Counterintuitive Lessons on How to Get Better as You Scale, From Twilio’s Jeff Lawson | First Round Review

13 years after Twilio first launched, CEO and co-founder Jeff Lawson opens up about the peaks and valleys to share a set of unconventional company building lessons on how to get better as you scale — from introducing new products and refining go-to-market strategies, to focusing annual planning and making post-mortems more effective.
— Read on’s-jeff-lawson

Understanding Lifetime Customer Value (or LTV)

Lifetime Customer Value or LTV is a crucial concept in analytics, a metric that simply express what the customer is worth (value), from the time she signs up, throughout her lifetime of interactions with your app, in the future. Through a general formula of value of same x number of repeated transactions x projected length of customer retention, you are able to reconcile how much money you spend on each user, the acquisition and retention costs, versus how much value the customer brings back over a period of time.

LTV also goes by the names of Customer Lifetime Value (CLV) or Lifetime Customer Value (LCV).

Let’s say you spend $8 procuring a customer, based on marketing and other efforts, once at the start, you ideally need to get something back in return, in a sustainable model. In return, your user subscribes to your service for $9.99 a month, for 8 months, the total LTV of $79.92.

Bare in mind that there are variables in here, which makes this an estimate rather than anything more concrete. The projected length of a customer retention is an estimate, as is number of repeated transactions, as past history isn’t always an accurate indicator of future behavior, and if any of the variables are way off the mark, LTV becomes useless. Therefore, use this tool diligently and in conjunction with other metrics, rather than as a sole metric.

LTV is also a long-term game, not a short one, and even if the cost of acquiring a user (Customer Acquisition Cost or CAC) is higher than the initial value, you should aim to have a longer projected length of customer retention, as value increases intangibly for your users, so longevity is key here. Often times, a happy customer over a longer retentative period means their value can increase through upgrades and other cross-selling measures.

Speaking of intangibles, last but not least, you cannot measure the viral value of customers as easily. Word of mouth, social media spreading and customer advocacy of your product through being a satisfied customer yields increase brand awareness and more users, thereby reducing customer acquisition costs for other customers. So, in conclusion, LTV is a strong tool, great in venture pitches, and works better with other metrics, for correlative purposes, rather than alone.

Get custom analytics, easily

In the startup-world, from conception to launch and acquisition, usually involves mass user acquisition, in order to increase brand value, and ultimately become a more attractive proposition for further investment, or even potential acquisition. Operating in such a competitive landscape, analytics has become a critical factor. Knowing and understanding app usage is crucial, understanding what features users are more receptive to allows startups to pivot dynamically, in order to provide a better offering, at the time-of-launch.  This is where Lean Analytics provides an even greater competitive differentiator, allowing companies to focus on what’s important. 


Fact is, the nature of startups is to operate on scarce resources and capital, where most can ill-afford to dedicate time to develop sophisticated analytics, resorting to picking  off-the-shelf analytics solutions that may not quite be ‘lean enough’ or may require too much customisation. Enter Keen, which brings to the table, a level of customisation and calibration that is scalable, whilst maintaining simplicity in implementation. 

So what is Keen?

Essentially, Keen  is devised to collect and store massive quantities of event data (Big Data), which are interaction action items that happen throughout the day. The type of events and data you want to track is completely selective, allowing startups to either use a simple REST API, or already-made client SDK, and track anything from signups, impressions, errors. 


As Keen claim, We believe in the power of data to uncover new truths about what’s important in your application.

Keen IO makes APIs to collect, analyze and visualize data. Keen IO is unique because of how flexible it is. We’re analytics by API – we don’t have a pre-defined solution or a finite set of use cases. Anything related to event data can be tracked and built on top of our platform. This empowers developers to collect and explore their data. Compared to a lot of solutions out there, with proprietary and closed solution sets, our platform approach allows for an amazing amount of flexibility, control, and customization for our users. On top of this, we make it incredibly easy to get set up and start sending events.


What makes Keen extremely robust, is in it’s ability to store arbitrary JSON data, based on custom properties and attributes, that makes sense to you, without having to worry about how ‘Big Data’ is stored, and scaled. The complexities involved in configuring complicated data analysis tools whilst balancing multiple servers is taken out of the equation, allowing startups to focus on their product, and tracking just the right amount of data needed for them to build their value proposition. 


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Start modelling your business hypotheses with a Lean Canvas template

Amazing ideas come to you in the most inconvenient of times, despite your mind being a great incubator of ideas, whether you are out and about, or going for a jog in the park, and you don’t want that idea to escape your mind and conscious. Lean Canvas Template is a template you should download now and save on your Mac/iPad on Keynote, as a handy way of modelling your business hypotheses.
With your iPhone handy, the next step is to post on your Evernote app, a quick line or two on what that idea is. When you get back home, and within reach of your iPad, or notepad, you start modelling your business hypotheses. Before you waste your time writing up a business proposal formally, drawing your ideas on a “Lean Canvas”, the modelling canvas that Ash Maurya (Running Lean: Iterate from Plan A to a Plan That Works, 2012) has derived from the work of Alex Osterwalder’s Business Model Canvas, drawn in a convenient one-page table.

Lean Canvas

Creating a Lean Canvas is a good way of making sense of your idea, quickly evaluating whether it is practical and feasable, minus the hours and weeks of labouring a formalised business plan. You will have some hits and misses with your ideas, and this is a quick filtered way to draw your idea, show it to your friends and associates.

This canvas allows you to identify your idea, the risks associated, whether your value proposition can withstand threats (sort of a quickie SWOT analysis) along with ideas on how this business model will sustain itself revenue-wise. It will also allow you to identify growth prospects, how this model will scale with the increased sales, as well as scoping minimum functionality, which could eventually be derived into a Kanban stack/queue.

You can download a Keynote template that will allow you to draw a Lean Canvas. As a tip, be sure to save the keynote file as a template on your mac, so you can re-use it at a later stage.

Lean Canvas keynote template

Lean Canvas Template.key


Finding your one Metric that Matters (OMTM)

A while back I reviewed a lovely book , Lean Analytics, which was posted by Alistair, called Finding your One Metric That Matters. In paraphrasing the post, the author emphasises whilst t’s not wise to neglect all other analytical measures and pigeon-hole yourself on one, giving one metric a focus over another one allows you to derive meaning through sustained measurement. Alistair points out:

Communicating this focus to your employees, investors, and even the media will really help you concentrate your efforts.

Choosing the OMTM falls down to three factors, the industry you are in, the stage of your startup growth and your audience.

Industry you are in

Big businesses track a few vital Key Performance Indicators (KPIs) aligned primarily with the corporation’s main goal, based on  transactional, collaborative, SaaS-based, media, game, or app-centric.


Someone buys something in return for something.

Transactional sites are about shopping cart conversion, cart size, and abandonment. This is the typical transaction funnel that anyone who’s used web analytics is familiar with. To be useful today, however, it should be a long funnel that includes sources, email metrics, and social media impact. Companies like Kissmetrics and Mixpanel are championing this plenty these days.


Someone votes, comments, or creates content for you.

Collaboration is about the amount of good content versus bad, and the percent of users that are lurkers versus creators. This is an engagement funnel, and we think it should look something like Charlene Li’s engagement pyramid.

Collaboration varies wildly by site. Consider two companies at opposite ends of the spectrum. Reddit probably has a very high percentage of users who log in: it’s required to upvote posts, and the login process doesn’t demand an email confirmation look, so anonymous accounts are permitted. On the other hand, an adult site likely has a low rate of sign-ins; the content is extremely personal, and nobody wants to share their email details with a site they may not trust.

On Reddit, there are several tiers of engagement: lurking, voting, commenting, submitting links, and creating subreddits. Each of these represents a degree of collaboration by a user, and each segment represents a different lifetime customer value. The key for the site is to move as many people into the more lucrative tiers as possible.


Someone uses your system, and their productivity means they don’t churn or cancel their subscription.

SaaS is about time-to-complete-a-task, SLA, and recency of use; and maybe uptime and SLA refunds. Companies like Totango (which predicts churn and upsell for SaaS), as well as uptime transparency sites like Salesforce’s, are examples of this. There are good studies that show a strong correlation between site performance and conversion rates, so startups ignore this stuff at their peril.


Someone clicks on a banner, pay-per-click ad, or affiliate link.

Media is about time on page, pages per visit, and clickthrough rates. That might sound pretty standard, but the variety of revenue models can complicate things. For example, Pinterest’s affiliate URL rewriting model, which requires that the site take into account the likelihood someone will actually buy a thing as well as the percentage of clickthroughs (see also this WSJ piece on the subject.)


Players pay for additional content, time savings, extra lives, in-game currencies, and so on.

Game startups care about Average Revenue Per User Per Month and Lifetime Average Revenue Per User (ARPUs). Companies like Flurry do a lot of work in this space, and many application developers roll their own code to suit the way their games are used.

Game developers walk a fine line between compelling content, and in-game purchases that bring in money. They need to solicit payments without spoiling gameplay, keeping users coming back while still extracting a pound of flesh each month.


Users buy and install your software on their device.

App is about number of users, percentage that have loaded the most recent version, uninstalls, sideloading-versus-appstore, ratings and reviews. Ben and I saw a lot of this with High Score House and Localmind while they were in Year One Labs. While similar to SaaS, there are enough differences that it deserves its own category.

App marketing is also fraught with grey-market promotional tools. A large number of downloads makes an application more prominent in the App Store. Because of this, some companies run campaigns to artificially inflate download numbers using mercenaries. This gets the application some visibility, which in turn gives them legitimate users.

Many businesses fall into more than one categories, as well as ‘blocking and tackling’ metrics common to all companies, which are captured in lists like Dave McClure’s Pirate Metrics.):

  • Viral coefficient (how well your users become your marketers.)
  • Traffic sources and campaign effectiveness (the SEO stuff, measuring how well you get attention.)
  • Signup rates (how often you get permission to contact people; and the related bounce rate, opt-out rate, and list churn.)
  • Engagement (how long since users last used the product) and churn (how fast does someone go away). Peter Yared did a great job explaining this in a recent post on “Little Data”
  • Infrastructure KPIs (cost of running the site; uptime; etc.) This is important because it has a big impact on conversion rates.

Second: what stage are you at?

A second way to split up the OMTM is to consider the stage that your startup is at, which includes generating attention to get people to focus on your product or service, through various media campaigns, as well as need discovery which is a qualitative method of finding out through surveys and interviews what fields aren’t being answered, different hot trending areas that are or aren’t being fulfilled. Finally, whether you are fulfilling the need, through tools such as metric amplification, (how much does someone tell their friends about it?), understanding whether your offering meets the entire need or is it a piecemeal.

Then there’s Feature optimization. As we figure out what to build, we need to look at things like how much a new feature is being used, and whether the addition of the feature to a particular cohort or segment changes something like signup rates, time on site, etc.

This is an experimentation metric—obviously, the business KPI is still the most important one—but the OMTM is the result of the test you’re running.

Another attribute is  to question whether your business model is correct, through business model optimization, calibrating the offer slightly, such as how you charge, how that affects the core KPIs, to determine how scalable you are for growth, and how your organic development is progressing.

Later, many of these KPIs become accounting inputs—stuff like sales, margins, and so on. Lean tends not to touch on these things, but they’re important for bigger, more established organizations who have found their product/market fit, and for intrapreneurs trying to convince more risk-averse stakeholders within their organization.

Third: who is your audience?

Who are you measuring the metrics for?  Understand the various stakeholders

For a startup, audiences may include:

  • Internal business groups, trying to decide on a pivot or a business model
  • Developers, prioritizing features and making experimental validation part of the “Lean QA” process
  • Marketers optimizing campaigns to generate traffic and leads
  • Investors, when we’re trying to raise money
  • Media, for things like infographics and blog posts (like what Massive Damage did.)


What makes a good metric?

Let’s say you’ve thought about your business model, the stage you’re at, and your audience. You’re still not done: you need to make sure it’s a good metric. Here are some rules of thumb for what makes a number that will produce the changes you’re looking for.

  • rate or a ratio rather than an absolute or cumulative value. New users per day is better than total users.
  • Comparative to other time periods, sites, or segments. Increased conversion from last week is better than “2% conversion.”
  • No more complicated than a golf handicap. Otherwise people won’t remember and discuss it.
  • For “accounting” metrics you use to report the business to the board, investors, and the media, something which, when entered into your spreadsheet, makes your predictions more accurate.
  • For “experimental” metrics you use to optimize the product, pricing, or market, choose something which, based on the answer, will significantly change your behaviour. Better yet, agree on what that change will be before you collect the data.


You can follow Alistair’s tweets by clicking here.