Calculate the Sales Performance of Any Company in Two Minutes Flat

Originally published on LATKA’s SaaS.

tl;dr: Run a few searches (patterns provided) on LinkedIn and Google to determine the current reps and churned reps per year for any given company. Divide churned reps per year by the current number of reps to compute the percentage of the current sales team that will churn in one year’s time – producing the Sales Team Churn (STC) metric. Can compute STC for any company (including privately held ones), and compare to other companies’ STCs (see benchmarks table covering several key SaaS companies below).

To compute this sales performance metric, you need to run three simple searches.

First, search for the company on LinkedIn – “in People” search:

assess_li_init_search

Then click “All Filters” near the top right of the search results page, check the company under “Current companies”, and in the “Title” field (near the bottom of the form), copy and paste the following string:

“sales development” OR “SDR” OR “account executive” OR “sales executive” OR “account manager”

Then click “Apply”.

assess_li_current_search_filter

Near the top left of the search results page is the number of results (highlighted in blue in the image below). Save this figure, which represents the current number of sales related heads at the company.

assess_li_current_count

Then click “All Filters” again. Uncheck the company under “Current companies”, and check the company under “Past companies”. In the “Company” field box, enter “-” then the company name (in quotes if company name is more than one token). 

assess_li_past_search_filter

Click “Apply”, and save the number of results, which represents the number of sales related heads that no longer work at the company.

assess_li_past_count

Then search the company name padded with “founded” on Google, and find the date the company was started.

assess_google_founded

These three searches give us current sales heads, past sales heads, and the founding date of the company.

With these three values, we can calculate the following:

Churned Sales Heads Per Year = Past Sales Heads / Number of Years Since Founded

Sales Team Churn (STC) = Churned Sales Heads Per Year / Current Sales Heads

This metric represents how much of the current sales team will churn in one year (so lower the number the better).

Let’s plug-in the example illustrated in the screenshots above:

Zendesk, founded in 2007, has currently 476 sales related heads, and has lost 146 heads.

Churn per year = 146 / (2019 – 2007) = ~12

STC = 12 / 476 = ~2.5%

So, what’s a good STC score? Here are the metric values for various enterprise companies (full table here):

assess_enterprise_benchmarks

The key columns are the last three. Lower STC the better. Been capturing monthly snapshots of STC values for these basket of companies – sparkline and percentage changed provided in the last two columns. Ideally, STC is below 5%, and the STC is steady or going down over time (in green if going down – otherwise red). See full table here.

 

The average of this basket of companies is 5.9% – so would recommend targeting under 5% in general. This sounds low, but that’s because churn per year is calculated over all time – since the incorporation date. This is because I was unable to filter LinkedIn search results to a select period of time (like over the past year). In the early years, sales and corresponding sales churn will be low or nonexistent, which heavily down weights this value. As companies age, the number of years increases, and the number of current sales reps increase, which can both lower this score. This is how even a mature company intentionally churning out 10-20% of their sales team annually (to remove bottom performers and raise the bar for all reps) can still score a STC below 5%. The key is to compare STC with other companies at similar stages versus directly comparing with internal annual attrition numbers given the differences in calculation methods and assumptions.

There are many interesting insights to glean from this table. For example, there are several companies with < 1% STC, including LaunchDarkly, Zoom (although growing), Twilio and GitLab – all of which have self-service trial flows.

There are also companies with > 10% STC percentages (to reiterate – this means more than 10% of their sales team will attrit in a year), including Domo, Dropbox, Gainsight and Zuora.

There are interesting competitive benchmarks as well – for ex. Intercom is 5.3% vs Drift’s 10.27% – nearly half of Drift’s sales churn.

I’ve been snapshotting this metric for several months, as it’s also important to look at how STC is changing over time (see last column above – is the sales health getting better or worse?). There are companies on this list who have consistently increased (Gainsight, Zuora) or decreased (Anaplan, Domo) their STC.

So, why the focus on sales team size for assessing the sales performance of a company?

When I was running Infer, I recall my syncs with Aaron Levie (CEO / Co-Founder of Box, and one of our angel investors at the time), and the first question he would always ask was:

“How many salespeople do you have now?”

This is a really great question for assessing a business quickly. The more reps you have, the more deals you can close. The more reps you have, the more market demand you have. The more reps you have, the more you’re spending to drive growth. The more reps you have, the better your hiring process, sales leadership and culture are for attracting talent.

The twist with STC, is that we’re not just looking at the number of present sales people, but also factoring in the attrition rate. So, the less reps churned, the more reps that are hitting their quotas.

C-level executives focus on key business metrics such as gross or net revenue retention. High churn means there’s a leaky bucket, which can sink even a high growth new ARR business.

This applies not just to customers, but to sales reps too – often overlooked compared to customer churn. If a company is losing a good chunk of their sales team each year, then the company is losing the ability to generate and close revenue making opportunities, and has to spend to hire new reps – and burn valuable time ramping them.

In general, sales reps face higher risks of attrition than those in other functions. Their goals are measurable, and if they miss, they’re fired or leave – and the best performing reps can receive promotions and make more money elsewhere. But even if higher attrition is expected, what is the healthy, right amount of attrition for any given company to experience?

Losing reps is very much a leaky bucket just like customer churn, and deserves metrics and magic numbers to abide to – hence STC and the 5% target.

The STC metric has several nice properties:

It’s accessible, and can be computed for any company (unlike revenue which is hard to reliably discern for private companies). Anyone can quickly and easily derive this metric with a free LinkedIn Account. It does not require internal financials – so it’s fully transparent internally and externally. The metric is normalized, so can compare companies’ STCs for benchmarking purposes. This metric updates often (as sales people tend to update their profiles quickly). It’s also a more forward looking indicator than revenue (need sales people first before closing more deals).

This metric can also be adapted to different roles outside of sales by simply changing the title query (could tailor to executive management roles with search tokens like “Chief”, “VP”, etc.).

VPs of Sales, CEOs, VCs, VPs of FP&A, job candidates, hedge fund quants, etc. should be leveraging STC-like metrics for planning models, researching competitive landscapes and evaluating investments or job opportunities.

Special thanks to the following for reviewing drafts of this piece:

Ajay Agarwal, George Bischof, Matt Cooley, David Gilmour, Amar Goel, Naren Gupta, Nathan Latka, Nick Mehta, David Kellogg, Vish Makhijani, Tomasz Tunguz and Jeff Weiner.

***

Do note, some technical caveats regarding this approach for computing STC:

Can be difficult to be precise with LinkedIn’s search. Two examples: (1) company name may overlap with other companies with similar names (2) can’t search past titles (at least via the free account) (3) have to negate ‘-’ company name in order to find people that worked in the past at a company and are no longer at that company

If a company is fresh and hasn’t had any churn (or really low churn), then this is meaningless (it has the best STC score). Just means they haven’t had enough sales team churn yet. Still useful to look at comparable companies who’ve been in-market longer and use their STC scores for headcount planning / forecasting.

Different companies use different sales titles. May need to adjust the title query on a per company basis.

The sales titles query does not exclusively correspond to quota carrying reps.

Not everyone is on LinkedIn. This is usually not a problem for sales reps as they typically want to advertise themselves in order to be able to connect with potential customers – esp. at tech companies. Even when not all sales reps are accounted for (when a rep is not on LinkedIn or the title query doesn’t capture that person), I find that this metric is still directionally useful especially on a relative basis with other companies (it’s consistently sampling each company in the same manner).

 

 

 

 

Why Atlassian will be a $50B company in 10 years

An op-ed I wrote for VentureBeat:

Last week, Atlassian made a very smart move by acquiring Trello. While $425 million implies a high multiple (given Trello’s revenue run rate was around $10 million last year), I believe it positions Atlassian to become the next big enterprise software company. I project it will reach a $50 billion market cap in 10 years by taking over software for teams. Here are four reasons why:

Read more

Amazon, Google and Apple vs the Big 5 Unicorns on Hiring and Churn

I’ve received multiple requests to analyze employee churn and new hiring rates for big companies and unicorns with the approach I took earlier for studying engineering and sales retention rates. I figured I’d give it a shot – and combine all of the key metrics in one chart …

amazon_google_apple_unicorns_retention_hiring

How to read this:

Blue bar represents the number of expected new hires that particular company will make in a 30 day (one month) period. Black bars (negative values) indicate how many employees will churn in a one month period. The orange line (the top most numerical labels correspond to the orange line plot) represent the net change in hires per month (new hires less churn). The companies are ranked by churn from left to right in descending order (so highest churn on the left).

As you can see in the chart, the big three companies included in this analysis are Amazon, Apple and Google. The unicorns are Uber, Lyft, Airbnb, Pinterest and Snapchat. “Big 5” combines these unicorns together as if they were one whole company. Also note, this is looking at employees worldwide with any job title.

Key Insights:

  1. Apple is not hiring enough new heads when compared with Amazon and Google. In fact, the Big 5 unicorns combined will hire more net heads than Apple with almost 50% less employee churn.
  2. Amazon’s churn is the highest – losing a little over 10 people a day. However, this is not bad relatively speaking – Google loses 8-9 people a day, and Apple is a tad over 9 a day (and Amazon has 36% more employees than Google). Given the recent press bashing Amazon’s culture and the periodic press envying Google’s great benefits, their retention rates tell a different a story – that it’s closer to a wash. Big tech companies with great talent churn people at pretty similar high rates regardless it seems (have some more thoughts on this but will save those for another post).
  3. At these current rates, all of the companies here (collectively) will increase their employee size by 20K (19, 414 to be precise) heads by year end (this is new hires less churn). That’s a measly 5% increase in their current collective employee size – and this is across the Big 3 Tech Companies and Big 5 Unicorns.
  4. Let’s compare Amazon to the Big 5 Unicorns. The Big 5 will hire 79% as many incremental heads as Amazon in a month, even though their collective employee size is 24% that of Amazon’s. Amazon has been in business for much longer (2-3x days since incorporation), and the Big 5’s churn is 43% of Amazon’s figure – both factors contributing to the closeness in the incremental head rate between the two.

Want more details?

How did I calculate these figures? Take a look at my previous post on engineering retention for more details. Same caveats listed there apply here, and then some (such as how this depends on the participation rates of LinkedIn which may differ considerably internationally compared to the US market which my previous posts exclusively focused on). Feel free to connect or email me if you have any questions or feedback.

 

Ranking Companies on Sales Culture & Retention

A company’s sales retention rate is a very important indicator of business health. If you have a good gauge on this, you could better answer questions such as: should I join that company’s sales department, will I be able to progress up the ladder, are reps hitting their numbers, are they providing effective training, should I invest money in this business, etc. But how does one measure this rate especially from an outside vantage point? This is where LinkedIn comes to the rescue. I essentially cross applied the approach I took to measuring engineering retention to sales.

sales_ret_2

This chart reveals several key technology companies ranked in reverse order of sales churn – so higher on the chart (or longer the bar) the higher the churn (so from worst at the top to best at the bottom).

So how are we defining sales churn here? I calculated the measurement as follows: I took the number of people who have ever churned in a sales role from the company and divide that by the number of days since incorporation for that respective company (call this Churn Per Day), and then I compute the ratio of how many sales people will churn in one year (the run rate i.e. Churn Per Day * 365) over the number of current sales people employed.

For ex. if you look at the top row, which is Zenefits, the value is 0.40 – which means that 40% of the current sales team size will churn in a one year period. In order to maintain that sales team size and corresponding revenue, the company will need to hire 40% of their team – and sooner than in a year as that churn likely spreads throughout the year as well as given new sales hire ramping periods (if you’re churning a ramped rep and say it takes one quarter to ramp a new sales rep, then you need to hire a new head at least one quarter beforehand to avoid a revenue dip).

A few more notes:

The color saturation indicates Churn Per Day – the darker the color, the higher the Churn Per Day.

Caveats listed in the previous post on engineering retention apply to this analysis too.

Want to compete with Salesforce? Buy Marketo

An op-ed I wrote for TechCrunch:

There are several enterprise players that want a share of Salesforce’s business, but just aren’t making headway by knuckling up against the company’s dominant, entrenched SaaS CRM offerings. Rather than competing head on, a smarter approach for these businesses is to “front door” Salesforce, instead.

By acquiring Marketo, a competitor could get into Salesforce’s accounts, then, over time, work themselves down the funnel and leverage better integrations with Marketo in order to eventually displace Salesforce. Marketo’s strategic foothold in the enterprise and its current market value relative to potential acquirers like IBM, Microsoft, Oracle, SAP and even Salesforce make this a great time to buy the leading marketing automation vendor.

Many industry watchers overlook the mission-critical role Marketo plays in its customers’ go-to-market operations. The majority of Marketo’s 4,000 customers also use Salesforce, but the marketing automation system has access to more data about the funnel than its CRM counterpart. Marketo can sync bi-directionally with Salesforce, capturing all the data stored there, while also holding top-of-the-funnel lead behavior data that doesn’t get stored in CRM. Hence, it has access to an invaluable superset of data about a company’s potential and existing customers.

Read More …

Predictive First – A New Era of Game-Changing Software Apps

A guest piece I wrote for TechCrunch on how predictive-first (like “mobile-first”) applications will change the software game: 

Over the past several decades, enterprise technology has consistently followed a trail that’s been blazed by top consumer tech brands. This has certainly been true of delivery models – first there were software CDs, then the cloud, and now all kinds of mobile apps. In tandem with this shift, the way we build applications has changed and we’re increasingly learning the benefits of taking a mobile-first approach to software development.

Case in point: Facebook, which of course began as a desktop app, struggled to keep up with emerging mobile-first experiences like Instagram and WhatsApp, and ended up acquiring them for billions of dollars to play catch up.

The Predictive-First Revolution

Recent events like the acquisition of RelateIQ by Salesforce demonstrate that we’re at the beginning of another shift toward a new age of predictive-first applications. The value of data science and predictive analytics has been proven again and again in the consumer landscape by products like Siri, Waze and Pandora.

Big consumer brands are going even deeper, investing in artificial intelligence (AI) models such as “deep learning.” Earlier this year, Google spent $400 million to snap up AI company DeepMind, and just a few weeks ago, Twitter bought another sophisticated machine-learning startup called MadBits. Even Microsoft is jumping on the bandwagon, with claims that its “Project Adam” network is faster than the leading AI system, Google Brain, and that its Cortana virtual personal assistant is smarter than Apple’s Siri.

The battle for the best data science is clearly underway. Expect even more data-intelligent applications to emerge beyond the ones you use every day like Google web search. In fact, this shift is long overdue for enterprise software.  (Read More)