Metrics that Matter in SaaS

Today, software entrepreneurs are very fortunate to have a wealth of information available on the indicators and metrics to focus on when running a SaaS business. There is so much out there that it can be a bit overwhelming to absorb. With that in mind, I’ve put together a one page summary of the core areas every SaaS founder should focus on when first starting and running a SaaS business.

This is not meant to be an exhaustive list of every KPI but rather an 80/20 “boil-it-down-to-what-matters-most” view of the qualitative and quantitative indicators of the overall health of a SaaS business. This also doubles as a checklist when going out to raise an institutional round of capital (most VCs will ask for these metrics as part of their diligence process.)

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The way to think about it is in 4 categories.

  • (1) Qualitative: Indicators in this category, while not as quantitative as the rest on this list, are likely to be the most important for early stage companies. They include a sharp focus on the team and the founder(s). The product/ service itself and early customer feedback are likewise very important.
  • (2) Market Metrics: Venture investors care a lot about the market in which a business is focused on (and entrepreneurs should as well to ensure they are solving a worthy problem!) Key metrics here include the overall TAM and growth (or stagnation/decline) of the industry. In addition the competitive landscape, both the number of competitors and share of each competitor, is key.
  • (3) Financial Metrics: Metrics in this category tend to be a bit more objective – but even here much is dependent on the idiosyncrasies of a particular business, what stage it is in and the market opportunity ahead of it. Here, most financial metrics boil down to 3 things:
    • Top-line revenue and growth: CMRR/CARR is the most accurate predictor here
    • Margin profile: some combination of gross and operating margin
    • Cash position:both burn rate and runway
  • (4) Operating Metrics: Operating metrics tend to be a bit more unique in SaaS than in other business models. A good way to think about operating metrics is through three sub-categories:
    • Customer willingness to pay: a combination of ACV, NPS, expansion revenue, etc. combined with the pricing model employed can help determine overall WTP
    • Sales efficiency: magic number (developed by Scale Venture Partners) is a great metric as are payback period and sales cycle length
    • Churn: gross revenue churn is closely tied to growth but cohort analysis and the quick ratio (developed by Social Capital) are also good metrics to track

As mentioned earlier, there is a wealth of information on all of 4 of these areas as well as best-in-class metrics based on revenue, stage, etc. Some of the best material out there for further reading includes: Byron Deeter’s State of the Cloud report, David Skok’s For Entrepreneurs blog and Jason Lemkin’s content on SaaStr.

Core & Emerging Platforms as we Move into 2017

Innovation at the platform level (whether it be improved hardware, changes in infrastructure or new ecosystems) has always led to new opportunity at the application level for both entrepreneurs and the investors that back them. As 2016 winds down and we look ahead to 2017, it’s as good a time as any to take stock of the innovation we’ve seen at the platform level in the last few years and the trends in tech that will drive new opportunity in application software.

More specifically, I see four core and emerging trends that will continue to dictate opportunity in B2B software: (1) continued dominance of cloud, (2) acceleration of mobile enterprise, (3) increased attention to AI (more specifically machine learning) and (4) the rise of AR & VR – particularly AR in the B2B setting. The figure below provides an overview that will be explained in further detail below:

tech-platforms

(1) Continued dominance of cloud

This is an “old” one but a good one. Of the four platform trends this is the most established one and has produced the most opportunity to-date.

From a horizontal perspective, the cloud has penetrated (though not yet dominated) every function within the enterprise. Salesforce is the prevalent choice for most in the sales / CRM functions. Companies like Workday, Cornerstone and SuccessFactors have gained real traction within HR. Eloqua, ExactTarget and Marketo are widely used marketing tools. NetSuite has a strong presence in ERP while Zendesk is a strong force in customer success. And there are many other more recent horizontal SaaS companies that have made big waves: Slack, Stripe, DocuSign and DropBox are just a few of many that had big years in 2016. And there are many more opportunities remaining in relatively untouched areas like: sales ops, SMB-focused HR tools, inventory management, market intelligence and customer care analytics.

Vertical software, is still very much in its infancy. There have certainly been some early winners like Veeva (life sciences), RealPage (real estate) and Fleetmatics (fleet management), but there are many more industry cloud winners to come. Industries like manufacturing, construction, logistics, agriculture, oil and gas and others have slowly begun moving to the cloud after remaining cloud-allergic for many years. 2017 will be a big year for many of these industries and the vertical-focused, category-winners that reshape them.

(2) Acceleration of mobile enterprise

Aggregate mobile enterprise revenue in 2016 was just under $100B –pretty solid for a platform that didn’t exist 10 years ago. However, this one is also just getting started. Forecasts show this number doubling by 2020 (and I wouldn’t be surprised if the growth rate is higher than that). Part of this growth is fueled by increased vertical software opportunities. Procore is a great example of a company delivering a vertical specific solution (in construction) via mobile enterprise. Industries like education, insurance and real-estate will soon follow.

(3) Increased attention to AI  

2016 really marked THE year when AI (or more accurately, machine learning) really came into focus in the startup and venture community. As seen in the figure above, deals done and investment dollars poured into the sector have grown exponentially in the last 2-3 years. In that time, AI has done a few interesting things:

  • It has re-opened the door in a real way to more horizontal software opportunities giving rise to the “disruption of the disruptors.” Suddenly, machine intelligence has allowed for greater insights and better products and services that opened the door to new entrants looking to enter horizontal spaces.
  • It has allowed for more focused solutions that really benefit from machine learning applied to large data sets to flourish. Little Bird (a market intelligence and data analytics company based out of Oregon) that was recently acquired by Sprinklr is a good example. AI powered point solutions like Little Bird, once bolted onto larger platforms (like Sprinklr’s social media management platform) can exponentially increase the utility to their enterprise customers.
  • It has brought back IBM’s relevance among innovators and early stage companies. Ironically, rightly or wrongly, IBM’s Watson is the most common machine associated with machine learning. Whether IBM is able to harness the potential of AI remains to be seen, but the company attempts to be mounting a bigger challenge to be a dominant presence in the space rather than giving way to the big four (Apple, Facebook, Amazon and Google) as it did with consumer devices, social, ecommerce and search.

Expect AI to be a powerful trend in 2017 and beyond, with both startups and established players getting involved, especially as the technological innovation becomes more advanced.

(4) The rise of AR & VR

AR and VR are the furthest off in terms of real platform potential and 2016 was largely a pretty big disappointment for these platforms. The biggest thing in AR/VR in 2016 was Pokémon Go, which was an entirely consumer play (and appears to largely have been a fad). I expect VR to still be a few years away from going mainstream –and even when it does, it will continue to be a consumer play.

That being said, I do think in 2017 we will see the start of some AR-based software applications that will gain traction among enterprises. And by 2020 forecasted revenues in AR will near $120B. Some of the important early verticals AR will start with will be healthcare, manufacturing, defense and architecture among others. Some of the early startups playing in these spaces, that I’ll be following in 2017 include: CrowdOptics, APX Labs and Pristine.

The Importance of Customer Success

A lot of the literature for startups and early stage companies focuses on revenue growth and customer acquisition. But over the last few years, customer success has begun to come into sharper focus, particularly given the movement to SaaS and cloud software, which naturally forces many software companies to think more seriously about customer success.

Nonetheless, churn benchmarks and insights on which metrics matter the most have often been hard to come by. Over the last few months, a few of us in McKinsey’s growth tech practice partnered with some leaders in the startup / venture ecosystem (thanks BCV & Gainsight!) to shed some light on the topic of customer success.

The results of this analysis, published here, were very interesting indeed!

Reassessing account coverage models in B2B Sales

Early stage B2B SaaS companies, and tech companies more broadly speaking, are typically very focused on adding new accounts, growing existing accounts (up-sell and cross-sell) and serving their account base as best they can (customer success). These activities drive revenue growth, which in turn drives valuation, market credibility, ability to hire talent and a whole host of other important things.

While this focus is certainly well placed, in the process of focusing on revenue generating activities, many B2B SaaS companies forget to periodically assess their accounts from a profitability perspective and ensure that their sales coverage models are appropriately positioned given the varying profitability of the underlying accounts being served. The result is that many of these B2B companies end up relying too heavily on large direct sales forces when:

  • The profitability of the underlying customer accounts doesn’t justify the use of direct sales reps in all situations
  • There are often lower cost to serve models that are not only cheaper but also more effective with many types of customers.

As such, if you rely heavily on a large direct sales team, it can often be helpful to do a quick coverage alignment exercise to determine whether it is worth re-assessing your account coverage model. While this is primarily a cost-saving activity (e.g. moving some accounts from a higher cost direct model to a lower cost indirect model) there can many times also be revenue-generating opportunities as well because many customers actually prefer non-direct sales channels.

The first step to reassessing your account coverage model is to plot out the full distribution of all your customer accounts from a gross profit (not revenue) perspective. It is important to look at profitability and not revenue here as revenue alone will not help identify accounts that could be moved to different coverage models. Breaking out the distribution into deciles is often helpful in terms of bucketing accounts into different categories. Many B2B tech companies have a distribution that looks something like this:

Long Tail

As seen in the chart above, a minority of customer accounts are driving the vast majority of the gross profit. In this example, 30% of accounts are driving ~90% of the total GP. In most cases, it makes sense to continue to serve these high-value customers through a direct model. Losing these customers or serving them in a different way could be devastating.

That being said, there is also a long tail of low or even unprofitable accounts. For the most unprofitable accounts (e.g. very negative accounts), in some cases it may be better to simply stop serving these customers altogether because you are losing money on each one with no way to profitably serve the account.

For the “long tail” of customer accounts that are low profit or even slightly negative, this is where it is worth re-assessing your coverage model and thinking through other lower cost to serve options with the end goal of improving GP. There are at least 3 viable coverage options that could be used to improve the profitability of these accounts (and maybe even grow them).

(1) Inside sales: Inside sales reps come with less of the overhead and expenses that direct sales rep comes with. They have much lower (if any) travel, lodging and entertainments costs. Inside sales reps also tend to be cheaper and can have more flexible roles (i.e. as hunters, farmers, sales support, customer care, etc.)

(2) Channel: Indirect reps or channel partners can also be used to lower the cost to serve and improve account profitability. This go-to-market strategy can be more appealing to customers who are used to making purchases from a trusted channel partner. In addition to saving money on expensive direct sales reps, many of the same levers used to compensate direct sales reps (tiered commissions, accelerated payout above 100% quota attainment, etc.) can be used with indirect sales reps. If managed correctly, these compensation levers combined with the right channel partner could actually drive further top-line growth.

(3) Off-shoring sales support: Reducing the cost of sales support is another lever lowering the cost to serve. Sales support (e.g. admin functions, sales operations, product support, etc.) can often be handled in near-shore or far shore locations via virtual support at a fraction of the cost of on-shore resources. While this approach should be balanced with a need to serve customers well, many sales support activities (including some that are customer facing) can be handled in lower cost locations.

Grow fast or die slow: Why unicorns are staying private

In today’s world, technology companies worth more than $1 billion—and many worth $10 billion—have fewer reasons to go public than they did in the past. It’s a new paradigm shift that has really changed many of the dynamics in the startup community. A few of us in McKinsey’s High-Tech practice put together an article on the software IPO environment and the implications for founders and VCs. We hope it’s an insightful read.

The full article is available here.

Revisiting EdTech: Opportunities for 2016 and Beyond

It’s been a few years since I’ve written extensively about education technology and the opportunities that exist in the space. Since my last set of posts back in December of 2012, the space has continued to be a fast growing sector with much opportunity. Back in 2012, the sector was a $4.1T industry globally. That number just topped $5T in 2015 with a 7% CAGR. Unsurprisingly, the education sector remains the second largest industry, trailing only healthcare in terms of global market size.

Likewise, venture capital investment has picked up substantially in the last 3 years. In 2012, Series B investments totaled just $159M—that number is expected to top $500M in 2015 once the final numbers are published. Similarly, deal activity across all stages has picked up. In 2012, the total number of deals across VC/PE was ~500 deals—that number will reach nearly 800 deals by end of year 2015.

Most importantly, exits have finally begun to provide some hope for returns. A scarcity of exits has long been one of the big problems for entrepreneurs and investors considering EdTech. Indeed M&A activity has historically been slow (<1% of all M&A exits from 2002-2012) and IPO showings have often been abysmal (e.g. Chegg which fell 23% during its IPO debut and now has a market cap of just ~$620M, half of its opening day valuation.)

In the last three years, however, there have been a handful of successful EdTech IPOs including companies like 2U and Instructure. Others, such as Coursera, Udacity and Edmodo, are all not far behind in the IPO pipeline. M&A activity likewise has been quite strong. In fact, U.S. EdTech companies tend to command higher revenue multiples than the average tech exit—3.2x for EdTech companies vs. 2.5x for the broader tech industry. Furthermore, M&A exits themselves over the last 5 years have been fruitful with 25 buyers spending more than $100M on U.S. EdTech companies.

exits-1438648868.jpgSource: EdSurge

Yet despite this progress, there remain a wide array of inefficiencies and unsolved problems. Specifically, I see 6 promising near-term opportunities for entrepreneurs to take advantage of and for investors to invest in. In no particular order here are a few thoughts of what we will see beginning in 2016.

1) Cloud SaaS will finally replace on-prem at the school district and system admin level

Having spent time working at the district level in education policy, I was always amazed at how archaic many of the tools districts and school systems use at the city-wide/admin level. Software tools that track important mission-critical information such as attendance, student demographics, building information, zone data, etc. across schools within a district are still often hosted on-premise, using archaic databases and outdated software with GUIs that look like they were designed in the ‘90s. Below is an example of what the NYC DOE ATS currently looks like:

Untitled.pngSource: NYC Department of Education

I suspect that in 2016, as much of the IaaS and PaaS layers begin/complete their moves to the cloud through services provided by the likes of AWS, Azure, SoftLayer, etc, we will begin to see more B2B SaaS applications layered on top to replace the traditional on-prem software solutions. This will bring much needed functionality, analytics and a cleaner user experience to the education world. This in turn will increase productivity for educators working at the district and administrative level across school systems.

2) Learning content will be far more personalized

Recent survey data showed that less than 50% of teachers reported having digital resources that could be used to meet teaching standards. Moreover existing technology solutions often are not tailored to individual students and their specific needs. The next generation of student-centric software tools (across grade levels and subjects) will provide high levels of granularity and insight into the specific needs of individual students allowing for an end-to-end customized experience across lesson planning/ delivery, class activities and periodic assessments. This will be even more important for special needs students in ICT, 12/6:1 or similar learning environments. Personalizing learning content will ultimately allow for a more tailored learning experience and better long-term knowledge retention.

3) K-12 teacher development will rely more heavily on software platforms and tools

As it stands today, professional development for teachers is largely untouched by software tools and applications. At the district level, spend on professional development for K-12 teachers in the U.S. is ~$3B and usually takes 1 of 4 forms: (1) periodic school-wide workshops, (2) observation of other teachers, (3) coaching (usually by a more experienced teacher) and (4) generic online research.

In 2016, we will begin to see more PD content move to the cloud as doing so makes training teachers: (a) less expensive, (b) more accessible and (c) more personalized. Horizontal HR solutions like Workday, Cornerstone OnDemand and PeopleSoft will be re-built / tailored for the education sector enabling professional development in education to be more sophisticated and effective.

4) Higher education software tools will focus more on degree completion  

As the Baby boomer generations’ offspring (Gen X) move beyond the college-age window, the college enrollment growth rate will begin to slow and the focus for many higher-education institutions, from a revenue perspective, will shift away from recruitment/ matriculation and towards retention/ graduation. As of 2012, ~50% of all college students were in at least 1 remediation course.

In the years ahead, there will be a greater focus on retention and remediation of students already admitted into colleges. Software tools will increasingly be used for (1) recruiting the right type of student to admit, (2) providing BI and predictive analytics platforms for identifying and tracking high at-risk students and (3) supporting remediation instruction for at-risk students to get them back “on track.”

5) Online courses and degrees will become more relevant

While online courses (including MOOCs) and degree programs will never replace the off-line experience, these offerings will increasingly be used to supplement off-line instruction as well as provide a new delivery format to non-traditional segments (such as continuing education students). Two important trends are happening that will accelerate the pace at which this happens in 2016: (1) online courses and degrees are becoming more socially acceptable (many programs have been accredited, employers are increasingly hiring graduates from these programs, etc.) and (2) the infrastructure (managing enrollment, handling payment, providing tech support, hosting platforms, etc.) to provide these offerings is cheaper and more readily available.

As such, we will see a greater number of higher education institutions join the ranks of UNC, USC, ASU and many others that provide courses and degrees online. This trend will create a range of software opportunities across: video collaboration, course development and delivery, student / faculty services and recruitment / retention.

6) Demand for software tools that teach skill-based training will increase  

As colleges increasingly charge exorbitant tuition fees while failing to equip graduates will real skills, demand for skill-based programs, vocational certifications and other alternative teaching tools will increase. In 2013, the number of vocational certificates granted was nearly 1M—up 35% from 2005. Similarly, from 2013 to 2015, the number of graduates who graduated from coding programs (such as Codecademy) increased 630%+.

In 2016, we will see an even greater emphasis on tools for skill-based training. Some of this will be purely software delivered via the cloud and some will be more hybrid: software mixed with in-person training. Companies like Lynda (acquired by LinkedIn), Udacity, General Assembly and Udemy have already made significant dents in this space. We will see much more of this in the upcoming year.

Bookings vs. Revenue in Early / Growth stage SaaS Companies

There is an important difference between revenue and bookings that comes into play for early stage SaaS businesses that are growing rapidly. This difference has implications on both the revenue and cost side of the equation and can also affect important decisions such as how much to raise when speaking with the investor community.

Before we get to growth SaaS businesses, however, let’s first talk about legacy software pricing. In the traditional software world, software was typically sold as a perpetual license. Customers would make a one-time payment for perpetual use of software and pay for annual support and upgrades each year. In this world, bookings = revenue and revenue was generally recognized at the time the contract was signed and the software provided to the customer. The benefit of this model was immediate revenue recognition; the downside was lumpy and unpredictable revenue.

In the SaaS world, software is provided on a recurring (often monthly) basis. The software, support and upgrades are all included in the subscription. This allows for stable and predictable revenues that are smoothed out over the life cycle of the contract. The down side, however, is that there is often a discrepancy between bookings and revenue that must be understood and accounted for appropriately.

This issue is best understood with an example. Assume for a moment that a SaaS business is selling software in 3 year contracts and that churn is 0% (for simplicity.) In a flat revenue business, where growth is 0% YoY, there are no major accounting issues or business implications because revenues = bookings. For example, if bookings are flat at $100M, the business would recognize $100M of revenue each year. $33M from 2 years ago, $33M from last year and $33M from this year.

The challenge that comes into play is when a SaaS business is in growth mode. For example, a SaaS business that books $25M in Y1, $50M in Y2 and $100M in Y3, would have the following revenue numbers:

Untitled.png

As seen in the table above, for a growth stage company that is doubling in bookings YoY, revenues do not equal bookings. There is a lag effect between bookings and revenue and it becomes necessary to take a haircut on bookings to get to revenue. This haircut will of course decrease over time as growth slows, but it does create an important accounting dynamic that has real business implications

One of the largest implications for early stage companies is in the amount of money a SaaS business must raise when going out to the VC market for financing. The underlying cost structure (COGS, sales expense, marketing expense, R&D, etc.) must be looked at as derived from bookings not revenue. This is a result of the fact that the SaaS business is incurring these expenses as a proportion of bookings not the revenue actually being recognized. Because expenses are incurred in line with bookings (and bookings > revenue), the result is that EBITDA is low (and generally negative for most early / growth stage SaaS companies). EBITDA ultimately ties to cash flow, burn rate and the required investment. Thus, it is key to make sure the raise is in line with what the business needs from a cash flow, and ultimately booking / expenses, perspective.