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?
Josh Pigford has an excellent post up titled Getting Out of the Startup Rat Race where he talks about the pressure and grind of trying to build a hyper growth SaaS startup. After spending three years comparing himself, and his startup, to the high flyers we read about, he realized that it’s not all about being the next billion dollar thing. Rather, there are many other ways to define success.
The highlights from the post:
Our revenue was definitely growing month over month, but my eternal optimism believed that it’d magically start really growing in “just a few months”.
We were treating our company like we were in some race for time. But there is no race. There isn’t another runner we could lose to and we can’t “come in first”.
Do not obsessively think about your startup “every single moment of the day”.
Because doing things your own way, on your own terms, is where you’ll find fulfillment. And really, that’s what we’re all after.
There is a real rat race element to the startup world and entrepreneurs would do well to step back and objectively think through what’s important to them.
What else? What are some more thoughts on the startup rat race?
At some point during the startup process the shine of a new venture starts to wear off. Some call it the Trough of Disillusionment and some just call it a normal part of being an entrepreneur. When I hear entrepreneurs talk about what they’re working towards, and the tone of their voice is optimism wrapped in fatigue, I know that they’re feeling the grind.
Here are a few thoughts on settling in for the grind:
Know that most “overnight successes” take 7-10 years
Tonight, the Atlanta Tech Village is hosting Atlanta Startup Village #44. The Atlanta Startup Village is a free, monthly gathering of 400+ people to hear startup pitches and ask questions. There’s one simple goal: build the startup community.
Here are the four presenters tonight:
TechStars Atlanta – Michael and Tyler will provide an update on last year’s cohort and the upcoming class.
Last week the topic of selling a product through resellers came up in two separate conversations with entrepreneurs. Historically, I haven’t had much success selling through third-parties but I know of plenty of other startups that have done it well. From what we’ve tried and what I’ve seen others do well, here are a few thoughts on startups selling through resellers:
Ensure Credit Towards Quota – One of the most important things in a reseller relationship is that the sales reps of the reseller get credit towards quota. Without quota credit there’s little, if any, incentive to sell the other product. Alignment is key and it starts with credit towards quota.
Monthly Champion Calls – The champions of the relationship in both organizations need to talk monthly about what’s going well and not going well. Too often, the reseller partnership is signed and everyone goes back to what they were doing before and nothing happens. Regular, recurring communication is key. Go head, put it on the calendar now.
Regular Training – Products, especially technology ones, are complex and nuanced. For the sales and marketing teams of the reseller to feel comfortable selling a new product they need to be trained initially as well as on a recurring basis when new functionality comes out. Ask a sales person if confidence is an important part of selling something and they’ll all say yes. If a product isn’t well understood, the sales rep isn’t going to be as confident, and that hurts the chances of success.
Selling through resellers is tough as most of the sales and marketing is outside your control. Increase the chance of success by aligning interests through programs like quota credit as well as recurring communication and training.
What else? What are some more thoughts on selling products through resellers?
Continuing with the previous post Not All Good Ideas Can be Good Companies, there’s a related topic that I’ve seen happen several times: an entrepreneur builds a good product, gets customers, and then realizes that it’s a poor market to be in. This is a tough one as good products, combined with some sales and marketing, often generate customers. Only, after a few customers sign on, there’s hope that the startup is off in a good direction, yet signs of a poor market become apparent.
Here are signs of a poor market for a product:
Required Product Customizations – Customer needs aren’t consistent from sale to sale requiring heavy product customization, and the product customizations aren’t following a pattern, making for a non scalable model. Constant one-off customer requests that are necessary for the customer to get serious value is a bad sign (unless you can charge a significant premium for them).
Acquisition Cost Relative to Price Point – Some people really want to buy the product, but don’t have budgets that warrant the cost of reaching them. This is more pronounced for products that require an outbound sales team to sell the product, thus requiring a higher price, yet the market won’t support a price that works.
Long Sales Cycle – Related to the acquisition cost issue, some types of buyers aren’t able to make decisions in a timely manner due to things like their own budget cycle, required internal approvals, and more. Long sales cycles, especially in areas like government, education, and others can make for a tough business model.
Entrepreneurs often have “happy ears” where they want to find the positive in every situation. When it comes to the overall market for their product, it’s important to objectively assess it on a regular basis, even after signing a few customers.
What else? What are some more thoughts on a good product in a poor market?
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:
Email Send Time – Analyze when leads opened emails in the past and automatically schedule future emails to be sent at the same time (time of day, day of week, etc.)
Email Message to Trigger – Analyze what emails (or other types of content) were most closely associated with pushing the lead through the current phase of the lifecycle (e.g. engaging with a sales rep) and trigger the email automatically (as opposed to human-defined static rules)
Lead to Opportunity Probability – Analyze every piece of historical data for leads that turned into opportunities (the training data or learning set) and come up with a probability for all other leads that haven’t turned into an opportunity based on how their behavior matches the training data
Lead to Customer Probability – Like the lead to opportunity probability, do the same thing for leads that became customers (not just those that had an opportunity in the pipeline associated with them) and come up with a probability that any given lead will become a customer
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?
Over the last few years the number of startups pitching machine learning as part of their special sauce has increased dramatically. Just a few days ago Tomasz Tunguz asked the question Is Machine Learning Overhyped? and argued it wasn’t.
Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed
So, if a computer is the figure out something without being told exactly what to do, there must be data with patterns that provides the basis for the learning. Here’s a Udacity video titled A Friendly Introduction to Machine Learning.
How does machine learning work?
Take a given set of data that’s known or correct (e.g. these are 10,000 pictures of cars) and have the software classify what it finds (e.g. size, shape, color, etc.) in this “seed” or “learning”, or “training” data (also called “supervised learning”). Now, give the software the overall data to analyze (e.g. 1 million pictures of anything) to find which ones have a high probability of being like what the classifier found (e.g. they have a car in the picture). Historically you’d have to “tell” the software what a car looks like in a photo. With machine learning, you give it examples to learn from for classification and then have it apply a probability of a match to each item in the overall data.
What’s a business example of machine learning in action?
Take SalesLoft and sales engagement software. In SalesLoft, sales reps are engaging with their leads via phone, email, social, etc. A common business use case is optimizing for when prospects are most likely to answer phone calls and then to optimize the engagement to coordinate the reps to make calls at those times. Using the SalesLoft data, machine learning would be trained with historical data of all phone calls that turned into conversations (as opposed to no answer, voicemail, etc.) with a variety of fields like time of day, day of week, day of month, day of quarter, timezone, job title, industry, etc. Next, the machine learning would trained on all the historical calls that weren’t answered using the same fields (the “error” data). Finally, the machine learning would process the “good results” and the “bad results” to find patterns and recommend the optimal times of day to make phone calls such that the quality and efficiency of time spent on the phone increased dramatically.
Why machine learning now and not 10 years ago?
Computing power has improved, data processing is better, proven open source machine learning libraries are available, and awareness has increased. Put another way, the challenge and complexity of doing machine learning has gone down 100x. With a lower barrier to entry and higher value, more startups are going to implement machine learning.
Machine learning is real and opens up a remarkable number of new opportunities to use computers to provide insights in a way that wasn’t possible before. Look for machine learning to grow in importance and the startup world to capitalize on it.
What else? What are some more thoughts on machine learning and the startup world?
Earlier today Kyle shared the details of raising their $15 million Series B to be the platform for sales engagement. On April 1, 2015 I blogged about SalesLoft raising their $10 million Series A and highlighted the company background. Quick recap: I tried to recruit Kyle at Pardot and instead we started SalesLoft. After two major product pivots Kyle, Rob, and the amazing SalesLoft team found an incredible opportunity with sales engagement and have been growing fast for years.
What’s sales engagement? Think of a system that empowers sales people to be significantly more productive by making more targeted phone calls and sending more personalized emails based on an automated process (a cadence). The ultimate goal is a more sincere, modern selling process.
Here are a few reasons I’m so excited about SalesLoft:
Mission Driven Business – The Lofters are on a mission. Truly. This isn’t idle talk. Everyone is focused on building the best, most transformative sales engagement platform on the planet.
Sincere Culture – Values are placed front and center as a core foundation of the company. And, with values Put Customers First, Glass Half Full, Team Over Self, Focus on Results, Bias Towards Action, you know I’m a fan.
Amazing Team – Kyle, Rob, and the rest of the team are amazing. Everything starts with the people and these are the people you want.
Big Market – Sales people. Productivity. Making more money. Sales engagement is a massive market with tremendous growth.
Atlanta Impact – To make an impact, Atlanta needs major success stories that create jobs, wealth, and inspiration for more entrepreneurs. Everything about SalesLoft is on the trajectory to achieve this.
SalesLoft will be the sales engagement platform of record. Every element is in place and now it’s time for Kyle and team to make their mark on modern sales. Here’s to SalesLoft achieving another milestone on their journey!
David Cummings on Startups 