell it at scale; and slow again, as it becomes a big company and encounters internal constraints or market saturation, and tries to overcome Porter’s “hole in the middle.”

stickiness comes before virality, and virality comes before scale.

Growing a B2B organization prematurely can alienate your core of loyal customers who are helping to build your business, stalling revenue and eliminating the referrals, case studies, and testimonials needed to grow your sales.

When you’re a pre-revenue startup at or near product/market fit, your line in the sand should be 5% growth for active users each week, and once you’re generating revenues, they should grow at 5% a week.

He says that for a web service or mobile application: 30% of registered users will use a web-based service at least once a month. For mobile applications, 30% of the people who download the app use it each month. 10% of registered users will use the service or mobile app every day. The maximum number of concurrent users will be 10% of the number of daily users.

Aim for 30% of your registered users to visit once a month, and 10% of them to come daily. Figure out your reliable leading indicators of growth, and measure them against your business model predictions.

A fundamental element of any pricing strategy is elasticity: when you charge more, you sell less; when you charge less, you sell more.

Unlike Marshall, you have the world’s greatest pricing laboratory at your disposal: the Internet. You can test out discount codes, promotions, and even varied pricing on your customers and see what happens.

If you make your pricing tiers simple, you’ll see better conversions. Patrick Campbell, co-founder and CEO of pricing service Price Intelligently, says that based on his data, companies with easy-to-understand tiers and a clear path up differentiated pricing plans convert customers at a much higher rate than companies with complicated tiers, features that aren’t always applicable, and hard-to-follow pricing paths.

“One of the biggest misconceptions around pricing is that what you charge for your product or service is directly related to how much it costs you to build or run it. That’s not the case. Price is related to what your customers are prepared to pay.”

This underscores the tricky balance in a freemium or tiered pricing model: how do you make sure that the features/services being offered fit into the right packages at the right price?

Price is an important tool for getting your customers to do what you want, and it should always be compared not only to cost of sales, but also to cost of goods sold and marginal cost.

Ultimately, what Patrick’s research shows is that despite the considerable rewards for getting pricing right, most startups aren’t looking at real data — they’re shooting from the hip.

There’s no clear rule on what to charge. But whatever your choice of pricing models, testing is key. Understanding the right tiers of pricing and the price elasticity of your market is vital if you’re going to balance revenues with adoption. Once you find your revenue “sweet spot,” aim about 10% lower to encourage growth of your user base.

Unless you have a good reason to do otherwise, don’t spend more than a third of the money you expect to gain from a customer (and the customers she invites downstream) on acquiring that customer.

A sustained viral coefficient of greater than 1 is an extremely strong indicator of growth, and suggests that you should be focusing on stickiness so you can retain those new users as you add them.

Put another way, virality is a force multiplier for your attention-generating efforts. Done right, it’s one of your unfair advantages.

There’s no “typical” virality for startups. If virality is below 1, it’s helping lower your customer acquisition cost. If it’s above 1, you’ll grow. And if you’re over 0.75, things are pretty good. Try to build inherent virality into the product, and track it against your business model. Treat artificial virality the same way you would customer acquisition, and segment it by the value of the new users it brings in.

Targeting your mailings by tailoring messages to different segments of your subscriber base improves clicks and opens by nearly 15%.

  • comprtir a tigo

Jason Billingsley recommends testing an individualized send schedule equal to the signup time of the unique user. So, if a user signs up at 9 a.m., schedule to send her updates at 9 a.m.

But by far the biggest factor in mailing list effectiveness is simple: write a decent subject line. A good one gets an open rate of 60–87%, and a bad one suffers a paltry 1–14%.[

Open and click-through rates will vary significantly, but a well-run campaign should hit a 20–30% open rate and over 5% click-through.

For a paid service that users rely on (such as an email application or a hosted project management application), you should have at least 99.5% uptime, and keep users updated about outages. Other kinds of applications can survive a lower level of service.

An average engaged time on a page of one minute is normal, but there’s wide variance between sites and between pages on a site.

Study after study has proven that fast sites do better across nearly every metric that matters, from time on site to conversion to shopping cart size.[

Site speed is something you can control, and it can give you a real advantage. Get your pages to load for a first-time visitor in less than 5 seconds; after 10, and you’ll start to suffer.

“In my experience, most e-commerce startups selling either their own product or retailing others’ products can expect conversion rates of 1–3% maximum,” says Bill D’Alessandro. “Startups shouldn’t plug 8–10% conversion into their models when deciding on the viability of their business — that’s never going to happen.

The three things that propel you from 2% to 10% are seriously loyal users, lots of SKUs, and repeat customers. And even then it’s a big accomplishment.”

Table 22-2. Conversion rates by vertical Type of site Conversion rate Catalog 5.8% Software 3.9% Fashion and apparel 2.3% Specialty 1.7% Electronics 0.50% Outdoor and sports 0.40%

If you’re an online retailer, you’ll get initial conversion rates of around 2%, which will vary by vertical, but if you can achieve 10%, you’re doing incredibly well. If your visitors arrive with a strong intent to buy, you’ll do better — but, of course, you’ll have to invest elsewhere to get them into that mindset. Kevin Hillstrom at Mine That Data cautions that averages are dangerous here. Many electronics retailers, which have a lot of “drive-by” visitors doing research, have conversion rates as low as 0.5%. On the other hand, there’s a correlation between average order size and conversion rate.

A 2012 study estimated that just over 65% of buyers abandon their shopping cart.[97] Of those who abandon, 44% do so because of high shipping costs, 41% decide they aren’t ready to purchase, and 25% find the price is too high. A February 2012 study estimated abandonment at an even higher 77%.[98] Improving on abandonment beyond 65% seems to be a challenge, but that doesn’t stop companies from trying:

Sixty-five percent of people who start down your purchase funnel will abandon their purchase before paying for it.

It has found that asking for a credit card during signup means 0.5% to 2% of visitors sign up for a trial, while not asking for a credit card means 5% to 10% of visitors will enroll.

Roughly 15% of trial users who did not provide a credit card will sign up for a paid subscription. On the other hand, 40–50% of trial users who did provide one will convert to a paid subscription.

A 2009 Pacific Crest study found that best-in-class SaaS companies manage to get their annual churn rates below 15%.[102]

Totango’s data shows that for most SaaS providers, 20% of visitors are serious evaluators, 20% are casual evaluators, and 60% are simply curious.

According to Totango’s research, the best approach is to not put up a credit card paywall to try the service, but to segment users into three groups — then market to the active ones, nurture the casual ones, and don’t waste time on those who are just curious bystanders (or at best, get them to tell friends who might be real prospects about you).

If you ask for a credit card up front, expect just 2% of visitors to try your service, and 50% of them to use it. If you don’t ask for a credit card, expect 10% to try, and up to 25% to buy — but if they’re surprised by a payment, you’ll lose them quickly. In our preceding example, not having a credit card up front gives you a 40% increase in conversions, provided you can tailor your selling efforts to each segment of your evaluators based on their activity.

Jules Maltz and Daniel Barney of IVP, a late-stage venture capital and growth equity firm, suggest that freemium models work for products that have:[107] A low cost of delivering service to an additional user (i.e., low marginal cost). Cheap, or even free, marketing that happens as people use the product. A relatively simple tool that doesn’t require long evaluations or training. An offering that “feels right” if it’s free. Some products (like homeowner’s insurance) might make prospects wary if they’re offered for free. An increase in value the longer someone uses the product. Flickr gets more valuable the more images you store in it, for example. A good viral coefficient, so your free users become marketers for you.

Even if you’re charging every customer, you can still experiment with pricing in the form of promotions, discounts, and time-limited offers. Each of these is a hypothesis suitable for testing across cohorts (if you use time-limited offers) or A/B comparisons (if you offer different pricing to different visitors).

Best-in-class SaaS providers are able to grow revenues per customer by 20% from year to year. This comes through additional users added to the subscription, as the application spreads through the organization, as well as a series of tiered offerings and an easy upselling path. Done correctly, the increased revenues from upselling should nearly offset the 2% monthly losses from churn. But these are the best of the best, and they offer a clear path for extracting more money from customers as each customer’s use grows.

Try to get to 20% increase in customer revenue — which may include additional seat licenses — each year. And try to get 2% of your paying subscribers to increase what they pay each month.

The best SaaS sites or applications usually have churn ranging from 1.5% to 3% a month. For other sites, it’ll vary depending on how you define “disengaged.” Mark MacLeod, Partner at Real Ventures, says that you need to get below a 5% monthly churn rate before you know you’ve got a business that’s ready to scale.

Certain products or services are very sticky, in part because of the lock-in users experience. Photo upload sites and online backup services, for example, are hard to leave, because there’s a lot of data in place, so churn for those product categories may be lower. On the other hand, in an industry with relatively low switching costs, churn will be substantially higher.

Try to get down to 5% churn a month before looking at other things to optimize. If churn is higher than that, chances are you’re not sticky enough. If you can get churn to around 2%, you’re doing exceptionally well.

Expect yourself to be at the mercy of promotions, marketing, and the whims of the app store environment. The app store battle can be demoralizing, but smart mobile developers use the abundance of information about competitors to see what’s working, emulate their successes, and avoid their mistakes.

Pay around 2.50 for a legitimate, organic one, but make sure that your overall acquisition cost is less than $0.75 per user (and, of course, less than the lifetime value of a user). These costs are increasing, in part because large studios and publishers are getting more heavily into mobile and driving costs higher, and in part because of the crackdown on some marketing service tactics for delivering paid installs.

but if you’re running a freemium model where users pay for enhanced functionality, then a good rule of thumb is that 2% of your users will actually sign up for the full offering.

For a free-to-play mobile game with in-app purchases, Ken Seto says that across the industry roughly 1.5% of players will buy something within the game during their use of it.

For a freemium model, aim for a conversion from free to paid of 2%. For a mobile application or game with in-app purchases, assume that roughly 1.5% of users will buy something.

SuperData Research has published ARPDAU benchmarks for different gaming genres:[113] 0.05 USD for puzzle, caretaking, and simulation games 0.07 USD for hidden object, tournament, and adventure games 0.10 USD for RPGs, gambling, and poker games GAMESbrief.com collected additional information from three game companies, DeNA, A Thinking Ape, and WGT:

A well-placed, relevant ad will get clicked more, but no matter what, ads are a numbers game: even the best ads seldom get as much as 5% click-through rates.

Global search marketing agency Covario reported in 2010 that the average click-through rate for paid search, worldwide, was 2% (see Table 25-2).

Your ads will get 0.5 to 2% click-through rate for most kinds of on-page advertising. Below 0.08%, you’re doing something horribly wrong.

Look at the outliers. “If a page has a large number of visitors and a low engaged time, think about why people are leaving quickly. Did they come expecting something else? Is the layout working? Or is it simply a page that isn’t designed to keep users for long?” asks Joshua. Show off your good stuff. If a page has a high engaged time but few visitors, consider promoting it to a wider audience. Ensure that the purpose of the page matches the engagement. “If you’re an e-commerce site, you might want your landing page to have little engagement time,” says Joshua. “But if you’re producing editorial content, you should aim for high engaged time on article pages.”

With a few notable exceptions, Steinberg and Krawczyk conclude that sharing happens from a groundswell of small interactions among colleagues and friends, rather than through massive actions between one person and an army of minions.

There’s no clear number, but if a content generation function (such as uploading photos) is core to the use of your application, optimize it until all your users can do it, and track error conditions carefully to find out what’s causing the problem.

Marc Andreesen says: “In a great market — a market with lots of real potential customers — the market pulls product out of the startup.”[

While it’s important not to overbuild beyond your initial feature set or core function — in reddit’s case, link sharing — a thriving community will pull features out of you if you know how to listen. Reddit included only basic functionality, but made it easy for users to extend the site, then learned from what was working best and incorporated it into the platform.

Leading web usability consultant Jakob Nielsen once observed that in an online population, 90% of people lurk, 9% contribute intermittently, and 1% are heavy contributors.[

23% of Internet users are passive, choosing only to consume 16% of users will react to something (voting, commenting, or flagging it) 44% will initiate something (posting content, starting a thread, etc.) 17% of users are contributing intensely, doing something even when it’s difficult or not core to the platform, such as reviewing a book on an e-commerce site

By our estimates, expect 25% of your visitors to lurk, 60–70% of your visitors to do things that are easy and central to the purpose of your product or service, and 5–15% of your users to engage and create content for you. Among those engaged users, expect 80% of your content to come from a small, hyperactive group of users, and expect 2.5% of users to interact casually with content and less than 1% to put some effort into interaction.

The reality is you’ll quickly adjust the line in the sand to your particular market or product. That’s fine. Just remember that you shouldn’t move the line to your ability; rather, you need to move your ability to the line.

Ultimately, the best the company will be able to do with all else being equal is achieve a conversion rate of around 9%. So on the one hand, that’s a good baseline, and gives a sense of the universe it’s in. On the other hand, all else is seldom equal. A new strategy for user acquisition could change things significantly.

He who rejects change is the architect of decay. The only human institution which rejects progress is the cemetery. — Harold Wilson

“While the enterprise can be as boring as hell, the whole goddamn thing is paved with gold.”[

The one thing that makes enterprise-focused startups different is this: B2C customer development is polling, B2B customer development is a census.

Much of analytics is about trying to understand large amounts of information so you can get a better grasp of underlying patterns and act on them. But in the early stages of a B2B startup, there aren’t patterns — there are just customers. You can pick up the phone and call them right away. They’ll call you and tell you what they want. You can get in a room with them. You can’t test something on a statistically significant sample of the population and write it off if the test fails — you’ll lose customers.

Enterprise buyers tend to be more regulated. They can’t make decisions on gut or emotion — or rather, they can, but it has to be justified with a business case. Big companies are often public companies with checks and balances. The person who pays for the product (finance) isn’t the person who uses it (the line of business). Understanding this dichotomy is critical for product development and sales. Initially, you may target early adopters, where the buyer is much closer to the user (they may be the same person at this point), but as you move past early adopters, the buyer and user diverge. Companies have formal structure for good reasons. It helps prevent corruption, and makes auditing possible. But that structure gets in the way of understanding things. Your contact at a company may be a proponent, but someone else in the organization may be a detractor, or have a concern of which you’re not aware. This is one of the reasons direct sales is common in early stages: it lets you navigate the bureaucracy and understand the part of the sales process that’s hidden to outsiders.

Those legacy issues are part of another problem — incumbents. If you’re trying to disrupt or replace something, you’ll have to convince the organization that you’re better, despite the efforts of an existing solution. Organizations are averse to change, and love the status quo. If you’re trying to sell to them, and your product is still in the early stages of the technology adoption cycle, you’re penalized simply for being new. Consumers love novelty; businesses just call it risk.

Of course, big, slow incumbents have plenty of weaknesses. New entrants can disrupt their market simply by being easier to adopt, because they require no training.

Simplicity isn’t just an attribute of enterprise disruption — it’s the price of entry.

A central theme to this new wave of innovation is the application of core product tenets from the consumer space to the enterprise. In particular, a universal lesson that I keep sharing with all entrepreneurs building for the enterprise is the Zero Overhead Principle: no feature may add training costs to the user.[

The rise of the SaaS market changes this, because it’s relatively easy to alter functionality without the market’s permission. But if you’re selling traditional enterprise software, or delivery trucks, or shredders, you’re not going to learn and iterate as quickly as you would from consumers. Of course, your competitors aren’t either. You don’t need to be fast — just faster than everyone else.

Because enterprise buyers can’t take the risks consumers can, they limit their own thinking. They demand proof that something will work before they try it out, which means great ideas can often become mired in business cases, return-on-investment analyses, and total-cost-of-ownership spreadsheets.

Because companies are full of people — for many of whom their job is just a job — their priority is to minimize the chance of them making a mistake even if the organization as a whole might suffer in the long term. It’s hard to inspire an organization if its employees are busy wondering whether the changes you promise will cost them their jobs.

For all these reasons, most B2B-focused startups consist of two people: a domain expert and a disruption expert. The domain expert knows the industry and the problem domain. He has a Rolodex and can act as a proxy for customers in the early stages of product definition. Often this person is from the line of business, and has a marketing, sales, or business development role. The disruption expert knows the technology that will produce a change on which the startup can capitalize. She can see beyond the current model and understand what an industry will look like after the shift, and brings the novel approach to the existing market. This is usually the technologist.

That’s because domain knowledge is essential. Important elements of how a business works — particularly back-office operations — are hidden from the outside world. It’s only by being an insider that the bottlenecks become painfully obvious.

It’s also necessary to “burn the boats” of the services business to ensure that you commit to the product. After all, you’re going to neglect some of your most-loved customers in order to deliver a product the general market wants instead, and it’ll be tempting to do custom work to keep them happy. You can’t run a product and a services business concurrently. Even IBM had to split itself in two; what makes you think you can do it as a fledgling startup?

Once the company saw what kinds of reports customers made, and how they used the appliance, it incorporated those into later versions.

Sometimes, environmental changes such as legislation or competition mean that validated business assumptions are no longer true. When that happens, look at what your core value proposition is and see if you can sell it to a different market or in a different way that overcomes those changes — in this case, keeping only a subset of a service and delivering it as an appliance.

Your existing clients may feel that a standardized product you plan to offer will be less tailored to their needs; you need to convince them that a standard product is in fact better for them, because the cost of building future versions will be shared among many buyers.

Imagine, for example, that you’re building a hiring management tool. The way that a legal firm finds and retains candidates is very different from the way a fast-food restaurant does it. Trying to build a single tool for them — particularly at the outset — is a bad idea. Everything from the number of interviews, to the qualifications needed, to the number of years someone stays with the company will be different. Differences mean customization and parameters, which increase complexity, and violates DJ Patil’s Zero Overhead Principle.

In the enterprise market, the risk is more, “Will it integrate?” Integration with existing tools, processes, and environments is the most likely source of problems, and you’ll wind up customizing for clients — which undermines the standardization you fought so hard to achieve earlier.

Managing this tension between customization and standardization is one of the biggest challenges of an early-stage enterprise startup. If you can’t get the client’s users to try the product, you’re doomed. And while your technology might work, if it doesn’t properly integrate with legacy systems, it’ll be seen as your fault, not theirs.

Assuming you’ve successfully sold the standardized product to an initial market segment, you’ll need to grow. Because enterprises don’t trust newcomers, you’ll rely heavily on referrals and word-of-mouth marketing. You’ll make case studies from early successes, and ask satisfied users to handle phone calls from new prospects. Referrals and references are critical to this stage of growth. A couple of household names as customers are priceless. Enterprise-focused vendors will often provide discounts in exchange for case studies.

With the pipeline growing and revenue coming in, you’ll worry about cash flow and commission structures for your direct sales team. To know if you have a sustainable business, you’ll also look at support costs, churn, trouble tickets, and other indicators of ongoing business costs to learn just how much a particular customer contributes to the bottom line. If the operating margin is bad, it will have a significant drag on profitability.

Scaling an enterprise software company takes years to accomplish. Zach estimates that it can be as long as 5 to 10 years before a company selling into the enterprise has established and validated channels, and mastered its sales processes.

Unlike B2C platforms where you’re looking at subscription and engagement, if you’re selling a big-ticket, long-term item, you’re looking at contracts. While you may not have recognizable revenue, you’ll have lead volume and bookings to analyze, and these should give you an understanding of the cost of sales once the product has launched.

It’s important — right from the very beginning — that you articulate the stages of your sales funnel and the conversion rates at each point along the way. The sales cycle needs to be well documented, measured, and understood after the first few sales, to see if you can build a repeatable approach. At that point, you can bring in additional salespeople to increase volume.

DJ Patil suggests using data to find where the friction is hiding in your usage and adoption. “If you can’t measure it, you can’t fix it,” he says. “Instrument the product to monitor user flows and be able to test new ideas in how to iteratively improve your product.”

In the heat of the moment, it’s hard to take notes, but integration plays such a big role in enterprise sales that you have to be disciplined about measuring it. What’s the true cost of pre- and post-sales support? How much customization is required? How much training, explaining, and troubleshooting are you doing in order to successfully deliver a product to a customer?

Get baselines from your clients that apply to their real-world businesses before you deploy. How many orders do they enter a day? How long does it take an employee to get payroll information? How many truck deliveries a day can their warehouse handle? What is the usual call hold time? Once you’ve deployed, use this information to measure progress, helping your advocates to prove the ROI — and turning it into case studies you can share with other customers.

Zach Nies suggests going even further, segmenting customers into three groups. “‘A customers’ are your really big customers who negotiated a big discount and expect the world from you. ‘B customers’ are customers who are fairly low maintenance, didn’t get a big discount, see themselves as partners with you, and provide useful insights. ‘C customers’ cause trouble, are a pain to deal with, and demand things from you that you feel will damage your business,” he explains. “Don’t spend too much time on the A’s — they sound good but aren’t the best for your business. Bring as many Bs on as customers as possible. And try to get your ‘C customers’ to be customers of your competitors.”

Zach’s advice is based on some fundamental truths. In many B2B-focused companies, the top 20% of customers generate 150–300% of profits, while the middle 70% of customers break even, and the lowest 10% of customers reduce 50–200% of profits.[

You’ll track support metrics like top-requested features, number of outstanding trouble tickets, post-sales support, call center hold time, and so on. This will indicate where you’re losing money, and whether the product is standardized and stable enough to move into growth and scaling.

You don’t actually have to fire customers, of course. You can simply change their pricing enough to make them profitable or encourage them to leave. This is part of getting your pricing right before you grow the business to a point where unprofitable clients can do real damage at scale.

Informal interaction with existing customers can be a boon to enterprise-focused startups, and resembles the problem and solution validation stages of the Lean Startup process — only rather than validating a solution, you’re validating a roadmap.

One important conclusion from this work is that people find it easier to discard something they don’t want than to choose something they do (which feels like commitment), so a series of questions in which they are asked to discard one of two options works well.

You’ve measured your effectiveness at setting up meetings in the early phases of your startup. It matters later on, when you’re about to bring on channels. Your channel partners aren’t as clever as you, and you’ll need to arm them with collateral and messaging that they can use to close deals without your assistance. If they try to push your product or service and encounter resistance, they’ll sell something else. With channels, you seldom get a second chance to make a first impression.

As you bring customers on at scale, you want to make them stick around. A vibrant developer ecosystem and a healthy API allow customers to integrate themselves with you, making you the incumbent vendor and helping you to counter threats from competitors and new entrants.

All innovations… are a gamble and whilst we can reduce costs we can never eliminate it. The future value of something is inversely proportional to the certainty we have over it; we cannot avoid this information barrier any more than we can reliably predict the future. However, there is a means to maximize our advantage. By making these utility services accessible through APIs, we not only benefit ourselves but we can open up these components to a wider ecosystem. If we can encourage innovation in that wider ecosystem then we do not incur the cost of gambling [and] failure for those new activities. Unfortunately, we do not enjoy the rewards of their success either. Fortunately, the ecosystem provides an early warning mechanism of success (i.e., adoption)…by creating a large enough ecosystem, we can not only encourage a rapid rate of innovation but also leverage that ecosystem to identify success and then either copy (a weak ecosystem approach) or acquire (a strong ecosystem approach) that activity. This is how we maximize our advantage.

Reward performance based on results, and get ready to break the normal compensation models. After all, you’re trying to keep entrepreneurs within a company, and if they’re talented, they could leave to do their own thing.

Products with high market share but slow growth are “cash cows.” They generate revenue, but they aren’t worthy of heavy investment. By contrast, products with high growth but small market share are “question marks,” candidates for investment and development. Those with both growth and market share are the rising “stars.” Those with neither — called “dogs” — are to be sold off or shut down.

“We found that building our own data set based on asking people questions and playing them music was the way to go.” The result was over 1 million detailed interviews, and hundreds of millions of data points.

“Bad data is a pain to sell to people. And even good data is a pain to sell to someone if it doesn’t actually help someone, whether that’s because it’s not in a form that helps them work out what to do or because it doesn’t actually answer the questions they are asking,” he says. “But when the data’s good and it really does help someone, then nobody can refuse it.”

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“If you really believe in the data and the recommendations that the data makes, then you focus on why the person doesn’t understand the data and you help them to understand it,” he explained. “When they understand, then their eyes light up, and they become a bigger fan of the data than I am!”

“Good data beats big data,” he concludes. “I am constantly surprised at how good it can be when done properly.”

“Successful business innovation isn’t about giving consumers what they need now, but about giving them something they’ll desire in the future.”[170

Your startup has succeeded when it’s a sustainable, repeatable business that can generate a return to its founders and investors. It might take on additional funding at this point, but the purpose of the funding is no longer to identify and mitigate uncertainties, it’s to execute on a proven business model.

“The most important figures that one needs for management are unknown or unknowable, but successful management must nevertheless take account of them.”

Nelson’s point was that we often do things without knowing they’ll work. That’s called experimentation. But experimentation — for companies of any size — succeeds only if it’s part of a process of continuous learning, one we hope to have instilled in you whatever the size or stage of your business.

If you’re a leader — the founder of a startup, or a C-level executive in a large enterprise — you can turn analytics into a competitive advantage simply by asking good questions. Earlier in the book we said that a good metric is one that drives decision making. As a leader within your organization, demand proof through data before making decisions.

As we’ve said before, Lean Analytics isn’t about eliminating your gut, it’s about proving your gut right or wrong.

There’s never been a better time to know your market. Your customers leave a trail of digital breadcrumbs with every click, tweet, vote, like, share, check-in, and purchase, from the first time they hear about you until the day they leave you forever, whether they’re online or off. If you know how to collect those breadcrumbs, you have unprecedented insight into their needs, their quirks, and their lives.

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