r/revops

Spoke to 4 founders over the course of last 10 days who are all building in the GTM agentic space (forecast, deal alerts, role-play, coaching, etc.).

Some of these folks are extremely talented - one of these startup founders built DeepTech at Google and then went on to found a company currently valued at $7B and now this new startup. Their underlying AI works really well.

However. However..

One common theme across all these startups I am coming to realize: the technical barrier to entry has come down dramatically and everyone's stitching a notetaker in weeks these days. The real differentiation will come from the GTM motions - how you position and package your product around the usecases of the prospect.

Technical founders are great. But hey you really need a very strong sales-sy co-founder now more than ever. Reason why I also strongly feel this is also the era of great sales reps who can position your product in the sea of similar products.

Got two more founders I will speak to over the weekend - a little research - and similar products to the rest four largely. Let's see how those go 😃

Hey RevOps folks — looking for input from anyone who has built or managed comp plans for quota-carrying Partnerships, Channel Sales, Strategic Partnerships, or Partner Manager roles, especially in fintech/payments.

I’m trying to understand how companies structure comp for partnership roles where the person is expected to source, onboard, activate, and grow strategic partners — but where there is also an existing direct sales team in market.

The part I’m trying to solve is how to measure performance fairly without creating conflict between Partnerships and Sales.

Some of the factors I’m thinking through:

- Should the role carry a revenue quota, pipeline quota, partner-sourced opportunity quota, partner activation quota, or some combination?
- How do you define partner-sourced vs. partner-influenced vs. co-sold revenue?
- How do you prevent double-crediting between the partner person and the direct AE?
- How do you compensate the partner person when the partner creates distribution, referrals, or influence, but the AE technically closes the deal?
- Do you pay on signed contracts, activated partners, live merchants/customers, revenue, GMV/TPV, or validated run-rate?
- How do you handle long sales cycles or partner onboarding timelines where the partnership work happens months before revenue shows up?
- For strategic partnerships, do you measure partner recruitment/onboarding separately from revenue production?
- How do you avoid incentivizing the partner person to compete with direct sales instead of enabling them?
- How do you design the plan so the partner team complements direct GTM — referrals, co-sell, enablement, partner channel development — rather than creating channel conflict?

For context, I’ve been looking at quota-carrying partner/channel roles at companies like Mastercard, Visa, Stripe, Square/Block, Affirm, etc. The job descriptions often reference things like partner-sourced pipeline, channel revenue targets, partner recruitment, partner onboarding/activation, GMV/revenue goals, Salesforce tracking, forecasting, and working with internal sales teams.

What I’d love to hear from people who have actually built or operated these plans:

- What metrics worked best?
- What metrics created bad behaviour?
- How did you define attribution and crediting rules?
- Did the partner role carry an individual quota, team quota, or shared quota with Sales?
- What would you absolutely avoid if you had to rebuild the plan?

Would especially appreciate examples from payments, fintech, SaaS channel sales, marketplace partnerships, or partner-led GTM motions.

If a buyer asks for a concession, the default move is usually to cut subscription price.

That sounds flexible, but it often creates the worst possible outcome:

• recurring revenue gets reduced
• buyers learn to ask for more
• and the team gives away value in the easiest place instead of the smartest one

There are usually cleaner ways to give value:

• renewal credits instead of a flat discount
• free months at the end of the term instead of the beginning
• sticky cross-sell value instead of cutting the core subscription
• implementation relief instead of discounting ARR

Same commercial value.
Better structure.

Sales is art. Pricing is math.

I put together a small interactive simulator for this to pressure-test different concession structures. I’m not dropping the link here because Reddit hates that, but I can share it in the comments if that’s useful.

When a deal needs a concession, what structure have you found works better than just discounting the subscription?

Marketing attribution feels like one of those things everyone relies on, but nobody fully trusts.

Every model tells a slightly different story. First-touch says one thing. Multi-touch says another. Pipeline attribution adds another layer. Then sales influence gets involved, and suddenly every team has a different interpretation of what “worked.”

And honestly, the more detailed attribution gets, the harder it sometimes feels to make clear decisions from it.

Because at some point, you stop asking: “What touched the deal?” And start asking: “What actually changed the outcome?”

What do you feel?

Has attribution genuinely improved decision-making in your org, or mostly improved visibility?

Over the last few weeks, we’ve all seen the posts.

Founders bragging about replacing their sales teams with AI.

Faster deals. More output. “Smarter” outbound.

And every time I read one, I think:
You’re probably not telling the full story.

Because behind closed doors, I’m hearing something very different.
Sales leaders are worried.

Then I see this:
‼️ Anthropic is paying ~$330K to manage 10 SDRs. ‼️

The company building the AI… is doubling down on humans ‼️ HUMANS ‼️

That tells you everything.

In the last 18 months, outbound production became cheap.

Prospecting, emails, follow-ups, CRM updates all near zero cost.

So the scarce resource changed.

It’s not messages anymore.

It’s trust.

Everyone can send outreach now.

Almost no one earns attention.

Trust comes from relevance:
One message that actually understands the buyer
Follow-ups that add value
Real effort, not templates
AI lowered the cost of noise — and buyers’ tolerance for it dropped to zero.

The real risk isn’t AI.

It’s scaling bad outreach faster.

Trust is the only thing left worth building.

for the last two months I’ve been studying RevOps deeply and building auditing documents while aligning the GTM team — obviously using Claude as a helper along the way. But the deeper I go, the more I realize how much there is to learn in order to deliver a truly solid audit and implementation.

I’ve seen so many companies install CRMs without creating proper customer journey maps, BPMN processes, EDRs, or data governance frameworks. They end up delivering mediocre implementations while charging companies huge amounts of money. In my country 10-30k for small teams...

What’s really hitting me lately is that the hardest part of RevOps isn’t even the CRM itself — it’s the marketing and customer acquisition side. Things like:

• Defining the ICP correctly
• Building accurate buyer personas
• Connecting social media and websites properly into the CRM (knowing that from this post we created some customer)s or new contacts)
• Understanding what’s actually working vs. what isn’t like A/B methods
• Tracking how customers enter the funnel, move through it, and eventually buy

There are so many moving pieces: lifecycle stages, attribution, automation, reporting, handoffs between teams, friction points, etc.

The last few days have honestly been overwhelming. RevOps looks simple from the outside, but once you start trying to build a real operational system that aligns marketing, sales, and customer success… it becomes a completely different game.

More often than not, the onboarding process os chaotic/goes sideways and we need to learn everything by ourselves, which is somewhat tricky because there are many systems, integrations and documentation is usually lacking. To make it short, that's my situation.

How would you approach self onboarding? What made your lives easier?

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I’ve seen duplicate cleanup become painful after migrations, imports, form submissions, or manual sales entries.

I’m testing a private beta for this workflow: upload a HubSpot CSV, scan duplicate groups, review risk/confidence scores, and export a cleaned file.

No HubSpot API connection is needed, and the CRM is not modified directly.

You can also test it with built-in sample data first, so you don’t need to upload real CRM data right away.

I’m looking for a few RevOps/CRM people to test it and tell me what feels useful or broken.

Comment or DM if you want to try it.

I work mostly around acquisition, CRM, tracking, reporting, and paid media, and I have been trying to make the whole client stack readable by AI.

The part that has actually helped is not "AI writes better copy." It is the read step across systems that usually do not talk cleanly.

Every client gets a context folder.

Emails, meeting transcripts, call recordings, offer docs, pricing, website content, CRM notes, pipeline logic, tracking notes, ad account data, conversion data, previous tests, all of it lives in one place.

Most of it is pulled in automatically through n8n, Codex automations, or whatever connector makes sense for that client.

The folder structure matters more than I expected. Same rough layout across clients, same naming conventions, same instruction files, same connection notes. When I open a client folder in Claude Code or Codex, the model is not starting from a blank chat. It can read the business first.

The useful workflows are very practical.

Stuff like:

• daily account checks
• CRM lead quality review
• broken conversion handoff checks
• form submission into CRM field checks
• offline conversion upload checks
• meeting transcript into open actions
• comparing ad platform numbers against CRM outcomes

The last one is probably the most important for my work. Ad platforms will happily optimise toward the wrong signal if you let them. If the CRM says the lead quality is bad, I do not care that the platform says performance looks good.

I trust scheduled reads more than autonomous decisions.

Most of the useful stuff in my setup runs on a fixed cadence.

Morning account checks. Weekly search term reviews. Monthly reporting passes. Tuesday and Thursday deeper account work.

Some of it runs through Codex automations, some of it through n8n, some of it is still me manually kicking off the workflow.

The point is that the agent is not the router. I am. The agent does the read work, runs the checks, drafts the output, and tells me what deserves attention.

My alerts are mostly email and Telegram, not Slack. Daily account summaries go to my inbox. Telegram is useful when I want a quick pulse or to trigger something from my phone. If I need detail, I open the folder.

Writes stay gated.

Budget changes, paused campaigns, negative keywords, CRM writes, conversion settings, pipeline changes, website deploys, anything that changes state or can cost the client money.

The model can draft, stage, queue, explain. I still review before it goes live.

That is not me being scared of automation. It is just the only version that survives contact with real accounts, messy tracking, delayed conversion data, platform policies, and clients who understandably do not want an agent freelancing inside their business.

I stopped trying to build a dashboard for this too. The folder is the view. The morning emails tell me what needs attention. If something looks off, I open the relevant client folder and inspect the files, logs, and outputs.

For RevOps-type work, I think this is the part people should look at more. Not "AI replaces ops." More like: can your systems be read together well enough that AI can catch gaps before a human spends three hours reconciling them?

Curious if anyone here is doing this around CRM and attribution. Where do you draw the line between read-only automation and writes?

RevOps and Enablement leader quietly looking for what's next.

8+ years in GTM, started carrying quota as a BDR and AE, moved into sales leadership, then built the ops infrastructure behind the teams I used to run. I'm a builder, specializing in budget efficient, scalable operations, tooling, and teams. I tend to get brought in when pipeline data can't be trusted and leadership needs to fix that before a board meeting. That's basically been my career pattern across multiple companies.

What I've done: full Salesforce custom builds from scratch including custom objects, fields, pipeline stage design, hygiene rules and executive dashboards. MCL/MQL/SQL flow ownership, board and investor level pipeline reporting, vendor negotiation, sales enablement programs including LMS builds and playbooks, BDR & Sales team management, and Clay for prospecting enrichment and data hygiene. Most recently in healthcare & tech.

Tools I've worked in: Salesforce, HubSpot, Marketo, Outreach, Salesloft, Gong, Clari, Clay, ZoomInfo, NetSuite, and a lot more beyond that list.

Pending leadership transition at in my current role has me looking quietly a little sooner than expected.

Looking for remote, Series B or later, real RevOps scope, base around $120k. Open to Director, Head of RevOps or the right fractional setup.

DMs open, thanks. & always happy to swap notes!

I'm a full stack dev, building a product around sync issues between hubspot and salesforce. Currently looking for an SME in revops.

I am running RevOps at a scale up. We use Salesforce as our CRM. Our set-up is very messy and reps hate using it. However, the Salesforce team invests little capacity into improving the system and allocates time/ resources to other functions as the team has a very broad scope. I am increasingly frustrated with this set-up as I don’t have control over capacity and prioritization. The SF team makes decisions around sales topics they want to pursue that don’t align with my priorities. Has anyone been in a similar situation? How can I fix this? How are other companies solving for this?

Running AI-generated cold outreach at scale and paranoid about what's slipping through unseen. Currently manually spot-checking a sample before sending but it doesn't scale. Curious what others are doing — any systems, tools, or workflows for catching AI mistakes before they hit prospects?

For RevOps/Sales Ops teams: do you have a good way to quantify how much rep time is lost to missing, stale, or incomplete contact data?

We're thinking about things like:

• reps manually finding contacts
• reps validating emails
• reps hunting for phone numbers
• reps fixing CRM fields
• duplicate account/contact cleanup
• stale job titles
• missing firmographics
• bad routing due to incomplete fields
• sequences failing because the list quality was poor

This seems like one of those problems that creates a ton of drag but is hard to measure because the work is distributed across every rep.

A few questions for anyone who has insights:

• Do you track this in your org?
• Who owns contact/account data quality?
• Do reps update CRM themselves, or is enrichment automated?
• What data quality metrics do you report on?
• Have you ever calculated the productivity cost of manual prospecting/research?

Would love to hear what’s working and what’s still painful.

I'm currently studying Revenue Operations and recently completed the HubSpot Academy RevOps Certification. It was a basic starting point, I’m looking for something more practical and implementation-focused.

• Revenue Wizards
• RevOps Training (RevOps Audit course)
• Pavilion
• RevOps Co-op / RevOps Scoop
• RevOps Academy by RevOps Careers

I’ve been researching a few options and would love to hear from anyone who has experience with them:

Edit: Recommended RevOps Co-op and RevOps Training; additionally the best way is to implement!

One thing I keep running into with marketing measurement:

It’s relatively easy to show that a campaign influenced a conversion. It’s much harder to prove the conversion wouldn’t have happened anyway.

That’s where incrementality testing gets interesting to me.

Because a lot of attribution reporting ends up rewarding participation in the journey, not necessarily impact. And once multiple channels are involved, almost everything starts looking valuable in some way.

Incrementality feels like an attempt to answer the harder question: “What actually changed behavior?”

But I’m curious how realistic this is in practice for most teams.

Are people genuinely running controlled tests and causal analysis regularly, or do most orgs still rely mostly on attribution models and directional judgment?

One pricing model collapse is getting all the attention. Another one is flying under the radar — and it might matter more.

The one everyone's talking about: per-seat SaaS giving way to outcome-based pricing. If agents do the work, you don't buy seats. You buy results.

The quieter one: Time & Material billing — the backbone of professional services for decades. Human hours were the proxy for value. Log them, bill them, track utilization. The entire operating model built on that single assumption. Agents don't have billable hours. They just execute. And the human-hours model is breaking.

Which brings us to Service-as-Software. SaaS meant software delivered as a service — humans still drove the work. Service-as-Software flips it: the service is delivered by software. In some workflows, agents are Copilots — first line, assisting humans who own the outcome. In others, Autopilots — executing autonomously, humans handling only exceptions. Most real engagements will run all three in parallel: human hours, assisted hours, agent-executed hours. our PSA needs to track all of it. No PSA today was built to.

Open questions — genuinely curious what people think:

• If human hours, copilot hours, and compute hours all contribute to delivery — how do you bill for all three?
• If utilization rate is the operating metric of a human-driven PSA, what replaces it in a Service-as-Software world?
• How do you track project margin when costs are split between salaries and compute?
• Does PSA need a new expansion — Professional Services Agents instead of Professional Services Automation?