But if you’re the CEO of a small to medium-sized tech startup, your true objective is to minimize your time to future revenue – and last quarter’s metrics alone aren’t going to get you there. Sure, historical performance can offer lessons in how to optimize going forward – but, for the most part, the past is the past, especially if the underlying data is insufficient or of low quality. The key to faster and greater revenue lies not in recapping last quarter but in accurately forecasting achievable performance for the rest of the year. That’s what the Board wants. That’s how you identify the resources necessary to hit your number. That’s how you win.

And what tools do today’s CEOs use for their forward-looking objectives? Little more than homegrown spreadsheets full of numbers, distributed across various computers in their companies. Between the CFO’s company model, the CRO’s bottom-up forecast or quota capacity model, the CMO’s marketing metrics, Revenue Operations’ pipeline calculations, and the CEO’s own model, there often are five or more spreadsheets … that don’t talk to each other and that often are based on different assumptions that can’t model the complexities of a company’s growth. And trying to cascade a coordinated set of changes through these disconnected spreadsheets takes days or weeks to ensure everyone is in sync – if ever.

It’s long past time to shift from backward-facing marketing analytics tools or simple, disconnected spreadsheets to forward-looking “Growth ArchitectingTM”. This means investing in planning tools and approaches built to produce reliable company growth plans, enabling CEOs and CROs to actually succeed in their jobs without having to report to the board at the end of the quarter being on the defensive (for a deeper analysis of these risks, see our recent blog, “Are you missing your number or is someone over-forecasting?”). By making these shifts, C-level leaders can accomplish two things:

• Faster time to revenue – They’ll have the data-driven plan they need to start producing revenue sooner.

• A steeper ramp – Not only will the money come in faster, there will be more and more of it as time goes on.

Legacy tools aren’t actually that bad…are they?

We get it: Change doesn’t always come easy in business – especially when you’re locked into a subscription deal with a SaaS vendor for the next seven months. But the longer CEOs put their forecasts in the hands of legacy tools that haven’t changed with the times, the more they – not to mention their investors – are going to be disappointed. 

Here’s where existing tools fall short.

Marketing analytics

Again, marketing analytics tools are great for recapping the past (if the date they contain is accurate and timely). But the only forward-looking value-add they provide is trial and error – if your data shows that you fell short last quarter, for example, then you know to try something different next time. 

By the time you can process these trial-and-error insights and turn them into an improvement plan, however, it’s too late to actually yield any benefits in a preventive manner – next quarter is already off to the races. No tech CEO with a hungry board of investors has time for that. 

Homegrown spreadsheets

What can tactfully be said about attempts to use spreadsheets, a product category first released in the 1980s, to make accurate sales forecasts in 2022? Homegrown, disconnected spreadsheets lack the functionality to seamlessly optimize for different budgets and other key variables. They don’t exactly make it easy to map out detailed, upstream KPIs. Owing to these factors and others, they’re prone to overprediction, meaning more missed goals, more uncomfortable conversations, and more lost jobs. 

Faster and steeper revenue production with Growth Architecting

The core difference between Growth Architecting technology and legacy systems comes down to this: Growth Architecting tools are actually designed for accurately planning growth within a modern annual-recurring-revenue business model. These tools are forward-looking rather than backward-looking, and they don’t require you to try to bend a simple – and, let’s be honest, ancient – spreadsheet tool to your specific, high-stakes needs. They’re built upon the reality that tech executives need to be able to define lead flows, account for key variables, and make data-supported adjustments on the fly if they hope to set a realistic revenue goal and actually hit it. 

Shifting to Growth Architecting means investing in tools and resources to level-up your revenue planning – and your successful revenue attainment. It means accelerating your time to revenue through data-driven decision making, clearer direction, and faster buy-in from key stakeholders. It means steepening the upward curve of your closed-won sales chart by optimizing your budgets, eliminating lead starving, and reducing waste. Once these pieces are in place, you’ll be in a position to stroll into your next board meeting not on the defensive, but with a confident smile on your face. 

Learn more about growth architecting in our related blogs.

The post Path to Faster Revenue Attainment – And Steeper Revenue Ramps appeared first on Premonio.

How did we get here? Why have one-year revenue forecasts proved as unreliable as one-week weather forecasts? And what can companies do to be more confident in their ability to hit their numbers?

When a sales team falls short of its quota goal, the common reflex is to assign the blame to the sales reps and their managers. But in our experience, the underlying problem just as often turns out to be overly optimistic, imprecisely formulated revenue growth forecasts.

With this in mind, it’s important for companies experiencing quota shortfalls to consider a fundamental question:

Is your team underperforming, or is someone over-forecasting?

This report will drill down into this question, diagnose some outdated norms that have led to such high rates of quota shortfalls, and show how teams can create a roadmap to consistent alignment between forecasted revenue and actual revenue using a process we call Growth Architecting^TM. This will empower teams to:

• Improve relations among C-Suite leaders, especially sales and marketing
• Keep the company in good standing with the board
• Validate their growth strategy without spending three or four quarters on trial and error – leading to accelerated time-to-revenue.

Underperforming vs. Over-Forecasting: A Case Study 

A good place to start exploring this issue is with a recent case involving an information security software company. When the company’s CEO announced the coming year’s forecast in Q4, the Revenue Operations teams took it at face value. The CRO mapped out a plan, and everyone was off to the races.

The Sales and Marketing teams were able to deliver expected growth numbers quarter after quarter – but they were one quarter delayed compared to the CEOs original Q4 forecast – and by the end of the year, they had fallen short of the CEO’s projection. Instead of rewarding the best quarter-to-quarter revenue growth the company had experienced in its history, despite the one quarter delay, finger-pointing ensued as the board looked for someone upon whom to hang the failure. In this case, that person ended up not the CEO but the Head of Sales. That VP lost their job – only to be replaced by someone far less competent, who led the team head-first into a series of losing years before being let go later, as well. When all was said and done, the company had gone through multiple executives, experienced turnover in the sales and marketing ranks due to a culture that had become unpleasantly political and failed to create any real value for the investors: Seven years later they were sold at a lower valuation than at the time when the CEO had made that original, fateful forecast.

As for the original VP of Sales and other key members of the Sales and Marketing team? They went on to have stellar careers, successfully scaling multiple startups. This suggests quite clearly that the problem isn’t always poor leadership or performance in the Sales and Marketing teams – more often than one might think, it can also be a case of unrealistic forecasting.

Why Forecasts and Actuals Get Disconnected

The case study above begs the question: What if the CEO had been able to see from the outset, using scientific and data-driven insights, that the projection was unrealistic – and understand the changes necessary to make it feasible?

Growth forecasting makes this possible – and it starts with understanding why over-forecasting occurs in the first place. As we survey common practices today, certain aging norms emerge as primary drivers of the problem:

1) Disconnect between decision makers and executors

The revenue figure that the CEO demands typically comes from a board of investors with one goal in mind: to achieve a corporate valuation that will yield large payouts. The growth pattern seen as the best way to achieve this goal might look something like this:

• Year 1: 3X revenue growth
• Year 2: 3X revenue growth
• Year 3: 2X revenue growth
• Year 4: 2X revenue growth

By Year 5, the common idea is to have created enough value to get acquired or go public.

The problem? These edicts delivered from on high are often completely divorced from realities on the ground, where CEOs or CROs, as well as other sales and marketing leaders, struggle to define a path to hitting their board’s revenue projections – or to determine whether such a path even exists with the resources at their disposal. Making these goals feasible requires an interplay between those making the revenue forecast and those responsible for hitting it, whereby leaders on the ground can either:

• Push back: Convey to the board, using data, what revenue attainment is actually feasible given the resources they have.
• Prescribe: Show what resources and practices will be necessary to hit the number that has been assigned.

Without the will or means to pursue a negotiation like this, CROs have little choice but to embrace a revenue goal that they may well lack the means to achieve, thereby setting up their CEO, their team, and themselves for a painful end of the year.

2) Failure to incorporate complexity

If hitting a projection were merely a matter of mapping out the number of leads and the conversion rate necessary for a team to hit their number, it would be easy. Rev-Ops leaders could estimate the number of reps they’d need, the CRO could toss some funding to the Marketing team to expand demand gen activities as needed, and everyone would watch the money come in.

Let’s look at an example of what such a calculation might look like. Say, the CEO forecast closed-won revenue for the year at $15 million – a not-atypical projection for a startup just beginning to stand on its own two feet. If the average deal size expected for the year – based on pricing and historical performance – were $30,000, then the Sales team would need to close 500 deals, either in new bookings or renewals. If we imagine they planned for a lead conversion rate of, say, 1 in 15 (6.7%), the team would need to generate 7,500 leads for the year.

• CEO annual forecast
  • $15 million
• Simple calculation
  • Average deal size: $30,000
  • Bookings needed: 500
  • Lead conversion rate: 1 in 15, or 6.7%
  • Leads needed: 7,500

In reality, however, things aren’t so simple. To truly understand what it would take to hit $15 million – or if that number is even feasible – the CEO and CRO would have to incorporate a lot more complexity. That simple calculation would be blown up by a plethora of variables, including, but not limited to:

• Conversion rates

A lot happens between “new lead” and “closed-won.” Leads at different stages of the sales cycle convert at different rates. How does the MQL-to-SQL conversion rate compare to SQL-to-SQO? What about SQO-to-closed-won? Conversion rates also vary by lead source, whether they belong to a cold call to an executive, an inbound demo request through the website, or something in between.

• Sales cycle

Selling a small deal to a startup flush with VC cash might take no more than two or three weeks. Selling half a million dollars to a Fortune 500 account, on the other hand, may take months of pitching, proposal writing, technical reviews, wining and dining, and negotiating. Depending on the underlying sales velocity, a deal that’s essential to hitting your revenue goal this year might not actually close until next year.

• Ramp times

If hitting a goal requires hiring new sales reps, accounting for variations in ramp time is paramount. Based on factors like industry experience and seniority, it typically takes between three and six months for a rep to be fully ramped. Hiring a rep today doesn’t necessarily mean their close rate will be tracking with a simple spreadsheet forecast by tomorrow.

Once all variables are accounted for, what was once a simple calculation might now include a lot of unknowns, and the resulting revenue growth looks something like this:

And drilling into first touch revenue contribution by lead source looks something like this:

The issue isn’t that CEOs, CROs, and sales leaders aren’t aware of all these factors. It’s that they have neither the time nor the tools or resources to incorporate them into their forecasting model and game plan, leaving them no choice but to rely on simple models – typically using ordinary spreadsheets that aren’t well-equipped or dynamic enough to handle the necessary complexity – and fall short as a result.

How to Use Growth Architecting to Make Things Right 

Aligning forecast revenue with actual revenue requires relying on an often incomplete or imperfect body of data. It requires using detailed calculations to either push back against an unfeasible revenue forecast or prescribe the changes necessary to make it feasible. It requires the flexibility to adapt when unexpected challenges emerge along your way to your goal. Growth architecting, at its core, is a methodology that incorporates all these measures.

Here are the key steps to successful growth architecting.

1) Define an integrated, detailed lead flow

As you map out your lead flow, consider the following questions:

• What lead sources will we need to tap to hit our goal, and that can be shown to target the precise segments or ICPs that we want to engage?
• What minimum lead conversion rates will we need to maintain, and how will they differ for each source?
• Given this information, and the resources at our disposal, how many leads will we need each source to deliver?
• What will the average deal size need to be?
• What additional budget and resources will we need to achieve all of this?

If, after answering these questions, the numbers look unfeasible, then either push back on the forecast or use the data to map out the necessary increases in, and reallocation of, resources and budget.

Keep in mind that when it comes to lead sources, there’s often an inverse relationship between volume and conversion rate. For example, an email campaign can pull in a healthy volume of content engagement-based leads – but few if any of them are likely to convert to opportunities; meanwhile, BDR prospecting or inbound web leads may pull in a much lower volume of leads, but at a higher conversion rate.

2) Account for key variables

Certain aspects of your calculations will involve a range rather than a fixed number. To keep forecast revenue aligned with actual revenue, teams need to account for variations in:

• Sales velocities

If your customer profile includes companies of all sizes, you’re likely to see a wide variety of sales velocities. A lead at a small company will probably close after a relatively short sales cycle – pulling in that lead near the end of the quarter or year is probably safe. But if your plan includes enterprise leads, you may need to either increase the volume, or create a mechanism for pulling them in before the end of Q3, to account for a sales cycle that often stretches for months at that level.

• Ramp time

Just as different leads take different lengths of time to convert, some sales reps ramp up more quickly than others. If and when you enter negotiations for more resources, keep in mind that getting new reps onto your payroll does not, by itself, get you to your goal. You have to make sure they’ll be ready to start closing early enough to produce the revenue you need from them.

3) Check in periodically

After you map out, in detail, what it will take to get to the revenue you need, it’s time to switch from planning to monitoring. Check in regularly to see how you’re tracking against your goals and determine what optimizations, if any, you need to make. We suggest starting at bi-weekly or monthly intervals, as this will allow for tidy recaps of the beginning, middle, and end of each quarter.

In some ways, this is nothing new. But while most teams today check in on their progress toward goal during their weekly or monthly syncs, these check-ins are often treated as informational, with little to no talk of optimization when the data reveals a shortfall. And quite often there is no line of sight between raw lead sources – say, search engine ads or BDR cold-calling – and their resulting contributions to closed-won bookings. This needs to change.

Beyond Revenue: The Fallout from Inaccurate Revenue Projections

Everyone has an interest in making sure forecasts and actual revenues line up. The board wants a return on their investment. The CEO, by committing to a number, is laying their reputation on the line, and therefore wants confirmation that that number is achievable. The CRO wants assurance that the available resources and budget are commensurate with the revenue forecast, so they’re not walking into a buzzsaw. The CMO, meanwhile, wants a clear roadmap for bringing in the necessary quantity and quality of leads, so that a miss can’t be pinned on their demand generation engine.

The true tragedy of missed forecasts is that even though all these leaders share the same goal, the reflexive instinct for self-preservation often leads to finger pointing and strained relations instead of a problem-solving attitude. This creates more harm than just lost dollars and cents:

• C-Suite relations can sour

Everyone in the C-Suite needs to be on the same side if the company is to succeed. Infighting will only stymie progress. Unfortunately, that’s exactly what often happens when a company misses its number and finger-pointing ensues.

• Careers can suffer

As the case study with the infosec company makes clear, a missed number and the resulting finger-pointing can have a serious impact not just on the health of the company, but on individual careers. People lose their jobs. What would otherwise be career-long, mutually beneficial professional relationships are strained and broken. Especially with the heads of sales, their careers end up having to take unexpected turns – even though the problem wasn’t them missing their number, but rather that their “number” came from an unrealistic, poorly-architected company financial forecast that didn’t take feasibility into account.

• The optics are unsavory

Just as bad as these events themselves is the fact that all of it is playing out in front of an audience. Leaders bickering as revenue falls short paints a picture of instability and incompetence, causing customers to lose faith – which, of course, further imperils future revenue generation. Never mind the loss of credibility and faith with the employees.

Conclusion

Growth architecting asks a lot of C-Suite leaders. It demands a level of meticulousness and detail in revenue forecasting that busy leaders have historically found impractical. It also demands a culture change, whereby leaders and their teams exchange finger-pointing for problem solving and a stronger sense of shared purpose, even in times of trouble.

This culture change comes from committing to a data-driven approach to pipeline management. In regular intervals – every two to four weeks is a good place to start – all revenue generating teams should convene to review what worked and what didn’t, what achieved the KPI goals and what fell short. It takes people a while to pursue their work this way, both dealing with the potential discomfort of having all one’s numbers out in the open and having to discuss other people’s ideas if their area happens to be the one not hitting KPIs.

But it also provides the tools to either push back on unrealistic forecasts or prescribe the means to make them realistic. Having a truly data-driven pipeline will make leaders more confident – in their revenue projections, in their negotiations with the board, and in the overall success of the company. Then they won’t have to consider whether their Rev-Ops team is underperforming, or the CEO is over-forecasting – because the answer will be neither.

The post Are you missing your number or is someone over-forecasting? appeared first on Premonio.

The problems? Few if any of these tools provide the kind of forward-looking insights that B2B startup leaders actually need today to drive growth quickly and thrive. 

Times are changing, and many C-level leaders are no longer looking to legacy martech solutions to carry them into the future. Even the well-known martech pundit Scott Brinker notes in a recent blog that “90% — that’s 9 out of 10 CMOs — are implicitly, if not explicitly, looking for better martech solutions” (scroll to his second graphic).

Keep reading as we explore the chasm between tomorrow’s C-level needs and today’s solutions and illustrate how this chasm should be driving the evolution of martech.

The Challenges: Too much marketing analytics, not enough “Growth Architecting” 

• An underserved market in martech
  One commonality among the vast majority of existing martech solutions is that they’re designed to serve operations specialists or mid-level marketing and sales managers. These professionals often have backward-facing goals: They aim to use marketing analytics to show how their specific activities were producing results. As such, their tech stack might help them attribute a certain source of leads retroactively or show where web traffic came from over the past X months.That’s all well and good – if you’re not tasked with creating an integrated, cross-company engine for growth. But if you’re a C-level leader, you’ll find that what these stacks don’t do is facilitate the kind of forward-looking, cross-functional “growth architecting” you’re looking for. C-level leaders don’t need software showing where the specific groups of leads came from last quarter – they need to figure out what they can promise the board in terms of pipeline and bookings for next quarter. That means precise and accurate growth forecasting, which implies an increased focus on top-down, forward-looking goal setting instead of bottom-up, backwards-looking analytics. Unfortunately, however, these issues are today hard to avoid in a martech landscape focused more on trying to measure growth than actually driving growth.
• Performance woes
  To make matters worse, to the extent that C-level leaders do rely on existing martech, the results are underwhelming. Ask any C-level leader how long it currently takes their Ops tech stacks to, for example, tell them the impact of Marketing lead sources on their bookings, and they’ll often tell you they’ve given up waiting and don’t trust the data. Never mind the costs associated with standing up a tech stack or a scalable ABM implementation.
• Current realities raise the stakes
  The C-suite has always been on the hook for growth commitments to the board, but current conditions have conspired to put even more pressure on leaders. For one thing, B2B companies today are increasingly questioning conventions pertaining to the org chart, resulting in marketing teams increasingly coming under the command of Chief Revenue Officers and other C-level acronyms that conspicuously lack a middle M. These leaders are new to the marketing game – and often lack experience in marketing operations and insight into historical lead performance. This makes it harder for them to know what to expect from the newly acquired marketing team under their command, which in turn makes it almost impossible for them to make accurate growth forecasts, amid multiplying variables, with nothing more than a spreadsheet and, in a startup’s case, limited staff. Hanging over all of this, meanwhile, is the ongoing COVID pandemic. For a year and a half, now, the virus has changed the business landscape, suddenly and repeatedly, in ways that are hard to foresee. Accurately forecasting B2B growth metrics amid all this uncertainty, as well as other volatile macro-economic conditions, requires the ability to quickly model and optimize different growth scenarios without having to wait for the analytics team to come back with their next forecast.

Looking ahead: The needed sea change in martech

The B2B martech world in 2021 is due for a pivot. C-level leaders need to be able to develop robust and dynamic forecasts that account for multiple growth scenarios; course-correct faster, when necessary; execute on target; and lead with clarity. As these leaders face ever more pressure to deliver accurate growth forecasts, and ever more challenges in doing so, it’s time for those leaders to demand a tech stack that facilitates their needs and goals, and that helps them navigate a business landscape in which variables change everywhere and visibility is low. 

Growth architecting from here on out will require an unprecedented level of sophistication, including:

• Understanding how much pipeline you need, when you’ll need it, and which sources you’ll get it from
• Optimally allocating budgets between and across those lead sources and across different growth scenarios
• Having a clear and measurable line of sight from raw leads all the way to closed deals
• Leveraging scenario modeling to plan for variations in sales velocity and other contingencies
• Creating forward-looking KPIs that can serve as an early warning system, to keep pipeline creation on track

This is what the martech of the future should look like, and what C-level leaders should be demanding of their martech partners in 2021. Anything less, simply put, will fail to meet the moment – and continue to leave the needs of C-level leaders in the lurch.

The post From Reactive Analytics to Proactive Growth: What B2B Martech Needs appeared first on Premonio.

Of course, being at that knee is associated with a marked increase in the public’s concern for everyone’s safety compared to a week or two ago. However, there continues to be little in the way of practical and fact-based guidance and information for marketing professionals to come up with concrete measures that probably should be undertaken now.

In last week’s blog (click here), I published a compendium of factual information sources. I suggested a) separating accurate reporting of the current public health crisis from media and political attention, and b) 8 practical planning steps for an organization. Many of those planning assumptions rest on the macroeconomic outlook, which in turn depends on an estimate of how much bigger the epidemic will become – or public perceptions of it as that also drives the economy.

How bad will it get?

In that vein of trying to publish rational, fact-based analysis, in the last section of this blog is my prediction of where we stand right now. It paints a milder picture. My review below suggests that the reproduction number (i.e., the number of new people infected by one infected person) in the US has already dropped from a high of 7 or 8 to now almost 2. If this trend continues, it would suggest we’ll be hitting the inflection point of when the epidemic’s growth will begin to decelerate reasonably soon.

One would thus hope for a moderate economic disruption, but don’t forget the economic effects of sensationalizing and fear. A lot of what companies do now depends on their expectations of how bad the economy will get. Depending on that assessment, companies then need to re-forecast Q2 and the rest of the year accordingly. So, the rest of this blog deals with some practical steps marketing and sales leaders can take to accomplish this re-forecasting quickly, as well as with an illustration of how an analyst could assess the epidemic’s likely ultimate size in the US.

A quick, practical approach to re-forecasting 2020 B2B bookings and pipeline

Here is a step-by-step process:

Marketing – Create a top-down booking and pipeline forecast:

1. Take your monthly or quarterly pipeline forecasts for 2020 and make percentage allocations to distribute them into four new, post-COVID-10 buckets:
   1. % of quarterly bookings with no change, i.e., they stay as is
   2. % price reduction needed, i.e., how much that portion of the pipeline is reduced
   3. Reduce closure rates by a %age, either by delaying lead and sales velocities to model later close dates or canceling deals altogether
2. Create high, medium, low estimates of the above, estimated % changes
   1. E.g., segment these four buckets by vertical and grow the forecasts for likely coronavirus gainers (e.g., healthcare, bidets, select tech sectors, etc.) and reduce the ones for losing verticals (e.g., hospitality, some manufacturing, travel, etc.)
3. That results in a new, top-down pipeline and bookings forecast for 2020

Sales – Create a matching, bottom-up forecast for at least the next two quarters:

1. Estimate which deals are at risk of delay, price reduction, and cancellation
2. Create four new columns next to the expected bookings for each deal, i.e., no change, price drop, delay, or cancel
3. Enter the deal amount into one of those columns.
4. And then add up the new totals for each of the four columns to create a unique, bottom-up forecast.

Compare and align bottom-up with top-down forecasts.

1. It is essential to calibrate both approaches against each other because the sales team may have an incentive to forecast low to create a safety cushion. While marketing has an incentive to forecast high to avoid the pressure of having to dramatically ramp up pipeline production to make up for anticipated reductions
2. After calibrating the two approaches against each other, it will be useful to make high, medium and low estimates of the resulting, combined forecasts to model different economic outlook assumptions (see above and bottom)

Plan and model compensatory measures to make up for likely forecast reductions:

1. Identify sales reps and territories that are most likely going to lose or delay sales and focus lead generation on them.
2. Shift marketing spend from events-based lead generation to digital sources (i.e., SEM, social media campaigns, email marketing) and teleselling
3. Ramp up content production, and in particular thought leadership content that is not self-serving in this crisis; people remember who helped them when times were tough

Rerun your forecast/revenue model and implement spend shifts

1. Model in all anticipated changes described above
2. Then layer in the budget shift to/from sales from/to marketing
3. And budget shifts within marketing (e.g., from events to digital)

Handoff to the finance department so they can rerun their company cashflow model and growth projections

As already said, the extent to which you’ll use an approach like the one outlined above to modify your forecast down (or up?) will depend on your assessment of the macroeconomic outlook. This, in turn, should be correlated with the extent of the ultimately resulting epidemic.

However, this raises an interesting dilemma: If you listen to some media coverage and watch stock markets plunge, the world economy may well crater, and we should all prepare for a recession. Which would suggest cutting your forecast to the bone (indeed, one of my pro-bono client’s head of sales just proposed a 90% cut; talk about cya). However, with that comes to the inevitable budget cuts or, worse, layoffs. Once those are executed, it will take months to turn the demand generation machine back on.

Now, what if the epidemic will be milder than it’s hyped up to be (as I’m suggesting in the following section based on China’s precedence and some virus propagation modeling)? If your marketing and sales budgets are cut to the bone, you’d be handing market share to your deeper-pocketed competition if they can hit the ground running faster than you can since they didn’t cut back as deeply.

So, the emotional knee-jerk reaction to the coronavirus of cutting way back risks being a massive over-reaction that would cost you market share. On the other hand, if the economy does indeed falter and you didn’t cut back budgets, you’ll risk making painful losses.

How you square that dilemma all depends on what you think the economic impact will be, and in the sea of contradictory, unclear, and sensationalized information, that’s hard to do. Hence, I’m concluding this post with a description of how I created my estimated outlook of the epidemic’s likely course in the US for the next one or two months as a way to calibrate and ground my thinking in all the noise.

The Estimated Epidemic Spread in the US

Please note that I am not an epidemiologist nor otherwise an expert of pandemics, and I am not a trained medical professional, nor am I trying to render an official prediction here. I am, however, illustrating a reasonably straightforward process a trained analyst could go through to form his/her own opinion, which is needed right now. Your management or board will want your advice on where this is all headed because they need to decide how to adjust the 2020 budgets and growth expectations. This blog aims at helping with the formulation of a well-reasoned response that can be replicated. Vs. Simply reacting from the gut or, worse, from a place of fear.

With that said, my estimation of the pandemic in the US suggests that the inferred reproduction rates underlying the daily infection statistics do not paint a doomsday scenario. I had used the same model with the Chinese infection statistics back in January and February, which predicted a stabilizing of their crisis by the end of March. This is indeed what has been happening there since late February.

Now, applying the same model to the current US data (as of March 15, 2020) suggests that the number of additional patients that a new patient infects has dropped from initially almost 8 to now near 2 (this number is called the “reproduction number” or R₀; for an explanation refer to this Wikipedia link). Once R₀ drops below 1, then more patients exit the infected pool than enter it and the epidemic will begin to die out. Here is the graph of my estimated R₀ for the US for the first 15 days in March:

In China, the similarly estimated R₀ had dropped below two by the end of January when about 8K to 10K patients were infected, and the epidemic peaked out a month later at 81K infected. I.e., an 8X to 10X multiple compared to the number of infected when R₀ had reached 2 for the first time.

Now, assuming that the voluntary quarantining in the US works as well as the government-imposed quarantine worked in China (in either method, the virus runs out of other people to infect), and assuming that similar social contact rates and discipline apply, this would suggest that the US’ total patient count would peak at between 30K and 40K infected (i.e., 8X to 10X over the 3,800 on March 15).

March 27, 2020 Update

Since we published the above estimate, it has become apparent that the number of undiagnosed cases was much more significant than had been published. With an acceleration of the growth of reported infections having additionally come from wider spread testing, not just the underlying spread of the epidemic. You can see the resulting update to the US’ reproduction rate estimate R₀ in the new graphic below.

There is good news and bad news: The good news is that R₀ is falling again in the US (of course, subject to the availability of accurate infection data), and is now again around 1.5. Still, an R₀ of 1.0 or below needs to be reached for the epidemic’s growth to be reversed. The bad news is that two weeks since the last estimate went by without consistently implementing the needed quarantining measures, and between the higher discovery rate due to wider spread testing now and the lack of social distancing (i.e. continued travel, the Miami beach episode, etc.), the virus continued its course mostly unperturbed.

Thus we now have over 100K infected, and assuming by now infection data is accurate and quarantining measures hold consistently (two big ifs, admittedly), and applying the above 8X multiple again would now suggest that the number of infected people in the US should peak out at around 800,000 people.

In these two weeks, the cost of inaction and indecision is these 760K people increase in the size of the ultimately infected population; that’s unfortunately how epidemics work. Second, it has made a damaging economic impact a self-fulfilling prophecy. Dealing with 40K infected and a roughly 4 to 6-week quarantine would have been one problem, coping with 800K+ infected, unprecedented layoffs, and economic contraction is now another. Funny what a difference two weeks make ?

Here is my most current R₀ estimate:

Now, this model’s purpose is to illustrate a simple estimation technique that a marketing forecaster can run to visualize R₀, a critical leading indicator that one should watch if one wants to have a sense of where the epidemic is trending. This model is not an official forecast.

However, if you are interested in a serious simulation of the epidemic, a MUST WATCH for anyone seeking to understand how COVID-19 spreads and what to do to minimize its impact is Grant Sanderson’s “3blue1brown” (deserves a huge Shout-Out). He has produced THE most coherent, yet easy to grasp video of how this epidemic grows and how to contain it. It’s 23 well narrated minutes and can be found here (link).

Important Caveats

1. As mentioned, this is not an official prediction; it is merely my modeling, which only has predicted well once thus far (China). I am not guaranteeing this outcome, and I instead want to illustrate a rational estimation process a business planner could go through.
2. Given the irrationality in the market right now as no one has a good feeling for how big the pandemic will get, the markets’ fears may well make a recession a self-fulfilling prophecy. I can’t model that.
3. And, most importantly, this more optimistic picture should not be taken as a suggestion to not self-quarantine; quite the opposite: The only way to keep R₀ low enough is precisely for everyone to stay home; probably for about the next four weeks if the Chinese experience is any guide (note that four weeks also is roughly the sum of the incubation period plus the probable outer limit of the time one is sick with coronavirus, i.e. it is the outer limit of the time an infected person can pass on the virus).
4. Last, this estimation is only as good as the officially available data. If the hidden number of infections in the US is much higher than what is being reported, the ultimate size of the epidemic would have to be adjusted upward accordingly, but not its principal behavior and timing if the self-quarantining holds.

So, what’s your choice: Hoist more sails, stay your current course, or turn into port?

The post Weathering the COVID-19 storm – How to Quickly Reforecast to not Get Blown Off Course appeared first on Premonio.

For one, a likely addition to the team needs to be business or revenue operations staffed with one or two, analytically minded folks that understand both, modern marketing techniques as well as sales psychology. They can build all the various models and / or implement some of the described monitoring tools.

Second, if the needed cultural transition to transparency in reporting, commitment to analysis of facts and data no matter what the conclusions, and an emphasis on accurate sales and marketing data entry is not supported or understood by upper management, then don’t even try. Ultimately the risk is they will find it an annoying slowdown at best or a threat to their authority, at worst. The crucially important point to make here is that there is cultural acceptance of scientific forecasting – as a complement to “gut feel” management (which also has its place). And that there is an environment where sales and marketing leaders are not punished for projecting pipeline or sales shortfalls. Fact-based and data driven business analysis is a company’s equivalent of a country’s free press. If they’re not allowed to flourish, find a new home if you’re one that does trust facts and data but doesn’t get listened to, or worse, is on the outside of the political currents driving the company because of it.

Another key group of stakeholders that need to be brought along typically is the board of directors. At one company, once we had built out a robust analytics and reporting functionality that was all based on real-time cloud applications like Marketo, Salesforce, InsightSquared, Salesloft, Bizible, and the like, we gave them login credentials. They loved it because if they wanted to see where the pipeline stood for the quarter, or what was coming in for next quarter at 3 am on a Sunday morning, they could see it. Real time. We had had the conversation about not then using that data to micro-manage us (we had seen it already at 2 am that Sunday morning :), that transparency did a lot to instill credibility in our forecasts.

Disagree with our theses? Please send us your comments.

In the meantime, for some further reading, here are links to some useful articles that further elaborate what investors are looking for beyond just growth but also around predictability:

• “Venture Capital Investors Like Revenue, But They Love Predictable Revenue”
• “The One Thing VCs Love to See Even More Than Growth”

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1. Different types of revenue forecasts:

In terms of forecasting techniques, we’ve often selected from three principal choices:

• Capacity vs. demand forecast
  • A common way to forecast, esp. for more than one quarter out, is to model the number of hired reps, with a hiring ramp if the model is more sophisticated, apply an average closure rate, and then use that for next year’s forecast. Technically that’s not a forecast, it’s a sales capacity model (i.e. “if we had X no. of reps and they closed $Y at closure rate Z%, then we should be making $XYZ next year”). If the pipeline velocity (how fast can they close deals?), the average selling prices (ASPs), and the average conversion and closure rates are reasonably accurate, a model like this can provide a sense of what’s possible to be sold next year.
  • A true demand forecast would be modeling the number of incoming leads, and then extrapolating from there to the eventual closed-won bookings, e.g. using a cohort model.
• On-off vs. weighted average forecasts:
  • Whether forecasting is done via spreadsheets (e.g. most sales leaders maintain a simple spreadsheet to help them track what’s out there, and also to help them sanity check what’s coming back from their business operations teams) or via applications like Salesforce or InsightSquared, an analytics overlay on top of Salesforce, there are two principal ways to calculate a forecast:
    • An “on-off method” where all possible deals are listed and their amounts, and the analyst “turns deals on or off”, i.e. totals the sum the “on” deals’ bookings. If, say, there are 90 deals in the pipeline, in this method the, say, 23 that are expected to close are added in their likely, total amounts, the resulting sum then is the forecast. As a rule of thumb, this is typically a more realistic approach for a limited number of high-priced deals.
    • Or a “weighted average method” where a likelihood of success in % is multiplied with each deal’s likely value, and the resulting multiplications are added up to a “weighted average total forecast” estimate. I.e. in this example, all 90 deals would be multiplied by their respective success probabilities and then those calculated, total expected values would all be added up together. This approach is typically better when there are many small, transactional deals.
    • This allows for some interesting comparisons: If, for example, sales management’s on-off method forecast is higher than the weighted average method calculated by a tool like, say, InsightSquared, then sales management is more optimistic about the close rate potential than the historical averages that InsightSquared uses to calculate the expected forecast. The reverse could be true, too. In any case, comparing the two methods’ forecasts can lead to interesting conversations to pressure test the assumptions behind the forecast.
  • The above two, principal techniques can be further refined by applying variations to the conversion and closure rate probabilities by sales stage, by lead source (e.g. inbound web leads or referrals typically close at higher rates than outbound, cold-call leads), by individual rep probabilities (different reps close at different rates), or by price bands (larger deals typically have lower closure rates than smaller deals).
• Gut vs. data:
  • As precise as modern forecasting methods based on tools like Salesforce, InsightSquared, or a sophisticated assembly of spreadsheets maintained by the revenue or business operations team are, they’re only as good as the data that goes into them. If, for example, the sales force does not maintain a good record of lead sources in Salesforce, then any correlations of closed-won rates with lead source types will be wrong. Or if the sales stages indicated for opportunities are overly optimistic or pessimistic, applying sales stage-based closure rate probabilities will result in biased forecasts.
  • To counteract those biases, it’s always helpful to get on the phone or send some validating emails to spot check individual opportunities and the accuracy of their data and descriptions. Prospects or reps, when spoken to in person and in private, often provide a level of insight of what’s going on in their marketplaces that the pure data can’t all convey.

2. Lengths of forecasting periods:

We talked about investors’ interest in predictable cashflows, and thus predictable revenue, and ideally, they’d like to have a rolling six quarter forecast that’s pretty accurate, and that also gets updated early enough if / when circumstances change. For example, a large deal gets pushed a year, macroeconomic conditions change, a major competitor is launching a predatory campaign against your company, or a new startup threatens your installed base. Forecasts typically get calculated for three time horizons, utilizing different approaches for each time horizon:

• In quarter forecasts:
  • Tools like Salesforce or InsightSquared (or others) handle this very well; and you can select from on-off or weighted-average options.
  • Or the oft mentioned, tried-and-true spreadsheets’ primary focus typically is on the ongoing quarter, as well.
• Two quarters out:
  • InsightSquared has several multi-quarter pipeline forecasting options that we have found to be quite accurate (assuming the data entered into the CRM system is accurate, of course).
  • If you do have a cohort model (i.e. can track how different lead source cohorts propagate through the pipeline over time), you can essentially reverse its calculational direction and estimate how much closed-won business you are likely to generate by extrapolating from incoming pipeline information in combination with known sales velocities and closure rates.
• Three to six quarters out:
  • For longer term forecasting, top down, market size models are an option. Top down refers to the process of starting with an overall market size (usually a “TAM” or total available market), and then breaking that down into smaller and smaller chunks that describe the “SAM” (i.e. the served available market). Breakdowns might be by major geography, by target customer size, by price point (i.e. enterprise deals vs. small, medium size business or SMB deals), or by vertical (say, finance and IT).
  • A second, longer term method is sales capacity modeling, hopefully layering in a hiring ramp, that estimates how much revenue could be booked by a sales force of a certain size by when. Marketing pipeline generating capacity typically gets estimated similarly but by calculating how much marketing spend would translate into how much $ pipeline.
  • A simple example illustrates this: If “marketing yield” (i.e. amount of pipeline generated per $1 of program spend) is $10, then a $1M marketing budget should generate $10M in pipeline. If in turn, sales reps, say, generate 33% of their bookings themselves and close 25% of all $ opportunities, then that $10M marketing pipeline should result in $2.5M in closed won from marketing sources, and another $1.25M from sales “self gen” (i.e. bookings they generated). The overall sales and marketing spend efficiency could then also be calculated once personnel costs are known.

Don’t turn that dial, we’ll be back with more …

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1. Overcoming common cultural challenges to scientific forecasting:

As more and more leads get generated from online behavior, which now can be tracked down to individuals’ browsing patterns, the amount of data that needs to be crunched to correctly attribute the origins of a pipeline is ever growing. And with it comes both, a greater burden on the analyst to be familiar with at least basic data collection and reduction techniques (e.g. multi-variate regression analysis, cohort analysis, basic probability theory, etc.; all available for study online), as well as a commitment to adapt and revise demand generation strategy based on what the data says.

Who hasn’t seen the classic battles raging over whether sales or marketing “sourced” a lead? As if that matters nowadays. It’s like trying to decide which shot won the basketball game. Did the prospect turn into a win because s/he read a white paper, came to the website, talked to an SDR, come to the booth, or followed the referral provided by a colleague? The answer is yes to all the above. The trick is to know which of these are the most efficient and success-prone channels, not who “sourced” them, so you know where to invest more and where less. To mitigate this issue, we now ask who “captured the lead”?

Which means there need to be forecasts across the entire pipeline – from customer success to marketing to sales. However, while marketing forecasts are in terms of leads, sales and customer success forecasts are in terms of $ pipeline or numbers of expected deals. For there to be a reliable model, there needs to be a good understanding of the analytics underlying a company’s lead lifecycle and specific pipeline conversion metrics.

Another, closely related issue is the need for management to support the conclusions of the data analysis, even if they head in a direction not aligned with prior thinking. A recent CEO we worked with was so convinced of the rectitude of his target market size instincts that any evidence to the contrary was being questioned to death, and the business analysts “punished” with oppressive data requests to justify their alternative conclusions. At some point his team just reported to him what he wanted to hear, and net new business pipeline generation continued to produce at 50% of the needed goal.

A variation on that theme can originate from the head of sales, where the implicit, old school message to the reps issuing their forecasts is that they better “make my number”, i.e. personally commit to a forecast. Vs. a more modern, analytics-driven approach to forecasting where they’re just reporting the state of their pipeline, say on a weekly basis, without pressure to generate a guaranteed outcome. Once SDRs and sales reps have begun to trust their management, they will report what’s in their pipelines so it can be made visible; and if need be, more pipeline generation initiatives can be launched. Vs. the old school approach where reps tend to wait with reporting what’s in their pipelines until a week or two before quarter end to not get shot over forecasted deals that didn’t pan out (see sidebar story below).

1. 1. :
   2. In-quarter corrective actions:

Guess with which of the above approaches is there more predictability and more of an opportunity to counter-steer early in the quarter if pipeline generation is below goal? The modern, analytics-enabled approach also allows the forecaster to normalize between different sales reps’ innate forecasting tendencies; some are optimists prone to over-forecasting while others are more cautious and prefer to “sandbag” their numbers. In the end it all settles out into whatever the closed-won number is, but during the quarter as one tries to predict the outcome, compensating for reps’ own forecasting tendencies increases the overall forecasting accuracy.

Knowing how to compensate for forecasting biases and have a sense, say 4 or 5 weeks into the quarter how it will turn out, allows sales and marketing management to counter-steer (see sidebar story). Here are some practical, rapid response options for in-quarter corrections:

1. 1. • Supporting sales pipeline building:
        • “Air strikes” – Reps can call in “air strikes”, i.e. help from upper management to help close iffy deals; e.g. the head of products or the CEO personally call a prospect to address their concerns. This can improve the “likelihood to close” of unsure deals.
        • “Local pipeline generation” – Marketing can utilize their demand generation infrastructure to drive leads to a local sales rep whose pipeline may be below goal. A call to action might be to attend a local event, and this allows driving in-territory brand awareness and introduce the local sales rep to prospects in his / her area.
      • Supporting marketing pipe generation:
        • Typically, this exercise for marketing revolves around which leads can be spooled up quickly? One example is “local pipeline generation” mentioned above. A challenge of in-quarter corrective action can be in terms of time: Some demand generation campaigns can take a few weeks to set up and be productive – so, unless there is visibility in the quarter, the in-quarter impact can be limited.
        • Another approach involves digging into the various pipelines and see which ones have rapid pipeline velocity (i.e. move to the next higher stage quicker than others). We’ve found that SEM campaigns can spool up quickly, as can additional nurturing emails around an interesting piece of content or a local event that prospects may be interested in. Also, improving the conversion and click-through rates of high-trafficked website pages is a relatively quick way of generating leads.

Don’t turn that dial, we’ll be back with more …

The post Scientific Forecasting – 2 of 4: How to’s appeared first on Premonio.

Well, here’s a common reality: While often significant amounts of time, energy and precision go into the accuracy of the expense forecast (aka “the budget”), on the revenue side the norm with many startups more often than not is to simply extrapolate growth with usually just a basic spreadsheet or “back of the envelope” calculations. Based on those “estimates”, executive teams often then commit growth plans to their boards (and with them EBIDTA and cashflow projections, profitability metrics, etc.) that ultimately not only drive department budgets, hiring plans, etc., but also their own compensation. All based on “rough estimates”. What could possibly go wrong, you say, right? Read the box below for one story of what did go wrong.

But before diving deeper into what’s at stake with good or bad forecasting, first a bit of theory: When you calculate a number (let’s call it P for Profit) by subtracting a highly precise number with a low uncertainty band (call it E for Expenses; say +/- 1%) from a highly imprecise number with a high uncertainty band (call it R for Revenue; say +/- 10%), the uncertainty band of the calculated number P is greater than the worst of the two uncertainties, i.e. the +/- 10%.

Here is an illustrative example: Say a start-up is expected to lose $2M a year on expected revenues of $25M, or -8%. Now, if the revenue uncertainty is +/-10%, then the year could end at $22.5M or at $27.5M. I.e. the expected profits might range from a loss of $4M to breaking even, depending on the how expenses scale with revenues. So, in this example, 10% revenue uncertainty could translate into a profitability range of -18% to breaking even. Now, how many investors would be willing to pay a premium for the resulting profit or cashflow prediction that has an almost +/- 100% uncertainty around it?

But instead of improving the precision of revenue forecasts, it’s too common to instead rely on adjusting expense budgets, typically quarterly and sometimes more often, to counteract the fluctuations in the actual revenues compared to their original, imprecise forecasts. The resulting rituals of quarterly expense adjustments, re-budgeting, vendor trimming, or even layoffs, etc. aim to ensure the expense lines track the undulating non-attainment of the revenue forecasts. The trouble is that while revenue can almost immediately go down, this common approach to managing the bottom line can cause problems because many expenses cannot be ramped down that fast – even if they would need to be adjusted after a revenue decline, it may not be possible due to existing expense commitments.

In companies not knowing how to precisely manage their forecasts, we’ve seen these expense budget re-adjustments literally consume 10% to 20% of affected departments’ time (often it’s the marketing department as they tend to have the highest non-personnel budgets). That’s wasted time with zero productivity gain (e.g. reduced pipeline from marketing), not to mention the resulting frustrations and morale hits. And to add insult to injury, all too often the hapless “expense victims” get blamed for having “runaway spending”, when the real culprits are companies’ inability to precisely forecast revenue.

Alright, I get why forecasting precisely is important, so what do I do about it?

Many organizations think they know how to forecast growth, but at the end of the day, their forecasts often really neither aren’t all that precise nor accurate. For one, many folks we talk to confuse “sales forecasts” with “forecasting sales”.

The former is what the head of sales puts together to manage, typically this quarter’s sales expectations. Which is different from the latter, which ideally is a rolling 2 to 6 quarter revenue and bookings forecast for the company that’s the basis of its financial projections and budget allocations, especially for marketing departments who have to have a longer planning horizon as some expense commitments get made a year out or more. As we’ve discussed, the latter, especially when done precisely, has a lot of upsides (investors pay a premium, more stable budgets) and avoids downsides (accurate and meaningful analytics, more stable organization, higher morale).

From years in the trenches and having made a living by pegging my own compensation to the accuracy of my forecasts, in the next three blogs we’ll publish tips and how-to’s on how to improve forecasting precision (and thus accuracy) by applying some basic common sense and scientific principles to forecasting.

Don’t turn that dial, we’ll be back with more …

The post Scientific Forecasting – 1 of 4: Why is it important? appeared first on Premonio.