Slides from Balderton Webinar on Aligning Product and GTM Using Customer Value Metrics

Posted on September 24, 2025 | 2 comments

Today Dan Teodosiu, Thor Mitchell, and I hosted a Balderton webinar entitled Aligning Product and Go-To-Market (GTM) Using Customer Value Metrics. We are all executives in residence (EIRs) at Balderton — Dan covers technology, Thor covers product, and I cover go-to-market — and, in a display of cross-functional walking-the-talk, we came together to present this session on alignment.

The session was based on an article Dan and I wrote, by the same title, which was published on the Balderton site last month and about which I wrote here. The purpose of this post is to share the slides from that webinar which are available here and embedded below.

Thank you to everyone who attended the session and who asked questions in advance or in the chat. I’m sorry that we didn’t have the time to answer each question, but if you drop one into the comments below, I’ll do my best to answer it here and/or ask Dan or Thor to weigh in as well. I’m not aware if Balderton is going to make a video of the session available, but if they do I’ll revise this post and put a link here.

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Posted in GTM, Management, Marketing, Metrics, People, Sales, Startups, Strategy

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Whence Will Come Tomorrow’s Sellers?

Posted on September 3, 2025 | 2 comments

To the extent that most sellers today started their careers as SDRs and to the extent that there is a strong trend to replace SDRs with AI agents (e.g., Piper from Qualified), I have a simple question: whence will come tomorrow’s sellers? [1]

It’s not news that this is a trend across all entry-level work, though I just found a new paper on the topic by three people at Stanford who examined ADP payroll data as the basis for their analysis: Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. And another one that analyzes resume and job posting data: Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data.

But in today’s post, we’re not going to look globally at the topic — no matter how interesting it is — but instead look specifically at just one question: if all the SDRs are AI agents, then where are we going to get sellers from?

I should also explain that I have a dog in the fight. My son Brian just graduated from NYU and started this summer as an SDR at Ramp. (If you’re a US-based company with 150+ employees and interested in spend management, please let me know and I can connect you.) I recommended that he take the job because it’s an amazing company, they have built an excellent sales machine (and the early-career learning on how to do things right is invaluable), and he definitely has both the raw material and the mettle to be successful in technology sales. But as I made the recommendation, I couldn’t help but wonder if he’d be in the final cohort of human SDRs.

My question actually has two parts, so let’s take them one at a time: (i) an assumption that SDRs will be replaced with AI agents, and (ii) the realization that doing so would seriously interrupt the sales career development pipeline.

Will All SDRs Be Agents?

I think the answer here is no, though I do think a good number of them will be. One easy division is inbound vs. outbound. Inbound SDRs primarily qualify and route people with intent (“hand raisers”) to sellers for a discovery and qualification meeting. Input: MQLs. Output: stage-1 opportunities. Outbound SDRs focus on some set of target accounts and work them via outreach sequences in order to get them to take a meeting. Input: contacts. Output: stage-1 opportunities. While they might also receive MQLs from their target accounts, they start higher in the funnel and are more responsible for developing interest in a meeting than someone who downloaded an asset, like it, and wants to speak to a seller.

I believe inbound SDRs provide less value than outbound SDRs and their job is more automatable. Ergo, I think inbound SDRs will be quickly replaced by AI or superannuated by targeted, hybrid inbound/outbound models (i.e., my job is to get into Citibank and I’ll take all the names, leads, and MQLs we have and leverage them to get meetings within the account).

I think outbound SDRs are here to stay. And Ramp, for what it’s worth, seems to agree. I know they’ve onboarded another cohort since Brian’s and they seem to believe that their SDR model works quite well for them. So if the old career path was inbound-SDR into outbound-SDR, I think the new one will start with a hybrid. You’re just an SDR and your job is to get meetings within some target. Sometimes you’ll have a lot of inbound interest to work with, sometimes you won’t.

The first-principles argument here is simple. When automated outreach sequences are table-stakes that every firm can easily do, the only way to break through the AI-generated and AI-automated noise will be via some combination of people/execution, message, and air support [2]. That’s why we’ll still need SDRs — and good ones — in the future.

Where Will We Find Tomorrow’s Sellers?

Since I believe there will be SDRs in the future, I think we’ll find our future sellers there. But in case that’s wrong, let’s examine where we might find them additionally or instead. I’m old enough to remember life before SDRs. So where did we find salesreps back then and where might we find them in the future?

  • Junior sales roles. You’d work your up from smaller companies to bigger ones and from managing smaller accounts to bigger ones. This should still work.
  • Sales training programs. Some companies were famous for their sales training programs, like Xerox or IBM. I’d differentiate those who emphasized entry-level sales training from those who hired sellers with some experience and who emphasized sales onboarding in a particular message or methodology (e.g., Salesforce, PTC). In the future, large companies who find themselves with a talent gap may need to create such programs, substituting Darwinian survival in the SDR ranks for a formal, and presumably demanding, training program. Once established, these companies will be targets for everyone else’s recruiting.
  • Sales consultants. A difficult path but those who survive the transition are often your best sellers. Everytime I hear an SC complain about salesrep compensation, I say the same thing: “quotas are available.” Go grab one and see how you do. (Or don’t and stop complaining,)
  • Customer success. I think this is an under-developed career path and hopefully, as CS gets more business-oriented and account-management-focused, that customer success will be more of a stepping stone into sales. Think: I developed my prospecting muscle as an SDR, I developed my closing and account management muscles as a CSM, and now I’m ready to be a salesrep.

As the SDR ranks shrink due to the pressure brought by AI, companies will have to be more creative about where they find their salespeople. Some will certainly walk up the SDR path. Others, the junior sales path. Some, the top sales training path. But I don’t believe there will be a shortage of sellers in the future. Just a shortage of good ones, as there is today.

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Notes

[1] Turns out that while both “whence” and “from whence” can be considered correct, technically standalone whence is still better in my humble opinion because whence means “from where” so “from whence” is, well, redundant.

[2] In the form of marketing, awareness, reputation, brand, etc.

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Posted in Career, Hiring, Sales, Startups

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Your ICP Starts as an Aspiration and Becomes a Regression

Posted on August 30, 2025 | 3 comments

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:

  • Is the ICP about a person or a firm? Both. It should include firmographic as well as role (or persona) information. Example: VPs of sales at technology companies between $500M and $2B in revenues. Here, we included the size and industry of the company along with the target buyer’s title.
  • Should an ICP include a problem to be solved? Yes. VPs of sales have lots of different problems from recruiting to training to pipeline management to forecasting, just to name a few. Thus, your ICP should include the ideal person at the ideal company and the problem you’re looking to solve for them.
  • Should the ICP include adjacent systems? Yes. Deciding at the outset if you want to focus on customers using specific, adjacent systems is often critical (e.g., NetSuite vs. Oracle vs. Xero for core financials, Salesforce vs. HubSpot for CRM). The alternative is drowning in integration work while never having the time to support the idiosyncrasies of a given package which, when you do it, is usually adored by customers.
  • Should an ICP include sales qualification criteria? No. The ICP is about the buyer: this is the person we’re looking for. They have this job at this kind of company. Whether they’re out shopping right now, whether they have budget, whether they have a buying timeframe and purchasing authority are all important qualification questions, but they are not part of the ICP itself. People differ on this, I know [1].

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.

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Posted in Customer Success, Data Science, Entrepreneurship, Marketing, Sales, Scaling, Startups

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Smart Conversations by Ian Howells: A Must-Read Book on Where B2B Marketing Strategy Meets Generative AI

Posted on August 18, 2025 | 8 comments

I first met Ian Howells in London long ago, as fellow footsoldiers in the early relational database wars. While you had to be pretty technical to do product marketing in those days, Ian was technical with a capital T, having just sprung from university with a PhD in distributed databases. We fought together on the losing side of the database wars [1], shared many of the same scars from the experience, learned many of the same lessons, and — I’m reasonably sure — both decided to aim our careers towards marketing to understand the dark and mysterious magic that was said to have been responsible for our misfortune [2].

I kept in loose touch with Ian over the years as he went to Documentum (content management) [3], SeeBeyond (supply chain), Alfresco (content management), and eventually to Intacct (accounting), later called Sage/Intacct after their subsequent acquisition by Sage.

So when I heard Ian wrote a book on how to use generative AI to improve marketing, I was intrigued. When I learned he was so excited about generative AI’s potential that he took a year off from work to dedicate all his time to the task, I was hooked. Whatever he produced, I was going to read it.

What he produced was a book called Smart Conversations, Revolutionizing B2B Marketing with the Generative AI Playbook. And in this post, I’ll share my conclusions based on a pretty in-depth reading of his book.

Here they are:

  • Anyone in B2B marketing with an interest in strategy should read this book.
  • This book isn’t what I expected. I feared the book might be full of prompts for generating content marketing (aka, AI slop), copy for marketing campaigns, presentations, or infographics.
  • Instead, Ian has produced an elegant work that teaches B2B marketing strategy while showing how to use generative AI to define and implement it. I’m not 100% sure what I was expecting, but this sure wasn’t it. It’s way, way better.
  • The book is both theoretical and applied. One page he’s explaining why you should target what I’d call sub-segments (that he calls micro-verticals). Five pages later he’s walking you through the prompts he uses to to build lists of them, right down to their NAICS codes.
  • On one page he’s talking about the definitions of ideal customer profiles (ICPs) and improved Geoffrey Moore positioning templates. A few pages later he’s got you in the prompts for getting ChatGPT to generate them. One minute he’s talking theoretically about the opportunities created by market discontinuities and, boom, several pages later, he’s back in the prompts showing you how to use ChatGPT to discover them.
  • What’s even more fun is how he shows what it used to take to do some of these exercises. Like building messaging by doing deep customer interviews, transcribing your notes, printing them, and then spreading them over a conference room for days so you can spot patterns. And then contrasting that to just how fast you can do it today.
  • This wonderful pattern repeats, through competitive analysis, all-in-one positioning, power messaging, and “wall of sound” campaigns [4]. Each time, the theory and then the ChatGPT practice.
  • Ian concludes with measurement. That section comes complete with a lesson on the benefits of becoming a market leader (that we both learned from the sting of Oracle’s lash), with lessons quite similar to what I describe in The Market Leader Play.

Congratulations to Ian on writing such a great book and sharing it with us. I’m glad you took the year off to write it! Now, every B2B marketing leader should go read it. Kindle version here.

Notes

[1] It’s not every day you find one of your company’s anti-competitor documents in the Computer History Museum!

[2] The company, by the way, was called Ingres. But since few have heard of Ingres today, I remind people they’ve almost certainly heard of its offspring: Postgres, which stood for Post-Ingres, an open source and extensible version of the system that achieved enormous popularity. I often say that “Postgres is corn” in the sense of The Omnivore’s Dilemma (i.e., it’s in everything) or quip that Postgres is Stonebraker’s revenge. While Larry Ellison made all the money, Stonebraker did win a Turing Award, create several new classes of database systems (e.g., column-oriented), and build Postgres which while ranking fourth on db-engines is generally acknowledged to have a higher market share than Oracle, in part due its open source heritage.

[3] And one of the original case studies in Geoffrey Moore’s classic, Crossing The Chasm.

[4] I’m not capable of typing the words “wall of sound” without referencing the Grateful Dead’s amazing and utterly impractical public address system. What Ian’s describing is what I call a backfire or surround-sound campaign, the goal being the economic buyer at your target can’t stop hearing about you from all sides. Regardless of the name, it’s a great idea, and a much more realistic goal on a limited budget than making “everyone” hear about you (e.g., super bowl ads).

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Posted in AI, Marketing, Silicon Valley, Startups

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Aligning Product and Go-To-Market with Metrics

Posted on August 11, 2025 | Leave a comment

My fellow Balderton Capital EIR Dan Teodosiu and I recently published an article on aligning product and go-to-market teams using metrics, specifically customer-value metrics. In this post, I’ll talk a bit about the article and how we came to write it, with the hope that I’ll pique your interest in reading it.

First, a bit on the authors. The definition of EIR (here meaning executive-in-residence) varies widely — as does the job itself. At Balderton, it means that we are on-staff resources available to help portfolio companies, on an opt-in basis, with the issues that founders and executives face in building a startup. Dan focuses on technology and engineering while I focus on sales and marketing. Dan’s founded two startups as well as having technology leadership roles at Criteo, Google, and Microsoft, and I’ve been CEO of two startups in addition to having served as CMO of three. That means we are both able to see the bigger picture in addition to our purely functional views. Not to be immodest, but I’d have trouble finding two better people to write an article on how to align product/technology and go-to-market. Heck, we even had the expected us vs. them disputes!

I write a lot about aligning sales and marketing (always remember the CRO is the #1 cause of death for the CMO), but I’ve not written before about aligning product and GTM. So this was a new, fun challenge that necessarily led to strategy, organizational behavior, and leadership. Yes, often, the CEO is the cause of the problem. I can’t tell you the number of times I’ve said: “You want to know whose fault this is? Grab a mirror!” But knowing that doesn’t necessarily help the particpants in a mess unless they know how to get out of it. Usually that starts by asking one simple question: why would anyone want to buy this again?

Does any of this sound familiar?

It’s a 2,750-word paper, which should take around 10 minutes to read, and I’d encourage everyone to check it out. We’ve got some nice, juicy historical examples in there where good companies, even great companies, lost the plot, forgot about customer value and wasted tons of resources as a result. Spare yourselves that pain. Or, if you’re in the thick of it already, step up and start asking the one big question: why would anyone want to buy this again?

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Posted in GTM, Product, Startups, Strategy