Nakul Mandan has an excellent tweetstorm on the importance of building sales capacity in advance of growth. The idea is that startups get caught up in the here and now and don’t start hiring and training AEs, SDRs, and SEs soon enough for the long term goals.
https://twitter.com/nakul/status/850392721625710593
The solution: plan 24 months in advance. Figure out the sales metrics and model the sales rep ramp.
When the startup is scaling fast, build sales capacity in advance.
With the basics of Account-Based Sales for More Predictable Revenue in place, next comes a deeper explanation as to how “standard sales” differs from account-based sales. First, let’s start with an example.
At Pardot, every August the new Inc. 5000 would come out and Account Executives (AEs) would would go through different relevant categories like software and claim “ownership” of any new accounts on the list that weren’t already in the CRM. Then, they’d go in to LinkedIn (or LinkedIn Sales Navigator) and find the right people based on their department and seniority level. Next, using a scraping tool like LeadIQ or Hunter, the names in LinkedIn would be turned into CRM leads with email addresses. Finally, the AEs would call and email the leads a few times, giving up quickly if there was no response.
This outbound approach, combined with following up to any inbound leads, represents how the majority of companies do standard sales. A few characteristics of standard sales:
Now, contrast that to the characteristics of account-based sales:
Standard sales is more “spray and pray” while account-based sales is targeted and deep.
Entrepreneurs would do well to initiate an internal shift to account-based sales and deliver more predictable revenue.
What else? What are some more thoughts on standard sales vs account-based sales?
Intercom is a fast-growing SaaS startup that provides customer communication software for doing live chat, in-app customer messages, email triggers, helpdesk support, and knowledge base content. Their CEO recently published an interesting blog post titled Vanity metrics, the future, and 100,000 thank yous with a number of interesting metrics.
Here are a few notes on the blog post and Intercom:
Impressive metrics all around and easily one of the fastest growing SaaS companies in the world. Congrats to Intercom on reimagining customer communication for the modern business and building an incredible company.
Thinking more about the post from a couple weeks ago titled Evaluating a Startup Based on Cash Burned vs Recurring Revenue and how the same idea was brought up again two days ago in Bessemer’s 2017 State of the Cloud Report, I’ve come to believe that $1 of cash burned for $1 of net new recurring revenue is the Golden Metric for SaaS.
As an idea, it’s easy to understand.
As a metric, it’s easy to track.
As a way to create value, it’s excellent.
As a benchmark for entrepreneurs to measure against, it’s perfect.
Some startups will choose to burn more than $1 for each $1 of new new recurring revenue, but most won’t have that luxury. Startups that achieve scale, and burn $1 (or less!) for every $1 of net new recurring revenue, will do well for all stakeholders involved.
What else? What are some more thoughts on the Golden Metric for SaaS being $1 of cash burned for $1 of net new recurring revenue?
There was so much good content at the SaaStr Annual that it’s going to time to get through it all. Next of the list is Bessemer’s 2017 State of the Cloud Report.
http://www.slideshare.net/AnnaKhan9/the-state-of-the-cloud-report-2017-bessemer-venture-partners
Here are a few notes from the Bessemer slide deck:
Thanks to the team at Bessemer for putting together the great information. Every SaaS entrepreneur should read Bessemer’s 2017 State of the Cloud Report.
Continuing with 12 Key Levers of SaaS Success from David Skok at SaaStr, Mamoon Hamid gave an excellent presentation Numbers that Actually Matter. Finding Your North Star.
http://www.slideshare.net/03133938319/numbers-that-actually-matter-finding-your-north-star
Here are a few notes from the presentation:
Read the presentation Numbers that Actually Matter. Finding Your North Star. and figure out the North Star for your product.
David Skok of forEntrepreneurs and Matrix Partners has a great new slide deck up from his presentation at SaaStr Annual titled 12 Key Levers of SaaS Success.
http://www.slideshare.net/DavidSkok/12-key-levers-of-saas-success/1
Here are the 12 key levers:
Haven’t read it yet? Head on over to 12 Key Levers of SaaS Success and read it now.
In the SaaS world, one of the common best practices is to have the cost of customer acquisition be equal to or less than the first year’s revenue (or even better would be gross margin). So, if on average it costs $5,000 in fully loaded sales and marketing expense to acquire a customer that pays $5,000 per year, things are going well. After learning that heuristic, and working with a number of entrepreneurs, I’ve come to take it one step further and judge the success-to-date of a startup based on the amount of money burned all-time vs the annual recurring run-rate today, especially if it’s one to one.
While burning $1 to get $1 of recurring revenue might not sound like much, it’s actually really good. Think of a company that’s growing fast at $5 million recurring on $5 million burned all-time. In today’s market, that company is likely valued at $30M .- $40M (6-8x run-rate). Spending $5M to build a company worth that much is likely a good scenario for everyone involved including founders, employees, and investors. A common phrase in the startup world is “if the company sells for 10x the amount of money raised, everyone does well.” While a valuation of 10x the capital raised is excellent, consider the ratio of capital burned all-time to current recurring revenue as another metric to evaluate the success of a startup.
What else? What are some more thoughts on evaluating a startup based on cash burned vs recurring revenue?
Continuing with yesterday’s post Machine Learning and the Startup World, a friend asked what machine learning applications might look like for marketing automation platforms (e.g. Pardot). Good question. Considering marketing automation platforms collect so much information about leads through email opens, form submissions, web page visits, ebook downloads, webinar signups, etc., there’s a tremendous amount of training data for machine learning to find insights.
Marketing automation is essentially human-defined rules based on what they think is best (e.g. send email A, wait two days, send email B, etc.) Machine learning “learns” over time and can figure out patterns humans can’t because there’s too many dimensions to analyze. Here are a few ideas on how to use machine learning in marketing automation:
Machine learning has applications in all fields, especially marketing automation. Look for existing vendors to add this type of functionality as well as new vendors to emerge that take advantage of this new technology.
What else? What are some more ways machine learning can be applied to marketing automation?
Updata Partners released their new SaaS Metrics Framework and it’s excellent. SaaS companies have a number of business model elements that are consistent from one company to another such that it’s possible to run them through a process and see how they stack up fairly quickly. Updata’s framework is one such model.
Here are a few notes from the SaaS Metrics Framework:
One big takeaway: SaaS companies need to be thinking about many of the popular metrics like the SaaS Magic Number in the context of gross margin, not revenue. And, thankfully, gross margin should improve with scale. Want to understand SaaS unit economics better? Head over to SaaS Metrics Framework.
What else? What are some more thoughts on Updata’s SaaS Metrics Framework?
David Cummings on Startups 