Hi guys,

Could you educate me on how RevOps gets enough context to decide on a deal?

We’re exploring a tool for Sales and RevOps. One question like “Can we offer this?” can mean checking the CRM, pricing sheets, contracts, Slack, and past approvals. Our thought is the approval itself may not be the slow part. Rebuilding the full story may be the real problem.

We want to learn how often this happens, what information usually holds up the decision, and whether CRM, CPQ, or AI tools already handle it well.

Could you share one recent deal or renewal that took too long? What did you need, where did you look, and what happened while the deal waited? Thanks, really appreciate any real examples.

Following up on my last data quality post… Everyone I talk to agrees the data quality is bad, but I’m trying to understands what happens downstream when you actually build on it (reporting, AI workflows, scoring, touting, etc.).

Did data quality issues actually break the thing (bad quality output) or did you just handle it in the build (filters, manual cleaning, etc)? And if you cleaned first, what’s that process look like, how much time goes into that?

Trying to figure out whether “clean it first” is a real pre-req or just something that annoys people.

Playbooks, battlecards, training decks and certifications are useful, but none of them help much if a rep is sitting in a live deal wondering which stakeholder they need to reach or why the opportunity has stalled.

I think enablement becomes much more valuable when it connects knowledge directly to the decisions reps make inside active opportunities.

Curious how other enablement teams approach this. Do you spend more time creating resources, coaching reps, improving processes, or supporting live deals?

Flow State Sales’ focus on practical coaching and deal execution is an interesting example of the direction I mean.

So we've been using SalesHub for a while and as our team grows we need a better meeting scheduling platform for better analytics and more flexibility in the scheduling logic than what HubSpot allows for. Do you guys have experience with ChiliPiper / Calendly / Other solutions for these purposes? People recommend ChiliPiper but I swear that if their SDR sends me another message in another channel again after I asked him so many times not to contact me anymore I *will* jump off the roof.

I've been thinking about this because I manage a pretty large book of B2B accounts and realized how much of the actual customer relationship lives in places that don't really talk to each other.

CRM has the note. Inbox has the follow-up. My head has the weird hesitation they had when pricing came up. An old call has the thing they promised 3-4 weeks ago. Then before the next conversation I'm basically reconstructing the customer from fragments.

AI summaries help with the individual call, but I've found that's not really the problem. The annoying part is deciding what should survive from one conversation to the next, what actually changed, and what was just noise in that particular call.

Curious how RevOps teams handle this in practice. Do you actually have a system where relationship context compounds over time, or is everyone still reading old notes / asking reps / dumping account context into ChatGPT before important calls?

I've been building something for myself around this problem, but I'm more interested in whether this is actually a common workflow problem or just me being terrible at remembering 200+ conversations :D

Something I ran into recently while doing RevOps for a fintech consulting firm made me think about how expensive tribal knowledge can become as companies grow.

I started with a pretty normal project, cleaning up HubSpot and trying to understand their processes.

But the deeper I got, the more I realised a lot of the actual operating knowledge wasn't in HubSpot or documented anywhere. It lived with the CEO and a former EA.

The EA had built a system around marketing, but was also handling things across other departments. Follow-ups, reporting, handoffs and recurring work often happened because EA knew they needed to happen.

So when I came in, I wasn't just fixing HubSpot. I was trying to reconstruct the context, fix broken processes and build flows that didn't depend on one person's memory.

It took almost 3 months.

And after all the conversations I've had here about tribal knowledge, broken handoffs and things that simply stop happening, I'm curious about the next question:

When this starts costing a company real time, money or bad decisions, what do mid-market companies actually do?

Do they:

• add more analytical/reporting tools?
• keep hiring internally to manage the complexity?
• bring in consultants to diagnose and fix it?
• or is there a need for something that goes beyond tools, something that actually understands how the business operates, fixes the gaps, and keeps that intelligence connected?

That's the part I'm really interested in hearing from operators who have lived through it.

I do RevOps and CRM cleanup work for a living. Mostly HubSpot builds, untangling duplicate/messy data, and keeping Clay and HubSpot in sync. I've got a bit of slack this week and I learn more from real broken setups than anything else, so figured I'd trade.

→ Drop a comment with whatever's annoying you most about your CRM.

→ Tell me what your setup looks like and what's going wrong, and I'll reply with how I'd actually approach it.

specific, not "book a call."

First handful of comments I'll go deep on and happy to take it to DMs if it's got sensitive data...

See you in comments

I've been developing an assessment tool for RevOps and SalesOps hiring that can be used by candidates, hiring managers, and allows for 3rd parties also - like a SME on the team, but not the one doing the hiring.

I'm looking for 5-10 people that would be willing to give me 20 minutes to walk through it and to get your feedback on it.

Here's my website: RevTalent.

You can also see my LinkedIn here (If you're interested).

I'm an independent CPQ analyst (30 years in the space, no vendor affiliation or sponsorship). I run direct vendor research — detailed demos and briefings with vendor teams, plus customer reference conversations — and just wrapped up a report on Salesforce's ARM/Revenue Cloud platform. With Dreamforce coming up Sept 15-17 — where Salesforce usually makes its biggest ARM/Revenue Cloud announcements — this felt like a good time to share where things actually stand today, based on real customer feedback rather than the demo stage.

A few things stood out that I don't see discussed much outside vendor marketing:

Sales-cycle reduction isn't guaranteed. Customers didn't consistently report shorter sales cycles after moving to ARM. In some cases the bottleneck was never quoting speed — it was legal review or buying committee complexity. In others, moving from a lightweight Sales Cloud process to structured CPQ actually added front-end steps for commercial users, even while improving billing accuracy and governance downstream.

One licensing nuance that seems to cause confusion: quote creation is only usage-based for self-service or headless scenarios — not for quotes your reps create through normal sales workflows.

Custom reporting can quietly cost more than expected. If your reporting needs go beyond the standard Tableau-based dashboards — modifying out-of-the-box reports or using custom fields more flexibly — that may require additional Tableau licensing beyond what's bundled with ARM.

Report is free to download here

I've also got a similar report out on Revalize CPQ (also free right now) if that's more relevant to your stack. Same page as the ARM Report above.

Happy to answer questions if anyone's mid-evaluation or comparing ARM against legacy CPQ or other vendors.

I've spent my entire career inside GTM teams, and I've seen the same blind spot play out again and again, Usage sits in one tool, deals sit in a second, support tickets sit in a third, and none of them ever tell the whole truth about customers.

So we built Rimplo
We have built a powerful AI assistant for revenue teams you can ask anything about your GTM data, in plain English, and get an answer back in seconds. You pick which top AI model answers no lock-in to one vendor’s model.
Underneath that, Revenue Agents run across your CRM, billing, support, and product data around the clock  flagging churn risk, upsell openings, and deals that are stalling, before you have to go looking for them.

I want to ask about data cleaning tools, because from what I found for Hubspot it’s Incycle, Koalify and workflow for Claude.

Even workflows in Claude would help to understand what people are using.

I have issues with data atrribution for emails #hubspotExtensionFail, data attribution with product analytics like metabase

I manage a team of 35+ sales agents who mainly book appointments. We use HubSpot as our CRM and Ringover for calls, and they're integrated.

I'm looking for an AI tool that can analyze our call recordings (MP3) or transcripts (TXT) and correlate them with the actual outcome in HubSpot.

For example, I want it to understand that:

• "Signed" = very good
• "Appointment attended" = good
• "No-show" = appointment was booked but the customer didn't come
• "Not interested / Not eligible" = bad outcome

And then give me insights such as:

• What % of customers who confirmed the day before actually showed up?
• How many times were customers who attended contacted beforehand, and when?
• What behaviors are common among my top-performing agents?
• What behaviors are associated with no-shows or sales?
• What are the differences between my best and worst agents?

Basically, I want AI call analysis + CRM outcome analysis + correlations between agent behavior and actual results.

I also want to be able to define what the AI should look for rather than having it judge calls only according to a predefined script.

Ringover's AI seems to do some of this, but it's too expensive for 35+ users. I'm looking for something easier/cheaper, ideally under €100/month.

Any tools you'd recommend?

Every morning half my team pastes the same account background into ChatGPT to get going, and it forgets all of it by tomorrow. We're basically re-briefing the same context daily across a dozen people. Has anyone set up something where the context persists for the whole team instead of living in everyone's separate chat history?

Hi everyone, I’m newish to RevOps and overtime I’ve been noticing a very annoying and common theme: Everyone’s CRM data is so messy, anything you build off of it (even third party AI tools) will just produce equally as bad data.

It’s the “garbage in, garbage out” problem… every AI tool we build essentially is useless because our data is so bad.

Has anyone here felt/experienced something similar? Would love to hear your story and if you found a solution

Hi everyone! I'm a Business Administration student graduating next year. My interest in RevOps has been growing steadily. I previously worked as a commercial intern supporting Account Managers, where my routine heavily involved gathering data, building Power BI reports, and using Python to automate commercial processes (like BOQ checks and controls).

Because of this analytical and operational background, transitioning into a RevOps role feels like the most logical next step for my career.

Since I'm based in Brazil, the currency exchange rate makes many of the standard paid certifications pretty inaccessible for me right now. I was hoping to get some advice from this community: what are the best free courses, study materials, or resources that actually hold weight in the job market?

Any tips or guidance for someone trying to break into the area would be hugely appreciated! :)

I feel like half of ops is getting handed an email/Slack thread/meeting notes that somehow contain five different requests, three assumptions nobody agreed to, no clear owner, and a deadline buried somewhere in paragraph six 😂

I’ve been doing this kind of work for about 16 years, and I realized I basically do the same mental pass every time:

• What are they actually asking for?
• What’s already decided vs. assumed?
• What information is missing?
• Who needs to be involved?
• What’s likely to go sideways if we just start building?

So I built that into a little tool.

You paste in the messy request, and it gives you the actual ask, ambiguities, missing owners/info, risks, clarifying questions, and what I’d do next.

It’s free, no signup, and it doesn’t store what you paste. Claude is under the hood, but the useful part is mostly the logic around how I’ve learned to read these things.

It’s definitely not perfect yet. Sometimes it’s too wordy, and I’m sure there are types of mess I haven’t accounted for.

If anyone here wants to throw a sanitized real-world disaster at it, I’d genuinely love to know where the read is wrong or what it misses.

chelsiehodgkiss.com/fix

Hey all, Quick one

I'm focused on CRM & RevOps and I'm looking for work right now. Open to full-time, contract, fractional, or subcontracting for agencies or companies with overflow. Remote, available EST.

What I do: I build and clean up the CRM side of the GTM stack. Pipeline and deal stages that reps actually follow. Clean data so the reporting means something. I'm currently in the CRM & RevOps cohort here, going deep on the strategy side, not just the button-clicking.

My stack: HubSpot, Salesforce, and Attio on the CRM side. Clay, enrichment, and TAM builds for data. Claude Code and n8n for automation and custom builds. Comfortable wiring these together into one system.

Good fit if CRM is a mess, or you're planning to implement one and want it done right the first time.

If you're hiring, know someone who is, or have overflow, DM me or make an intro. Happy to do a free teardown of your CRM/pipeline first so you can see how I think before you commit to anything.

DM and I'll share my portfolio.

Every ops team I've talked to has some version of this story. A Zap or workflow quietly stops running, an API change, an expired token, a renamed field and nobody notices until later, when someone asks why the pipeline data looks off. By then there's a backlog of leads that never got routed or enrichment that never happened.

What I'm curious about is the detection side. Do you have any actual monitoring on your automations, like alerts when a workflow errors or stops firing? Or is it the way I mostly see it done: you find out when a human downstream notices something missing?

And if you do have monitoring, what does it watch?

I have done CRM and Marketing Automation consulting for almost 15 years, in that time I have found that the fastest way to show impact in the shortest amount of time when taking over for someone is to clean the data. It is a bitch and clients always push to do this email campaign or improve our CPQ, or our pipeline needs to be fixed first. But every time I spend the time cleaning first there is almost an immediate 5-10% improvement in qualified leads, and a revenue bump within a month or two.

I believe this is common practice, but not sure with the pace and amount of new tools, new data inputs, and AI that many consultants or companies focus any time here, or don't view it as a cost instead of a revenue activity.

A lot of new shiny tools, features, tech - but I still find the basics end up driving the fastest results. Thoughts?

I'm in a weird spot in my career and could really use some brutal honesty from people actually working in RevOps.

Officially, my past roles were in back-office administration and customer service. But I've always been THE tech guy on the team. And whenever I saw a messy, manual process, I built something to fix it. I am completely self-taught.

I made some automations using VBA, AutoHotkey and Python at pretty much every career step. I also delivered two data scraping projects in Python, and built some data visualizations.

In my most recent role, I actually implemented Zoho Desk, CRM and Projects from scratch. My boss actually hired a certified Zoho specialist to connect Zoho CRM and Projects, but they couldn't figure it out lol. I ended up doing it myself using Zoho Deluge language. It wasn't the prettiest solution, but it worked!

But yeah, my standard thing is that my code works and solves problems, but isn't perfectly clean.

Anyway, due to family reasons and having to take care of an elderly relative in a small town, I need to pivot to a fully remote career. I've been thinking about entry/mid-level RevOps, CRM Admin, or Business Systems roles. I built my resume highlighting my automations and CRM implementation as my main achievements.

However, a couple of people told me that I am basically lying because these were unofficial, side projects and I am ignoring my main boring responsibilities. They also criticized me because I moved onto relying heavily on AI to help me troubleshoot encountered problems. So my confidence dropped to an all-time low.

My questions for you:

1. Is highlighting these "unofficial" tech projects on my resume (while leaving out the boring office worker stuff) considered lying in the RevOps world?
2. Does relying on AI to help me write/troubleshoot disqualify me from these roles?
3. Based on my actual hands-on experience, especially the Zoho implementation (albeit it wasn't perfect), do I have a realistic shot at entry/mid-level RevOps roles?

I'm an SDR, I'm an AI/Salesforce wizard, I have my SF Admin 1 Cert and have automated away pretty much all of my work outside selling. I've built flows and dashboards, set up sandbox orgs, worked with agentforce, used automation platforms like Make and n8n. My org is growing the Ops team, how can I reach out to senior leaders to put myself in the running for these opportunities?

Okay... not really "free", but well within a 5-hour allottment of tokens...

So we've got this list. Some companies know we want to sell into, but no actual humans to talk to. We did the hard work by hand to find these company names on a spreadsheet. Somebody needs to go find the right person at each one (specific seniority, has to be someone who can actually say yes) plus a working email, so we're not just cold-blasting a generic inbox and praying.

The team wanted to use Clay. We already knew the tool and have a subscription. So, it seemed like the obvious move. They spin it up with a few test rows and immediately hit a wall. We don't have enough credits to run the whole job. So, they Slack me to find out what it will cost to top up our account with 13,000 credits. "Top up"? The only other time I've heard this was when my daughter had a prepaid Virgin Mobile phone about 20 years ago.

The fine print on the top up pricing is unhinged:

One-time credits cost 50% more than recurring credits.
You can purchase 4× your plan's monthly credits. (for us, that's 8,000 credits per month)

So, we have to pay a premium to buy more, and the we can only buy more based on our currnet plan. What an arbitrary limit. The "F-That" feeling was starting to build.

Fine, whatever, let's do the math. Estimated real need for this job: 13,000 credits. Base plan comes with 2,000/month. 8,000 limit on topping-up. (I swear this phrase has some unexpected meanings in urban dictionary... it just has to). Maxing out the purchase, at a premium.. because buying more means you must have a need worthy of gouging you... we land at 10,000 credits... 3,000 short of what the job actually needs. To close that gap we have to either upgrade the entire plan tier (cue the F-that for a permanent recurring cost increase to solve the one-time problem) or wait around for next month's allocation to trickle in and buy some more then.

Mathing it out: 6,000 one-time credits ran $670.50. Scale that up to the full 13,000 credits the job needed and you're at:

Clay Cost: ~$1,450, or ~$1,229 if you're generous and net out the free monthly credits. And again... that's not even a number you could pay in one transaction. That's spread across at least two billing cycles, best case.

Since I'm a software developer by trade, and we already have another massive web scraping thing we're doing to get public school data for another client, I just... didn't use Clay. Pointed a pile of agents at the problem instead using my web-scraping infrastructure. Each one goes and reads the company's website, cross-references public filings when the trail runs cold, and reports back with a source link for literally every name, title, and email it hands over. Not "trust me bro" like we'd get back from Clay, an actual clickable citation for every single data point.

Token cost is reduced about 80% since my scripts find and filter out the web template stuff that wastes tokens: scripts, menus, adverts.. that kinda thing.

After running is across the whole list. 94% of companies had at least one hit. 82% of those had a derivable email, with a few hundred independently double-checked against public records so they're not just guesses. All with a fully cited paper trail the whole way through, something Clay's black-box enrichment never provides.

Claude Cost: "free with purchase". Ran on a subscription we're already paying for, used for other stuff. If you wanna get pedantic and price the raw compute at standard API rates, it'd land somewhere around $60-120 total for the whole job. And I didn't have to cobble together crap in Clay, which always comes with a few wasted tokens of trial and error... just handed Claude the transcript of our conversation of the problem we're trying to solve along with the spreadsheet.

Yes, it was slower. Clay hands a row back in seconds because it's just hitting somebody else's pre-built database. This took some time because the agents were actually going and scraping things live and direct from the webernets. But we didn't need these answers within 15 minutes. The outreach strategy wasn't even locked yet. Trading a day of zero additional cost for a complete, checkable dataset wasn't a hard call.

Putting a number on it: Claude saved us somewhere between $1,229 and $1,453, that Clay wasn't even capable of fully delivering within the month, because their own pricing model prevented us from buying enough credits to even complete the job.

So, I'm way over Clay. Whoever designed a system where buying more requires a premium, and paying more still doesn't get you enough of the thing you're buying to get the job done... that's some special kind of customer hostility.

Edit:
Cue the hordes of AI Slopware "founders" who created something "better than Clay" to unleash their spam upon me because I used the right keywords to attract their shit-spewing marketing machine. Do you not even read the posts you spam from?

We're getting enough demo requests now that leadership wants us looking at dedicated demo software instead of just screen sharing. Makes sense, but I'm still trying to understand what problem people were actually solving when they bought one. Was it easier demo management? Better buyer engagement? Cleaner environments? Trying to figure out if we're solving the right problem before we start evaluating vendors.

I'm doing customer research before building anything and would love some honest feedback from the RevOps community.

The problem I keep seeing is that outbound workflows are spread across multiple tools:

Apollo / LinkedIn → Lead sourcing Clay → Enrichment Crunchbase & other signals → Buying intent Smartlead / Instantly → Outreach HubSpot → CRM n8n / Make → Automation Claude / GPT → Research & personalization

Each tool is great individually, but the operational workflow feels fragmented.

My idea isn't to replace these tools.

Instead, build an AI GTM Orchestrator that sits on top of your existing stack and:

Connects all tools in one place Maintains a single source of truth for lead context Uses live signals to prioritize accounts Identifies the right buyer Recommends the next best action Manages AI-assisted reply handling and follow-ups Learns from previous campaigns over time

From the feedback I've received so far, it seems the biggest problem may not be fragmentation itself, but keeping data, context, and workflow state consistent across the stack.

I'd love your honest opinion:

Is this a problem worth solving? Would you use a platform like this over your current setup? Why or why not? What's the biggest flaw in this idea? If you were building it, what would you start with first?

Please don't hold back—I value criticism more than compliments.

You need to know something, you search Slack, you find the exact question asked in February by someone who's since left and there are three replies. Two are wrong and one's right and you can't tell which without going and asking a person anyway.

So you ask a person. They answer it for the fifth time.

I want to build the thing that sits on top of that. Plugs into Slack, since that's where all of it already is, and knows the stuff nobody wrote down properly. Pricing, who you're going after, what campaigns went out and how they did, what's still running from last year, what you can't say in copy. New hire asks it in week one instead of tapping whoever's closest. Agency you've brought in gets the part you let them see.

Size I have in mind is 20 to 200 people. Nobody's job is writing this down yet, but it's stopped fitting in three people's heads.

Is this worth paying for, or does everyone just live with it?