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Your VP of Sales Shouldn’t Be Perfect




We’ve talked a lot on SaaStr over the years about all different types of VPs of Sales.  From Stretch VPs to stage-appropriate VPs.

Screen-Shot-2016-01-18-at-3.24.49-PMIt’s worth also focusing on a related, high-level point.  Especially if you sell to multiple customer segments (i.e., small, medium, and large) and through multiple processes (in-bound, out-bound, upsell, channel, partner) … you can’t expect your VP of Sales to be perfect at everything.

And surprisingly, many founders do.

Here’s the thing.  Usually:

  • Elephant Hunters aren’t really very good at Inside Sales.   VPs of Sales that have come up through field sales, hunting six- and seven-figure deals … usually are actually kind of terrible at high velocity inside sales for four and five-figure deals.  It’s just too much effort for deals that seem way too small.  They can’t handle all the noise.  And all the endless questions. And the endless hiring to keep up.   And the sheer number of issues and drama around such small deals.  Versus a great VP of Sales that really is focused on inside sales loves it.  Loves being in the pit.  Loves managing a large team, and pushing them all to goals.  And dealing with the same set of issues, and honing the answers, and winning.  And winning fast.
  • Inside Sales Leaders (Often) Leave Money on the Table in Very Big Deals.  Most VPs of Sales that are good at inside sales can also close larger deals — but they don’t totally optimize it.  They want to close fast and easy by nature.  And so it sounds great when they close a $250,000 deal.  But maybe a VP of Sales out of true enterprise sales, field sales … could have closed the same customer for $500,000 a year.
  • Inside Sales Leaders Don’t Love Mapping Out All the Stakeholders, Doing Demo-after-Demo, Filling our RFPs, Getting on Jets (When We Can Again). And Elephant Hunters May Spend Too Much Time Here in Smaller Deals. If you are used to managing dozens of inside sales reps, you don’t want to talk to every stakeholder, do a dozen demos, fill out that RFP … unless it’s a crucial deal. But elephant hunters know you have to get on a jet to close the big ones.
  • VPs of Sales Trained in Inbound May Have to Learn Outbound, More or Less.  If your VP of Sales has grown up in an in-bound environment, she may have to learn how to build a true outbound team.  More and more VPs of Sales have done both these days, but the reality is VPs of Sales that grew up in-bound are rarely great at outbound.  It’s not just “picking up the phone”.
  • VPs of Sales Trained in Outbound May Not Know How to Optimize Working with a VP of Demand Gen / Marketing.  VPs of Sales that have done it the hard way, without many in-bound leads … may not know how best to work with a great VP of Demand Gen.  So — importantly — they may not process all those in-bound leads as well as a VP of Sales trained in in-bound.
  • Many VPs of Sales Just Want to Be Closers.  They Often Aren’t Perfect at Upsell or Customer Success.  Many true closers only want to be involved in the minimize necessary once the contract is e-signed.  On the other hand, VPs of Sales that have also owned customer success often want a broader purview, to own the whole customer lifecycle, including upsells years down the road.  That can be good — but these folks that want to own all the revenue often aren’t as strong of pure closers.

I could keep going.

My real point is to do your best to optimize your VP of Sales hire around your target ACV.  If your average customer is $50,000 in ACV, and you hire a great VP of Sales with lots of experience at that price point … then she may not be as good at $5,000 and $500,000 deals.  But at least she’s optimized around your core deal.

And beyond that, help her … and if she has materially increases sales, well cut her some slack.  Let her be good at what she does, especially on the path to $10m ARR and Initial Scale.  Put her in her zone.  Then, help her add managers under her that are good at the stuff she is less experienced in.

No VP of Sales is great at every deal size, every type of customer, and every type of sales.  Expecting that is a recipe for frustration.  Even if she kills the plan for this year.  Because you’ll also see all the places she could have done even better.

Note: an update of a SaaStr Classic Post

Published on June 29, 2020



We need a new field of AI to combat racial bias




Since widespread protests over racial inequality began, IBM announced it would cancel its facial recognition programs to advance racial equity in law enforcement. Amazon suspended police use of its Rekognition software for one year to “put in place stronger regulations to govern the ethical use of facial recognition technology.”

But we need more than regulatory change; the entire field of artificial intelligence (AI) must mature out of the computer science lab and accept the embrace of the entire community.

We can develop amazing AI that works in the world in largely unbiased ways. But to accomplish this, AI can’t be just a subfield of computer science (CS) and computer engineering (CE), like it is right now. We must create an academic discipline of AI that takes the complexity of human behavior into account. We need to move from computer science-owned AI to computer science-enabled AI. The problems with AI don’t occur in the lab; they occur when scientists move the tech into the real world of people. Training data in the CS lab often lacks the context and complexity of the world you and I inhabit. This flaw perpetuates biases.

AI-powered algorithms have been found to display bias against people of color and against women. In 2014, for example, Amazon found that an AI algorithm it developed to automate headhunting taught itself to bias against female candidates. MIT researchers reported in January 2019 that facial recognition software is less accurate in identifying humans with darker pigmentation. Most recently, in a study late last year by the National Institute of Standards and Technology (NIST), researchers found evidence of racial bias in nearly 200 facial recognition algorithms.

In spite of the countless examples of AI errors, the zeal continues. This is why the IBM and Amazon announcements generated so much positive news coverage. Global use of artificial intelligence grew by 270% from 2015 to 2019, with the market expected to generate revenue of $118.6 billion by 2025. According to Gallup, nearly 90% Americans are already using AI products in their everyday lives – often without even realizing it.

Beyond a 12-month hiatus, we must acknowledge that while building AI is a technology challenge, using AI requires non-software development heavy disciplines such as social science, law and politics. But despite our increasingly ubiquitous use of AI, AI as a field of study is still lumped into the fields of CS and CE. At North Carolina State University, for example, algorithms and AI are taught in the CS program. MIT houses the study of AI under both CS and CE. AI must make it into humanities programs, race and gender studies curricula, and business schools. Let’s develop an AI track in political science departments. In my own program at Georgetown University, we teach AI and Machine Learning concepts to Security Studies students. This needs to become common practice.

Without a broader approach to the professionalization of AI, we will almost certainly perpetuate biases and discriminatory practices in existence today. We just may discriminate at a lower cost — not a noble goal for technology. We require the intentional establishment of a field of AI whose purpose is to understand the development of neural networks and the social contexts into which the technology will be deployed.

In computer engineering, a student studies programming and computer fundamentals. In computer science, they study computational and programmatic theory, including the basis of algorithmic learning. These are solid foundations for the study of AI – but they should only be considered components. These foundations are necessary for understanding the field of AI but not sufficient on their own.

For the population to gain comfort with broad deployment of AI so that tech companies like Amazon and IBM, and countless others, can deploy these innovations, the entire discipline needs to move beyond the CS lab. Those who work in disciplines like psychology, sociology, anthropology and neuroscience are needed. Understanding human behavior patterns, biases in data generation processes are needed. I could not have created the software I developed to identify human trafficking, money laundering and other illicit behaviors without my background in behavioral science.

Responsibly managing machine learning processes is no longer just a desirable component of progress but a necessary one. We have to recognize the pitfalls of human bias and the errors of replicating these biases in the machines of tomorrow, and the social sciences and humanities provide the keys. We can only accomplish this if a new field of AI, encompassing all of these disciplines, is created.


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As Q3 kicks off, four more companies join the $100M ARR club




Welcome back to our $100 million annual recurring revenue (ARR) series, in which we take irregular looks at companies that have reached material scale while still private. The goal of our project is simple: uncovering companies of real worth beyond how they are valued by private investors.

The Exchange is a daily look at startups and the private markets for Extra Crunch subscribers; use code EXCHANGE to get full access and take 25% off your subscription.

It’s all well and good to get a $1 billion valuation, call yourself a unicorn and march around like you invented the internet. But reaching material revenue scale means that, unlike some highly valued companies, you’re actually hard to kill. (And more valuable, and more likely to go public, we reckon.)

Before we dive into today’s new companies, keep in mind that we’ve expanded the type of company that can make it into the $100M ARR club to include companies that reach a $100 million annual run rate pace. Why? Because we don’t only want to collect SaaS companies, and if we could go back in time we’d probably draw a different box around the companies we are tracking.

$100M ARR or bust

If you need to catch up, you can find the two most recent entries in the series here and here. For everyone who’s current, today we are adding Snow Software, A Cloud Guru, Zeta Global and Upgrade to the club. Let’s go!

Snow Software

Just this week, Snow Software announced that it has crossed the $100 million ARR mark, according to a release shared with TechCrunch. The Swedish software asset management company has raised a few private rounds, including a $120 million private equity round in 2017. But, unlike many American companies that make this list, we don’t have a historical record of needing extensive private capital to scale.


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U.S. Fintech Saas Company HighRadius Continues European Expansion with Opening of New Frankfurt, Germany Office




HighRadius, a U.S.-based fintech enterprise Software-as-a-Service (SaaS) company specializing in integrated receivables, announced on Thursday it has continued its European expansion efforts by opening its new Frankfurt, Germany office.

Founded in 2006, HighRadius claims its HighRadius Integrated Receivables platform optimizes cash flow through automation of receivables and payments processes across credit, collections, cash application, deductions, electronic billing and payment processing.

“Powered by the Rivana Artificial Intelligence Engine and Freda Virtual Assistant for Credit-to-Cash, HighRadius Integrated Receivables enables teams to leverage machine learning for accurate decision making and future outcomes. The radiusOne B2B payment network allows suppliers to digitally connect with buyers, closing the loop from supplier receivable processes to buyer payable processes.”]

HighRadius reported it had a 250% increase in bookings, 25 new customers, and a fourfold increase in employees in EMEA in the last 12 months. HighRadius noted that the new office will it to support more customers to accelerate their recovery from the impact of COVID-19.

“The pandemic has increased demand for agile and intelligent credit and collections solutions as organizations focus on maintaining cash flows and strengthening business resilience.”

Speaking about the expansion, Jon Keating, HighRadius’ Vice President and General Manager, added:

“Frankfurt’s position in central Germany makes other parts of the country readily accessible, and its status as the financial center of the country opens up a gateway to a deep pool of talent and relevant partnerships.”


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