Conspiracy Theories Don't Scale

Donald Trump is promoting yet another conspiracy theory. This time, he’s suggesting that the economy itself is a sham. He’s asserting that:

1. Janet Yeller, Chair of the Federal Reserve, has been a puppet of Obama, and that Obama has instructed her to keep interest rates artificially low to bolster the economy.
2. Unemployment figures reported by the Bureau of Labor Statistics (BLS) are outright and knowingly false. Trump suggests that the actual unemployment rate is over 40%, whereas the figure reported by the BLS is about 4%.

This isn’t the first conspiracy theory that Trump has promoted. He continues to deny the impact of carbon and climate change, he continues to state that Obama is a Muslim that wasn’t born in the United States, that the electoral system is “rigged”, among many other accusations.

There are lots of problems with conspiracy theories. But there’s a common thread I’ve observed in most of them: conspiracy theories don’t consider the challenge of human coordination and human propensity to leak facts. As a result, conspiracies don’t scale.

What do I mean by “scale?” Let’s look at a few high profile examples:

Enron — it the wake of Enron’s collapse, the SEC learned that only a handful of individuals who really knew what was going on prior to the collapse: Chairman Ken Lay, CEO Jeff Skilling, and CFO Andy Fastow. It seems that 1 or 2 of Arthur Anderson’s (Enron’s auditing firm) partners had some inclination of what was going on, but not more than that. On the eve of Enron’s demise, which ultimately destroyed tens of billions of dollars of value, only a handful of people knew knew the truth.

Bernie Madeoff — Bernie’s children turned him in after he admitted to them in private that his entire wealth management operation was a Ponzi scheme. Not even his children, both of whom were senior executives at the firm, knew of any fraud. In the investigation afterwards, it was discovered that Bernie ordered two junior staff members to produced fraudulent investment reports when clients requested redemptions. In a $65B fraud case — the largest in history — only ~3 people knew what was going on, and it’s likely that only Bernie knew the true extent of fraud.

9/11 — All together, only 15–20 people were involved in the planning of the 9/11 attacks. Of those, approximately 8 were pilots who actually flew the planes. No more than 10 others were involved. Based on what we know, the pilots didn’t even know what their targets were until weeks before the attack. All the pilots knew was that they were being recruited for a secret mission. Only a handful of Al Queda leaders actually planned the operation.

When you hear statements that fly in the face of common sense, the simple litmus test to think through is is “How many people would have to be lying for this to be true?” If that number exceeds 20, it’s likely a conspiracy theory, and nothing else. This is especially true when conspirators know that the truth is incriminating. As the number of people involved in a scandal grows, the truth will eventually leak. It’s simply human nature.

Let’s look at some of Trump’s claims through this lens. How many people would have to be lying for Trump’s statements to be substantiated?

False unemployment numbers from the BLS — dozens, if not hundreds of people work on these reports each month. There is a lot of transparency provided during the process. The process itself is scrutinized by many others. All together, hundreds of people are involved in this process, and it’s ongoing. It never stops. There is simply no way that hundreds of people are keeping secrets on this issue over the last 8 years of the Obama administration.

Climate change — even if you ignore all of the independent research done on the subject and only read the research of the EPA, the data is clear: climate change is real. The EPA has published dozens if not hundreds of reports on the issue, which represent the culmination of hundreds of researchers working for thousands of hours. There is simply no way all of these highly knowledgeable and intelligent researchers are producing reports suggesting there is a problem when in fact there isn’t one.

Electoral fraud — when Trump’s polling numbers began to dip in early August, he began to hint that, if he loses in November, that people should suspect that the electoral system is rigged. This statement is utter nonsense. Since votes are reported on a county basis to the state level, then if there were to be any fraud, state and county level officials would have to coordinate. Hundreds of county level and dozens of state level staff would have to knowingly commit fraud to make sure the numbers added up in plausible way to move the needle in favor of one candidate. There is no way that this many people can plan, in anticipation of losing, to adjust their votes, and to coordinate in real time on one day. It is simply not possible.

Conspiracy theories don’t scale. So please, when you hear people call out organizations, ideas, or movements as frauds, please just think: if this theory were to be true, what would it take to be true? If more than 20 people would have to conspire to make it so, then it’s overwhelmingly likely that the theory is false.

Rethinking The Customer Relationship In Medtech

This post was originally featured on TechCrunch.

In light of Dollar Shave Club’s recent sale, I started thinking about other industries that haven’t yet rethought the customer relationship by leveraging the cloud. A significant majority of FDA-approved medical devices — and their associated business models — should be rethought around the idea that the cloud is a core feature of the product.

Cloud-connected diapers

Pixie Scientific makes smart diapers that monitor Alzheimer’s patients for urinary tract infections (UTIs). They are innovating on at least five distinct technical fronts: hardware, software, chemistry, cloud and AI/machine learning/image analysis.

Today, the process of monitoring for UTIs in Alzheimer’s patients is rudimentary, at best. Diapers are changed every 6–12 hours, or if the patient smells too bad. This process repeats until the patient ends up in the ER screaming of abdominal pain caused by a UTI that’s made it to the bladder or kidneys.

Pixie rethinks the entire process by monitoring urine content in near real time. As urine content begins to show signs of a UTI, care teams can act proactively.

This is a profound shift: Not only are computers making recommendations (not diagnoses), but Pixie is building a library of data that will make their products the best in the world at detecting UTIs in Alzheimer’s patients. This medical device will leverage data network effects to improve recommendations. This medical device will get better as more people use it. That’s insanely awesome.

Cloud-connected vitals monitors

Stasis Labs makes a cloud-connected vitals monitor that can be used in any setting outside of the ICU. Rather than selling the hardware, Stasis provides the end-to-end system for a few dollars per bed per day. Traditionally, hospitals would have spent $10,000+ per monitor to buy and install a GE or Welch Allyn vitals monitor. These monitors require manual integration with electronic medical record (EMR) systems. To get alerts (e.g. “warn me if pulse drops below 50 for more than 10 seconds”), the EMR needs to integrate with a clinical decision support (CDS) system, adding even more cost.

Stasis rethinks the entire process of vitals monitoring: their box just needs an internet connection, and that’s it. All alerts (e.g. “warn me if O2 dips below 93 percent”) can be managed from a browser/Android interface. Within a few years, Stasis will have the world’s largest database of vitals monitoring for every single disease state. They’ll use that data to make recommendations and predictions.

Cloud-connected inhalers

Propeller Health makes an add-on that fits into any inhaler and records ambient data. Propeller helps asthma and COPD patients understand which environmental factors cause symptoms.

Again, the cloud is paramount to deliver this service: There is no way Propeller could ever understand every environmental trigger and their associated impacts on patients. They didn’t have to. They simply record sensor data, ask the users for some input and aggregate and analyze the data to deliver insights.

But what about pharma and implantable medical devices?

The examples I’ve provided are all FDA Class 1 devices — devices that are generally considered low risk in the event of failure. As the cloud invades medtech, it makes sense that entrepreneurs are starting with the lowest hanging fruit — Class 1 devices. Implantable medical devices and pharmaceuticals are Class 2 and 3 devices, respectively, so naturally, the capital requirements are higher for those.

But the opportunities are easy to imagine:

• An artificial knee that records range of motion over time to record improvements.
• A pacemaker that records pulse and correlates it with activities, and provides activity recommendations that are coordinated with a care plan.
• A pill that records when it’s taken (or not taken). This has huge implications for insurance, chronic disease management and more.

Of these examples, Proteus has been working on the third for about a decade. They’ve raised hundreds of millions of dollars with the fundamental aim of bringing the cloud to the business of pharma (and not just research, where pharma already does lots of computational biology/simulations). The opportunity is staggeringly large.

Other than the cloud, what’s so special?

The cloud changes not only the product, but the entire business model.

No one in the adult diaper business has a direct relationship with the patient or caregiver. Because Pixie uses an app, Pixie has a front to build to engender that relationship. And that means Pixie can go direct to consumer, cutting out 50 percent of the cost that’s associated with retail. Althoughdiapers.com (owned by Amazon) has been going direct to consumer for a while, their business model has no lock-in beyond a recurring diaper subscription. Pixie’s does. Once customers go Pixie, they’re never going to go back. That changes how Pixie can think about CAC because their LTV will be much higher.

Stasis is SaaS-ifying what was traditionally a capital expense. This will dramatically increase the size of the market by making vitals monitoring available to almost anyone in any disease state for just a few dollars per day. Home care, ERs and more. And they will become the first out-of-the-box solution that can predict adverse outcomes. In time, this function will be considered a basic requirement for all vitals monitors.

Propeller, like Pixie, will build direct relationships with consumers. They’ll make recommendations. They’ll know who the consumer is, and deliver products, messages and services accordingly. They won’t rely on retail or other traditional channels of distribution because they will own the customer relationship. Once customers go with Propeller, they’ll be locked into their ecosystem and look to Propeller for the next innovation in inhalers and asthma/COPD management.

For implantable medical devices, there will be less opportunity to build a consumer relationship. Patients aren’t going to decide between a Stryker or Smith Nephew knee replacement; surgeons make that decision (at least for the foreseeable future). But by layering the cloud into Class 2 and 3 devices, medical device manufacturers will have an opportunity to unlock mammoth revenue streams: true pay for performance from payors. Once devices can actually report real-world performance, insurers will gladly pay for surgeons to use the best devices, and for physicians to prescribe the best drugs based on real-world data and performance.

Medtech companies will build direct relationships with consumers, providers and payors. Every medtech product and business will be infused with the cloud as the industry inverts over the next 20–30 years.

In What Contexts Should Messaging Be The UI?

Note: this post was originally featured on TechCrunch. Also, I advise Well, which is mentioned in this post.

The current messaging hype is overstated. There are certainly some interesting and unique opportunities for messaging as an interface, but I contend the number of practical use cases is a fraction of what the current hype cycle suggests. Facebook and Microsoft in particular have been pushing messaging because their proprietary messaging platforms give them a way to gain some leverage and autonomy on top of iOS and Android — but this reasoning is supply-driven, not demand-driven.

One of the key premises of messaging as a UI is that users may not have or don’t want to install an app to interact with a given service. By abstracting the UI to a messaging interface, the tech giants are trying to solve the “go to the App Store and download the app and create a username” problem. This should, in theory, increase long-term user engagement.

Although messaging can help in these scenarios, there’s no reason this problem can’t be solved in the current app model on iOS and Android. Case in point: Google just showcased Android Instant Apps: partial, on-demand app downloads with integrated identity services. They have blurred the lines between HTML and native apps to offer the best of both worlds.

Apple is likely working on a similar solution for iOS. That function, coupled with persistent OS-level logins for Facebook/Google/Twitter/LinkedIn/iCloud/Apple Pay can easily solve the “go to the App Store and download the app and create a username” problem.

I’m therefore not convinced that a messaging interface should exist to circumvent the “go to the App Store and download the app and create a username” problem. Although messaging can help with this challenge today, this problem will be addressed at the OS level. Apple and Google are not oblivious to this.

So the question is, when does messaging as a UI make sense? I’ve developed a couple of litmus tests to answer this question:

Does the user actually want to talk to someone to complete the transaction?

Could a reasonable user want to engage in more than 10 different types of transactions?

In Facebook’s first messaging bot demos, they showcased ordering flowers and pizza via a messaging interface. Both are simple, straightforward transactions with a few customization options.

You don’t need to talk to a sales rep to purchase flowers or pizza. Perhaps if you’re in a store, you may want to speak to a florist because she’s there and you want her opinion. But if you’re buying flowers online, all you need to do is select an occasion, look at some pictures, then select a type, number of flowers, a vase and write a personal message. The number of options to choose from are limited, and the options themselves are easy to understand. That interface should be delivered in a graphical way, and not as a messaging conversation.

Or put more simply: Would you rather buy flowers over the phone, or via an app? The app is clearly the superior choice.

The same can be said of purchasing pizza: a linear transaction flow and a few customizations.

Neither of these transactions warrants human conversation in the real world. Why should users try to engage in these transactions as if they were talking to a human?

However, there are really interesting messaging use cases where I, as a user, want to “talk” with someone.

I have money with a private wealth manager at Morgan Stanley. I like talking to him because I can get his feedback on what’s going on in the markets, and discuss the rationale behind asset class allocation decisions. I also have money with Wealthfront. Using Wealthfront, my entire asset allocation decision effectively boils down to a few multiple choice questions that can be approximately simplified to: “How much risk do you want to take?” The computer decides the rest. Although I can look at the transaction details and determine which trades the computer is making, it’s hard to get a summary sense for the reasons behind decisions, and future outlook. A conversational UI would be awesome in this context:

“Hey Wealthfront, I’m concerned about the recent market volatility in the wake of Brexit. What’s going on in my portfolio?”

“Great question Kyle. In light of recent volatility, we’re doing X and Y and Z, and our outlook is A and B and C. I’ll give you another update in 2 or 4 weeks. What frequency would you like to be updated?”

Or …

“How are falling oil prices impacting my portfolio?”

“Well you don’t have any direct exposure to the energy industry. But you do have lots of exposure to the airline industry. Low oil prices reduce fuel costs, boosting airline profits.”

Right now, all I get is a single line graph showing the aggregate value of my portfolio. Any further analysis is virtually non-existent. I’m sure Wealthfront is trying to address this fundamental problem programmatically, but the UI complexity to pull this off is likely impossible. There are simply too many questions an investor could ask given the massive number of investing options. A messaging-driven UI makes a lot of sense here, given the vast breadth and depth of questions that a user may have.

(BTW, whether the messaging interface is delivered in a generic messaging app or in the Wealthfront app is immaterial for this use case.)

Or take Well. They are a messaging interface between patients and the front desk of a doctor’s office. Well automates appointment reminders, sends patients forms to complete before visits, helps patients reschedule appointments, manages insurance information, gets prescription refills, requests copies of medical records, manages bills/payments, etc. There are 1–2 dozen types of transactions a patient may have with the front desk of a physician’s practice. A graphical UI for navigating 12 different types of transactions will become unwieldy quickly. A messaging UI addresses this by letting the user simply drive the conversation naturally.

ATMs probably represent the limits of graphical UIs. ATMs today give users 3–6 options: check balance, deposit check, deposit cash, withdraw cash, cancel, etc. But as the number of transaction types balloons past ~10, UIs become unwieldy. Messaging can address option-overload.

As we increasingly use our phones to interact with the world around us, messaging as a UI will prosper. But today, messaging is overhyped. Companies are trying to offer messaging UIs where one isn’t really necessary. Many are too focused on circumventing the “go to the App Store and download the app and create a username” problem, as it’s no doubt a huge source of drop-off in the customer acquisition funnel.

But Apple and Google will solve this problem at the OS level. Messaging should not exist simply to circumvent a temporary shortcoming in mobile OSes circa 2016. Messaging apps should instead focus on areas where users want to feel like they’re actually talking to a person. This is a much harder technical problem, but, once solved, it will unlock enormous value.

A World Divided... By Tech

I contend that this is the single most important graph in the world circa 2008 - 2020:

It explains many of the socioeconomic changes we’re seeing across the globe:

Nationalism and xenophobia in Western Democracies.

Massive growth of middle class in Asia, Africa, and Latin America.

The sharp accumulation of wealth among the world’s richest.

Why is this graph shaped the way it is? Why isn’t economic growth accruing more equally? Why is there a huge dip in the 70–90% range?

In short, the answers are technology and globalization. And globalization is a by product of improvements in technology. So really just technology. The only constant in the history of humanity is the progression of technology.

Beginning around 1980, it became economically efficient to begin manufacturing jobs overseas and in Mexico. This was due to a few major advancements in technology: shipping (in particular the standardization of containers), computers to coordinate global logistics supply chains (in particular databases to keep track of this stuff), the Internet (to empower people to communicate across the globe), and falling costs of plane and sea travel.

As the computer revolution continued, more jobs moved overseas: customer support, accounting, software development, and even law jobs. As the Internet has become ubiquitous, many traditionally local services have been abstracted to an API or a web interface whereby the user didn’t care who is doing the work on the other side of the interface. Video chat, faster Internet, and general acceptance of software interfaces have compounded this phenomena across many industries and services.

As these manufacturing and services jobs moved overseas, billions of people were lifted out of poverty. That represents the massive growth on the left 2/3 of the graph. The incredible economic good of globalization over the last 30 years cannot be overstated.

Conversely, most of Western society benefited from globalization on two fronts: lower prices for goods, and greater selection of goods — the breadth of consumer goods to choose from today is simply staggering. This wouldn’t have been possible without an exponential growth in cheap and varied manufacturing capability across the globe.

Since ~2000, we’ve seen software begin to directly replace humans: Google obviated the need for thousands of local ad salesman at media organizations, Expedia/Orbitz obviated the need for travel agents, etc. In the last few years, we’ve begun to see computers take on roles and responsibilities that were traditionally reserved for humans: managing asset allocation (Betterment, Wealthfront, others), accounting (inDinero), law (Ross Intelligence), sales forecasting (Clari, many others), appointment scheduling (x.ai, Clara), logistics scheduling (Service Max, many others). And there are many jobs that are on the verge of automation: self-driving cars/trucks (and their massive support and tertiary industries), automated and prefabricated construction, automated food preparation, and more. These technologies explicitly displace jobs in a clear and obvious way.

The people who have been losing their jobs to globalization and technology have been in the lower middle and middle classes of Western Democracies. They comprise the majority of the huge dip in the graph above.

These people are unhappy. They feel some combination of: being left behind, that their home country isn’t great anymore, that immigrants are taking their jobs, that overseas working are taking their jobs, and that “the system” is rigged.

Many of these people were raised to believe that as members of the middle class, they would lead healthy, financially prosperous lives. That they were destined to fulfill the American dream. But the world has changed, and these people are no longer competitive on a global scale. These people have been economically stagnating for years, if not decades. And they are pissed off.

This economic stagnation has given rise to the the far-right xenophobia that fueled Brexit and Donald Trump’s campaign. This same economic stagnation has also fueled the far-left Occupy Wall Street movement the Bernie Sanders campaign.

The underlying causes of these changes are accelerating. Software is getting better, faster. Global, cheap cloud computing and Internet access means that once software solves a problem once, it can automate the problem anywhere in the world instantly at marginal cost. Data science is just being unleashed and will change almost every industry vertical by automating human decision making. And robotics and battery technology are finally becoming capable of replacing humans in narrowly-defined mechanical tasks.

Economic inequality is going to get structurally worse before it gets better. As far as I can see, the only solution to this massive inequality will be government redistribution of wealth (which is of course, rife with problems). The “haves” are literally separating from the “have-nots.” This is happening across many dimensions, but particularly along two axes: geography and age. Cities are becoming overwhelmingly liberal and progressive, and the countryside increasingly xenophobic and conservative (see the US presidential election map and the Brexit vote map). The old feel left behind, are are vying for the to make the world the way it was once (hence the slogan “Make America Great Again”). These divisions are becoming more pronounced as Western Democratic societies are aging due to falling birth rates and tighter immigration policies since 9/11.

In 30 years, I suspect we’ll look back at 2016 as the symbolic beginning of a new socioeconomic global era (though it’s likely that the 2008 financial crisis was the real catalyst; it just took 8 years to manifest into mainstream global scale socioeconomic politics). The rise of Trump, Brexit, broad Euro-skepticism, xenophobia, and authoritarianism (fantastic 6 minute video) represent massive shifts in socioeconomic and geopolitical structures across the globe. The reverberations through other countries have yet to be felt, but it’s clear that tensions are high across the globe. The Spanish and Italians have had unemployment rates of about 20% for years, with no indication of improvement on the horizon. The Chinese economy is slowing as they finished picking most of the low hanging fruit associated with industrialization. And we are still only at the cusp of what machine learning will do to job automation.

I love Marc Andreessen quotes, but I think he understated this one: “There will be two kinds of people in the world. Those who tell computers what to do, and those who are told by computers what to do.” This quote misses a 3rd category: people who no longer have anything to do because computers automated their jobs. As software and automation permeate manufacturing and services jobs globally, the countries that were lifted out of poverty by globalization will be forced to reckon with poverty once again because of globalization and software. There will be massive instability as this unfolds over the next 20 years.

Those with capital are investing in tech, which begets more capital. The virtuous (vicious?) cycle of capitalism is fueling the right end of the graph. And there are no signs that this will change. The dip in the graph above is the perfect representation of this division. Tech is dividing the world.

Why Does Sales Ops Matter?

Credit to InsideSales.com for the graphic.

This post is intended for B2B SaaS founders.

“Companies fail for one of two reasons: spending too much money before achieving product/market fit, and not spending money fast enough after achieving product/market fit.” — Marc Andreesen

The first reason is intuitive: the company couldn’t build the right product, or find the right customer acquisition channels, or the market just wasn’t there. These are all obvious reasons why companies fail.

The second reason is less intuitive. After you’ve really, truly found product/market fit, failing feels a lot less acceptable. After all, if you’ve built something that solves a real problem, you’ve got word-of-mouth growth, you’ve established customer acquisitions channels, then why would your company fail if everything is working?

Competition. The more successful you are, the more competition you’ll beget. If you don’t out-execute them, they’ll out-execute you.

This leads us to sales ops. It is one of the most under-appreciated forms of “execution.” Sales ops processes can lead to improvements across all fronts: more lead volume, higher conversion rates through the funnel, shorter sales cycles, higher ACVs, and longer customer life cycles.

Once you’ve found product/market fit, you need to scale marketing, sales, customer success, product, and back office (finance, HR, etc) organizations. There are lots of operational best practices to employ in each area.

A VC once told me “sales is sloppy.” By this, he meant that, in general, sales professionals tend towards entropy. Without specific boundaries and guidance, they do whatever they want, and not what’s necessarily in the best interest of the company. They want to do things their own way. They generally have little regard for how their activities impact others (eg finance for forecasting, customer success, engineering, etc),

Sales ops is fundamentally about automating every aspect of the sales process that can be automated: building sales training materials, figuring out lead assignment, and all the way through sending out contracts, signing them, and receiving payment. By automating these functions, the entire customer lifecycle becomes more predictable: deals close on time and for the forecasted amount; customer success doesn’t have to over-deliver; and engineering doesn’t have to perform miracles to make customers happy.

It’s not intuitive for many VPs of Sales to think “how can I design a series of processes using software that prevent my reps from making any mistakes in our otherwise well-known and established sales process?” VPs of Sales think in terms of people, not how to systematically reduce the number of decisions their people make. In other words, many VPs of Sales haven’t fully digested the magnitude of the statement “software is eating the world.”

Most founders don’t recognize just how much of the sales process can be automated, and the enormous value that derives from automation. In the best run sales organizations, virtually no value exists at the “edge” of the network — the sales people. Instead, all of the value lies in the infrastructure itself — all of the processes. Every path through the sales cycle has been considered, and the best response is known. The fewer decisions that that sales professionals have to make, the more scalable, repeatable, and successful the sales organization will be.

Most importantly, sales professionals lose leverage against the organization as sales ops matures. As training new sales professionals becomes more clear, concise, and automated, it’s easier to hire and train new sales professionals. It’s also easier to assess which ones aren’t going to make the cut. And by automating the daily sales functions, sales people require less training to get through the daily logistics (finding documents, setting pricing, sending proposals, etc) of their jobs. This leads to faster ramp ups / sales professional, more efficiency / sales professional as they’ll make fewer mistakes, and larger deals as they don’t make stupid pricing mistakes that force the company to leave money on the table. The multiplier of good sales ops on sales efficiency, CAC, and LTV can be enormous.

A Simple Example

For example, in the early days, startups are typically building sales collateral as it’s needed. After achieving Initial Scale — $1–1.5M ARR — the startup will amass 10–30 different pieces of sales collateral that can be used throughout the sales cycle. Those PDFs will be organized and stored in a folder somewhere. Since the marketing team probably made the collateral, the documents will probably be in the Marketing folder somewhere. Hopefully they’re all in 1 folder, and not spread out across 5 folders.

There’s nothing particularly difficult about grabbing a file, attaching it to an email, and getting it out to a customer. Even if it’s in the marketing folder, it shouldn’t be a big deal for a sales professional to grab it, right?

Wrong. This seemingly trivial process is fraught with opportunities for error:

When you onboard new sales people, they will not read all 15 pieces of marketing collateral you have. They will read 2, maybe 3. So they don’t even know what content is available.
Even if they knew all of the content that was available, each piece of content is designed with a specific audience in mind, and designed to be shared at a particular stage in the sales cycle. Sales professionals definitely won’t remember when to share what piece of content with whom.
At some point, marketing will start archiving old iterations of documents, and names of particular files will change. This will befuddle the sales professionals as their cherished documents are seemingly gone.

There are probably 10 other stupid logistical problems that can arise from this trivial process of attaching a PDF to an email. Instead, the right approach is to implement a tool like Showpad that automates all of the “thinking” so that sales professionals can’t make these kind of mistakes or run into these problems. With Showpad, it’s virtually impossible to make any of the mistakes outlined above.

Another Example: Repeatable Outbound Cold Outreach

Managing cold out reach is incredibly challenging for humans. Even with just a few sales development reps (SDRs) reaching out to 50+ leads / day on a standard 7x7 touch schedule, knowing who you should reach out to on what day can be a huge pain. SalesForce in no way provides infrastructure to do this correctly.

Then imagine compounding this problem by layering in A/B testing of various messages.

It’s pretty much impossible to know: how much work each SDR is doing, who each SDR should contact on each day, what messages are actually effective, and how each SDR is actually doing. There are just too many intertwined variables. Enter SalesLoft, which solves all of these problems, and more. SalesLoft is the purest expression of sales ops: design the system, and plug people into it, and let them focus on pure execution.

Sales Ops: How You Scale Your Brain

Another way to think of sales ops: as you scale an organization, you can’t make every decision. But what if you could? If you had the time, wouldn’t you? As founder, you can synthesize far more variables, draw on more experience, and understand customers better than your sales professionals. Other than time constraints, you are the best person to make many decisions. Sales ops is an awesome way to multiply your decision-making ability by codifying your decisions into software that everyone else can follow and learn from.

Sales ops is literally a multiplier of your brain. If you and your leadership really understand the best things to do at each stage of the sales process, in consideration of every know-able variable, you can automate that process. Every automation is worth investing in. As you get past Initial Scale — $10M ARR — you’ll find that every marginal improvement in sales ops has a huge impact downstream on ramping up new employees, close rates, revenue growth, and customer success.