SaaS Economics Blog

    Management through Inspiration

    by Brad Coffey

    HubSpot has a unique culture.  We hire people who are thoughtful, smart and fight for what they think is right.  And we try to hire as many as possible of them.  Our core tool for deliver great value to our customers, is to have great leaders in the company. 

    It turns out (not unsurprisingly) that these type of leaders don't like to be told what to do.  They want direction on where the company is headed, but not directions on how to get there.  As Dharmesh likes to say - they want to be inspired, not managed.  

    To that end I find the best leaders at HubSpot do 3 things really well:

    • Obsess over creating a vision (with a matching set of high goals)
    • Obsess over driving alignment around those goals
    • Empower people with the freedom to achieve those goals

    And they do this regardless of where they are in the company - not just 'managers'. In fact, some of our best leaders are not 'managers' at all and have zero people reporting to them.  

    • Anyone at HubSpot can set and sell a vision at a certain level - there are no offices, everyone's calendar is open to be booked for coffee, and we have an active wiki where ideas are shared and debated.  It takes a lot of selling, but the opportunity to be heard is there.
    • Many people drive alignment through direct reporting relationship (and may complain when its not there) - but there are other (often better) tools to drive alignment including: Managing meeting agendas, creating regular reports, adjusting commissions or compensation structures, and running internal contests
    • Finally most managers can empower people to achieve their goals by getting out of the way.  Often its as simple as asking 'who owns this project' - and once that person is identified, funneling all questions through that person.  If they see a roadblock ask again 'who owns this project', and make a decision. At HubSpot we call this person a DRI - Designated Responsible Individual.
     
    I think this is different than how a lot of companies are managed.  In many companies an executive will come up with an idea - and instead of taking the time to set and sell the vision for that idea, they'll simply tell someone to do it.  Instead of giving people autonomy to solve the problem, they'll tell them exactly what to do.  They'll do more managing than inspiring.
     
    And its easy to see why.  It far easier to tell someone to do something, than to create a vision.  Its much faster to tell someone how to do something than to give them enough context to make the right decision themselves.  It's seductive because its easy and gets to a short term goal quicker.
     
    But that's not what seems to work at HubSpot and I think that's probably a good thing.  We want to nurture future leaders and to avoid uninspired compromises - and I think this culture helps that.  It's not for everyone, but it works for us. 
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    No uninspired compromises

    by Brad Coffey

    No Uninspired Compromises

    One truth I've found about startups as they transition to become growing companies is that decisions tend to get tougher - not easier.

    I attribute this to the fact that for any sufficiently novel or innovative idea - someone is going to think it's terrible.  If no one thinks it's terrible - then it's really not that good.  And the math behind that at even a small scale can be astounding.

    Lets say for your brilliant idea there is only a 20% chance someone in the room thinks it's terrible.  So if you tell one person - there's a 80% chance you'll move forward with.  Pretty good.  But as the number of people in that room grows, the odds of everyone agreeing to the idea gets bad. Fast.  By 5 people there is only a 33% chance that brilliant idea will move forward, by 20 there is only a 1% chance everyone in the room will agree it's awesome.  1% - that's it.

    What happens then, at most companies, is that to get any idea pushed through they settle on uninspired compromises.  They dilute their brilliant ideas down to something everyone can agree on, and lose all the magic and inspiration from that original idea.   It makes sense, it's the only way to get everyone in the room to agree.  It's just also path to bad management.

    So at HubSpot we have a policy:  No uninspired compromises. 

    We do our best not to let ourselves fall into this trap, and we do a couple of good things to avoid that mistake.

    1. Give teams goals, and the autonomy to hit those goals.  Don't legislate the process - only the end results
    2. Keep teams small. Don't let the room get too large so people can make decisions and move forward.
    3. Identify a DRI (designated responsible individual).  That person owns the decision, not the entire room.  This is critical for cross functional decisions.
    4. Require transparency. Hold people accountable to these decisions by reviewing the success of the decision, and then keep iterating.
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    Pricing Model Lessons: Matching the Timing on Value & Price

    by Brad Coffey

    Nate Weiner posted a brilliant article today articulating why his company - Pocket - moved from an upfront paid model to a freemium model.  The insightful post had lessons for all software companies looking for a simple framework to drive developing their pricing strategy.

    Nate's argument boiled down to a failure of his previous (upfront) model to match the value the customer received over time to the price charged for that app over over time.  Pocket (formally ReadItLater) was charging upfront for an app that didn't deliver value until much later in time.  Nate's insight was that when the value is received upfront - charge upfront; but when the value is received over time, charge over time.

    Here is a copy of the graphic from the post that neatly summarizes the different models for how value is delivered over time:

    ValueOverTime resized 600

     

    Expanding on Nate's post - with these three types of value-over-time models, there are a matching set pricing models available to most digital companies:

    1. Value upfront >> Charge upfront (e.g. video games)
    2. Value over time >> Subscription fees (e.g. Carbonite - desktop backup software)
    3. Value grows over time >> Freemium (e.g. Evernote - note taking software)

    In each cases, the pricing model allows the company to match the timing of the preceived value to the price charged.  Additionally there is a 4th type - when the value grows exponentially over time - specifically when there are network effects involved.  In these cases the matching pricing model is to be free, and then to find alternative ways (e.g. advertising) to monetize the value created.  That's why social networks like facebook or twitter don't charge the heavy 'users' of the platform that are actually helping them create value.

    While this is a bit simplistic the core philosophy of matching the timing of the value delivered with the timing of the price charged is very powerful.  Taking this one step further - the best companies in the world have found out how to not only match the timing of value delivered as described above, but the amount of the value delievered.  This is where models like Google's Adwords, or Salesforce.com per user pricing are some brilliant. These comapnies have mastered the ability to match the timing and amount of value delivered - therefore creating successful, retainable customers and build remarkable businesses.

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    Why Shopify's churn article doesn't matter for 95% of SaaS companies

    by Brad Coffey

    Shopify recently posted an article on defining churn rate and the 'correct' way to calculate the metric and the limitation of the more standard approach.  Steven Noble is completely right in the importance of churn.  It's a massively important metric for SaaS companies and can kill even the most successful companies and cap potential any revenue potential if not held in check.  However his assertion that many companies get it wrong is incorrect (well at least for the reasons he states)

    Why the Shopify article on churn is wrong for 95% of SaaS Companies

    Where the article fails is in the proposition that most companies are calculating churn wrong because of the impact of churns from new customers within the period.  

    "The problem here is that the [number of churns over period] value is affected by the entire period but the [number of customers at beginning of period] value is a snapshot from the beginning of the period. This might not have much impact if new customers only make up a small percentage of your user base but for a company that's growing this can lead to some major misinterpretations."

    Noble goes on to describe how you have to not only account for the customers at the beginning of the period but also the customers who churn over the period.  He shows some interesting math on the topic - but what he misses is that for most SaaS companies they bill customers monthly (at the shortest interval) so customers only have one opportunity to churn in a month - and similarly new customers can't churn within the first month they sign up. For instance if you have a product that is $100 a month, and a customer signs up and pays you on the 15th of July - that customer can't churn until their next billing date, the 15ht of August.  So that 'churn from new customers' isn't relevant within the month your calculating churn. 

    The real open questions on defining churn

    There are however some more interesting questions surrounding the right way to calculate churn.  Specifically there are two large questions we think about at HubSpot:

    • Should upsell be included in churn?
    • Should customers locked into long term contracts be included in churn?

    For HubSpot we've determined that we should actually calculate both customer churn (excluding upsell) and revenue churn (which includes upsell and is weighted by the recurring revenue from each customer).   Customer churn ensures that you're delivering continued value to all your customers and MRR churn ensures you're getting the economics of the business correct.

    For locked-in customer we've determined that yes - you should include those customers.  The simple reason for this is that if only looked at customers up for contract renewal you could incorrectly move the metric short term by selling more month-to-month deals (at a higher overall churn rate) once at steady state.  For instance if customers who sign 12-month upfront contracts churn naturally at 1% monthly - when the contract was up this month the end of the year they'd have a 1-(.99%)^12 = 12% retention rate in that one month their contracts are up.  If you instead sold month-to-month last month that churned at a higher 2% monthly rate, those month-to-month contracts would also renew this month and blend in with the 12% churn from annual contracts lowering the calculated churn rate that month even though overall they were a worse cohort of customers.

    As illustrated by this topic SaaS metrics are at best inconsistently applied and can be really hard to compare from company to company.  The best thing to do is find an approach you can apply consistently internally and work to push those metrics in the right direction - and worry less about the exact benchmarks you're hearing from friends and VCs. 

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    SaaS Revenue Growth Benchmarks & Thoughts on Eloqua S1

    by Brad Coffey

    Eloqua filed to go public yesterday and it generated a lot of buzz around HubSpot.  We are thrilled to see marketing software companies succeeding (Responsys went public earlier this spring as well) and think it helps validate the space.  Big congratulations.

    On HubSpot's internal wiki I posted some take-aways after reading the s-1 and wanted to reposted it below.

    ------------------

    SaaS Revenue Growth Comparison Chart

    This chart shows the growth rates of several public SaaS companies from a 'base' year. This base year was taken as the year when the company had $5 to $12m in revenue.  The market cap valuations are based on Aug 2011.  Capital raised is from various public sources and press releases.

    SaaS Revenue Growth Comparison

    Eloqua S1

    • Company
      • Raised $40m (most recently from Bessemer Ventures)
      • Upstream with many brand name clients
      • 500 customers in 1/1/2009, 1,000 today
      • 284 Employees
      • They have a 'Revenue Performance Management' Suite which includes: 
        • Marketing Automation, 
        • Sales Effectiveness and 
        • Revenue Suite
      • Key benefits are listed as:
        • Comprehensive On-Demand Revenue Performance Management Platform
        • Ability to Track, Capture and Analyze a Potential Buyer’s Digital Body Language
        • Real-Time Insight to Facilitate Revenue Growth and Operational Efficiency
        • Seamless Integration with Other Key Enterprise Systems
        • Differentiated Proprietary Knowledge and Professional Services
      • Revenue was listed as 50.7m last year, $40.9 in 2009 (see chart below for revenue growth numbers)
        • Pretty standard split between services and software revenue
    • Marketing/Sales
      • 90 People in Marketing & Sales
      • Direct Sales is 85% of new sales (rest re-sellers)
      • 10% of sales outside US
      • Typically 1 or 2 year contracts billed quarterly, semi-annually or annually. No month-to-month
      • Sales team split between enterprise and SMB
      • Sales are mostly hunters and 'Customer Success Managers' handle renewals and 'add on software and services'.
    • Services
      • 65 ppl in customer support, 26 in professional services
      • Services sales are 10% of total revenue - pretty standard
      • Services are unprofitable (Total Services Costs about 50% of total Services Rev in 2010)
      • Professional services include application configuration, system integration, business process re-engineering and mapping and data migration.
      • They started outsourcing much of this in 2008 - when the recession hit - but in 2011 are re-investing and growing the team


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    Combating the Innovator's Dilemma – HubSpot’s Experiments Framework

    by Brad Coffey

    I recently posted an article on OnStartups about HubSpot's Experiments Framework.  I've reposted some of the relavent parts below.  The full article can be read at OnStartups.com 

    The foundation of this framework is based heavily on Clay Christensen’s work inThe Innovators Dilemma.  We’re huge Clay Christensen fans at HubSpot (even have a conference room named after him) and have been life-long students of his work.  In his work Clay asks a very straightforward question without an obvious solution - specifically: Why do well managed, successful companies repeatedly fail to create new disruptive innovations?

    This framework was developed fundamentally to combat that challenge and create a lasting culture of entrepreneurial exploration.


    HubSpot Experiments Framework

    HubSpot’s Experimentation Framework

    The framework has 3 stages, each with a distinct goal and approach.

    Alpha – Lowering barriers to experimentation

    No bureaucracy, no red tape, full access to information.  This stage is simply focused on enabling anyone with energy and an idea to try a new solution.  Tests are run by everyone and anyone – but are generally done in spare time (nights and weekends) and with few resources. You don’t need to ask permission to run these tests - and by design no one ever knows all the alpha stage experiments actively being pursued.  It's open and distributed.

    Beta - Determining proper funding

    When an experiment reaches Beta stage the ‘founders’ are fired from their day job and work on the experiment full time.  While founders determine their own goals and metrics – these leaders are encouraged to be patient for growth but impatient for profitable economics. Like many founders these people also report to a 'board' regularly and are subject to evaluation on future funding.  At its core this stage is about providing access to funding for entreprenurial folks with new ideas and transparency/accountability into the success of those early tests.

    v1 – Scaling successful experiments

    v1 projects have proven economics and now are looking to scale the success.  Often this requires growing the team beyond the founders, building dedicated systems and developing regular tracking of core metrics.  Founders with experiments graduated to v1 are now considered 'mini-CEO’s' and are tasked with running their project as a start-up within HubSpot.   

    We established this framework in the hope of driving innovation and empowering the entrepreneurial edges of our organization to create change.  It seems to be working - we’ve had several successful founders graduate from the program (Pete Caputa with VAR programJordyne Wu with the Services Marketplace) and we created a culture to be proud of.   It's enabled us to focus on the core business without foregoing the entreprenurial engery and creativity of our team.

     


     

     

     

     

     

     

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    SaaS Unit Economics - Calculating COCA by Segment (and why its hard)

    by Brad Coffey

    LTV COCA

    At a high level calculating the cost of customer acquisition (COCA) is relatively straightforward to calculate for a SaaS company.  It's simply the total Sales and Marketing expense for the period divided by the number of customers acquired in the period.  Easy.

    Unlike other SaaS unit economic metrics, unfortunately, this simplicity breaks down rapidly when calculating COCA for a specific segment.  How do you break down the marketing spend aimed at the software industry vs marketing spend targeting manufacturing?  How do you break down the sales spend for SMB segment vs enterprise segment?  Its often very difficult to calculate reliable numbers within these narrow bands.

    At HubSpot we've looked at several different calculations over the years and have come up with one simplifying approach - that although imperfect - can drive COCA when applied consistently.  

    Measuring COCA by Segment:

     

    At HubSpot we calculate COCA by segment as:

    Marketing COCA =  Total Marketing Spend * % of Total Leads in the Segment

    Sales COCA = Total Sales Spend * % of Sales Reps selling into Segment

    Total COCA = Marketing COCA + Sales COCA

    A few notes on this.  First, the total marketing and sales spend includes everything (managers salary, overhead, etc.) - not just direct program spend or commissions.   Some people do this when calculating COCA and I think its wrong and ignores some of the real costs there.  Second, this assumes all leads are the same price.  It's a simplifying assumption in the wake of more information around specific channel spend, we find works well for most companies.  Finally, this also assumes all sales reps have the same costs associated with them.  While this isn't always accurate depending on the mix of inside and outside sales reps - much like the marketing spend we find works well for most companies and is relatively robust.

    With these assumptions and this approach we find that we can segment our SMarketing (Sales + Marketing) funnel down to very refined levels and measure the health of our business in specific markets.  

    When we conduct this analysis we do not only look for segments with the lowest cost however - but instead try to identify segments that have the highest return on our sales and marketing spend.  So although COCA is valuable, the real insights come when compared to other unit economic measures such as the lifetime total value of the customers in that segment.  (lifetime total value, LTV is measured as avg. Monthly recurring revenue * gross margin / monthly churn rate)   

    By most VC standards, SaaS companies should target a LTV to COCA ratio of 3, and a COCA payback of <12 months.  Many public companies are well within those ranges or much beyond them.  However - these numbers heavily dependent on growth rates - its much more difficult to maintain high unit profitably and solid economics while growing 100% year-over-year than it otherwise could be.  Said another way - in most companies if they fired 1/2 their sales team these numbers would improve, but the growth would stall.

    For many of these reasons its essential for companies to understand the economics and COCA of the markets they target and the segments their business sells into.

     

    So what do you think?  Are there better alternatives for calculating COCA by segment?  For some additional information also check out David Skok's piece on the Startup Killer: Cost of Customer Acquisition.

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    One Measure of Operational Capabilities: Gross Margin

    by Brad Coffey

    Harvard Business Review Daily Stat reported recently that for software firms operational skill is the key to avoiding corporate failure.  In the short post the reference a study done by Why Software firms Fail.

    From the HBR snippet (accents are mine):

    The best way for a software firm to increase its survival chances is not to boost R&D or marketing skills but to increase its operating capability — its knack for efficiently deploying people and capital, according to researchers led by Shanling Li of McGill. Their study of 870 U.S. software companies over the period 1995-2007 shows that companies with high levels of innovation-related competitive actions but low operating capability were 466% more likely to fail than the average.
    Additionally from the research note itself:
    "The impact of OP capabilitieson firm failure rates is almost twice that of MK  and five times that of RD."
    "Comparing firmswith high OP (top 25th percentile) to low OP (bottom25th percentile) further supports the importance of OP capability: the firms with low OP capability fail atfive times the rate as those with high OP capability."

    The study shows the importance of operational capabilities and the need to for management focus in this area in order to sustain its competitive advantages.  

    As always though, the first step to improving Operational Capabilities is to first measure it. But how do you do that?

    One specific potential measure the researchers hit upon (though they roll it up with other inputs) is gross margin.  That is - the difference between your operating revenue and the cost of goods sold.  I actually think this makes a lot of sense.  The goal of operations, as suggested in this study, is to  "use resources cost effectively" and that firms gain "an operating advantage byefficiently leveraging their resources to create operating income".

    So what do you think?  Do you have someone at your firm whose job it is to focus on improving gross margin? 

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    Sitting on a pile of information Gold?

    by Brad Coffey

    HubSpot, like most companies, focuses much of its businessinformation gold operations efforts of making processes more efficient and collecting hoards of information to report on the success of those initiatives:

    • Marketing and sales collect information about our leads - to help engage and convert these leads
    • Customer team collects information on what marketing actions our customers have taken how successful they have been - to help guide the onboarding process more effectively
    • Product team collects information on how our customers are using the product - to gain valuable feedback that drives the product development process

    In most cases these collection points are completely self serving for the organization at hand.  They need this information to run their division more effectively and share it regularly within their organization.

    This is common for many companies.  Using business intelligence to drive internal processes is nothing new.  But where many companies fall short is answering:  How can all the data I collect help our customers?

    At HubSpot we've started down this path. 

    • Marketing leverages data from the product team about how our customer do marketing to create content for the blog (often its our best performing content)
    • Sales team leverage data from the customer team to create a 'inbound marketing calculator' that can help prospects understand the potential for inbound marketing in their business
    • Customer team leverages data from the product team to create a monthly newsletter informing customers of what parts of the application they should explore next
    • Product team (soon) will leverage to demographic data from the sales/marketing team to create a benchmarking tool specific to a customers industry

     

    In each of these cases we've tried to look at the pile of data we collect at HubSpot and understand how we can re-purpose it to the benefit of prospects, leads and customers.

    Is there information your organization already collects that you're just sitting on?

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    Quality vs Quantity when Defining Business Metrics

    by Brad Coffey

    At HubSpot we've recently started a series of projects aimed at defining some new metrics for running our business.  We do this because (admittedly) we are dorks and love data - but also because we need to solve some real problems in the organization.  These problems generally range from aligning incentives, evaluating project success, or managing day-to-day operational flow.  In each of these cases, however, a common challenge emerges centered around finding the right balance quality and quantity.

    Below I have identified a series of touch points between organizations where this has been particularly interesting for HubSPot. As you'll see we've tried a couple different approaches to identifying the right balance.  Nothing is ever perfect - but I think there are some take-aways here for others, 

    Marketing and Sales: Sales 'Points' SLA

    • Balance Need: Volume and Fit
    • Tendency: Simply count leads because its simpler
    • Solution: Score each conversion event (not simply lead) and hold Marketing accountable for a sum of those points

    Sales and Customer Team: LTV base compensation

    • Balance Need: New Customer Velocity and Retention
    • Tendency: Compensate only for new units
    • Solution: Measure sales reps by Total Lifetime Value generated each period - not just new units.  For SaaS this strongly influenced by a sales rep individual churn rate

    Onboarding Consultants and Account Managers: Customer Happiness at handoff

    • Balance Need: Pace of Consulting and Success of Consulting
    • Tendency: Don't measure anything because its hard
    • Solution: Determine 'customers happiness' objectively (frequency of use, features set-up, survey results) and hold consultants to a onboarding schedule

    Product/Engineering & Customer Team (specifically services): Daily Pain

    • Balance Need: New Innovation and Fixing Existing Innovation
    • Tendency: Allot some % of time to bugs and hope it works
    • Solution: Score open bugs by severity and hold engineering to a cap - and allow time for new development while below that limit

    In each of these cases we have tried to understand the underlying motivations that lead to imbalances and identify objective metrics that when monitored regularly can keep us from tipping too far in either direction.

    So what do you think?  How have we done?
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