Rohan Punamia’s Post

If you've never told a prospect they're a better fit for a competitor, your sales process isn't truly "consultative". As a founder, building trusted relationships is much more important than jamming a deal through with a company that might not be a good fit. Unfortunately, the incentives for reps at large companies aren't aligned with this mindset. Reps need to hit quota, aren't responsible for renewals, and often won't be with the company long-term. It's amusing when people claim their sales process is consultative. It's like going to a doctor who can only prescribe one medication. How "consultative" is that really?

I’m not an expert at cold calling. Far from it. My background is in GTM Ops and Product at LinkedIn. So why was this CMO who gets hundreds of calls a week impressed with my cold call at the end of her work day? It has to do with a study Nooks ran on the top 1% dialers on their platform: “71% of tops reps highlight prospect-based over company-based personalization. They use tactics like mentioning role-specific pains, job changes, previous conversations, and interactions with colleagues.” Bluebirds queued this CMO up in our dialer with the following hyper-specific context: 1) Name of a BDR, BDR leader, and growth marketing leader that just joined their company 2) Open job postings for an AE role responsible for cold calling 3) Their BDR team size All I did was start the conversation with the specific names of new team members that report into her, then transition to a hypothesis of the problem they might have based on these signals. The best signals give you clues on problems. It’s really that simple. If I called and mentioned their Series B, I’m sure she would’ve hung up on me. It’s so overused and doesn’t tell me much about problems they might have. We’re leaning into building more person-specific signals at Bluebirds, because this is the alpha in outbound today. Feels like every team that’s cold calling should be using this to their advantage.

“Can your AI write messaging too? - one of the most common feature requests we get. The short answer is not yet. The long answer is that this is a trap for most GTM use cases. Great messaging is much more art than science, and in marketing (and outbound), copywriting is judged by its ability to stand out from competition and win over buyer mindshare. Take this Avis billboard. The genius of it is flipping a disadvantage, Avis being #2 in the market, into two key strengths: trustworthiness and the human desire to root for the underdog. Both gave them a leg up on competition, and after 50 years of using this positioning, they finally became #1 in the market. This is the exact kind of thing that AI is not good at. And with the current architecture of LLMs (next token prediction), I don’t see it changing. The premium on world-class positioning and copywriting only increases as it becomes easier to build products, AI platitudes flood the market, and communication channels saturate further. Outbound messaging is competitive and zero-sum, which is why cute tactics work for 6 months than fizzle out (e.g. remember the trend of sending an image with your prospects’ name on a cup of coffee) . AI copywriting is similar. It was novel for a brief period of time, but lacks the ingenuity and creativity required to stand out from a sea of noise your buyers are hammered with every day. So why did I say “not yet” instead of “never” to this feature request? Because I can see 3 notable exceptions where AI messaging does make sense: 1) Use Bluebirds signals + AI to scale messaging from the best reps / marketers. The Avis messaging example is truly distinctive. Let’s be honest, most reps are far from this this level of creativity and market understanding. But in the best companies we work with, I’ve noticed there are always a few people who have a deep understanding of their product <> market fit and translating that into differentiated messaging. Bluebirds will give them 100x leverage by scaling their messaging ideas to the entire GTM team. A few creatives will be able to influence outbound across hundreds of reps. 2) Inbound follow-up doesn’t require as much creativity as outbound, because you already have the buyer’s attention. While this isn’t a focus of Bluebirds, I do see AI messaging playing a role here. AI should automate the inbound SDR email / schedule tag with demo request leads. 3) Companies with deep reputation / brand moats can get away with mediocre copy. The outbound conversion rates of more established companies we work with are 3 - 5x higher than startups. Counter-intuitively, their messaging is usually less punchy vs. startups with a chip on their shoulder. But the reality is that it doesn’t matter. Buyers will always give mindshare to companies with strong reputation, regardless of copywriting (though it can only help!) Nobody got fired for buying IBM, right?

Context switching is THE ongoing challenge as a founder. But I love it... let me explain. In most roles, it's best to minimize context switching and stay focused on one main project for days or weeks at a time. As a founder, this is just impossible. We're a team of 6 engineers, a designer, and an AE. That means I'm running point on product, marketing, revops, finance, and helping Max / the eng team whenever possible. The extreme context switching is both challenging and thrilling. It captures what I love most about being a founder -- learning many different disciplines and piecing them together into an engine that hums. Of course, context switching every hour isn't very productive. So I lean on habits and rituals to structure my day, minimize thrash, and make space for top priorities that I need to push on. A few of my favorites are: - Making a cappuccino every morning - Hitting the gym with Deena before starting work - Minimal meetings until 11am so I start each day focused I've been practicing these habits for many years now, and while they help a ton with being a founder, perhaps the biggest win is how much better my latte art has gotten 😁

At Bluebirds, pricing strategy is second in priority to building a product that creates immense value for GTM teams. To keep things simple, our first pricing model was “unlimited” for a flat annual contract. Last week we hit the breaking point of this KISS (keep it simple, stupid) model. Prospects were confused because this doesn’t align to how competitors price, making it harder for them to compare. This is especially true as we move up-market, where clarity on how pricing works is often more important than the actual price itself. Our realization: unlimited plans which offer incredible value to price, are no longer be the best answer given our product scope and ICP. As we refine our pricing / packaging for sharper clarity, I noticed a few reasons for why pricing Bluebirds is tricky: 1) Bluebirds is not a point solution, and our customers use us for a wide range of account and contact signals that they would otherwise have to buy via expensive point solutions. Point solutions for each signal (e.g. customer job changes, credible tech stack data, team size / growth rates, etc.) have the luxury of optimizing their pricing model for that one signal. For example, vendors that track customer job changes often charge based # of customer contacts to track or # of job changes delivered. If we did the same, pricing would get increasingly complicated as we add more signals. We want to make it easier (not harder) for customers to use more signals for their GTM motion! 2) The obvious answer, seat-based pricing, doesn’t make sense for us since Bluebirds provides the entire GTM team with signals directly in their CRM. Bluebirds isn’t another workflow for the sales team to learn. As such, we can’t just follow the Outreach / Salesloft model of per-seat pricing. Our signals get pushed to the CRM for everyone to use, so BDRs, CSMs, and AEs can all use the insights. One enterprise customer told me point blank that we are way under pricing for their organization. 🤷♂️ 3) It’s too early for outcomes-based pricing (per meeting booked via signals). This is the holy grail, because it aligns price to value almost perfectly. Unfortunately, outbound is a tough nut to crack for any organization and has a lot of moving pieces. I previously wrote about why I’m not bullish on outcomes-based pricing for outbound (AI doesn’t have control over your email deliverability, differentiated messaging, and product offer). I’d love to revisit this in the future, it just seems too early and doesn’t set customers up for success. While there isn’t an easy answer, there’s a few ideas I’m excited about that revolve around three principles: - Aligning price to value as much as possible - Without over-complicating it - And striking a balance between pricing innovation (exciting, but risks prospect confusion) and following tried-and-true models As we continue on this journey, I appreciate feedback from both prospects and customers. It’s the only way for us to get better.

Gen AI will force GTM away from spray and pray outbound, and towards thoughtful and targeted outreach. Here’s why: On Wednesday I shared the 3 major inflection points in outbound over the last 2 decades: 1) Readily available contact data to find prospects (e.g. LinkedIn, Zoominfo, Apollo) 2) Sales engagement tools that enable SDRs to 10x their volume (e.g. Outreach, Salesloft) 3) Gen AI automating writing and sending sequences (e.g. AI SDR type tools) With each inflection point, outbound became cheaper, more companies started to do it, and conversion rates dropped because there’s a limited amount of buyer attention. We’re in the first inning of Gen AI, which will almost certainly drive the cost to outbound to be free. If history taught us anything, we should except conversion rates will be decimated, and outbound will die, right? Not quite. There’s a new constraint on outbound emerging. It’s shifting from capital and labor (the last 20 years) to reputation (the next 20 years). Apple, Google, Microsoft, etc. have every incentive to protect the attention of their users, who are your buyers and decision makers. Here’s what this means: A) The playing field will not be equal, and reputation will be king. Reputation includes email deliverability and broad moats like brand awareness. Enterprises with stronger reputation will get larger allowances to contact more of their TAM, and conversion rates will be higher because of brand awareness. Outbound will continue to thrive here. On the other hand, startups without established reputation won’t be able to achieve the same economics. I expect outbound will only work for startups with 10/10 GTM execution, or those in markets with little competition (lucky them). B) Targeting will become 10x more important. For both Enterprises and startups, it’ll become even more important to make the most of whatever reputation they have. Reps need to outbound to the accounts and people most likely to convert (e.g. signals / warm outbound), and ignore the rest. C) Investment in channels that increase reputation long-term. I expect to see more investment in reputation accretive activities like founders posting on LinkedIn (hello 👋), hosting local dinners with a tailored guest list, dedicated content teams (Hubspot buying The Hustle), etc. If you pay close attention, you’ll see the best companies in competitive markets are already doing this. That’s the prediction. A bit ironic that Gen AI will end spray and pray. It might take some time, but there’s already evidence of this playing out. Which side do you want to be on?

Thought experiment: what is the future of outbound if email and cold call conversions drop another 10x? Let’s predict what will happen. 20 years ago, the problem with outbound was finding who to target and getting their contact information. Before LinkedIn, there wasn’t a source of truth on people’s jobs and responsibilities. In the 2000’s, companies like LinkedIn and Zoominfo solved this problem, making it easier (and much cheaper) to find prospects and contact them. When a foundational piece of the economy becomes 10x cheaper and more accessible (i.e. selling your product via outbound sales), it unlocks latent demand of businesses who wanted to do outbound sales but couldn’t previously justify the cost. Fun fact: this is the same phenomenon that tripped up McKinsey in the 1980’s, which predicted that the market size for mobile phones would be less than 1 million users. The result is 100x more sellers getting in touch with buyers via email and phone, a shift away from field sales, and a golden era of GTM efficiency. Fast forward to 5 years ago, and we underwent a second transformation with ZoomInfo competitors like Apollo and the rise of sales engagement via Outreach / Salesloft. Apollo brought the cost of contact data down dramatically with a generous free trial and credit card swipe. Outreach / Salesloft made outbound reps more productive by scaling volume and staying organized across hundreds (or thousands) of active sequences. That once thoughtful follow up email has long been automated by every seller in the market using "sequences". Contacting prospects via outbound, once again, became order of magnitude cheaper. Today, nearly every YC founder goes through the right of passage of excitedly swiping their credit card on Apollo and messaging thousands of prospects in their TAM, thinking they found the golden ticket to GTM — only to hear crickets. With the influx of startups, small business, and others now outbounding, the channel saturates quickly and conversion rates fall off a cliff. There are 10x more sellers trying to contact buyers, but there isn’t 10x more buyer attention to go around. Email conversion drops to 0.3% or less, and phone connection rates tank to sub 5%. Which brings us to the third transformation — Gen AI. This is the final nail in the coffin on cost to outbound — it is (or will soon be) basically free. If history taught us anything, we should except another 10x influx of sellers clamoring to contact a limited group of buyers. Conversion rates will drop another 10x, and email and phone will die, right? Not quite. I think the laws of gravity in outbound are about to change. To be continued…

I was texting a prospect who’s deep into looking at vendors for signals to drive outbound pipeline. He shared an elegant framework on how he’s thinking about various vendors: - Public and Web signals: Bluebirds - Product signals: Pocus - Community signals: Common Room - Website: RB2B / Warmly / Koala This is an actionable framework for GTM teams because answering a simple question can dramatically focus the evaluation. Where do your reps spend time researching prospects today? - Digging into LinkedIn profiles, job postings, recent news, or prospect websites → Bluebirds - Sitting on tons of valuable product data that gives reps a pond to fish from → Pocus - Have engaged communities to tap into → Common Room - Lots of website traffic from a strong marketing engine → RB2B / Warmly / Koala Yes, many vendors (including 6Sense and ZoomInfo) may claim they do all of the above. But keep these two realities in mind when evaluating demos: 1) Many signals are not fungible, and quality varies a lot. For example, most GTM teams were duped with a black box Tech Stack signal that’s wrong 50%+ of the time. The data is so corrupt that reps don't trust it. Our tech stack signal (along with all of our signals) come with clear sources and confidence scores. This is possible because we’re laser focused on building signals from trusted public data, and have the infrastructure to do this. 2) Each vendor’s product and workflows are heavily designed around the main signal they are known for. When evaluating options, make sure that workflow actually makes sense for your teams' needs across the spectrum of signals. Otherwise you're stuck with a workflow that doesn't make sense for most of the signals you were promised. All our customers want signals to enrich directly into their CRM and Sales Engagement tools, so reps don't have to learn new tools. Bluebirds is designed from the ground up for exactly this, and prospects are pleasantly surprised when they see how simple our demo is. It can be an overwhelming decision, hope this framework helps.

There’s a lot of excitement about AI agents automating outbound and charging for qualified pipeline or C/W deals, rather than inputs like lead volume (Apollo / Zoominfo) or emails sent. The holy grail of pricing is to align as closely as possible to customer value, and Aaron Levie's post is a good summary. There’s already evidence of this playing out in customer support, where successful AI agents are answering tickets without a human in the loop 70% of the time. This works because the agent has full control over driving the outcome, assuming it’s well designed with access to a company’s knowledge base. As a result, an support agent vendor like Decagon can consistently sell against this outcome and price relative to the value delivered. But outbound is fundamentally different because AI agents have much less control over the outcome (a qualified meeting). Outbound, unlike most other tasks in an enterprise, is highly competitive. Your conversion rate is almost entirely driven by what you’re doing differently relative to the rest of the market. Quality meetings booked = deliverability * targeting * messaging * product offer Agents can automate targeting, but they don’t have much control over deliverability, differentiated messaging, and your product offer. In fact, as the market is flooded with more outbound agents, deliverability and differentiated messaging will only get harder. It’s possible the entire channel collapses and the way we think about “outbound” evolves beyond email / calling. I’m still excited about how AI and agents can help reps and GTM teams become more productive. Just not sold on pricing relative to meetings booked or C/W outcomes.

One of the most foundational concepts in Machine Learning is the tradeoff between precision and recall. Interestingly, I’ve noticed the best GTM teams use this framework for their strategy on building outbound pipeline with signals. Here’s how it works: When a prospect of ours engages with content around “outbound dying” or “signals” on LinkedIn, it’s a strong sign of interest. Emails can convert at 10% positive reply rates, so Max will manually dig for this information because he knows they are much more likely to be a high quality opp and successful / happy customer. The problem is that not all prospects who are actively considering tools like Bluebirds are posting or engaging on these topics. Signal strength is high, but volume is low, so we we can’t build our entire company pipeline just off this signal. This is an example of a high precision, low recall signal. The flip side is the legendary “job change” signal. A simple but universal truth is that many products are bought when a new leader joins a company because they're shaking things up (it's literally their job). The data backs this up -- several GTM teams backtested their C/W deals and found 50%+ of their C/W opps happened after a key leadership job change. In our market, there’s a LOT of VP of Sales / Marketing / RevOps job changes. The problem is that this signal on an individual basis is fairly weak. Just because a VP of Sales is new to a company, doesn’t mean they have a pipeline coverage problem, are outbound-driven motions, or thinking about signals at all. As a result, the positive response rate is closer to 1%, but the volume is 10x-100x higher than other signals. This is an example of a low precision, high recall signal. Now, here’s the key insight the best teams have already figured out and operationalized: you need both. By blending both types of signals together, you have a high precision and high recall set of signals so that your reps are focused on the 5% of the market that’s will contribute to 90% of your C/W deals. This is exactly why we built Bluebirds as a platform for the full portfolio of signals, because we realized doing one signal (our first was customer job changes) wasn’t enough to move the needle drastically on quality pipeline.