I've been working on a CRM in Notion and I'd love some honest feedback from people who actually use CRMs.
My goal was to keep things simple while covering the basics:
Contacts
Companies
Deals
Follow-ups
Revenue tracking
If you were looking for a Notion CRM, what would make you choose one template over another?
Hey everyone,
I've been researching the CRM migration industry before building anything. Recently I made a post asking everyone what some of their biggest pain points are and most manual parts of the process. So far from the responses I found that the actual data movement has been largely solved. The most consistent complaints have been field mapping, data cleanup and pre-migration decision making. From what I've read it seems pre-migration is the most underserved phase.
Some new questions I have:
-which roles or teams suffer from these problems the most and how frequently?
-how much time, money or project risk is created from these problems?
-Are there any workarounds or tools people use to make these problems manageable?
I really appreciate all the help and support I've gotten from this community. Thank you for all the responses and everything!
Client’s HubSpot workflow hit the marketing email quota. Their instinct: “let’s just upgrade the plan.”
I asked one question instead: “Who actually needs this email?”
Turned out “Enroll all associated contacts” was ON. So one workflow was blasting every contact linked to a company — 120 people per company — instead of just the one department that mattered.
Client’s answer when I asked who should get it: “Only Billing.”
Fixed the enrollment logic to target the Primary/Billing contact only.
Result:
Login**•** Recipients per send: 432 → 17 **•** No plan upgrade needed **•** Cleaner automation, fewer confused replies from random contacts Moral: before paying more for software, spend 5 minutes checking why you’re hitting the limit. Sometimes it’s not growth — it’s just bad workflow logic quietly emailing people who never needed to be in it.
Anyone else caught a “quota problem” that was actually a “workflow problem”?
Hi! I am an independent rep for a huge manufacturing company and my sole job is recruiting clientele and partners.
I run meta ads and 90% of my conversions come from them.
I've got the ads down. But the system stumps me. Training new marketers to use it is completely impossible with where I am currently at.
I use privyr as a current CRM but I need a way to TRAIN newbies, have them receive their own leads and directly pay for their own leads. That way I can create the lead gen, and they can benefit from it..
I've looked at Go High Level- but I am not 100% sure.
If anyone has any expertise I would be all ears.
I have seen people use apps like Word, OneNote, Excel and other note taking apps for taking notes outside of the CRM. What's your use case and why aren't you doing it inside the CRM? Is the CRM software lacking a notes section?
I operate a digital transformation services company. I recently landed a project with a small maintenance company doing 150 - 250K annually(conservative estimates). They are scaling and planning to franchise.
The engagement:
- CRM audit
- Deliverables:
How would you go about pricing this engagement?
I am considering a fixed project fee, a monthly retainer, and revenue share as options.
They have already signed on with a relatively flexible budget.
Is there anywhere else I should post this question? Thank you!
Been doing CRM audits for a few agents/teams lately — data hygiene, pipeline health, tagging, that kind of thing — and the pattern across every single one has been strikingly consistent:
None of this needs new leads or new tools. It's all sitting in the CRM they already have.
Curious if this tracks with what others here are seeing, or if I've just gotten an unlucky sample size. If anyone wants to compare notes on their own setup, happy to talk through what I'm checking for.
Has AI actually saved you time in your CRM, or is it mostly hype? What features do you use regularly?
I build an AI-native CRM for Swiss and DACH SMEs, firms that have run the same accounting software for fifteen years. After a year of selling into this market, here's the uncomfortable GTM summary: almost nothing from the standard SaaS playbook works.
Product-led growth assumes people try things. Our buyers don't try things. Cold outreach assumes people respond to strangers. Our buyers, almost by definition, don't buy from strangers. Virality assumes users share tools. Nobody here has ever shared a tool.
What's left is one mechanism: borrowed trust. These firms buy through people who already have access their Accountant, their IT partner, the agency that built their website, a peer at an industry event. So our entire GTM reduces to a single question: how do we become the thing trusted people recommend?
In practice that means the "channels" look strange on a dashboard. Partner relationships instead of ad spend. Content written to be found when a consultant researches on a client's behalf, not when an end user browses. Being findable and credible at the exact moment someone is about to spend their own reputation on us, because that's what a recommendation is.
It's slow. Painfully slow compared to any funnel chart I've seen at a SaaS meetup. But it compounds, and once a trusted advisor recommends you twice, you're not a vendor anymore, you're part of how they do their job.
Curious how others sell into markets where the buyer only buys through existing relationships. What actually moved the needle for you?
(Disclosure: I'm the founder of Uliasti, the team behind the product: Advanzo. Happy to go deeper in the comments.)
Confession post. Some background so you know where this is coming from: I co-founded a VC-backed logistics marketplace, we raised 8 figures over a few rounds, and I ran the commercial org as COO. At peak that was 30+ sellers and account managers across two markets, all running through one CRM that I was personally accountable for at board level. So when I say the pipeline data was bad, I mean I was the one presenting that bad data to investors every quarter.
A few years in I got so fed up with stale deals that I did the thing every advisor tells you to do. Tied money to it. Small bonus component for data completeness, fields filled, activities logged, stages current. Reps keeping clean data finally get rewarded, messy ones shape up. Made total sense in the spreadsheet.
Within a month the CRM was 100% complete and maybe 40% true.
Fields stuffed with placeholder junk. Activities bulk-logged Sunday nights from memory, you could literally see the timestamps clustered between 9 and 11pm. One rep copy pasted the same call notes onto six different deals. I was paying people to make the data LOOK maintained. Killed it after a quarter, and undoing the damage took way longer than that because now I couldn't tell which historical data was real either. Try building a board forecast on that.
The part that stung: my best closer had the worst data. And he was right. At 7pm after a day of calls, typing notes into a database is genuinely the lowest value use of his time. The guy was correctly prioritizing. The bonus didn't change that math, it just changed what the corner cutting looked like.
What actually helped, ranked by impact:
Cutting required fields from ~15 down to 5. Boring, free, biggest single win. Half our fields existed because someone wanted a report once in 2019 and nobody ever deleted them.
Weekly deal-by-deal reviews, me or a team lead. This worked. It also ate 5-6 hours of senior time every week and collapsed the month I was traveling for a fundraise. Renting a human sync engine, basically.
The bonus. Negative impact lol.
The pattern I never escaped: everything that worked needed a senior person burning hours enforcing it. Everything that tried to automate the motivation (KPIs, dashboards, the "CRM is our source of truth" speech I gave at least four times) did nothing or backfired.
I've compared notes with a lot of founders in the 10-50 seller range since and it seems near universal. Big enough that bad data actually costs you real money in forecasting, too small to justify a RevOps salary.
So genuinely asking: anyone here solved pipeline hygiene at that size without it hanging on one person's weekly discipline? And has anyone else tried paying for data quality directly? Did it backfire the same way or did we just design ours badly?
Planning to implement AI quote generation at the end of this year but we already ran into problem during testing. The tool quickly produced a clean quote, but for a multi-year deal it ignored our discount floor without any warning or approval step.
The vendor suggested adding the rules to the prompt. But that approach is more like a suggestion than a real policy engine. So, where should we put our pricing guardrails?
we burned about $14k in tooling and 3 months of pipeline time running this test so hopefully some of you can skip the expensive part and just read this instead
ok the raw numbers first because thats what matters. this is across our full SDR team of 6, selling cybersecurity solutions into mid-market and enterprise, $98k ACV, 30 months into this role so i have decent baseline data to compare against
TOTAL SENDS (3 month test period, jan through march 2025) human written campaigns: 11,400 emails across 3 reps AI written campaigns: 10,800 emails across 3 reps
REPLY RATES human: 3.1% overall (positive reply rate 1.4%) AI: 4.7% overall (positive reply rate 2.2%)
BOUNCE RATES human group: 1.8% AI group: 1.6% (slightly lower because the AI reps happened to get cleaner lists that month, not really meaningful)
MEETINGS BOOKED human: 38 AI: 54
COST PER MEETING human: ~$312 (just tooling, not counting rep salary) AI: ~$287 (tooling plus AI costs)
ok so context. our CRO came to me in december and basically said she wanted to see real data on whether AI written sequences could outperform what our reps were writing manually. weve been using Smartlead for sending for about a year, running 22k emails a month across the full team, Maildoso inboxes, the whole setup. the question wasnt about infrastructure it was purely about copy.
the test design was pretty simple. i split the team into two groups of 3. group A kept writing their own sequences the way they always had. group B got access to Claude and a custom GPT we built that was trained on our best performing sequences from the last 18 months. group B was told to use AI for first drafts of every sequence and they could edit from there but had to start with AI output. both groups targeted the same ICPs, same titles (mostly CISOs, VP Security, IT Directors at companies 200-2000 employees), same verticals.
we kept the enrichment and verification pipeline identical for both. lists built in ZoomInfo, enriched through Prospeo for email finding, verified with NeverBounce, loaded into Smartlead. same warmup protocols same sending schedules same everything except the copy.
what surprised me was where the AI group won and where they didnt.
the AI sequences were noticeably better at first touches. like significantly. reply rates on email 1 were 5.3% for AI vs 2.8% for human. thats almost double. and i think the reason is that the AI was better at writing concise openers that felt research-driven without being fake. our reps have a tendency to write these long intros where theyre clearly just restating the prospects linkedin headline back to them and it reads as exactly what it is. the AI drafts were tighter, usually 3-4 sentences for the opener, and they got to the pain point faster.
but on follow ups the gap narrowed a lot. by email 3 and 4 in the sequence the human written stuff was actually performing about the same. reply rates on email 4 were basically identical, 1.1% human vs 1.2% AI. my theory is that follow ups are more about timing and persistence than copy quality and the humans were fine at writing short bumps.
the other thing, and this is where it gets interesting for anyone managing a team, the AI group was significantly faster at producing sequences. group A spent on average about 4 hours per new sequence (research plus writing plus internal review). group B was averaging about 90 minutes. thats a massive time savings and it meant the AI group could test more variations. they ran 14 distinct sequences in the 3 months vs 8 for the human group. more sequences means more data on what resonates which compounds over time.
now the negatives because there are real ones.
the AI copy had this tendency to sound... samey after a while. by month 2 i could read a cold email and tell you immediately whether it was AI drafted even after editing. there was this pattern of "noticed [company] is [doing thing], curious if [pain point]" that kept showing up in different variations. we had to actively fight against that by feeding in competitor examples and telling the model to avoid certain structures. our VP sales actually flagged this independently, he said the emails were starting to feel like they came from a template factory which... yeah.
the other issue was personalization quality. the AI could pull in surface level personalization really well but it would sometimes make connections that didnt actually make sense. one email referenced a prospects company "expanding into european markets" based on a job posting for a UK role, but the company was already operating in 12 countries across EMEA. the prospect actually replied to tell us we clearly hadnt done our homework. stuff like that happened maybe 4-5 times over the 3 months which isnt a lot percentage wise but each one felt bad.
also, and this drove me crazy, getting the meeting data synced properly into Salesforce was its own nightmare. we tag campaigns by sequence type and the custom fields we set up to differentiate AI vs human kept getting overwritten by our Smartlead integration. spent probably 6 hours across the quarter just fixing attribution data. i swear half my job is fighting Salesforce integrations and the other half is pretending i enjoy it.
wait i should mention cost breakdown on the AI side specifically. we were spending about $120/mo on Claude team plan, roughly $80/mo on ChatGPT plus for 2 seats, and then maybe $40-50/mo on various API calls for the custom GPT. so call it $250/mo in AI tooling. thats pretty cheap relative to the lift we got. the human group obviously had zero AI costs but their time cost was higher and they produced fewer meetings.
the conclusion im landing on, and we've since rolled this out to the full team, is that AI should be writing first drafts of everything. no exceptions. but you need a human doing a real edit pass, not just skimming it and hitting send. the reps who performed best in group B were the ones who used AI as a starting point and then rewrote 30-40% of each email. the ones who basically sent the AI output with minor tweaks had good numbers early but they degraded by month 3 as the copy got stale.
one more thing. we tested this exclusively on cold outbound to net new prospects. i have no idea if these results hold for re-engagement sequences or inbound follow up or anything else. our ACV is $98k so the math on spending time on copy quality makes sense. if youre selling a $2k product the calculus might be totally different.
since rolling it out to all 6 reps in april our reply rates have been sitting around 4.2-4.4% which is below the test group's 4.7% but well above our historical 3.1%. good enough. the time savings alone would have justified it even if reply rates stayed flat honestly.
anyway thats the data. take it for what its worth, n=22k emails across one company in one vertical selling one product at one price point. not exactly a huge sample but its real money and real pipeline so it meant something to us
If you're making a call to a regular phone number in the US, how is the call getting recorded, transcribed and summarized back into the CRM? Is a VOIP system or a dialer being used?
Is Whatsapp being used?
I am asking about the recording part in particular when making a regular phone call. I know once a recording has been captured, voice to text and AI can do the transcribing and summarization. Does the CRM have like an api to receive the text including the metadata, so it knows where to place it?
I keep running into the same issue with CRM imports:
Some rows are clearly fine.
Some rows are clearly trash.
But the painful ones are in the middle.
Examples:
same email, different phone
same phone, different name spelling
same company, slightly different title / city / source
records that look mergeable, but not safely
The obvious stuff is easy.
What I’m trying to understand is the middle zone.
How do you actually handle that at scale before import?
Do you:
hold them back in a review bucket
manually label them
merge conservatively
import and fix later
just accept some contamination
I’m not asking for theory. I mean your actual workflow.
I specifically want to be able to export those notes, weekly, and use them for a sales report, but Hub Spot dont do this unless you pay for the next tier up, and Nimble dont have the functionality. Can anyone recommend one? Its esentially something we could run out of an excel spreadsheet so we're looking for something cheap. Thankyou!
I built an AI Agent that fully controls my CRM. Contacts, tagging, workflows, communication, internal notifications, pipelines, scheduled tasks, posting, etc. Anyone else build something like this that runs on auto-pilot. Curious to learn who else is doing this.
For people who actively use a CRM for outbound sales, do you prefer keeping prospecting, enrichment, outreach, and CRM as separate tools, or would you rather manage more of the workflow in one place?
For example, would it be useful to define your ICP and receive a smaller list of already-researched and qualified companies, including the right decision-makers, enrichment data, why they match, and why they may be worth contacting?
Only approved leads or positive replies would then be added to the actual CRM, rather than filling it with every prospect.
Would that make your workflow easier, or do you prefer using tools such as Sales Navigator, Clay, Apollo, outreach software, and your CRM separately?
What would a combined platform need to do before you would consider using it instead of your current setup?
Thanks for any constructive feedback!
We (our team) recently made a few websites for a college, integrating a CRM system to make the application process and customer service easier. It was AI-integrated.
We want to know whether a manually managed CRM system is more effective than an AI-integrated CRM, if so, then in what ways? Advantages, limitations and other things.
Was there a specific moment when spreadsheets started becoming frustrating?
What was the biggest challenge?
Some of you might remember a post I wrote about capping our CRM pricing at CHF 350/month flat. Buried in it was one line that got more DMs than the pricing itself: that predictability resonated with our buyers more than any AI capability we led with. A few people asked what I meant by that, so here is the longer version.
Quick context: I spent about 12 years building software inside Swiss regulated banks before leaving to build an AI-native CRM for German-speaking SMEs. And I mean AI-native literally. The product's whole reason to exist is that the AI does the CRM work people hate: logging activity, keeping records current, drafting follow-ups, surfacing what needs attention. Take that away and there is no product.
So naturally, our early pitch led with it. AI-native CRM, intelligent automation, the whole vocabulary. And in demo after demo with our actual buyers, conservative Swiss and DACH SMEs, it landed with polite nodding and no second meeting.
It took me embarrassingly long to understand why. For this buyer, "AI" is not a capability claim. It is a risk claim. It translates to: my data goes somewhere I can't see, the vendor will change things under me, and my industry association just sent a newsletter warning about exactly this. These are firms that kept their accounting software for fifteen years because it never surprised them. Leading with AI meant opening every conversation with the thing they were most skeptical of.
What we changed: we stopped describing the technology and started describing the Tuesday evening. Nobody types meeting notes into a database at 7pm anymore. Your pipeline is current without anyone maintaining it. Same product, zero mystery vocabulary. And we moved data residency, auditability, and "here is exactly where your data lives" from the compliance footnote to the second slide, because it turned out that was the real question hiding behind the AI skepticism all along. My banking years finally paid off there; I can talk about audit trails with genuine enthusiasm, which is a strange superpower.
What happened: conversations got longer and more concrete. Instead of debating whether AI is trustworthy in the abstract, we were debating whether our tool fits their process, which is a discussion you can actually win. Interestingly, once trust was established, customers started asking about the AI themselves, on their terms. The feature didn't change. The sequence did.
The honest cost: our marketing and our sales pitch have split personalities. In search and directories, "AI CRM" is the category people look for, so our website has to say the words our sales calls avoid. And there is a real risk that as AI normalizes over the next few years, the vendors who shouted it loudest early will own the category label while we deliberately whispered. That trade-off is not settled, and it is the part I second-guess.
What I would do differently: I would have talked to our buyers' actual objections before writing a single line of positioning, instead of importing the vocabulary of the SaaS bubble I was reading. The market told us within ten demos. I just needed three months to listen.
Curious about others selling AI products into AI-skeptical or conservative markets: do you lead with the technology or bury it? And has anyone seen the "whisper strategy" backfire once the market caught up?
(Disclosure: I'm the founder of Uliasti, the team behind the product: Advanzo. Happy to go deeper in the comments.)
