Python, React (web), Angular, machine learning, and Docker will be the five most popular tech skills in 2020.
TensorFlow is the most popular tech skill of the last three years, exponentially increasing between 2016 and 2019 based on Udemy’s
Udemy sees robust demand for AI and data science skills, in addition to web development frameworks, cloud computing, and IT certifications, including AWS, CompTIA & Docker.
SAP expertise is projected to be the fastest-growing process-related skill set in 2020.
These and many other fascinating insights are from Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future (48 pp., PDF, opt-in). The report provides compelling evidence of how important it is to prepare workforces for the future of work in an AI-enabled world. Udemy predicts 2020 will be the year AI goes mainstream. The report states that “In the world of finance, investment funds managed by AI and computers account for 35% of America’s stock market today,” citing a recent article in The Economist, The rise of the financial machines. The following are the key insights from the report:
Python, React (web), Angular, machine learning, and Docker will be the five most popular tech skills in 2020. TensorFlow, OpenCV, and neural networks are the foundational skills many data scientists are pursuing and perfecting today to advance their AI-based career strategies. Mastering those three skills is essential for understanding and developing AI apps and platforms. TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. The following is a comparison of the top 10 most popular tech skills in 2020 and the top 10 tech skills that grew in popularity between 2016 and 2019.
Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future
The top 10 emerging tech skills in 2020 will be web development, quantum computing, and Internet of Things IoT). Udemy analyzed the emerging skills that over 40M people are learning on Udemy today, and found that Gatsby.js, a new web development framework tool, is gaining rapid adoption. Additional web development tools include React Hooks, Next.js, and SwiftUI, a user interface tool for Apple apps. Entirely new skills, including quantum computing and ESP32, used in the IoT development, are also among the top 1 emerging tech skills of 2020.
SAP enterprise software expertise, knowledge of the ISO/IEC 27001 standard, information security, and Microsoft Dynamics 365 are projected to be the four of the fastest-growing process and tools skills in 2020. Udemy also found strong interest in Robotic Process Automation (RPA) and Business Process Management (BPM). Robotic Process Automation (RPA) refers to the use of process automation tools to quickly replicate how human beings perform routine daily office work using popular productivity apps, including Microsoft Excel, databases, or web applications.
Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future
Chef Software expertise, network security, penetration testing, Linux security, and AWS Certified Cloud are predicted among the fastest-growing skills for IT professionals in 2020. Chef software is prevalent in IT organizations and is used for streamlining the task of configuring & maintaining a company’s servers. Chef has invested in integrating with many of the most popular cloud-based platforms, including Rackspace, Microsoft Azure, and Amazon Elastic Compute Cloud, to automatically provision and configure new machines.
I had an extensive debate recently on why monoliths are not universally such a terrible idea, where a young developer told me “Most of the code you would have written in your time as a developer is now what we call legacy code” .
I don’t blame the young man for thinking “legacy = bad”. I believed it too when I had his role many years ago . And then my thinking evolved over time and I am sure his will too.
I wrote this blog “Nuke that code, lets start over” nearly a decade ago, and I think many of the points discussed in there still hold true . There is a lot of wisdom in the comments section under the blog that I just finished reading again .
The young man’s comment made me think a bit about all the code I have written – from games in BASIC that I have sold to others for 50 rupees on cassettes while in high school to C/C++ , ABAP and java Programs that did more “Enterpisey” stuff for big companies across the world .
“Most” of the code I wrote probably have not lasted very long at all !
Some of the best code I wrote never made it to production – because priorities changed, projects were shelved , acquistion happened and a host of other reasons .
Most of the code I wrote has been refactored – some times by me , but mostly by developers who owned those repos after my tenure . I know of a couple of places where a bit of my code still runs in production – but it’s a tiny fraction of what I have written in total .
Legacy code survives because of many reasons – some good and some bad
1. It does something exceedingly well and it’s hard to make a case to replace II
2. It is way too complex for anyone in the team to rewrite now – usually due to poor knowledge management , not because the code itself is bad . Even with sophisticated tooling – it’s hard to understand logic completely from code, DB schemas and logs .
3. Refactoring is a routine activity in good tech shops . But the truth is that vast majority of large enterprise shops have ignored the importance of refactoring for a long time . They paint themselves to a corner and then need massive modernization projects to get out of the mess and cater to the business needs of today’s and tomorrow’s market . It also shocks me that some shops after finishing the modernization, they still refuse to follow any structured process to refactor along the way !
4. Another common pattern I have seen is that modernization is attempted purely for “tech fashion” – and usually with tragic results . I was quite amused once to watch the code of MRP process be rewritten to a new technology only to realize that even though the results now showed up in a fraction of the time of the old system, it was all wrong and hence useless !
Being honest about the need to rewrite is a good starting point in all modernization projects . There is only limited time, effort and money available and there is never a good reason to waste it by doing a project for the wrong reasons .
“we need to modernize because mainframe is dead” or “we need to move to NoSQL because RDBMS is dead” or “Our code base is a big monolith and we need to be serverless for our future” . It’s very common to hear these kinds of reasons being touted as the reason to start a modernization project . These are not bad reasons by themselves !
These statements are all good starting points to explore what is the real issue – do you take more time to deliver a new feature that business wants , and is that leading to less than optimal business results ? If the answer is yes – is it a tech problem or a process problem primarily that causes it ? Can you quantify the value of changing to a new system and prove that it is greater than the estimated cost ? What happens if the modernization project fails – how much risk can you take ?
Bottom line – you need an honest case for why you want to modernize . This case will then serve as your North Star in your modernization journey . It also will serve as a dose of reality check – which we will all need from time to time . The case itself has a shelf life – so it’s important to revisit periodically to check what changes are needed .
Big Bang vs Incremental changes – how do you decide which is the best way to go ? I will share some thoughts on that in another post . For now , I need to get back to my day job
In the previous post, I introduced the idea of an inverted demand generation (demandgen) funnel which we can use to calculate a marketing demandgen budget based given a sales target, an average sales price (ASP), and a set of conversion rates along the funnel. This is a handy tool, isn’t hard to make, and will force you into the very good habit of measuring (and presumably improving) a set of conversion rates along your demand funnel.
In the previous post, as a simplifying assumption, we assumed a steady-state situation where a company had a $2M new ARR target every quarter. The steady-state assumption allowed us to ignore two very real factors that we are going to address today:
Time. There are two phase-lags along the funnel. MQLs might take a quarter to turn into SALs and SALs might take two quarters to turn into closed deals. So any MQL we generate now won’t likely become a closed deal until 3 quarters from now.
Growth. No SaaS company wants to operate at steady state; sales targets go up every year. Thus if we generate only enough MQLs to hit this-quarter’s target we will invariably come up short because those MQLs are working to support a (presumably larger) target 3 quarters in the future.
In order to solve these problems we will start with the inverted funnel model from the previous post and do three things:
Quarter-ize it. Instead of just showing one steady-state quarter (or a single year), we are going to stretch the model out across quarters.
Phase shift it. If SALs take two quarters to close and MQLs take 1 quarter to become SALS we will reflect this in the model, by saying 4Q20 deals need come from SALs generated in 2Q20 which in turn come from MQLs generated in 1Q20.
Extend it. Because of the three-quarter phase shift, the vast majority of the MQLs we’ll be generating 2020 are actually to support 2021 business, so we need to extend the model in 2021 (with a growth assumption) in order to determine how big of a business we need to support.
Here’s what the model looks like when you do this:
You can see that this model generates a varying demandgen budget based on the future sales targets and if you play with the drivers, you can see the impact of growth. At 50% new ARR growth, we need a $1.47M demandgen budget in 2020, at 0% we’d need $1.09M, and at 100% we’d need $1.85M.
Rather than walk through the phase-shifting with words, let me activate Excel’s trace-precedents feature so you can see how things flow:
With these corrections, we have transformed the inverted funnel into a pretty realistic tool for modeling MQL requirements of the company’s future growth plan.
Other Considerations
In reality, your business may consistent of multiple funnels with different assumption sets.
Partner-sourced deals are likely to have smaller deal sizes (due to margin given to the channel) but faster conversion timeframes and higher conversion rates. (Because we will learn about deals later in the cycle, hear only about the good ones, and the partner may expedite the evaluation process.)
Upsell business will almost certainly have smaller deal sizes, faster conversion timeframes, and much higher conversion rates than business to entirely new customers.
Corporate (or inside) sales is likely to have a materially different funnel from enterprise sales. Using a single funnel that averages the two might work, provided your mix isn’t changing, but it is likely to leave corporate sales starving for opportunities (since they do much smaller deals, they need many more opportunities).
How many of these funnels you need is up to you. Because the model is particularly sensitive to deal size (given a constant set of conversion rates) I would say that if a certain type of business has a very different ASP from the main business, then it likely needs its own funnel. So instead of building one funnel that averages everything across your company, you might be three — e.g.,
A new business funnel
An upsell funnel
A channel funnel
In part III of this series, we’ll discuss how to combine the idea of the inverted funnel with time-based close rates to create an even more accurate model of your demand funnel.
The spreadsheet I made for this series of posts is available here.
As 2019 is heading to its last few weeks, I had a chance to catch up with a lot of friends, mentees and my own mentors and also take some time to reflect how things worked out for me this year . The consultant in me immediately started grouping and classifying the common themes, apply MECE , and map it all to a 2 X 2 . Don’t worry – I somehow resisted the temptation and stopped at just grouping
I thought three repeated themes were worth sharing here on my blog .
1. You need to tell some managers what they want to hear
So there is this guy who comes from a non tech background and does a good job managing a team of developers to hit milestones ahead of schedule (mostly by overworking them) . He is a flight risk, and the only way to keep him is to promote him to an executive rank . If he flees, his manager probably won’t earn his own promotion any time soon – so there is plenty of motivation to make this happen.
The only trouble is that this promotion needs the blessing of a very senior tech exec . There is no way this non tech dude is going to pass that level of tech scrutiny . That’s when the manager has a flash of brilliance in his thinking . The senior techie is somewhat of a philosopher and his current pet topic is “culture”. He coaches the up and comer to focus heavily of culture when the interview happens – which he masterfully does and gets promoted .
About half the people I caught up with had a similar story where promotions or raises were secured by telling their bosses what they wanted to hear .
2. Diversity seems to be a bit easier than inclusion
While it’s still a long ways to go, a lot of managers have been acting on improving diversity in their teams . There is a lot more awareness and training at all levels . In many cases there are top down directives like “by end of the year there needs to be X% women in your team , Y% URM in your team” etc . In a some of these cases, these are KPIs tied to the manager’s bonus too . All of these are great of course – and I hope it’s not just a one time exercise .
Then comes the question of inclusion . Are these “new” members of the team supported and set up for success ? Are their voices considered with the same importance as when your team was homogenous ? I was a bit surprised that even the people who actively champion the need for diverse teams haven’t done as much thinking on how to make inclusion happen in their day to day work.
That said – I was quite happy to hear that almost everyone I spoke to had done something good about making sure people doing similar roles are paid the same.
3. Being a newly promoted executive continues to be really hard
Before I made Partner , I had attended an exceptional internal training course in IBM called “Cornerstone”. And that’s where it was drilled into me that “what got you here won’t get you there”. Excellent advice which helped me and many others who attended that course . It is also very hard to put it into practice !
Everyone wants to make an immediate impact as an exec . Most have someone else they treat as their role model and want to be like him or her. Nothing wrong with any of it – just that what works for one exec in one context might not translate 1:1 to you in your context . Almost every single story I heard from the newly promoted folks made me say in my inner voice “oh no – I did that too” .
Here is one story I heard from someone who got promoted and took over a new team as its leader in January 2019. All six direct reports were asked to make 1 hr presentation to the new boss . The first one made a less than stellar presentation in the new manager’s opinion and the next day it was announced that this person will have a new role to be announced soon . Long story short – we are in December now and that position is still not filled , and two of the key next-in-line leaders quit because they couldn’t stand the ongoing chaos.
I asked them what were the words of wisdom they got from their leaders after their promotion and it was the usual list of “Have a bias to action”, “Lead with courage”, “Make your voice heard” etc . Those are all invaluable in their own rights – but perhaps we should add “slow down a bit now to speed up later” to the list .
SaaSnet is an emerging business model that combines a marketplace to create value driven by network-effects, and Enterprise SaaS to deliver deep enterprise value. In this post I’ll share the defining attributes, give three company examples, and growth strategies for this new disruptive business model. But first, here’s how models have evolved:
Quick History of Enterprise Software Business Models
Enterprise Software business models continue to evolve. New models are driven by increased vendor competition and an increasing sophistication and choice by customers. Here’s a quick list of the primary innovations in Enterprise software business models:
Licensed: The initial shift from software services to software products with software licenses that enable the vendor to retain intellectual property and sell copies to multiple customers are a high margin.
Perpetual: The creation of a Perpetual License plus Support and Maintenance fees. Perpetual licenses had the benefit of being capital expenditures paid up front, combined with 20–30% fees as operating expenses, resulting in attractive +90% margins.
Open Source: The license to use and modify the software was free, and vendors and outsourcing firms charge for Support and Maintenance.
Hybrid Open Source: Combines a paid software product and a free open source project, plus the vendor charges for Support and Maintenance. Typically the deployment of the software reaches a certain scale that creates the demand for the paid product
Subscription: Software-as-a-Service combines the rights to the software, support and maintenance in a recurrent subscription.
Freemium: A recent innovation you see with companies like Intercom is enabling self-service in-app purchases, creating a third tier in a freemium (free, core paid product, additional paid).
Platform as a Service: PaaS is an extension of SaaS with rights to build upon the platform, to modify and extend the product.
SaaSnet: Combines a marketplace business model with SaaS. A new disruptive business model with the benefits of the subscription model with the power of network effects….
SaaSnet Attributes
The SaaSnet business model combines a marketplace with network effects and subscription enterprise software. Attributes include:
A N-sided marketplace offers transactional value between the parties
Use of the marketplace is largely driven by self-service
The transactional value in the marketplace contributes a core value to the SaaS product
The SaaS offering provides enterprise workflow and data owned by the customer
In some cases, customers may start using the marketplace and grow into the SaaS offering
There are some parallels with PaaS (e.g. Salesforce’s app marketplace and SaaS products). But as often the case with the enterprise, inspiration is drawn from consumer innovation such as gig economy marketplaces like Uber and Lyft, Social Networks like Facebook and LinkedIn. Market Networks described by James Currier like Houzz and Honeybook combine attributes from marketplaces, social networks and SaaS workflow — but after we go through some examples you’ll see a difference in deep enterprise value.
In our advisory and coaching practice, it happens that three of our startups are SaaSnets.
OnFrontiers
OnFrontiers builds enterprise knowledge networks. A double-sided marketplace for expert consultations facilitates request matching, call scheduling, recording, transcription and more. Sales, business development, consulting and other expert requests are matched with existing profiles or sourced outside the network. For experts, it’s akin to a high end gig economy job for white collar work. After a customer gains success (for example, positioning and winning deals, or better project outcomes), they upgrade to the SaaS Platform.
The SaaS Platform creates a customer-owned knowledge network. The customer enables their employees, 3rd party experts and marketplace on the platform. Employees gain expert consultations, managed with enterprise admin and compliance. A knowledge base is created as a byproduct of consultations, valuable for both reference and future expert discovery.
One part of the future of work I’m excited about is that everyone will have a 3rd Voice, human or not, aiding individual employee productivity and growth. Costs to create these services have fallen, and so too is the boundary of the firm. A knowledge worker depending upon the context may have an expert consultant, a mentor or coach, a peer, community or other available helping them advance in ways their management cannot.
Let me illustrate the network effects at play with a flywheel:
OnFrontiers Flywheel
Marketplace Experts increase the potential matches for expertise requests with Marketplace Customers and SaaS Customers. Marketplace Customers can grow into SaaS Customers. SaaS Customers of the Platform add SaaS Experts (employees, 3rd parties). SaaS Customers of the Platform can add Customer Partner organizations to their knowledge network, which contributes experts, and drives referral sales. A subset of experts from SaaS Customers and Customer Partners may independently become Marketplace Experts. And while the arrow would have to go all the way around the flywheel, of course the more Marketplace Customer requests, the more demand for experts.
With this context, the OnFrontiers business model combines the network effects of a marketplace and the deep enterprise value of SaaS. The marketplace product adds direct value and growing value to the SaaS product. Given it’s network effects, the marketing benefits include a lower cost of customer acquisition, a way for buyers to start small on a self-service basis and upgrade as they realize value (although some buyers skip this step and go straight to the Platform). A moat is built with the value of the network and data.
Kea
Kea.ai is a cashier in the cloud for restaurants, taking orders over the phone with AI-augmented Agents. The company has a single-sided marketplace of Agents and a SaaS product sold to Quick-Service Restaurants and Franchises.
While recently more meals are from outside the home than not, restaurants are a tough business with small margins, highly competitive, it’s hard to recruit and attrition rates are high. Order completion rates suffer at peak hours and customer satisfaction is hard to maintain. Even with the rise of food ordering and delivery apps, in many cases the bulk of orders come over the phone (that refrigerator magnet isn’t going away soon). DoorDash, GrubHub and others cut into their margin (charging 25% per order) and dis-intermediates demand.
Kea Flywheel
Kea takes a specific business process, the cashier taking orders over the phone while interfacing with customers and delivery people in person, and in some cases engaged in food prep, and moves it to the cloud. Remarkably, this grows restaurant revenue by over 20%, saves 10 hours a day in labor and increases customer satisfaction. Some of their Agents used to be cashiers, and now they work from home supporting multiple restaurants at higher pay. An attractive gig economy job. I’ve taken orders as an Agent myself, and it’s an amazing experience to shepard orders at high volume with a bot doing much of the work.
With this context, Kea’s business model combines a single-sided marketplace and SaaS offering. As the network of agents grows, and data yields optimization, product/service efficiencies are realized. Customers retain ownership of the phone as marketing channel, increase margin and customer satisfaction. A moat is built with the value of the network and data.
Skillprint
Skillpint.co is a LinkedIn for inner-talents derived from playing games. By playing existing casual games (think Tetris on your phone), your gameplay behavior can tell you a lot about who you are. Assessing strengths (e.g. are you creative, bold, precise, and/or strategic?) in this engaging way enables them to continuously engage people and recommend ways to up-level their skills.
The product is in private beta, and you can read about their vision in VentureBeat. The business model is a double-sided marketplace where consumers, employers and marketers may pay, that will evolve to include a SaaS Product. I mention them here to segway on how to grow this model…
SaaSnet Growth Strategies
First, principles:
To grow any marketplace, you have to primarily focus on either the buy side or sell side to secure it first.
If the marketplace includes consumers or individual professionals, you serve them first and foremost. LinkedIn codified this into their culture as Members First.
Initial go-to-market with the marketplace.
You may have to have a shallow SaaS offering to aid product discovery, but after you gain basic marketplace liquidity and network effects, invest deeply in the SaaS Product.
In Skillprint’s example, they could chose a different business model, just pure SaaS. Employers could pay them for inner-strength assessments of recruits and employees. But if they chose that path, they would lose out on the bigger prize. Pure SaaS has less growth potential and lower barriers to entry than the network effects of SaaSnet. A PaaS like Salesforce’s app marketplace can start out with a SaaS product and add a marketplace when there is scale attractive to developers on the platform. But if you achieve scale with a SaaS product for soft skill assessment, it won’t be an advantage for launching a marketplace, and in fact your customer traction may put you at a disadvantage. The attribute of the marketplace providing a core value to the SaaS product means you can’t just choose to focus solely on marketplace or SaaS.
At the beginning of any startup, you will have to make changes to get it right, even to the point of pivot or restart. But I believe the product you start with is the product you end with. For example, Evernote was personal productivity, making it very hard to gain adoption and monetize for group productivity. The business model you start with is also most likely the business model you end with. You could be like Google and start without one, and later discover targeted search ads. Or you could do a bold restart like Netflix from CDs to streaming.
Please learn from my lesson on how not to innovate. Once I started with a SaaS product and tried to shift to a Hybrid Open Source business model.
Also while at SlideShare we had a media business model and realized even while it was successful it had it’s limits (you can only sell ads on trafficked inventory). We launched a freemium offering which was doing well at the time LinkedIn acquired us, but if freemium was part of the business from the onset, as the network effect grew so would this line of revenue. Even with a massive base of users to market to, it was hard to change the perception of SlideShare only being free. And given that the community loved us, it’s not like we could take things away from free!
In consideration of this learning, if you are aiming towards a SaaSnet business model, here’s the balance to strike. Focus on proving out the above principles in sequence, but lay out the full business model in a lightweight way. That might mean your initial SaaS product is just one feature, available only on a self-service basis.
Feedback appreciated
I see this as a new trend. I have conviction in the power of the model because of growth in companies I’m close to, and how it relates to business models I’ve operated before. I’m looking for examples of other companies developing this model. Without many other examples, it wouldn’t really be a trend. And if you are operating it, I’d appreciate if you could share the principles that you think matter for growing it.
Salesforce.com was co-founded by Marc Benioff in 1999. At that time, multi-tenant software as a service was a relatively new concept in business. Salesforce eventually adopted the slogan “no software,” to draw the distinction between their new SaaS method of delivering software and established, on-premise software vendors.
The SaaS approach to software distribution, coupled with a subscription business model, created a new paradigm for the relationship between an enterprise software company and its customers. Historically, on-premise enterprise software contracts required buyers to make large, upfront investments. The software vendors’ incentive to close these large deals sometimes resulted in failed projects and unhappy customers. The problem was so severe that I originally called this ZDNet blog “IT Project Failures,” back when I started writing in 2006.
The SaaS software subscription model pioneered by Benioff and Salesforce shifted the buying model from large, upfront payments to smaller, but ongoing, subscription fees. Because customers could cancel at any time (even, if it was not easy to move your data and processes to a new system), Salesforce had to prioritize customer success as a core company value. Creating a positive, inclusive culture was an existential decision for Salesforce because happy customers retained their subscriptions. High customer retention continues to fuel the company’s growth.
By aligning the interests of buyers with Salesforce’s revenue goals, the subscription model drove a corporate culture that embraced customers by making them happy. Given Salesforce’s current revenue run rate of almost $20 billion [PDF], the company’s strategy has obviously worked.
To learn about the role of culture and customer success in software, and to explore the history of Salesforce, I spoke with John Taschek, the company’s Senior Vice President of Market Strategy. The discussion took place at Dreamforce 2019, Salesforce’s annual conference, which had over 135,000 on-site attendees in San Francisco.
Watch the full video of our conversation embedded above and read the edited transcript:
When did you join Salesforce?
A little over 16 years now, in 2003. There were 300 people at the company when I joined.
What was the vision when Salesforce was a small company?
Marc [Benioff] used to say, “Why can’t enterprise technology be as easy to use as Amazon.com. That was the vision and it’s still true today. At that time, software was very complex. It took teams of people and months to install before the first person could use it. Amazon was very easy. You signed up and you bought a book. That’s how easy it was. And they handled everything else, including the e-commerce part. Everything. Enterprise offer wasn’t like that.
How does internal culture affect customer relationships at Salesforce?
The focus of always was on the customer, it wasn’t on sales. Since we were a subscription business, we had to work all the time to get renewed. In the early days, somebody could just turn off Salesforce right away and go to something else (even though it wasn’t practical to do that). So, the culture internally made us empathetic to the customers, the consumers.
We created programs for people who were heroes. At the first Dreamforce in 2003, you saw posters of people, people who actually did things and not just logos.
It really was an issue of trust. Definitely, trust was the key.
We sold to lines of business because that’s who bought and it was also easy. Small businesses could buy us with a credit card, and we made it very easy. We took away that friction point. They could also set it up themselves, they could become administrators themselves, they could do a lot of things themselves.
At some point, we were successful in those small businesses, medium-sized businesses, and lines of business within large companies. And it spread. In some of our first customers, like Cisco or ADP, it spread throughout the company.
Eventually, you became a major enterprise software company.
Definitely. The CEO’s became involved. The CMOs became involved after we got seriously into the marketing cloud business with the acquisition of Exact Target. Everybody’s kind of involved now. It’s a very C-level type of relationship now, especially with the larger companies, multinational companies.
And we still sell to small businesses too. We still have Salesforce Essentials. That’s huge. And still, a third of our business is small business. So, we don’t want to lose anything. But we do gain access to that C-level, helping people and companies transform.
Have the core values of Salesforce changed since the beginning?
No. Values are values. They stay the same. Our methods change and our goals change. Our measurements change, our obstacles change.
But we believe that our values are going to make us run for the next 21 years and beyond.
Does my company spend too much on marketing? Too little? How I do know? What is the right level of marketing spend at an enterprise software startup? I get asked these questions all the time by startup CEOs, CMOs, marketing VPs, and marketing directors.
You can turn to financial benchmarks, like the KeyBanc Annual SaaS Survey for some great high-level answers. You can subscribe to SiriusDecisions for best practices and survey data. Or you can buy detailed benchmark data [1] from OPEXEngine. These are all great sources and I recommend them heartily toanyone who can afford them.
But, in addition to sometimes being too high-level [2], there is one key problem with all these forms of benchmark data: they’re not about you. They’re not based on your operating history. While I certainly recommend that executives know their relevant financial benchmarks, there’s a difference between knowing what’s typical for the industry and what’s typical for you.
So, if you want to know if your company is spending enough on marketing [3], the first thing you should do is to make an inverted demand generation (aka, demandgen) funnel to figure out if you’re spending enough on demandgen. It’s quite simple and I’m frankly surprised how few folks take the time to do it.
Here’s an inverted demandgen funnel in its simplest form:
Let’s walk through the model. Note that all orange cells are drivers (inputs) and the white cells are calculations (outputs). This model assumes a steady-state situation [4] where the company’s new ARR target is $2,000,000 each quarter. From there, we simply walk up the funnel using historical deal sizes and conversion rates [5].
With an average sales price (ASP) of $75,000, the company needs to close 27 opportunities each quarter.
With a 20% sales qualified lead (SQL) to close rate we will need 133 SQLs per quarter.
If marketing is responsible for generating 80% of the sales pipeline, then marketing will need to generate 107 of those SQLs.
If our sales development representatives (SDRs) can output 2.5 opportunities per week then we will need 5 SDRs (rounding up).
With an 80% SAL to SQL conversion rate we will need 133 SALs per quarter.
With a 10% MQL to SAL conversion rate we will need 1,333 MQLs per quarter.
With a cost of $250 per MQL, we will need a demandgen budget [6] of $333,333 per quarter.
The world’s simplest way to calculate the overall marketing budget at this point would be to annualize demandgen to $1.3M and then double it, assuming the traditional 50/50 people/programs ratio [7].
Not accounting for phase lag or growth (which will be the subjects of part II and part III of this post), let’s improve our inverted funnel by adding benchmark and historical data.
Let’s look at what’s changed. I’ve added two columns, one with 2019 actuals and one with benchmark data from our favorite source. I’ve left the $2M target in both columns because I want to compare funnels to see what it would take to generate $2M using either last year’s or our benchmark’s conversion rates. Because I didn’t want to change the orange indicators (of driver cells) in the left column, when we have deviations from the benchmark I color-coded the benchmark column instead. While our projected 20% SQL-to-close rate is an improvement from the 18% rate in 2019, we are still well below the benchmark figure of 25% — hence I coded the benchmark red to indicate a problem in this row. Our 10% MQL-to-SQL conversion rate in the 2020 budget is a little below the benchmark figure of 12%, so I coded it yellow. Our $250 cost/MQL is well below the benchmark figure of $325 so I coded it green.
Finally, I added a row to show the relative efficiency improvement of the proposed 2020 budget compared to last year’s actuals and the benchmark. This is critical — this is the proof that marketing is raising the bar on itself and committed to efficiency improvement in the coming year. While our proposed funnel is overall 13% more efficient than the 2019 funnel, we still have work to do over the next few years because we are 23% less efficient than we would be if we were at the benchmark on all rates.
However, because we can’t count on fixing everything at once, we are taking a conservative approach where we show material improvement over last year’s actuals, but not overnight convergence to the benchmark — which could take us from kaizen-land to fantasy-land and result in a critical pipeline shortage downstream.
Moreover because this approach shows not only a 13% overall efficiency improvement but precisely where you expect it to come from, the CEO can challenge sales and marketing leadership:
Why do you think we can improve the SQL-to-close rate from 18% to 20% and what you are doing next-year to drive that improvement? [8]
What are we doing to improve the MQL-to-SAL conversion rate?
How are we going to improve our already excellent cost per MQL by $25?
In parts II and III of this post, we’ll discuss two ways of modeling phase-lag, modeling growth, and the separation of the new business and upsell funnels.
Notes
[1] For marketing or virtually anything else.
[2] i.e., looking at either S&M aggregated or even marketing overall.
[3] The other two pillars of marketing are product marketing and communications. The high-level benchmarks can help you analyze spend on these two areas by subtracting your calculated demandgen budget from the total marketing budget suggested by a benchmark to see “what’s left” for the other two pillars. Caution: sometimes that result is negative!
[4] The astute reader will instantly see two problems: (a) phase-lag introduced by both the lead maturation (name to MQL) and sales (SQL to close) cycles and (b) growth. That is, in a normal high-growth startup, you need enough leads not to generate this quarter’s new ARR target but the target 3-4 quarters out, which is likely to be significantly larger. Assuming a steady-state situation gets rid of both these problems and simplifies the model. See part II and part III of this post for how I like to manage that added real-world complexity.
[5] Hint: if you’re not tracking these rates, the first good thing about this model is that it will force you to do so.
[6] When I say demandgen budget, I mean money spent on generating leads through marketing campaigns. Sometimes that very directly (e.g., adwords). Other times it’s a bit indirectly (e.g., an SEO program). I do not include demandgen staff because I am trying to calculate the marginal cost of generating an extra MQL. That is, I’m not trying to calculate what the company spends, in total, on demandgen activities (which would include salary, benefits, stock-based comp, etc. for demandgen staff) but instead the marketing programs cost to generate a lead (e.g., in case we need to figure out how much to budget to generate 200 more of them).
[7] In an increasingly tech-heavy world where marketing needs to invest a lot in infrastructure as well, I have adapted the traditional 50/50 people/programs rule to a more modern 45/45/10 people/programs/infrastructure rule, or even an infrastructure-heavy split of 40/40/20.
[8] Better closing tools, an ROI calculator, or a new sales training program could all be valid explanations for assuming an improved close rate.
Cloud-based endpoint protection platforms (EPP) are proliferating across enterprises today as CIOs and CISOs prioritize greater resiliency in their endpoint security strategies going into 2020.
Gartner predicts that Global Information Security and Risk Management end-user spending is forecast to grow at a five-year CAGR of 9.2% to reach $174.5 billion in 2022, with approximately $50B spent on endpoint security.
Endpoint security tools are 24% of all IT security spending, and by 2020 global IT security spending will reach $128B according to Morgan Stanley Research.
70% of all breaches still originate at endpoints, despite the increased IT spending on this threat surface, according to IDC.
There’s a surge of activity happening right now in enterprises that are prioritizing more resiliency in their endpoint security strategies going into 2020. The factors motivating CIOs, CISOs, IT, and Practice Directors to prioritize endpoint resiliency include more effective asset management based on real-time data while securing and ensuring every endpoint can heal itself using designed-in regenerative software at the BIOS level of every device. CIOs say the real-time monitoring helps reduce asset management operating expense, a big plus many of them appreciate give their tight budgets. Sean Maxwell, Chief Commercial Officer at Absolute, says, “Trust is at the center of every endpoint discussion today as CIOs, CISOs and their teams want the assurance every endpoint will be able to heal itself and keep functioning.”
The Endpoint Market Is Heating Up Going Into 2020
There are over thirty vendors competing in the endpoint security market right now. A few of the most interesting are Absolute Software, Microsoft,Palo Alto Networks, and others who are seeing a surge of activity from enterprises based on discussions with CIOs and CISOs. Absolute Software’s Persistence self-healing endpoint security technology is embedded in the firmware of more than 500 million devices and gives CIOs, CISOs and their team’s complete visibility and control over devices and data. Absolute is the leading visibility and control platform that provides enterprises with tamper-proof resilience and protection of all devices, data, and applications.
Like Absolute, Microsoft is unique in how they are the only vendor to provide built-in endpoint protection at the device level, with the core focus being on the OS. Windows 10 has Windows Defender Antivirus now integrated at the OS level, the same System Center Endpoint Protection delivers in Windows 7 and 8 OS. Microsoft Defender Advanced Threat Protection (ATP) incident response console aggregates alerts and incident response activities across Microsoft Defender ATP, Office 365 ATP, Azure ATP, and Active Directory, in addition to Azure.
Further evidence of how enterprise customers are placing a high priority on endpoint security is the increase in valuations of key providers in this market, including Absolute Software(TSE: ABT) and others. Absolute’s stock price has jumped 13% in just a month, following their latest earnings announcement on November 12th with a transcript of their earnings call here. Absolute’s CEO Christy Wyatt commented during the company’s most recent earnings call that, “The ability to utilize near real-time data from the endpoint to… to deliver actionable insights to IT about where controls are failing and the ability to apply resilience to self-heal and reinforce those security controls will become a critical skill for every one of our customers. This is the essence of Absolute’s platform, which adds resiliency to our customers’ operations.” It’s evident from what CIOs and CISOs are saying that resiliency is transforming endpoint security today and will accelerate in 2020.
Key Takeaways From Conversations With Enterprise Cybersecurity Leaders
The conversations with CIOs, CISOs, and IT Directors provided valuable insights into why resiliency is becoming a high priority for endpoint security strategies today. The following are key takeaways from the conversations:
Known humorously as the “fun button” cybersecurity teams enjoy being able to brick any device any time while monitoring the activity happening on it in real-time. One CIO told the story of how their laptops had been given to a service provider who was supposed to destroy them to stay in compliance with the Health Insurance Portability and Accountability Act (HIPAA), and one had been resold on the back market, ending up in a 3rd world nation. As the hacker attempted to rebuild the machine, the security team watched as each new image was loaded, at which time they would promptly brick the machine. After 19 tries, the hacker gave up and called the image re-build “brick me.”
IT budgets for 2020 are flat or slightly up, with many CIOs being given the goal of reducing asset management operating expenses, making resiliency ideal for better managing device costs. The more effectively assets are managed, the more secure an organization becomes. That’s another motivating factor motivating enterprises to adopt resiliency as a core part of the endpoint security strategies.
One CIO was adamant they had nine software agents on every endpoint, but Absolute’s Resilience platform found 16, saving the enterprise from potential security gaps. The gold image an enterprise IT team was using had inadvertently captured only a subset of the total number of software endpoints active on their networks. Absolute’s Resilience offering and Persistence technology enabled the CIO to discover gaps in endpoint security the team didn’t know existed before.
Endpoints enabled with Resiliency have proven their ability to autonomously self-heal themselves, earning the trust of CIOs and CISOs, who are adopting Absolute to alleviate costly network interruptions and potential breaches in the process. 19% of endpoints across a typical IT network require at least one client or patch management repair monthly, according to Absolute’s 2019 Endpoint Security Trends Report. The report also found that increasing security spending on protecting endpoints doesn’t increase an organizations’ safety – and in some instances, reduces it. Having a systematic, design-in solution to these challenges gives CIOs, CISO, and their teams greater peace of mind and reduces expensive interruptions and potential breaches that impede their organizations’ growth.
Politics is mostly responsible for getting us to the verge of impeaching and removing a president. While there are good reasons for initiating the process, the best legal arguments in the world won’t finish the job. As a political process, the electorate has to be convinced of the validity of the outcome or public faith in rule of law could be further eroded. Current polling suggests that slightly more people favor removal but those numbers can shift especially with the daily revelations likely to come out of a senate trial. Game theory may offer clues about how the process can be brought to a satisfying conclusion for the majority of Americans.
Impeachment fervor is high, but is it enough?
Conventional wisdom has it that the house of Representatives might be able to impeach Donald Trump, but the GOP dominated Senate won’t vote to remove him from office. That calculation is focused on the Constitutional reality that it takes two-thirds of the Senate to vote for removal which would require 20 GOP senators to cross the aisle to affect the deed. The arithmetic involved is what provides Trump with a firewall against removal from office. But how valid is that logic?
The math depends on a straight party line vote which would leave Trump in office by something close to the 53–47 senate split that currently favors the GOP. Some moderate and progressive prognosticators often allude to a patriotic duty for senators to examine the evidence against Trump, which is damning, and to vote accordingly. But others consistently fall back on party affiliation with an argument of the type that blood is thicker than water. Many ordinary citizens simply throw up their hands and wait for the drama to play out.
Nevertheless, these analyses, while often well intended, only scratch the surface; there’s a straight-line logic to them that brooks no possibility of variation. But the situation is completely fluid and opportunities to think in straight lines may be few and far between. A deeper look via game theory reveals something else. Something that Trump supporters should view with raised eyebrows.
The prisoner’s dilemma
Basic game theory tries to reveal how humans might act under pressure and without all of the available data and it shows that people can act erratically for self-preservation. One of the classic gambits in game theory is the Prisoner’s Dilemma in which two people accused of the same crime are separated by inquisitors for questioning. It’s a sound police tactic.
If the perpetrators are allowed to communicate, they can more easily keep their story straight. But if they are separated, neither knows what the other is saying to the authorities and there is great temptation for each to throw the partner under the bus, especially if the authorities are offering leniency to the first to offer sound evidence implicating the other. Even if there was a pact between the partners in crime not to divulge anything about the effort, the threat of a long sentence, especially if coupled with an insinuation by the authorities that they already have a confession from the other partner, is sometimes enough to get evidence.
The gambit works a fair amount of the time, and not simply with naïve criminals. For instance, the recent depositions before the House Intelligence Committee were held in private and transcript releases were delayed until related witnesses could deliver their testimony without influence from earlier proceedings. This provided an incentive for all to tell the truth or risk a perjury charge. Witnesses like Ambassador Gordon Sondland were induced to amend their testimonies once transcripts of other witnesses’ testimonies were made public. As a result, the House Intelligence Committee has a cohesive body of evidence that it has referred to the Judiciary Committee for drafting into articles of impeachment.
Foreshadowing how the senate votes
Now imagine you are a prisoner and also imagine you are seeking the best treatment from your jailer. Much as you despise your keeper, you’ll likely not make any significant opposition to him or her if you believe opposition is futile. Eventually that’s the position many prisoner’s dilemma players find themselves in. Your strategy might likely revolve around an effort to go along and get along while biding your time looking for an opportunity to make a break. This strategy could take years to play out but jailbreaks do happen.
The U.S. Senate
Republican Senators are currently in a situation that mimics the above scenario. They are prisoners of the Republican Party and their jailer is none other than Donald Trump who can summon a backlash against any senator with the temerity to oppose him. The party’s preferred method of retaliation is introducing a primary challenger for any senator deemed not sufficiently loyal or supportive of the president.
There are 22 GOP senators up for re-election in 2020 and the state primary filing deadlines are all over the calendar; though a cluster of them happen in March or earlier, some happen as late as June. Many of the senators up for re-election come from safe seats in which the greatest threat for the incumbent is being removed in a primary challenge and not the general election so senators are indeed cautious. Senators who opposed Trump during the first two years of his presidency like Bob Corker of Tennessee and Jeff Flake of Arizona retired from the senate after discovering how difficult their re-elections would be in 2018 having opposed Trump.
It would seem that Trump’s hold on the Senate and therefore his acquittal is nearly secure, but this analysis omits one critical feature. If the scuttlebutt coming out of the Senate is true, most Republican senators loathe Trump in private but nonetheless support him in public. That’s just the behavior you’d expect from risk averse prisoners and it’s logical.
But all is not lost for the GOP or its senators. If any of them is thinking of a jailbreak, the impeachment trial offers the best opportunity, and the stakes are high. Preserving Trump’s presidency is one thing but arguably more important is the future of the Republican Party.
A strategy
Trump’s power stems from an ability to pick off individual senators who cross him so he can almost always defeat his opposition in detail, one at a time. But impeachment and a Senate trial present a unique moment. During a Senate trial all senators are compelled to attend and to be quiet actively listening. The only defenders of the president would be Republican House members designated to defend him and at the end of the trial there would be a simple up or down vote: “aye” for removal and “nay” to acquit.
Up to the point of the vote, game theory suggests that senators and GOP members of the house in general would support their man, forcing the Democrats to make their case without their assistance. You’d expect the same in a court of law: The defense never aids the prosecution in its work.
It’s exactly at the vote that Republican senators could for the first- and only-time work in unison to rescue themselves from their jailer. But would they? At this point many senators might have a longer purview than the next presidential election. Senators are elected for 6-year terms and they can and do run for re-election repeatedly. So, an argument can be made that they have, or should have, the longer-term interests of the nation in mind. But even more fundamentally, they have history to reference–not in Watergate and Nixon’s resignation but in Herbert Hoover.
Herbert Hoover
Hoover was in office less than a year when Wall Street sank in October 1929 into a sea of bad debt that initiated a significant economic downturn. Hoover’s laissez faire management of the economy turned a bad recession into the Great Depression. By the 1932 election the nation was flat on its back with 25 percent unemployment and Hoover lost to Franklin Roosevelt in a race that wasn’t close. But the important part of the story for today’s GOP is that Hoover’s maladministration poisoned the well for the Republican party for 5 election cycles, 20 years in total.
Herbert Hoover and Franklin D. Roosevelt riding to Roosevelt’s inauguration. FDR beat Hoover in an election during the Great Depression.
Any student of history, and we can assume that senators have more than a passing interest in it, can see the dominoes setting up for a prolonged Republican turn in the wilderness. It’s doubtful that the wilderness years could have the same result as FDR’s triumph over Hoover, for example, the 22nd amendment sets a two-term limit on presidents.
After Nixon-Ford, the Democrats were only able to hold power with Jimmy Carter for four years before Ronald Reagan served two terms and G.H.W. Bush served one. But Carter was not a great politician and it’s questionable how a better candidate would fare today assuming Trump is not removed and is instead defeated at the ballot box.
But let’s stipulate that it would be bad for the Republican brand to look the other way on Trump’s indiscretions, especially if the Democrats can put on an air-tight case.
Analysis
It would be better for GOP senators to vote to remove Trump at the trial than to appear to be derelict in their duty during a televised trial and vote. There’s also insurance for senators working in unison. Each could mount the argument that they were with the president throughout the trial but that the mountain of evidence they reviewed was simply persuasive and, in the end, each voted his or her conscience. No doubt many members of the party would be upset with the result, but we’re talking about a portion of the base, which is a plurality, not a majority, of the American people.
With that logic, the GOP could affect its jailbreak and reclaim the party from extremists on the right. Some GOP senators could still lose their re-election fights, but the net effect would be to save the party, something more important. That’s why predicting a 98–2 outcome favoring removal is not out of the question though it will remain out of sight for the duration of the process.
Slowly and steadily, over the past decade, the industry has evolved from a mentality of “all salesreps must do everything” – including some percent of their time prospecting — to one of specialization. We, with the help of books like Predictable Revenue, have collectively decided that in-bound lead processing is different from outbound lead prospecting is different from low-end, velocity sales is different from high-end, enterprise sales.
Despite the old-school, almost-character-building emphasis on prospecting, we have collectively realized that having our top hunters dialing for dollars and digging through inbound leads isn’t, well, the best use of their time.
Industrialization typically involves specialization and the industrialization of once purely artisanal software sales has been no exception. As part of this specialization the sales development representative (SDR) role has risen to prominence. In this post, we’ll do a quick review of what
SDRs typically do and discuss the relative merits of having them report into sales vs. marketing.
“Everyone under 25 in San Francisco is an SDR.” – Anonymous startup CEO
SDRs Bridge the Two Departments
SDRs typically form the bridge between sales and marketing. A typical SDR job is take inbound leads from marketing, perform some basic BANT-style [1] qualification on them, and then pass them to sales if indicated. While SDRs typically have activity quotas
(e.g., 50 calls/day) they should be primarily measured on the number of opportunities they create per week. In enterprise software, typically that quota is 2-3 oppties/week.
As companies get bigger they tend to separate SDRs into two groups:
Inbound SDRs, those who only process in-bound leads, and
Outbound SDRs, those who primarily do targeted outreach over the phone or email
Being an SDR is a hard job. Typical SDR challenges include:
Adhering to service-level agreements for all leads (i.e., touches with timeframes)
Contacting prospects in an increasingly spam-hostile, call-hostile environment
Figuring out which leads to work on the hardest (e.g., which merit homework to customize the message and which don’t)
Remembering that their job is to sell meetings and not product [2]
Supporting multiple salespeople with often conflicting priorities [3]
Managing the conflict between supporting salespeople and executing the process
Getting salespeople to show-up at the hand-off meeting [4]
Avoiding burnout in a high-pressure environment
To Which Department Should SDRs Report: Sales or Marketing?
Historically, SDRs reported to sales. That’s probably because sales first decided to fund SDR teams as a way getting inbound lead management out of the hands of salespeople [5]. Doing so would:
Enable the company to consistently respond in a
timely manner to all inquiries
Free up sales to spend more time on selling
Avoid the problem of individual reps not
processing new leads once they are “full up” on opportunities [6]
The problem is that most enterprise software sales VPs are not particularly process-oriented [7], because they grew up in a pre-industrialized era of sales [8]. In fact, nothing drives me crazier than an old-school, artisanal, deal-person CRO insisting on owning the SDR organization despite the total inability to manage it. They rationalize: “Oh, I can hire someone process-oriented to manage it.” And I think: “but what can that person learn from you [9] about how to manage it?” And the answer is nothing. Your desire to own it is either pure ego or simply a ploy to enrich your resume.
I’ll say again because it drives me crazy: do not be the VP of Sales who insists on owning the SDR organization in the annual planning meeting but then shows zero interest in it for the rest of the year. You’re not helping anyone!
As mentioned in a footnote in a prior post, I greatly prefer SDRs reporting to marketing versus sales. Why?
Marketing leadgen and nurture people are metrics- and process-oriented animals, naturally suited to manage a process-oriented department.
It provides a simple, clear conceptual model: marketing is the opportunity creation factory and sales is the opportunity closing machine.
In short, marketing’s job is to make opportunities. Sales’ job is to close them.
# # #
Notes
[1] BANT = budget, authority, need, time-frame.
[2] Most early- and mid-stage startups put SDRs in their regular sales training sessions which I think does them a disservice. Normal sales training is about selling products/solutions. SDRs “sell” meetings. They should not attempt to build business value or differentiation. Training them to do so tempts them to do – even when it is not their job.
[3] A typical QCR:SDR ratio is 3-4:1, though I’ve seen as
low as 1:1 and as high as 6:1
[4] Believe it or not, this sometimes happens (typically when your reps are already carrying a lot of oppties). Few things reflect worse on the company than a last-minute rescheduling of the meet-your-salesperson call. You don’t get a second chance to make a firm impression.
[5] Although most early models had wide bypass rules – e.g., “leads with VP title at this list of key accounts will get passed directly to reps for qualification” – reflecting a lack of trust in marketing beyond dropping leaflets from airplanes.
[6] That problem could still exist at hand-off (i.e., opportunity creation) time but at least we have combed through the leads to find the good ones, and reports can easily identify overloaded reps.
[7] While they may be process-oriented when it comes to the sales process for a deal moving across stages during a quarter, that is not quite the same thing as a velocity mentality driven by daily or weekly goals with tracking metrics. If you will, there’s process-oriented and Process-Oriented.
[8] One simple test:
if your sales org doesn’t have monthly cadence (e.g., goals, forecasts)
then your sales VP is probably not capital P process-oriented.
[9] On the theory you should always build organizations where people can learn from their managers.
