Getting Attention

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Mark Evans’ post about Do Canadian Startups Get Enough Attention? annoys me. It is not Mark, it is not Canadian startups, it is the assumption that Canadian media outlets should write about Canadian startups.

“But the fact is there are a lot of great startup stories that go unreported or receive a smidgen of the coverage they deserve. It is a situation that frustrates entrepreneurs, investors and people within the startup community who believe the spotlight should be burning a lot hotter.”

Let’s start by answering Mark’s implication that “startups stories go unreported or receive a smidgen of the coverage they deserve“. Bullshit! They don’t deserve coverage. They have to earn coverage. They are noise. And as an entrepreneur you need to learn how to rise above the noise and tell stories that the media want to share with their readers.We have many examples of Canadian startup success and failure stories that have managed to figure out how to tell media friendly stories. Sarah Prevette at Sprouter managed to become a media darling:

Why was Sarah Prevette so much more successful in getting press coverage for her startup than other entrepreneurs? Is she smarter? Does she have more hustle? Is her startup more successful? I challenge Mark’s assertion that Canadian startups deserve coverage. I think the first step is doing something worthy of coverage. And as entrepreneurs we need to understand the stories that media want to tell, and begin to hustle to take away time and space from the big players. Tim Ferriss told his story about getting on national television, From First TV to Dr. Oz: How to Get Local Media…Then National Media. You have to work at crafting a story, building relationships and being newsworthy. So rather than assume that all good startups deserve coverage, how about we as entrepreneurs go out an earn it. Aim higher. Make something newsworthy.

Resources for Getting Media and PR Coverage

  • PR Tips for Startups: How to Get and Keep Media Attention
  • Getting Press and Media Coverage for your Startup Company – Who needs a PR Firm?
  • Tips for Getting (Follow Up) Press Coverage for Your Startup
  • From First TV to Dr. Oz: How to Get Local Media…Then National Media
  • Your Funnel is a Finite State Machine

    Editor’s note: This is a cross post by Joseph Fung (LinkedIn, @josephfung), the CEO of TribeHR (@tribehr). Joseph has recently raised $1MM from David Skok (@bostonVC) at Matrix Partners in Boston, MA. He is building and automating the SaaS metrics for TribeHR. He has a unique engineering view of sales and marketing that allows him to be nimble and correct his efforts based on real customer behaviour data. This post was orignially published on September 23, 2011

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    I’m of the opinion that the startup journey is really just the process of repeated work between “a-ha” moments of key insights. The faster we get to new insights, the better we are at ongoing improvement. I’m writing this post to describe an a-ha moment that happened early on (although earlier would have been better) in the lifecycle of TribeHR.

    Figure 1: Exciting! An Arbitrary State Machine

    To engineers turned entrepreneurs: your customer acquisition funnel is a finite state machine.

    This statement implies three specific premises:

    • your funnel can and should be modeled as a Finite State Machine (FSM)
    • your funnel FSM should map to explicit in-app states
    • investors care about the funnel state as much as (if not more) than anything else in your app

    Your Funnel Should be Modeled

    This point is best described in terms of my experiences with TribeHR:

    When designing features within TribeHR, it was intuitive to think about our software in terms of moving objects through a series of states: a review was “in-progress”, “completed”, then “filed”; a vacation request was “pending review”, then “approved” or “rejected”. Similarly, the users of the system would also be moved through states – “employee”, “manager” or “admin” for example. When I thought about the marketing process, however, I treated “sales and marketing” as the entry point into the state machine – I saw it as the entry arrow rather than a separate series of states.

    Because we didn’t start by planning our marketing and sales states, it was easy to rely on 3rd party services for our definitions. Unfortunately, implementing multiple services led to confusion. Some customers subscribed using PayPal, others paid through our payment gateway, and others found us via third-party app stores – each system had a different way of defining the state of a customer, so simple numbers like “how many customers are active” was a difficult thing to determine. This was compounded by our shift from a freemium model to a free-trial model earlier this year.

    If we had clearly defined and tracked our states from the start (which we have since done) it would have been easier to map third-party terminology to our own, making analyses and improvements much easier. You can see the results of subsequent mapping in the diagram below:

    Figure 2: TribeHR Funnel as a Finite State Machine

    As you can see, our entry state is “trialing”, thus the primary objective of our website is to convert visitors and leads into trialing users (our lead nurturing program is a state-machine still being designed). Once someone is trialling, they have two potential transitions: they can become either a paid “active” customer or an “abandoned” trial. Once someone becomes an active customer (and ideally remain one for a long time) they will exit the state only as a “cancelled” or “suspended” account. By clearly defining our states in the above format, we are now much better equipped to modify our messaging and features to optimize the experience. Before identifying the above state machine, we wasted a lot of time manually analyzing and identifying states, often on a case-by-case basis.

    The “should” part of my assertion follows from my conversations with investors and advisors. I’d frequently be asked for information such as our conversion rate from trials to paid customers or our re-activation rate of suspended accounts – without a clear FSM, we’d have some accounts that occupied more than one state, which made answering these questions impossible. By defining our funnel/FSM we were then able to answer such questions with ease, which made a world of difference to our working relationship with investors and advisors.

    If you haven’t defined your Funnel/FSM yet – do so. If you’re early-on in your startups, ot might not be perfect, but it will save you significant stress, time, and effort as you continue to work with mentors and investors. If it helps, put the model up on the wall at your office – it’ll keep it top of mind with your team.

    Mapping to Explicit In-App States

    Once you finalize your model, it’s critically important that you then track these states explicitly within you app. For example, if you offer a 15-day trial, during which users have to cancel or continue, it might be tempting to calculate “trialing” customers as those who are subscribed and whose date subscribed value is within the last 15 days. While this calculation might yield a correct result, formulating queries becomes significantly more complex when you can’t simply evaluate whether a field “state” is set to “trialing”.

    These queries are important because as your company and customer base grow, you’ll need to generate reports and dashboards that highlight this information in near real-time. You’ll need to answer questions like what percentage of users that sign up for a trial convert to a paid customer, and how is it changing over time? As soon as you can answer that, you’ll then be asked to segment by lead source, user characteristic, or time window. For example how does that conversion rate over time vary according to lead source or engagement level?

    To put it into an example, below are two examples of queries that would generate a summary of states of a single cohort from January 2011, assuming a 15-day trialing period. The first uses explicitly defined states, and the second assumes you calculate a real-time trial period, and simply delete records when they terminate their account.

    Explicitly defined:

    SELECT COUNT(state) AS total_users, state
      FROM users
        WHERE date_registered >= "2011-01-01" AND date_registered < "2011-02-01"
      GROUP BY state;

    Calculated on the fly:

    SELECT SELECT COUNT(state) AS total_users, IF(date_registered >
        DATE_SUB("2011-02-01" , INTERVAL 15 DAY); "TRIAL"; "ACTIVE") AS state
      FROM users
        WHERE date_registered >= "2011-01-01" AND date_registered < "2011-02-01"
      GROUP BY state;

    As you can see, the query in the first is much easier to use and read, and it includes all states, whereas the second is challenging to use (even more challenging to modify if you have more states) and doesn’t track cancelled accounts.

    By structuring your database such that the state is explicitly identifiable, you’ll be able to generate queries much more readily, which will then let you automate standard reports (like conversion and churn rates) for dash boarding, and will allow you to more easily connect business intelligence tools to your database. The ultimate goal is to let your business-oriented team members manipulate the data as readily as you can.

    An added benefit of explicit states is that they act as assertions. Although it’s possible to determine that a customer is active by checking the date of their last successful payment, it’smuch better to have an explicit “active” state as you can then run automated tests to verify that your assertions are true. Having a recurring task that iterates through your customer base to confirm that accounts with a most recent payment made within the last month are correctly identified as “active”, is a good way to follow monitoring-driven-development approaches. Any assertion errors can help identify critical flaws in your system.

    Investors Care About the Funnel State

    Although this may seem obvious, it still needs stating. The platitude what get’s measured gets done has a corollary – what we care about gets measured. Technical founders often measure and know details like server load, traffic metrics, lines of code and number of commits or push requests. Because we innately care about those tasks, we tend to measure and follow them. What can’t be over-emphasized is how much investors, advisors and partners will care about your funnel states. Below is a representative subset of the metrics we’ve been asked to report at our board meetings – you’ll notice that none of them are related to in-app usage or infrastructure performance:

    • Total # Of Customers (overall and by customer segments)
    • Visitor-to-Trial Conversion Rate (overall, and by lead source)
    • Trials-to-Active Conversion Rate (overall, and by lead source and by segment)
    • Churn Rate (overall and by lead source)
    • Customer Acquisition Cost (overall and by lead source)
    • Average Revenue per User (overall and by lead source)
    • Life Time Value (overall and by lead source)

    Most of these numbers depend on measuring our customers’ states as well as various additional segments. Because our segments will vary frequently as we experiment and optimize with marketing campaigns, if we don’t have explicit (and easily determined) states, rapid iterations on our reporting become exceptionally difficult.

    Investors and advisors will assume that you have infrastructure running smoothly – you don’t need to hammer home evidence of it, so skip on reporting the infrastructure stats I mentioned earlier. For them to provide valuable advice, however, they need to be able to understand and trust the business metrics I listed. If you can speak as confidently about your Funnel/FSM as you do your application, and if you can deliver transparency into the funnel by automating reports and dashboards, you’ll build your investors confidence and trust in you as an entrepreneur.

    Bonus Reasons

    As a bonus, here are a few cool things you can then do once you have this funnel modelled and embedded within your software:

    1. More easily build dashboards with tools like Geckoboard
    2. Delegate data-mining and analysis to non-technical staff, by tacking on BI tools like Qlickview
    3. Automate segmentation and lists for automated email campaigns and lead nurturing using MailchimpPerformable, and others
    4. Simplify cohort analyses by customer segment

    If you have a state machine for your funnel or customer base, especially if it deviates significantly from mine above, please share it in a comment or an email to me. It would be interesting to see what approaches others are taking.

    Editor’s note: This is a cross post by Joseph Fung (LinkedIn, @josephfung), the CEO of TribeHR (@tribehr). Joseph has recently raised $1MM from David Skok (@bostonVC) at Matrix Partners in Boston, MA. He is building and automating the SaaS metrics for TribeHR. He has a unique engineering view of sales and marketing that allows him to be nimble and correct his efforts based on real customer behaviour data. This post was orignially published on September 23, 2011

    Quota is not a dirty word

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    “We are ALL in sales” – Dale Carnegie

    I used to think that quota was a dirty word. It struck me as restricting freedom and potentially forced the exploitation of trusted customers and prospects to drive the bottom line results. But I was wrong. In reality, a quota is a number that is useful to incent certain behaviours. The trick is to incent the appropriate behaviours. It is a contract between a sales person and an organization about how to compensate behaviours based on outcomes.

    “Quota is a direct path to clarity and accountability.” – Shawn Yeager

    So many entrepreneurs can benefit from contracts with defined outcomes. I was chatting with a startup last week about the numbers he agreed to with his VC to unlock the next tranche of funding. He mentioned that he wasn’t going to meet the numbers, but he still expected the VC to unlock the funding. My advice to him was very straight forward, it was to figure out how to achieve the agreed to numbers, or immediately open a conversation with the VC about missing the numbers due to changing market conditions and see if the tranche can be renegotiated. In the case of this entrepreneur, the numbers were in the funding contract, and I fully expected the VC to hold the entrepreneur to deliver on these numbers. The numbers and metrics exist to help assess the risk and the ability of an entrepreneur to deliver.

    The secret with an early stage company is to set appropriate metrics, quotas and growth numbers that incent the correct behaviours out of entrepreneurs. The good news is that there are a lot of examples of SaaS, B2B and consumer metrics that can be used.

    There are a lot of different sources of metrics and numbers. Each of the numbers needs to be considered in corporate revenue goals, past historical performance, current product development stage, market share, budget, etc. The targets and growth numbers need to be established.

    I’ve taken to requiring all of the startups I mentor, to establish 3 metrics that we discuss in our mentorship meetings. Each of the metrics must be clear enough for me to understand, for example:

    • Number of paying customers
    • Number of registered users
    • Churn rate
    • Number of pageviews or unique visitors

    And each metric should have the current measurement, the predicted growth rate and the actual target number. I try to start each conversation around the metrics. And any issues related to the market conditions, learnings, corrections, etc. Then together we set the targets as part of the planning for the next meeting. This may include a redefinition of the metrics. The trick for me as a mentor is to try to help identify what metrics I think are most useful for the startup and founder to focus on next.

    What are the metrics other entrepreneurs track? How do you set your targets and quotas?

    What are the metrics and growth rates that investors like ExtremeVP, Real Ventures, iNovia Capital, GrowthWorks, Rho and others want to see from prospective early-stage companies?