Benefits of Sales Territories in a Startup

After the post on Startups Should Avoid Sales Territories, several people reached out and offered reasons why they like sales territories for startups. Here are a few benefits of sales territories in a startup:

  • Face-to-Face Selling – With up-market, enterprise deals, prospects often expect some amount of face-to-face meetings, and that’s easier being in a specific territory.
  • Coordinating In-Person Meetings – Even with an inside sales team, some companies do quarterly trips to major population centers for relationship building (e.g. hey, I’m going to be in Atlanta next week for two days, do you have time to get together at your office for 30 minutes?). With reps working specific territories, it’s easier to meet with multiple prospects on the same trip.
  • Reputation in the Local Community – Another element of sales territories is to have field sales rep that lives in the territory. By living in a major population center, it’s easier to build a reputation in the community and work through a variety of civic and philanthropic channels to build rapport.

Overall, sales territories still should be avoided for most startups. Startups that have significant scale and/or large deal sizes that warrant face-to-face selling are good candidates for sales territories. Otherwise, it’s better to take advantage of the latest sales and marketing technologies to achieve greater levels of sales productivity without territories.

What else? What are some more benefits of sales territories in a startup?

Startups Should Avoid Sales Territories

Recently I was meeting with an entrepreneur who’s startup is growing nicely. They just raised a round of financing and will be expanding the sales team. After catching up for a few minutes, he asked about implementing sales territories and I recommended against it.

Here are a few reasons most startups should avoid sales territories:

  • Distribution of Best-fit Accounts – While sales territories are often divided based on certain states and their corresponding population centers, in actuality the ideal customer profile isn’t evenly distributed. Apps can automatically find the total addressable market and build smart lists of the best-fit accounts. Having each rep work a set of named accounts ensures all best-fit accounts get worked, not just the best in a certain territory (e.g. 250-500 accounts per rep is recommended).
  • Growth in Sales Reps – As the startup grows, and hires more sales people, territories for existing reps must shrink to make room. Shrinking territories results in disillusionment for the existing reps and creates ongoing realignment challenges.
  • Inbound Lead Distribution – Just as the ideal custom profiles aren’t evenly distributed across territories, quality inbound leads aren’t evenly distributed either. By not having territories, inbound leads can be qualified and parsed out in a more dynamic fashion.

Sales territories are a relic of the pre-internet era and no longer make sense for most startups. Entrepreneurs would do well to avoid sales territories and take advantage of the opportunity to target the best accounts anywhere, not the best accounts in a certain territory.

What else? What are some more reasons startups should avoid sales territories?

Taking a Customer Up the Value Curve

One of the popular startup strategies is getting in the door with a customer at a basic level and then upselling them with more functionality so they get more value. Best known as taking a customer up the value curve or the trojan horse strategy, the key is providing value quickly to start building the relationship before working towards the more complex solutions.

Here are a few thoughts on taking a customer up the value curve:

  • Start Quick and Easy – The consumerization of IT is real. Companies want the ease-of-use and feel of a consumer app to solve their business problems. Enable the customer to start quick and easy.
  • Provide Real Value – Quick and easy onboarding doesn’t matter if the product doesn’t provide real value. Ensure that it’s a must-have and not a nice-to-have.
  • Make Upselling Product-Native – Once the customer is in and getting value, incorporate nudges and upsells natively in the product. The most common example of this is an “invite people” feature to add more users in the product.

Entrepreneurs would do well to think through ways to take customers up the value curve and ensure a thoughtful approach.

What else? What are some more thoughts on taking a customer up the value curve?

Professional Services in a Startup

At Pardot we debated internally whether or not we should offer professional services to our customers. Our services team did an amazing job with implementations and onboarding while our customer success team regularly checked-in with customers to proactively help. Only, customers would ask for more services like custom work with email templates, inbox deliverability, landing pages, and nurture programs. Our recommendation: work with an agency partner.

In hindsight, we missed out on an opportunity. Startups should have a professional services strategy. Here’s why:

  • Stronger Relationships – Professional services are a great way to build stronger relationships with the customer as it requires more one-on-one time and a deeper understanding of their business.
  • Additional Revenue – Professional services result in a new revenue stream. While not as high margin as SaaS, professional services often has strong margins, especially when done as a true add-on (some services are a loss leader to make a product sale). One key point: services revenue shouldn’t be more than 20% of total revenue otherwise the company doesn’t look like a true SaaS business.
  • Product Ideas – Professional services becomes a day-to-day user of the product resulting in more product ideas and use cases. This internal team acts as a new customer voice.

Entrepreneurs would do well to develop a professional services strategy for their company, especially when there’s enough scale and existing customer demand to make it worthwhile.

What else? What are some more thoughts on professional services in a startup?

Compounding Value Over Time

One of the most profound forward-looking concepts is the power of compounding value. I’ve mentioned the idea several times before, and it’s worth repeating many more. Let’s take the simple example:

  • Start with $1,000 of value today
  • Compound at 5% annually
  • After five years you get $1,276

Now, expand that value in a hyper-growth ( > 50%) fashion:

  • Start with $1,000 of value today
  • Compound at 50% annually
  • After five years you get $7,594

By growing at 50% per year instead of 5% per year, there’s 6x the value in only five years. Carry it out further in the future and the multiple becomes even more dramatic.

Finally, apply the compounding value concept to ownership in a startup with a much larger dollar base and fast growth rates. The SaaS valuation growth rate multiplier shows the importance of growth, and the corresponding compounding value, in a startup. Combine that with Tomasz Tunguz’s When Should I Sell and it’s clear how to create real value.

Do the simple math. Run the numbers around compounding value and appreciate just how powerful it is to create incredible value over time.

What else? What are some more thoughts on compounding value over time?

5 Questions to Ask When Evaluating a Market

Looking back on the recent posts, including Bessemer’s 2017 State of the Cloud Report and 12 Key Levers of SaaS Success, it’s clear that the core market is critically important for any of the metrics and ideas to matter. Without a great market, worrying about things like how much cash to burn won’t even be relevant. Here are five questions to ask when evaluating a market:

  1. Where is the market in the adoption lifecycle? Ideally, you want to be 2-3 years early so that there’s a great foundation in place when the market really heats up.
  2. How big can the market become? Most entrepreneurs talk about a market being X big (say $2 billion/year), when in reality that’s all the spend in the market and not the spend on software in the market (which might only be $100 million/year). Potential market size is crucial.
  3. Why now? Besides the size and adoption component, ask the core why question. There should be a compelling reason why now is the time to enter the market (market shift taking place, new innovation, new trend, etc.).
  4. Where does the budget come from? Customers have to figure out how to pay for the solution. Existing budget vs new budget makes for a different dynamic. Departments that are used to buying solutions vs ones that rarely do makes for a different dynamic. Figure out the budget question.
  5. How bad does the market need the solution? The must-have vs nice-to-have dynamic never goes away. Pain killers demand a premium over vitamins.

Picking a great market and timing it perfectly are two of the most important things an entrepreneur can do. Never underestimate the importance of these when evaluating the potential for success.

What else? What are some more questions to ask when evaluating a market?

4 Quick Ways to Evaluate a Startup Idea as an Investor

Earlier this week an entrepreneur casually threw out an idea he had on the side that wasn’t related to his startup. My recommendation: don’t judge an entrepreneur’s idea. Push them to do customer discovery and let the market plus their internal motivation decide if the idea makes sense or not.

Now, as an investor, once you get past the common requirements of a great team and market, there are four quick ways I like to evaluate an idea:

  1. Must-Have vs Nice-to-Have – If the app is taken away from customers tomorrow, how much do they complain? How replaceable is the app if they just went back to email and spreadsheets?
  2. In the Path of Revenue – Where the app in the path of revenue? How clear is it that the app helps the company make more money?
  3. System of Record vs Utility – What functional category does the app fall in? Do people live in the app most of the day? Once a week? Set it and forget it?
  4. Timing – Where’s the market in the adoption lifecycle? Is it too early? Too late? Timing is 10x more important than people realize.

Evaluating an idea is hard. These four quick ways help me develop a mental model of a startup idea to see if I should pursue it further.

What else? What are some other quick ways to evaluate a startup ideas as an investor?

Good Product in a Poor Market

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?

Machine Learning and Marketing Automation

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?

Seven Spectrum of Outcomes for AI

Ray Wang has an interesting post up titled Understand The Spectrum Of Seven Artificial Intelligence Outcomes. From the post, here are the seven:

  1. Perception – What’s happening now?
  2. Notification – What do I need to know?
  3. Suggestion – What do you recommend?
  4. Automation – What should I always do?
  5. Prediction – What can I expect to happen?
  6. Prevention – What can I avoid?
  7. Situational Awareness – What do I need to do right now?


Just think of how artificial intelligence is applied to every B2B SaaS application using these seven questions as a thought exercise — there are so many amazing possibilities.

It’s a great time to be an entrepreneur.