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Which are the ripest areas for startups to disrupt using machine learning? At the core, machine learning/artificial intelligence relies on two key ingredients: advanced algorithms and data sets to train those algorithms. Novel algorithms are increasingly making their way into the public domain in the form of open-source libraries. So, the key differentiator for startups and ultimately long-term competitive advantage is access to proprietary data sets.

In the consumer world, there are natural and intrinsic monopolies at play. Google’s search has a natural feedback mechanism of users clicking on the best search results that results in Google continuing to have the best search. Facebook’s network effects of it social network, in combination with its strategic acquisitions of other fast-growing social networks like Instagram and WhatsApp, reinforce its natural monopoly.

How does a chat bot company transform a technology advantage into a distribution advantage? This is the key question facing startups in this part of the ecosystem.

Deep learning is advancing at an incredible rate. Google has released TensorFlow and Parsey McParseface. These advances will improve natural language understanding, enabling chat interfaces to amaze and delight users.

So let’s assume that the technology works. Let’s take for granted that despite the myriad ways a human can ask a question, the computer will understand the query. And let’s also assume that on top of the messaging platforms, these chat services flourish. Distribution resembles that of the early days of the iTunes Store, when users were rapt to try out new products and startups aggregated massive user basis overnight. Then what?

When is the right time to increase sales headcount for a SaaS startup? It is one of the most strategic decisions for early-stage business to make given the amount of effort and expense involved in building, managing and scaling a sales team.

While there is no single absolute sign, neither qualitative nor quantitative, these are some of the signals I have found indicate it might be a good time to scale sales.

There are two main ways of building a SaaS sales team: top down or bottoms up. Top down SaaS sales organizations start with a VP of Sales who often hires senior account executives. Bottoms up sales teams hire the first account manager and promote from within. Which is better?

Let’s create a framework to compare the bookings capacity, the ramp period and the cost for three levels of sales hires. These figures will vary by company and sales team.

Three billion dollar SaaS acquisitions were announced this week with Salesforce paying $2.9B for DemandWare, an eCommerce platform provider, Vista Equity Partners paying $1.8B for Marketo, the marketing automation company, and Thoma Bravo buying Qlik, a business intelligence companies for $2.9B. In addition to the absolute size, the first two trans transactions distinguish themselves high valuation multiples of 12x and 8x on trailing revenues.

Marketo generates more revenues is growing faster and burning less cash than OPower and Textura. Presumably, these three factors and the market opportunity afforded to a horizontal SaaS company led to a greater multiple.

We each know that focus is the most effective way to work, but hearing the mantra to focus doesn’t help narrow our scope. What’s the best way to focus? Start with the premise that everything is noise and then work to find the exceptionally valuable or important things for each day and for each project. That’s the thesis of a book called Essentialism.

“There are three different types of channel relationships for SaaS companies,” a seasoned executive told me recently. Which is the right one for your SaaS startup?

In a classic reseller relationship, the value-added reseller sells, builds, services and operates a solution to a customer. After signing the deal, the VAR crafts and customize the software to the needs of the customer, invoices the customer and supports the customer. The VAR pays some royalty or license fee back to the vendor. Most of the time, when companies say they sell through the channel, they refer to this type of arrangement.

One question founders often ask is which is the right customer size to target? What is the optimal ACV for a SaaS startup? One way of answering this question is to reflect upon the success of previous SaaS companies and analyze how they did it.

The chart above plots the total revenue of publicly traded SaaS companies by ACV bucket. Enterprise companies average contract value is greater than $100,000. Mid-market companies span $10,000-$100,000, and SMB companies generate less than $10,000 per year per customer. These demarcations are not industry-standard, but my own.

To thrive, venture capital firms must perform three things well: raise capital from limited partners, source companies to invest in, and pick the best opportunities. Historically, each of these three activities has been highly centralized in a small partnership often perched on Sand Hill Road. But new networks are changing this. The latest called DAO attempts to decentralize all three at once.

At Google, we tested everything. User interfaces, advertising targeting models, even hiring practices. One product team tested 41 different shades of blue to ensure maximum click through rate; but the company is now testing black links. That’s one enormous advantage of A/B testing - all the sacred cows must prove their beatitude to maintain their divinity.

As Looker founder Lloyd Tabb explained to me, it doesn’t work for early stage SaaS companies. There’s a key ingredient to AB testing that most SaaS companies don’t have, something that Google profited tremendously from. Huge amounts of user traffic. The image above is a screenshot from A/B Testing marketshare leader Optimizely.