I hate firing people. It’s the worst part of my job. Even after all these years I still spend days or even weeks agonizing over a decision to let someone go. I feel absurd complaining about this, given that of course it’s a hundred times worse for the person being fired than it is for me. Still, I hate firing people.

My first firing at Top Hat was our VP Sales. He was employee number two, he joined right after we raised our angel round. In retrospect it was doomed from the start, and it was entirely my fault. I had no idea what I was doing when it came to building a sales organization and brought him into a role that didn’t make sense (read about the lessons learned in building a sales team). It took me 6 months before I finally pulled the trigger. In the end, it was undoubtedly the right decision and set the company back on track. But at the time it was an extremely tough call. It was admitting failure – to myself and to our investors – that this first major hire was a mistake. I felt  ashamed about it for months and kept convincing and re-convincing myself that we could still make it work.

As a general rule once you’ve lost faith in an employee, things rarely get better. You can sometimes fix a skill-level problem by giving someone time to grow, but you can never fix a personality problem. If you’ve identified that someone isn’t a fit you need to move on it quickly and decisively. The longer you wait the worse it will be for both parties.

Firing is an essential part of running a successful company.

In a narrow way, it’s actually more important than hiring. You could, in theory, use a shit-against-the-wall style hiring strategy and as long as you filter out the bad apples quickly enough you’ll be able to build up a functional team over time. Of course that’s probably not the best approach.

The reality is that even the most effective interviewers are rarely more than 70% or 80% accurate. The average interviewer is quite a bit worse than that and isn’t much better than chance – often even worse, because the naive approach just selects people who are great in interviews, which disproportionately selects for bullshitters. However, even if you’re some kind of super-human talent screening machine with a 95% success rate, that 5% will accumulate and degrade the culture until you’re surrounded by bozos.

The Best Firing Process is a Better Hiring Process

Of course the best “firing process” is not to have to fire people, which can only be done through effective hiring. That being said, not having an effective firing process is like not having an immune system – the first cold will eventually kill you.

It’s fairly common knowledge these days that A players only like to work with other A players. A slightly more subtle observation is that someone’s status as an A player isn’t fixed. Bringing a weak player onto a team has a tendency to poison the culture and downgrade the rest of the team (especially if that weak player has a shitty attitude.) This bad apple syndrome has been observed to happen fairly reliably in studies on organizational dynamics.

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The Bad Apple Syndrome

We’ve experience this at Top Hat a couple of times. One of the most instructive was with our inside sales team. Early on when we were in a pinch to fill the team we lowered our standards and brought on a few people that we should have passed on. The results were disastrous. The quality of the team degraded and eventually hurt not only the inside team but also other parts of the company that came into contact with it. It took nearly a year of solid effort to rebuild the team. For a time it seemed hopeless. No matter what changes we put in place, no matter how much talent we threw at the team, the cancer of negativity and poor morale just wouldn’t go away. The most profound mistake we made in the process of trying to fix the team was to keep those who were performing well but had a negative attitude.

There was a pattern we observed a few times: we’d put a new person into the team, their performance would be great and they’d be super enthusiastic. Then like clockwork after a week or two their numbers would slowly drop, and they’d become engrossed in the culture of negativity and gossip. It was only after the cleared out the ringleaders who were perpetuating the negativity (who happened to have decent performance numbers!) and put in strong positive management that things finally began to change. The most amazing thing is that many of the people who were B or even C players when the team was dominated by negativity shot up to solid A player status. The overall output of the team per person went up by nearly 300%. In addition it seems as though life was trying to setup a lab experiment for us to prove just how much things had improved – we had a person who had left the company a few months prior re-join the team. His feedback was that he was blow away, he couldn’t believe it was the same team.

Lessons Learned

The first lesson we learned was that no matter how strapped for manpower you are, no matter how much it seems like the world will end if you don’t fill a position, compromising on the quality of talent will surely be more damaging. Second, we learned that in fixing a damaged team the key is to identify the cultural sources of the underlying problem and focus on those. Finally, we learned to use a divide and conquer approach – we would pull all the top talent into a separate team while rehabilitating the broken remaining team separately – it really helped prevent the “negativity cancer” from spreading while we were fixing things. These are simple things in retrospect, but it took a while to pull it off.

One of the most revealing questions I tend to ask when interviewing potential managers is whether they’ve ever had to make the decision to fire someone. The answer and subsequent discussion usually tells you two things: first, it tells you if the person has ever had to deal with the most difficult problems in management, second it tells you if they know how to handle those problems through the process they followed. Assuming the person has ever had to hire and manage a team of a decent size for any length of time, it’s almost certain they’ve made hiring mistakes, and their answer tells you that they know how to detect and correct these mistakes. If the person simply walked into a mature team, or has had HR handle all the hiring/firing decisions for them, then they’ve been living on easy street.

The process of firing someone is always somewhat unique to each situation. That being said there are some basic principles that you should always follow:

  1. Give people plenty of notice and regular feedback. Give people several chances to improve. The actual firing should never be a surprise – if it is then you almost certainly did something wrong in setting expectations. Depending on the role the whole process should take 1-2 months (longer for senior roles.)
  2. Try to be generous with severance and leave the person in a good spot to find their next employment. I know it’s not always possible in a startup, but do what you can. It’s the decent thing to do.
  3. Take time to reassure the rest of the team and explain (with discretion) the process that was followed and why the decision was made. Letting someone go is always a huge morale hit (even if the person wasn’t well liked, it still scares people.) You need to make people understand that their job is not in danger.

Firing someone is always a brutal experience. Anyone who says otherwise is either lying or is a psychopath. That being said, it’s unfortunately a necessary evil and understanding when and why it needs to be done is essential to the success of any business.

Michael Silagadze

Mike is the founder and CEO of Top Hat Monocle. He is an engineer from the University of Waterloo.

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This post is a recap from a how-to created by Mark MacLeod . I feature some of the most impressive startup strategies we encounter at StartupPlays and share them free, here at Startupnorth.ca. Enjoy.

This guide is available in an interactive how to format Free at StartupPlays.com – Get it here

The number one focus of Mark’s investment and advisory work is SaaS companies. There are lots of reasons to focus on this segment. For one, it’s large ($21B and growing at 20% per year). In addition, it lends itself well to (metrics) and is great for investors because of the visibility and predictability that the best SaaS businesses generate.

The SaaS Terminology Cheat Sheet:

AARRR: A pirate war cry or more importantly, an acronym coined by Dave McClure to summarize the flow of SaaS users from first activation to monetization and referral.

Activation: The first time someone uses your service.

Acquisition: A new user sign up. This does not necessarily mean a paid customer. It means a new user on a free trial or permanently free version. If you don’t have a free trial or free product and the only way for someone to use your product is to pay then acquisition for you is a new paid customer. In this series “user” will refer to people that don’t pay and “customers” will be people that do pay.

ARPU: Average Revenue Per User: Total revenue / # of paying customers.

CAC: Customer Acquisition Cost. Total costs of customer acquisition / # of new customers acquired. This should be calculated both for gross new customers and net new. Net new is net of customers that you lost in the period.

Churn: The % of users / customers that abandon the service over time. This can be measured weekly, monthly, quarterly, etc. You will want to measure churn for users and churn for customers (assuming you have a free trial or freemium product).

  • Customer churn: % of paying customers that cancel their subscription.
  • User churn: % of free users that stop using the service.

CLTV: Customer Lifetime Value. The expected total revenue from a customer over their lifetime less the cost of generating that revenue less the cost to acquire that customer.

Cohort: Also called cohort analysis or class analysis. A cohort is a group of users that are grouped together based on a common attribute. That could be the month they signed up, the source through which you acquired them, the method in which they use your service (web vs. mobile vs. desktop app), etc. Say, you’re looking at cohorts based on month of sign up. You can then look at usage and monetization patterns for those users over time. For example all users signing up in January are a cohort. You can then look at the % of them that use, subscribe for, churn out, cancel their account etc. in February, March, April, etc.

Conversion: Every time a user moves forward a step in your funnel from visitor (just visiting your web site) to user (signed up) to customer (paying you money) to referrer (helping bring you new users).

  • UV conversion: % of new unique visitors that become users.
  • Active conversion: % of users that use the service for the 1st time.
  • Paid conversion: % of free users that upgrade to a paid account.

Engagement metrics: These are softer metrics that are specific to your application that don’t measure core conversion but measure specific feature uses and overall engagement with your service. Examples include # of likes, session length, # of comments, # of connections, etc.

Freemium: A goto market strategy where you have a permanently free base version of your service. This, hopefully, replaces the need for a big marketing budget and reduces friction for user sign up enabling you to acquire lots of users. From that large user base you convert a small portion to a paid premium version. There are other freemium scenarios such as free content monetized by ads but in SaaS this is the primary meaning for freemium.

K Factor: Also known as “viral co-efficient“. For every active user how many new users do they bring on. If your K factor is > 1 then your user base grows virally or exponentially. This applies well for social games and freemium services that have a built in viral aspect that introduces the game or service to new potential users.

Retention: Subsequent usage of your service. Any usage after the initial (activation use). As you will learn, retention is the most important aspect of a successful SaaS business.

Retention rate: The % of users that continue using the service over time. This can be measured weekly, monthly, quarterly, etc.

Tenure: The # of months or years that you keep a paid customer. Calculated as 1 / churn rate.

Upgrade %: The % of customers that upgrade from you basic plan to a higher plan.

The Business Model

Business model viability, in the majority of startups, will come down to balancing two variables:

  1. Cost to Acquire Customers (CAC), and,
  2. Lifetime Value of a Customer (LTV)

Successful web businesses have long understood these metrics as they have such an easy way to measure them. However there is a lot of value in looking at these same metrics for all other businesses, especially in the SaaS model.

Image source: http://www.forentrepreneurs.com/startup-killer/

Calculating Customer Acquisition Cost – To compute the cost to acquire a customer (CAC) you would take your entire cost of sales and marketing over a given period, including salaries and other headcount related expenses, and divide it by the number of customers that you acquired in that period. Use the “Cost To Acquire” sheet of the workbook to help calculate this.

Calculating Lifetime Value – To compute the Lifetime Value of a Customer, LTV, you would look at the Gross Margin that you would expect to make from that customer over the lifetime of your relationship. Gross Margin should take into consideration any support, installation, and servicing costs. Use the “Lifetime Value” sheet of the workbook to help calculate this. SaaS businesses are usually initially very high cash intensive businesses because you pay upfront to acquire a customer and the customer only becomes profitable over time. So, you have a gap in cash flow. You can grow organically by saving up enough margins from your existing customers to acquire more, but this is slow. If you want to dominate your market, you need outside capital to maximize the pace of growth (more on this later). At the seed and series A stages, I recommend startups spend no more than 6 months of revenue to acquire a customer. This is because i.) cash tends to be tight; and ii.) the startup does not have enough cohort data to know for sure how many months customers stay on average. Later, when you have more data and more cash you can be more aggressive and spend more. It’s important to do this calculation both for your overall customer base and by price plan. You will likely find your higher price customers stick around a lot longer.

Tipping the Balance – Tactics for Optimizing your Model

If you are building a data driven company then i.) your entire team should have daily access to key stats (just put up Geckoboard on a big monitor and connect everything to it); and ii.) each team member should own a metric.

Brainstorm – with your team – 5 ideas/strategies around the two key elements (Monetization and CAC) with help from the examples given. Calculate Your Monthly Recurring Revenue (MRR) And Average Revenue Per User (ARPU) - Monthly Recurring Revenue (MRR) is calculated by multiplying the total number of paying customers by the Average Revenue Per User (ARPU). This is usually a key indicator of profitability. Use the “Monthly Reoccurring Revenue” sheet of the workbook to help calculate this.

Look at your CAC ratio monthly: this is the new Monthly Recurring Revenue (MRR) you added in the month * gross margin / the customer acquisition costs incurred that month. You can read more about this important ratio here.

This and more is available in a step-by-step interactive format at StartupPlays.com

Mike Mason

Mike heads up metric driven growth at MercuryGrove. He specializes in marketing strategies for early stage high growth internet companies.

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I’m guessing this is confirmation that Waterloo Region is really just part of the Toronto ecosystem.

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I have seen The Communitech HubSpot space. Not a bad name given the facility that houses Communitech is called The Hub, calling a smaller zone in Toronto “The HubSpot” would make sense. Other than one of the leading CRM vendors based in Boston that has raised a combined $65MM in financing is also called HubSpot. That’s got to be bad for SEO.

I first saw the Communitech HubSpot space when I looked at moving the Maintenance Assistant offices earlier this year. It’s space located on the 2nd floor of 170 University Ave, that doubles as offices for Phil Deck. Phil was previously the CEO and Chairman of MKS Inc. which was acquired by PLC for $305MM in 2011. Phil is also on the Board at Communitech, which features other key players in the Waterloo and Toronto ecosystem including John Ruffolo of OMERS Ventures, Carol Leaman of Axonify, Ali Asaria of Well.ca and John Baker of Desire2Learn.

“It’s been a lot of hard work and even more fun putting Waterloo Region tech on the map with the help of a hugely supportive community.”
Iain Klugman, Communitech Marks 15 Year Anniversary of Supporting Waterloo Tech

Communitech in Toronto

It’s great news as Well.ca already has offices in Toronto. Previously Waterloo-based companies like Top Hat Monocle have moved to Toronto. There are a large number of Waterloo alums actively working in the Toronto startup ecosystem (I’m a UWaterloo alum along with Farhan Thawar, Zak Homuth, Zach Aysan, John Phillip Green, Dan Holowack, Oshoma Momoh, Bruce Chin, Garry Seto, Mike Rhemtulla, Monica Goyal and that’s just off the top of my head).

Too bad they chose to leverage an existing StartupNorth brand for the event. The least they could have done was invite us. Oh well, they stuck with our own “invite only” model.

 

David Crow

David Crow focused on product design, customer development and go-to-market implementation on $0. He is available as a consultant. He is a mentor at UW VeloCity, Jolt and FounderFuel. Follow him on Twitter @davidcrow or at DavidCrow.ca

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