Kellblog covers topics related to starting, managing, leading, and scaling enterprise software startups. My favorite topics include strategy, marketing, sales, SaaS metrics, and management. I also provide commentary on Silicon Valley, venture capital, and the business of software.
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.
In reality, your business may consist 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.
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  from OPEXEngine. These are all great sources and I recommend them heartily to anyone who can afford them.
But, in addition to sometimes being too high-level , 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 , 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  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 .
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  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 .
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 are we expecting to increase our ASP by $5K to $75K?
Why do you think we can improve the SQL-to-close rate from 18% to 20% — and what you are doing to drive that improvement? 
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 part II and part 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.
You can download my spreadsheet for this post, here.
 For marketing or virtually anything else.
 i.e., looking at either S&M aggregated or even marketing overall.
 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!
 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.
 Hint: if you’re not tracking these rates, the first good thing about this model is that it will force you to do so.
 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).
 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.
 Better closing tools, an ROI calculator, or a new sales training program could all be valid explanations for assuming an improved close rate.
Most startups today use some variation on the now fairly standard terms SAL (sales accepted lead) and SQL (sales qualified lead). Below see the classic  lead funnel model from marketing bellwether Sirius Decisions that defines this.
One great thing about working as an independent board member and consultant is that you get to work with lots of companies. In doing this, I’ve noticed that while virtually everyone uses the terminology SQL and SAL, that some people define SQL before SAL and others define SAL before SQL.
Why’s that? I think the terminology was poorly chosen and is confusing. After all, what sounds like it comes first: sales accepting a lead or sales qualifying a lead? A lot of folks would say, “well you need to accept it before you can qualify it.” But others would say “you need to qualify it before you can accept it.” And therein lies the problem.
The correct answer, as seen above, is that SAL comes before SQL. I have a simple way of remembering this: A comes before Q in the alphabet, and SAL comes before SQL in the funnel. Until I came up with that I was perpetually confused.
More importantly, I think I also have a way of explaining it. Start by remembering two things:
This model was defined at a time when sales development reps (SDRs) generally reported to sales, not marketing .
This model was defined from the point of view of marketing.
Thus, sales accepting the lead didn’t mean a quota-carrying rep (QCR) accepted the lead – it meant an SDR, who works in the sales department, accepted the lead. So it’s sales accepting the lead in the sense that the sales department accepted it. Think: we, marketing, passed it to sales.
After the SDR worked on the lead, if they decided to pass it to a QCR, the QCR would do an initial qualification call, and then the QCR would decide whether to accept it. So it’s a sales qualified lead, in the sense that a salesperson has qualified it and decided to accept it as an opportunity.
Think: accepted by an SDR, qualified by a salesrep.
Personally, I prefer avoid the semantic swamp and just say “stage 1 opportunity” and “stage 2 opportunity” in order to keep things simple and clear.
# # #
 This model has since been replaced with a newer demand unit waterfall model that nevertheless still uses the term SQL but seems to abandon SAL.
 I greatly prefer SDRs reporting to marketing for two
reasons: [a] unless you are running a
pure velocity sales model, your sales leadership is more likely to deal-people
than process-people – and running the SDRs is a process-oriented job and [b] it
eliminates a potential crack in the funnel by passing leads to sales “too early”. When SDRs report to marketing, you have a
clean conceptual model: marketing is the
opportunity creation factory and sales is the opportunity closing factory.
“Wait, hang on. How is that pipeline distributed by quarter? By stage? By forecast category? By salesrep? You can’t just look at it as a giant lump and declare that you’re in great shape because you have 3x the F4Q coverage. That’s lazy thinking. And, by the way, you probably don’t even need 3x the F4Q target, but you sure as hell need 3x this quarter’s coverage  and better be building to start next quarter with 3x as well. You do understand that sales can starve to death and we can go out of business – the whole time with 3x pipeline coverage — if it’s all pipeline that’s 3 and 4 quarters, out?”
I’ve got a crazy idea. How about as a first step, we stop looking at annual pipeline  and start looking at this-quarter pipeline and, most importantly, next-quarter pipeline?
What people tell me when I say this: “No, no, Dave. We can’t do that. That’s myopic. You need to look further out. You can’t drive looking at the hood ornament. Plus, with a 90-day average sales cycle (ASC) there’s nothing we can do anyway about the short term. You need to think big picture.”
I then imagine the CMO talking to the head of demandgen: “Yep, it’s week 1 and we only have 2.1x pipeline coverage. But with a 90-day sales cycle, there’s nothing we can do. Looks like we’re going to hit the iceberg. At least we made our 3x coverage OKR on a rolling basis. Hey, let’s go grab a flat white.”
I loathe this attitude for several reasons:
It’s parochial. The purpose of marketing OKRs is to enable sales to hit sales OKRs. Who cares if marketing hit its pipeline OKR but sales is nevertheless flying off a cliff? Marketing just had a poorly chosen OKR.
It’s defeatist. If “when the going gets tough, the tough get a flat white” is your motto, you shouldn’t work in startup marketing.
It’s wrong. The A in ASC stands for average. Your average sales cycle. It’s not your minimum sales cycle. If your average sales cycle is 90 days  then you have lots of deals that close faster than 90 days, so instead of getting a flat white marketing should be focused on finding a bunch of those, pronto .
Here’s my crazy idea. Never look at rolling F4Q pipeline again. It doesn’t matter. What you really need to do is start every quarter with 3.0x  pipeline. After all, if you started every quarter with 3.0x pipeline coverage wouldn’t that mean you are teed up for success every quarter? Instead of focusing on the long-term and hoping the short-term works out, let’s continually focus on the short-term and know the long-term will work out.
This brings to mind Kellogg’s fourth law of startups: you have to survive short-term in order to exist long-term.
This process starts by looking at the this-quarter (aka, current-quarter) pipeline. While it’s true that in many companies marketing will have a limited ability to impact the current-quarter pipeline — especially once you’re 5-6 weeks in — you should nevertheless always be looking at current-quarter pipeline and current-quarter pipeline coverage calculated on a to-go basis. You don’t need 3x the plan number every single week; you need 3x coverage of the to-go number to get to plan. To-go pipeline coverage provides an indicator of confidence in your forecast (think “just how lucky to do we have to get”) and over time the ratio can be used as an alternative forecasting mechanism .
In the above example, we can see a few interesting patterns.
We start the quarter with high coverage, but it quickly becomes clear that’s because the pipeline has not yet been cleaned up. Because salespeople are usually “animals that think in 90-day increments” , next quarter is effectively eternity from the point of view of most salesreps, so they tend to dump troubled deals in next-quarter  regardless of whether they actually have a next-quarter natural close date.
Between weeks 1 and 3, we see $2,250K of current-quarter pipeline vaporize as part of sales’ cleanup. Note that $250K was closed – the best way for dollars to exit the pipeline! I always do my snapshot pipeline analytics in week 3 to provide enough time for sales to clean up before trying to analyze the data. (And if it’s not clean by week 3, then you have a different conversation with sales .)
Going forward, we burn off more pipeline to fall into the 2.6 to 2.8 coverage range but from weeks 5 to 9 we are generally closing and burning off pipeline  at the same rate – hence the coverage ratio is running in a stable, if somewhat tight, range.
Let’s now look at next-quarter pipeline. While I think sales needs to be focused on this-quarter pipeline and closing it, marketing needs to be primarily focused on next-quarter pipeline and generating it. Let’s look at an example:
Now we can see that next-quarter plan is $3,250K and we start this quarter with $3,500K in next-quarter pipeline or 1.1x coverage. The 1.1x is nominally scary but do recall we have 12 weeks to generate more next-quarter pipeline before we want to start next quarter with 3x coverage, or a total pipeline of $9,750K. Once you start tracking this way and build some history, you’ll know what your company’s requirements are. In my experience, 1.5x next-quarter coverage in week 3 is tight but works .
The primary point here is that given:
Your knowledge of history and your pipeline coverage requirements
Your marketing plans for the current quarter
The trends you’re seeing in the data
Normal spillover patterns
That marketing should be able to forecast next quarter’s starting pipeline coverage. So, pipeline coverage isn’t just an iceberg that marketing thinks we’ll hit or miss. It’s something can marketing can forecast. And if you can forecast it, then you adjust your plans accordingly to do something about it.
Let’s stick with our example and make a forecast for next-quarter starting pipeline 
Note that we are generating about $250K of net next-quarter pipeline per week from weeks 4 to 9.
Assume that we are continuing at steady-state the programs generating that pipeline and ergo we can assume that over the next four weeks we’ll generate another $1M.
Assume we are doing a big webinar that we think will generate another $750K in next-quarter pipeline.
Assume that 35% of the surplus this-quarter pipeline slips to next-quarter 
If you do this in a spreadsheet, you get the following. Note that in this example we are forecasting a shortfall of $93K in starting next-quarter pipeline coverage. Were we forecasting a significant gap, we might divert marketing money into demand generation in order to close the gap.
Finally, let’s close with how I think about all-quarters pipeline.
While I don’t think it’s the primary pipeline metric, I do think it’s worth tracking for several reasons:
So you can see if pipeline is evaporating or sloshing. When a $1M forecast deal is lost, it comes out of both current-quarter and all-quarters pipeline. When it slips, however, current-quarter goes down by $1M but all-quarters stays the same. By looking at current-quarter, next-quarter, and all-quarters at the same time in a compact space you can get sense for what is happening overall to your pipeline. There’s nowhere to hide when you’re looking at all-quarters pipeline.
So you can get a sense for the size of opportunities in your pipeline. Note that if you create opportunities with a placeholder value then there’s not much purpose in doing this (which is just one reason why I don’t recommend creating opportunities with a placeholder value) .
So you can get a sense of your salesreps’ capacity. The very first number I look at when a company is missing its numbers is opportunities/rep. In my experience, a typical rep can handle 8-12 current-quarter and 15-20 all-quarters opportunities . If your reps are carrying only 5 opportunities each, I don’t know how they can make their numbers. If they’re carrying 50, I think either your definition of opportunity is wrong or you need to transfer some budget from marketing to sales and hire more reps.
The spreadsheet I used in this post is available for download here.
# # #
 Assuming you’re in the first few weeks of the quarter, for now.
 Which is usually done using forward four quarters.
 And ASC follows a normal distribution.
 Typically, they are smaller deals, or deals at smaller companies, or upsells to existing customers. But they’re out there.
 Or, whatever your favorite coverage ratio is. Debating that is not the point of this post.
 Once you build up some history you can use coverage ratios to predict sales as a way of triangulating on the forecast.
 As a former board member always told me — a quote that rivals “think of salespeople as single-celled organisms driven by their comp plan” in terms of pith.
 Or sometimes, fourth-quarter which is another popular pipeline dumping ground. (As is first-quarter next year for the truly crafty.)
 That is, one about how they are going to get their shit together and manage the pipeline better, the first piece of which is getting it clean by week 3, often best accomplished by one or more pipeline scrub meetings in weeks 1 and 2.
 Burning off takes one of three forms: closed/won, lost or no-decision, or slipping to a subsequent quarter. It’s only really “burned off” from the perspective of the current-quarter in the last case.
 This depends massively on your specific business (and sales cycle length) so you really need to build up your own history.
 Technically speaking, I’m making a forecast for day-1 pipeline, not week-3 pipeline. Once you get this down you can use any patterns you want to correct it for week 3, if desired. In reality, I’d rather uplift from week 3 to get day-1 so I can keep marketing focused on generating pipeline for day-1, even though I know a lot will be burned off before I snapshot my analytics in week 3.
 Surplus in the sense that it’s leftover after we use what we need to get to plan. Such surplus pipeline goes three places: lost/no-decision, next-quarter, or some future quarter. I often assume 1/3rd goes to each as a rule of thumb.
 As a matter of principle I don’t think an opportunity should have a value associated with it until a salesrep has socialized a price point with the customer. (Think: “you do know it cost about $150K per year to subscribe to this software, right?”) Perversely, some folks create opportunities in stage 1 with a placeholder value only to later exclude stage 1 opportunities in all pipeline analytics. Doing so gets the same result analytically but is an inferior sales process in my opinion.
 Once you’re looking at opportunities/rep, you need to not stop with the average but make a histogram. An 80-opportunity world where 10 reps have 8 opportunities each is a very different world from one where 2 reps have 30 opportunities each and the other 8 have an average of 2.5.
I can’t tell you the number of times, as we were tearing down our booth after having had an epic show, that we overheard the guy next door calling back to corporate saying that the show was a “total waste of time” and that the company shouldn’t do it again next year. Of course, he didn’t say that he:
Staffed the booth only during scheduled breaks and went into the hallway to take calls at other times.
Sat inside the booth, safely protected from conference attendees by a desk.
Spent most of his time looking down at his phone, even during the breaks when attendees were out and about.
Didn’t use his pass to attend a single session.
Measured the show solely by qualified leads for his territory, discounting company visibility and leads for other territories to zero.
Does this actually happen, you think? Absolutely.
All the time. (And it makes you think twice when you’re on the other end of that phone call – was the show bad or did we execute it poorly?)
I’m a huge believer in live events and an even bigger believer that you get back what you put into them. The difference between a great show and a bad show is often, in a word, execution. In this post, I’ll offer up 10 tips to ensure you get the best out of the conferences you attend.
Ten Ways to Get the Most out of Conferences and Tradeshows
1. Send the right people. Send folks who can answer questions at the audience’s level or one level above. Send folks who are impressive. Send folks who are either naturally extroverts or who can “game face” it for the duration of the show. Send folks who want to be there either because they’re true believers who want to evangelize the product or because they believe in karma . Send senior people (e.g., founders, C-level)  so they can both continue to refine the message and interact with potential customers discussing it.
2. Speak. Build your baseline credibility in the space by blogging and speaking at lesser conferences. Then, do your homework on the target event and what the organizers are looking for, and submit a great speaking proposal. Then push for it to be accepted. Once it’s accepted, study the audience hard and then give the speech of your life to ensure you get invited back next year. There’s nothing like being on the program (or possibly even a keynote) to build credibility for you and your company. And the best part is that speaking a conference is, unlike most everything else, free.
3. If you can afford a booth/stand, get one. Don’t get fancy here. Get the cheapest one and then push hard for good placement . While I included a picture of Slack’s Dreamforce booth, which is very fancy for most early-stage startup situations, imagine what Slack could have spent if they wanted to. For Slack, at Dreamforce, that’s a pretty barebones booth. (And that’s good — you’re going to get leads and engage with people in your market, not win a design competition.)
4. Stand in front of your booth, not in it. Expand like an alfresco restaurant onto the sidewalk in spring. This effectively doubles your booth space.
5. Think guerilla marketing. What can make the biggest impact at the lowest cost? I love stickers for this because a clever sticker can get attention and end up on the outside of someone’s laptop generating ongoing visibility. At Host Analytics, we had great success with many stickers, including this one, which finance people (our audience) simply loved .
While I love guerilla marketing, remember my definition: things that get maximum impact at minimum cost. Staging fake protests or flying airplanes with banners over the show may impress others in the industry, but they’re both expensive and I don’t think they impress customers who are primarily interested not in vendor politics, but in solving business problems.
6. Work the speakers. Don’t just work the booth (during and outside of scheduled breaks), go to sessions. Ask questions that highlight your issues (but not specifically your company). Talk to speakers after their sessions to tee-up a subsequent follow-up call. Talk to consultant speakers to try and build partnerships and/or fish to referrals. Perhaps try to convince the speakers to include parts of your message into their speech .
7. Avoid “Free Beer Here” Stunts. If you give away free beer in your booth you’ll get a huge list of leads from the show. However, this is dumb marketing because you not only buy free beer for lots of unqualified people but worse yet generate a giant haystack of leads that you need to dig through to find the qualified ones — so you end up paying twice for your mistake. While it’s tempting to want to leave the show with the most card swipes, always remember you’re there to generate visibility, have great conversations, and leave with the most qualified leads — not, not, not the longest list of names.
8. Host a Birds of a Feather (BoF). Many conferences use BoFs (or equivalents) as a way for people with common interests to meet informally. Set up via either an online or old-fashioned cork message board, anyone can organize a BoF by posting a note that says “Attention: All People Interested in Deploying Kubernetes at Large Scale — Let’s Meet in Room 27 at 3PM.” If your conference doesn’t have BoFs either ask the organizers to start them, or call a BoF anyway if they have any general messaging facility.
9. Everybody works. If you’re big enough to have an events person or contractor, make sure you define their role properly. They don’t just set up the booth and go back to their room all day. Everybody works. If your events person self-limits him/herself by saying “I don’t do content,” then I’d suggest finding another events person.
10. No whining. Whenever two anglers pass along a river and one says “how’s the fishing?” the universal response is “good.” Not so good that they’re going to ask where you’ve been fishing, and not so bad that they’re going to ask what you’ve been using. Just good. Be the same way with conferences. If asked, how it’s going, say “good.” Ban all discussion and/or whining about the conference until after the conference. If it’s not going well, whining about isn’t going to help. If it is going well, you should be out executing, not talking about how great the conference is. From curtain-up until curtain-down all you should care about is execution. Once the curtain’s down, then you can debrief — and do so more intelligently having complete information.
 In the sense that, “if I spend time developing leads that might land in other reps’ territories today, that what goes around comes around tomorrow.”
 In order to avoid title intimidation or questions about “why is your CEO working the booth” you can have a technical cofounder say “I’m one of the architects of the system” or your CEO say “I’m on the leadership team.”
 Build a relationship with the organizers. Do favors for them and help them if they need you. Politely ask if anyone has moved, upgraded, or canceled their space.
 Again note where execution matters — if the Host Analytics logo were much larger on the sticker, I doubt it would have been so successful. It’s the sticker’s payload, so the logo has to be there. Too small and it’s illegible, but too big and no one puts the sticker on their laptop because it feels like a vendor ad and not a clever sticker.
 Not in the sense of a free ad, but as genuine content. Imagine you work at Splunk back in the day and a speaker just gave a talk on using log files for debugging. Wouldn’t it be great if you could convince her next time to say, “and while there is clearly a lot of value in using log files for debugging, I should mention there is also a potential goldmine of information in log files for general analytics that basically no one is exploiting, and that certain startups, like Splunk, are starting to explore that new and exciting use case.”
This post builds on my prior post, Win Rates, Close Rates, and Milestone vs. Flow Analysis. In it, I will take the ideas in that post, expand on them a bit, and then apply them to difficult problem of ensuring you have enough marketing demand generation budget to hit your sales targets.
Let’s pretend it’s 4Q17 and that we need to model 2018 sales based solely on marketing-generated SALs (sales accepted leads). To do that, we need to decompose our close rate over time because knowing we eventually close 40% of SALs is less useful than knowing the typical timing in how they close over time.
In a perfect world, we’d have 6-8 cohorts, not two. The goal is to produce the last line, the average of the in-quarter, first-quarter, second-quarter, and so on close rates for a SAL.
Using these time-based average close rates, we can build a waterfall that takes historical, forecast (for the current quarter), and planned 2018 SALs and converts them into deals.
This analysis suggests that with the currently planned SALs you can support an ARR number of $16.35M. If sales needs more than that, you either need to assume an improvement in close rates or an increase in SAL generation.
Once you’ve established the required number of SALs, you can then back into a total demand-generation budget by knowing your cost/SAL, and then building out a marketing mix of programs (each with their own cost/SAL) that generates the requisite SALs at the targeted overall cost.
I’m Dave Kellogg, technology executive, investor, independent director, adviser, and blogger. I’m also a hiker, oenophile, and fly fisher.
From 2012 to 2018, I was CEO of cloud enterprise performance management vendor Host Analytics, where we quintupled ARR while halving customer acquisition costs in a highly competitive market, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of Service Cloud at Salesforce and CEO at NoSQL database provider MarkLogic. Before that, I was CMO at Business Objects for nearly a decade as we grew from $30M to over $1B. I started my career in technical and product marketing positions at Ingres and Versant.
I love disruption, startups, and Silicon Valley and have had the pleasure of working in varied capacities with companies including ClearedIn, FloQast, GainSight, Lecida, MongoDB, Recorded Future, Tableau and TopOPPs. I currently sit on the boards of Alation (data catalogs) and Nuxeo (content management) and previously sat on the boards of agtech leader Granular (acquired by DuPont for $300M) and big data leader Aster Data (acquired by Teradata for $325M).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).