Just a quick post to highlight that Exit Five has published the audio from my presentation at their recent leadership retreat.
The presentation was entitled How To Be the CMO Everyone Wants To Work With, and the audio is about an hour long. Exit Five founder Dave Gerhardt published it as Episode 342 of The Dave Gerhardt Show.
Thanks to those who attended and thanks to Dave for inviting me to speak at the event.
The concept of an ideal customer profile (ICP) has been around for a long time, but like its cousin, the minimum viable product (MVP), it is often misunderstood. In this post, I’ll offer some background commentary on the ICP concept and then build into one of my favorite sayings: your ICP starts out as an aspiration and becomes a regression.
There are four common questions around ICPs. Here they are, along with my answers:
Because the world is imperfect and it’s difficult to find “Mr. or Ms. Right” every time, it’s useful to think of the ICP as a bullseye. The absolute perfect customer is in ring 0, the next level off in ring 1, after that ring 2, et cetera. Note that I have no religion about the things you vary across the rings, but the usual candidates are: job title, industry, size, adjacent systems, and problem (aka use-case). And you might do them in unusual combinations. For example, if you think a director of finance with a budgeting problem is about as good as a manager of finance at a bigger company with an operational reporting problem, then you can put them both in ring 2.
The idea is to give you a simple and flexible model to agree on who to target and who to prioritize across sales, marketing, and product.
For a zero-to-one startup, you might focus exclusively on ring 0. As you grow you will typically get more use-cases, more industries, more adjacent systems, and thus more rings. That’s fine as long as you’re defining the rings clearly and triaging them into: hot pursue, pursue, and slow-roll or some similar encoding system.
With a few clearly established tiers you are now ready to report on ARR and pipeline by ICP tier to see if “you’re walking the talk” when it comes to your ICP. At many companies, you will find the majority of the ARR and pipeline [2] outside ring 2 or 3. In these cases, you simply aren’t living your ICP and instead suffering from a faux focus. The usual cause is an inability to control the sales force and prevent their default “chase anything” behavior [3].
The ICP is typically born in the founder’s head as an hypothesis. Think: I bet if we can build something like this, it will solve a problem like that. By the time a company has been founded and a product built, it becomes an aspiration. Think: I want to sell to people like this to solve a problem like that. So you sharpen your definitions of this and that, and add some additional targeting criteria like company size, industry, or adjacent systems. And then you go off and sell.
Let’s say it works. One day you look up and you’re now $50M or $100M in ARR. Congratulations. Should your ICP still be an intuition-driven aspiration? No. It should be a regression. Reality happened. Let’s find out what reality is telling us.
Are the people in our ICP ring 0 really our best customers?
Well, what do we mean by best? Do they have higher ASPs? Do they have shorter sales cycles? Do they renew at higher rates? Do they expand at higher rates (e.g., NRR)? Do we win new deals at higher rates? Do they give us higher CSAT scores?
At the first order, these are all just simple segmented metrics calculations that you can and should do. Your QBR and board decks should show these key metrics segmented by ICP tier [4]. And — since not all these metrics can be important — your e-team also needs to have the conversation about “what do we mean by best” so you can have a common, precise definition of the “best” customers that you are trying to target [5].
But the best answers to these questions are not performed using segment analysis [6]. Segment analysis is great for finding anomalies — e.g., why do we have a higher win rate in ICP tier 3 than tier 1? But it’s not a great technique for actually finding the impact of different variables on the success criteria.
For that, we need regression analysis. Regression analysis will tell us which variables most strongly correlate with the outcomes we want (e.g., that the strongest predictor of renewal is company size, not CSAT) [7]. A good regression analysis will tell you not only which factors most correlate with the outcome, but it can also be the best way to bucket those variables (e.g., the real breakpoint is at 250 employees, but your initial segment went from 0 to 500).
Odds are, when we do this kind of analysis we’ll find lots of surprises. Some of your intuition will be proven correct, but some won’t. And you’ll likely find entirely new variables (e.g., number of data scientists) that you didn’t even consider in your initial ICP exercises.
So this is why I like to say that your ICP starts as an aspiration — about who you want to sell to — and over time becomes a regression. Because one day you will have lots of data to analyze to determine who your best customers are — subject to your definition of best, of course — as opposed to who you thought they would be.
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Notes
[1] Regardless of where you land at least be aware there are two types of criteria: those that change slowly or not at all (e.g., company size, adjacent systems, industry) and those that can change overnight (e.g., out shopping, budget, authority). My analogy here is dating: you can meet the right person at the wrong time. It doesn’t change the fact that they’re the right person. (And that’s why God made nurture tracks.)
[2] Think of pipeline as a potential leading indicator of ARR. Well, it should be, at least.
[3] Using the ICP in territory and compensation plan definitions can help with that. Think: you only earn commissions on customers in ICP rings 1 through 3 within your geographic territory. That will get your sellers’ attention.
[4] Note that I’m kind of using ICP tier and ring synonomously here and that’s generally OK. However, in cases where you have lots of rings, I would then sort those rings into tiers, so ring is the more specific and tier the more general term. For me, because I like simplicity, I want to see ICP segmentation in at most 3-4 buckets, so if there are N rings underlying those, I’d prefer to hide those by using 3-4 tiers.
[5] You probably don’t want marketing targeting high LTV prospects when sales wants to target high win rate ones. We should all be on the same targeting page.
[6] One of the key problems being that the segments themselves were somewhat arbitrarily chosen. Sure, we did our best to guess who’d be our best customers. But who are they actually? We may have used not only the wrong bucket boundaries (e.g., 100 emps vs. 500 emps) but even the wrong dimensions (e.g., maybe company size is a poor predictor and industry or use-case a powerful one).
[7] I cheated here on purpose to see if you were paying attention. Thus far, we’ve largely said the ICP is about a company (firmographics) and a role/persona. But here I’ve said that company size is a better predictor or renewal than CSAT — and CSAT isn’t a ICP-style criteria. The reality is these tools can do precisely that, looking across a wide range of input variables to see which most influence the output. Obviously, for marketing targeting purposes we don’t want CSAT to be an input variable to the model, but for renewals analysis we sure would.
Posted in Customer Success, Data Science, Entrepreneurship, Marketing, Sales, Scaling, Startups
Tagged ICP
I remember how silly the board looked that day. Reviewing the slide with performance by salesrep, an alpha board member impatiently shouted the seemingly obvious conclusion: “Look, only one rep — Joe Schmedley — is performing. Everyone else is struggling. The solution to our problem is quite simple: we need more Schmedleys.”
I had to kick about three executives under the table to prevent them from bursting into uproarious laughter. The last thing we needed, the team knew, was more Schmedleys.
Joe Schmedley wasn’t a bad guy. A competent, if somewhat bumbling, enterprise rep. Affable. Could talk about local sports teams. Not a bad guy to have a beer with. Not a particularly good guy at driving paperwork through procurement. Average to a skooch above in most respects. The last thing we needed to do was define our hiring profile by his background and tell a recruiter to go find ten more of them.
So what was driving this false signal in the data? The numbers don’t lie, right? And by the numbers, Schmedley was crushing it. Consistently our top rep.
What’s going on here?
What if I told you:
Like some successful salesreps, Schmedley had found himself in the right place at the right time. We knew it. Heck, he wasn’t arrogant, I think he knew it. The only people who didn’t know it were the board, who was actively telling us to hire ten more Schmedleys, making him the archetype for all future sales hires.
The problem was, of course, that the big customer was effectively a “house account,” and Schmedley merely a steward. We were in the midst of the transition from founder-led sales to sales-led sales and the board was confusing the account’s success with Schmedley’s.
What can you do to avoid this problem?
My marketing professor once said, The answer to every marketing question is, “It depends.” Thus, the important part is knowing on what.
So, how do you calculate the cost/opportunity? Well, it depends! On what? On the specific question you’re trying to answer. When people ask about cost/opportunity, they usually have one of two things in mind:
Knowing which question you’re being asked has a big impact on how to calculate the answer. Let’s illustrate this by looking at this typical marketing budget, which is allocated roughly 45/45/10 across people, programs, and technology:
If this marketing team generated 1,000 oppties, then the average total marketing cost/oppty is $9,000 = $9M/1K oppties. You might argue that’s a good overall marketing efficiency metric and try to benchmark it. But those benchmarks will be hard to find.
Why?
Because there’s a better overall marketing efficiency metric: the marketing customer acquisition cost (CAC) ratio = (last-quarter marketing expense)/(this-quarter new ARR). Why is the marketing CAC a better marketing efficiency metric than average total marketing cost/oppty?
So if someone’s asking you about marketing efficiency by looking at average total marketing cost/oppty, I’d politely redirect them to the marketing CAC ratio.
But say they’re looking at cost. Specifically, that the company is forecasting a pipeline generation shortfall of about 50 oppties and the CEO asks marketing: How much money will it take for you to generate 50 more?
Is $9,000 * 50 = $450,000 even correct?
The answer is no. To get 50 more oppties, you don’t need to hire 5% more marketers, boost the CMO’s salary by 5%, up the PR agency retainer by 5%, increase the userconf budget by 5%, spend 5% more on billboards, or increase tech infra spending by 5%. Thus, you should not multiply the average total marketing cost of an oppty by the number of oppties. You should multiply the incremental cost of an oppty by 50.
And the best answer we have here, at our fingertips, for the incremental cost of an oppty is the average demandgen programs cost/oppty. In our example, that’s $3,250. So, to generate 50 more oppties would cost $162,500. That’s good news because it’s a whole lot less than $450,000 and because it’s correct.
In short, cost/oppty = total demandgen cost / number of oppties.
This begs a potential rathole question which I call the low-hanging fruit problem. Most demandgen marketers argue that picking oppties out of the market is like picking apples out of a tree. First, you pick the easy ones, which doesn’t cost much. But the more apples you need, the higher up the tree you have to go. That is, the cost of picking the 1,000th apple is a lot higher than the cost of picking the first one. That is, the average cost of picking 1,000 apples is less than the incremental cost of getting one more.
While I think there’s some truth to this argument — and a lot of truth when it comes to paid search — you can’t let yourself slide into an analytical rathole. As CMO, a key part of your job is to always know the incremental cost of generating 50 more opportunities. Because — as veteran CMOs know well — either or both of these things happen with some frequency:
That’s what you might call “agile marketing.” And you get agile by doing the math in advance and having the incremental spending plan in your pocket, waiting for the day when someone asks.
To make things easy, unless and until you have a spending plan that answers the cost of getting 50 more oppties, just use your average demandgen cost/oppty and uplift it by 25% to adjust for the low-hanging fruit problem. That way you can answer the boss quickly and you’ve left yourself some room.
Let’s close this out by raising a common objection to using demandgen costs only. It sounds something like this:
If I use demandgen cost only, someone might say that I’m understating the true cost of a marketing-generated opportunity and I’m going to get in trouble.
Well, that certainly can happen. People can accuse you of anything. There are two ways to avoid this.
And finally, remind them if they want to discuss overall marketing efficiency, they should change slides and look at the marketing CAC ratio, which does proudly include every penny of marketing expense. And if you’re really, really good, ask them to skip to the slide that shows the sales/marketing expense ratio and discuss that.
Posted in Demandgen, Marketing, Metrics, SaaS, Scaling, Startups
Tagged Agile Marketing, Cost/Opportunity, Marketing productivity
Why is it that when we want to add an account executive (AE) to the plan, we always think about some ratios but not others? For example, most people think: Boom! Then we’re going to need:
With the chosen ratios varying as a function of your sales model. If we’re good, we might even include:
If we’re really good, and we have a large organization, we’ll also get the next layers of sales, SC, and SDR management.
But what’s the one thing that almost never comes up in these support ratio discussions? Demand generation (aka demandgen). Money for marketing to build pipeline for the incremental AE.
When we don’t treat demandgen as a ratio-driven, support resource, we get what I call the baby robin problem.
We throw our model out of whack by hiring more sellers than planned and thus everyone ends up with insufficient pipeline. The sellers turn into baby robins, mouths extended upwards, waiting for someone — e.g., SDRs, marketing, alliances — to drop opportunities in.
How can we avoid the baby robin problem? By treating demandgen budget as you would any other sales support resource. We instinctively think about SDRs and SCs (even if we don’t always go hire them). But we don’t do the same for demandgen. So part of this is self-discipline. The other part is math.
Let’s assume steady state, so we can ignore timing and ramping:
That’s $240K/year. A lot more than 1/3rd of an SDR and 1/6th of a manager. Yet, we routinely model these lesser costs and forget the demandgen.
Why? Silos.
It’s a different budget. Oh, that’s marketing. But it’s sales that’s asking for incremental money to hire the seller. The marketing budget is someone else’s problem. Until you repeat this 5-10 times and now every seller is starving for pipeline. Then it’s everyone’s problem.
So how can you avoid this? I’ll say it a few different ways, so you can take your pick:
I’m Dave Kellogg, advisor, director, blogger, and podcaster. I am an EIR at Balderton Capital and principal of my own eponymous consulting business.
I bring an uncommon perspective to enterprise software, having more than ten years’ experience in each of the CEO, CMO, and independent director roles in companies from zero to over $1B in revenues.
From 2012 to 2018, I was CEO of Host Analytics, where we quintupled ARR while halving customer acquisition costs, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of the $500M Service Cloud business at Salesforce; CEO of MarkLogic, which we grew from zero to $80M over six years; and CMO at Business Objects for nearly a decade as we grew from $30M to over $1B in revenues.
I love disruptive startups and and have had the pleasure of working in varied capacities with companies including Bluecore, FloQast, Gainsight, Hex, Logikcull, MongoDB, Pigment, Recorded Future, Tableau, and Unaric.
I currently serve on the boards of Cyber Guru, Light, Scoro, TechWolf, and Vic.ai. I have previously served on boards including Alation, Aster Data, Granular, Nuxeo, Profisee, and SMA Technologies.
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