r/CustomerSuccess

I posted here earlier asking how teams handle support → engineering escalations, and the comments were really helpful. Here

Quick disclosure: I’m building Packtly, a small tool in this space, so I’m trying to understand the problem better rather than pitch.

My biggest takeaway from the earlier thread:

Most teams don’t seem to need more “AI replies.”
They need cleaner escalation evidence.

Usually the missing pieces are:

• what the customer was trying to do
• repro steps
• browser/env details
• logs or screenshots
• impact/severity
• what support already tried

The best test someone gave was:

Can an engineer make a first move without asking support anything?

That’s the direction I’m exploring with Packtly[dot]dev: a readiness gate + evidence packet before a customer issue reaches engineering.

For CS/support teams: would tooling around escalation readiness actually help, or is this usually solved well enough with templates and process discipline?

1. 0% — we haven't implemented AI yet
2. Under 10% — it's there but barely used
3. 10-30% — handles the easy stuff
4. 30-50% — meaningful chunk of volume
5. 50%+ — AI does most of the heavy lifting

Is this a viable career path for someone who doesnt like coding much and wants to build a career in solution consulting?

Context: I have experience in deployment. automation, devops etc

Dont like coding, hate object oriented code.

Want to focus on career path which little coding and more on product, definitely not a product manager which is ambigous.

Which is why i was checking this out like solutions/sales/customer engineering kind of profile than a typical technical expert which will make me burnout.

We recently had a painful moment where a customer stopped us mid-meeting and said: "I literally just answered this for your sales rep yesterday." It was incredibly awkward, and a clear sign that our internal communication is broken.

Right now, our context is scattered everywhere: Sales has their notes in the CRM, Support is in Zendesk, Implementation has a checklist on a random Jira board, and CS is trying to piece it all together. Everyone has a piece of the puzzle, but nobody actually knows the current ground truth.

I'm not looking for a massive enterprise platform right now. I just want to find the simplest, lowest-friction shared tracker—something that clearly shows:

• Account name & owner
• Current risk level
• Open questions
• Immediate next action

We tried a basic shared Google Sheet, but it quickly became an unmaintained mess because updating it felt like a chore.

For those of you in smaller teams, how did you solve this without adding a massive admin burden? What did your tracker or lightweight process actually look like?

Hey everyone,

I am a clinician looking to eventually transition into the health tech world as a CSM.

I am wondering what the daily stress levels are like from all of your experience?

Do you find significant stress when it comes to renewals? How about from management?I know it is probably mostly company dependent.

I am used to a fairly high level of autonomy, but I am no stranger to stress working in a busy primary care clinic with high acuity and complex patients.

Thanks again!

Currently a top performing sales rep at a company (been #1 past 2 quarters in a high competition environment) but the whole quota thing and non stop proactive work (cold approach mostly) has been really wearing me down. I've had previous experience as a CSM (Not exactly in this branch) and find it more stimulating and exciting (more my element). Was curious if someone has been in a similar situation and if so what would be a good approach for me to actually transition. Any advice is welcomed and appreciated. (I'm on real good terms with everyone from CEO to my actual manager just for context)

Three startups. Three times I built something people said they wanted. Three times they didn't actually buy it.

The pattern was obvious. I was asking "would you use this?" and collecting yeses like they meant something. They didn't.

The problem is structural. When you ask someone if they'd use a product, they're predicting their future behavior. Humans are awful at this. We're biased toward yes because we don't want to be negative, and the cost of saying yes is zero, they're not actually committing to anything.

Contrast that with asking about behavior that already happened. "How do you handle this today?" Nobody can lie about what they're already doing. That's real, verifiable behavior.

These are the five questions I run through every customer discovery conversation now:

- How do you solve this today? → Your actual competition

- What's the most frustrating part? → Your positioning, in their words

- What does it cost you? → Whether there's a business here

- What have you already tried? → Active seekers vs. passive complainers

- What does ideal look like? → The outcome they want, not the feature they imagine

The third question is probably the most important. If someone can't tell you what the problem costs them, in real time or real dollars, it's probably not painful enough to pay to fix. That's the filter that saves you from building vitamins.

I now do a minimum of 25 conversations before writing a single line of Vibecode. At around conversation 15-20, you start hearing the same language. The same metaphors. The same workarounds. That's when you know you've found something real.

One thing that changed how I listen: I stopped pitching. Most founders do "discovery" where they pitch for 15 minutes and ask for feedback for 5. That's backwards. You should be quiet 80% of the time.

What's the question you've found most revealing in these conversations?

I have been working in Ireland & uk for 4 years and I am planning on moving back to India in the next 6 months. I have been out of indian job market the whole time, so I request for a reality check from the experienced because I keep going back and forth on this.

Background:

• MSc from TCD. About 4 years experience.
• Currently on the founding team of a small B2B SaaS in ecom reg tech (£20 Mil Valuation), as product owner and product manager (small company, so the two blur).
• I own the backlog and roadmap, ship features, ran 5 country integrations, work with 2 dev teams daily, comfortable with sql & system design. A-CSPO and AWS Solutions Architect certified.
• I am also the point of contact for 5 enterprise clients plus around 30 SMBs. I run onboarding, understand their business, and upsell and cross sell, but with no sales quota. I brought in 4 partnerships and 2 are doing well.

I actually like product work. My hesitation is the Indian market. From the outside it looks like the strong product roles expect an iim or a proper tech degree, and I have neither.

I am torn between aiming for product (PO / PM) and account management, the farming kind, not cold hunting (I have zero experience chasing new logos). I do have equal liking and interest in Account Management.

To start, I need roughly 1.3L in hand per month atleast as am the sole earner in the family.

1. With this profile, would you qualify me to pivot to account management / customer success?
2. Honestly, any directional advise would be considered gold. Please slap me hard with any thoughts you have!

Curious how this actually works on different teams.

- Is there a real process (documented guides vs shadowing a senior?) or is it mostly figure-it-out?

- Roughly how long until a new CSM owns their book without needing backup?

- And what's the harder part for new hires, learning the product or learning how your team does things in your tools?

- What are ramp/ training applications you'd recommend for new CS teammates

Our org has been struggling with silo’d departments over the past year and wanted to share something that’s been working well for us.

We’re a small(ish) startup and have some “flagship” AI features that have been live for a while now with around 3 people on our CX team. Since launch there has always been a weird gap between the engineers and the CX team handling tickets. A customer would get a weird response, complain, and the agent would deal with it in a ticket. However, none of that info would make its way back to the people improving the features. I mean, sometimes it would, obviously the teams talked, but there wasn’t any systematic process in place.

This ended up being really frustrating and finally we built a lightweight version of a human review queue. When a support agent runs into a bad AI response, they flag it and slap a quick label on it (wrong info, weird tone, didn't follow instructions, etc.). That flagged + labeled example then feeds straight into our eval dataset (currently using Braintrust for our eval platform). Now the agents' labels turn into actual test cases we run against future changes.

We’ve now got the extra benefits of:

A. The support team genuinely likes it. NGL just kinda assumed they’d be annoyed haha.

B. The dataset got way more realistic.

C. It quietly bridged the technical/non-technical divide.

Still early and the labeling taxonomy needs work (agents disagree on categories more than I'd like). But overall it's turned our support team from the last to know into the first line of quality signal.

Hey everyone. I few months ago I posted about my work as a junior CSM in a SaaS B2C startup.

Lately I’ve realized I’m not actually getting better at being a CSM, mainly for 2 reasons:

1. I didn’t have any onboarding or guidance when I stepped into the role. I came in, learned the software, and was tasked with creating a new onboarding process our user base. I wasn’t really checked on my work (if I had set it up correctly), and wasn’t “taught” by anyone (especially considering I had no experience in the field).

2. I’m not actually measured on how “good” of a CSM am I. I have weekly meetings with the CEO of the startup and my direct manager (PM), but the metrics are usually revolving around user activity like: how many users were active, how many came back to the software, how many new users signed up, etc. These metrics have to do with product quality as well as marketing, and so when there’s decline in metrics, it feels like it’s my “fault”.

But the thing is, I actually love the part of the job that is ACTUALLY Customer Success. I conduct demos for a certain user base, and I ALWAYS receive positive feedback during the calls. Users tell me to my face how much they enjoyed the meeting, because I actually love what I do. The sad thing is that positive feedback I receive during a call stays in that call. There’s no system in place to actually test how well I’m performing in my job. Regardless, I still give my all cause I love to help people.

Now this long realization brought me to think that I need to do SOMETHING to actually improve, and have a chance of being offered a position in a proper CSM position.

Is there a thing where I can be coached by a senior CSM? I don’t have the funds to pay someone, and honestly even once a month I think will be a great impact. Preferably, if any of you know about a platform that maybe matches coaches to people who seek guidance that would be amazing.

And if you’re a Senior CSM and have any advice for me, please feel free to share it as well.

A bit of additional background: this is a part time position while I peruse my bachelors degree. I would love to have a more “organized” position, but unfortunately it’s VERY hard to find a part time position as CSM.

for 3 years my meeting prep was open calendar, skim linkedin for 30 seconds, figure it out live. broke down when a client brought up 3 issues from our support tickets on a renewal call and i had no idea. they could tell.

manual prep in google docs - pulling notes, checking emails, skimming history. effective but 15 mmin per meeting is not sustainable at 6 to 8 calls a day. lasted 2 weeeks.

read*ai / ot_terr - good for transcription and summaries after tthe call. doesnt help with prep before the call which was my actual problem.

denc**h - meeting prep agent runs automatically before every call. pulls account history, recent emails, open tickets, last meeting notes, drops a briefingg 30 min before. not always perfect but i havent been blindsided since.

clau_de manually - works great if you paste enough context. quality is high, the effort of gathering and pasting everything each time is not sustainable for every meeting.

the best prep system is the one that runs without you remembering to doit.

Ialways knew preparation mattered i just never did it consistentlyy because it required effort at the moment i had the least time…

One question changed the way I approach customer meetings.

Instead of asking myself, “What should I present?”, I started asking, “What is this customer trying to achieve?”

That simple shift made a huge difference.

Customers rarely care about our products as much as we do.

They care about solving a business problem.

When I started focusing on business outcomes instead of features, conversations became more collaborative, and account growth followed naturally.

Has anyone else experienced a similar shift in the way they approach customer conversations?

Recently joined a company as a new CSM - 2/3 months in so still learning the product. I have also got about 6+ years of experience in CSM and CSE roles, so the actual role and job function isn’t new to me.

I have joined and been given a book of business where about 30% - 40% of the customers are already about to churn off the platform - most of them had very little CSM exposure the past 4-5 months due to previous CSM’s leaving without much of a handover in sight.

My manager isn’t particularly supportive and I am under a lot of pressure to turn the accounts around from full churn.

Has anyone been in this sort of situation before and what would your advice be?

Working in a smaller company and we’ve been encouraged to consider AI (Claude in this case) first before we do something manually. But to give you an example of how this impacts my day, instead of manually entering in stuff into my CRM like I normally do, I spent about 2 hours working on connectors and perfecting skills/prompts that can make us efficient when this needs to be done again (the manual way would have taken me about 20 mins). 2 hours where I basically ignored my emails and multitasked during calls to try to figure something out (which still isn’t fully complete).

I understand that the benefits would outweigh the initial time spent on using AI to automate workflows but how have you all balanced your AI experimenting with your normal CS (manual) tasks? Some people in the company admit to working late hours because this stuff is so fascinating to them to figure out but cmon - I have a life and my eyes are bleeding at the end of a work day lol.

We're building this for millions of people across India who don't know how to use their smartphones confidently. That includes older adults, many middle-aged users, and even younger people who struggle to navigate certain features or complete specific tasks. Instead of figuring out complicated menus or settings, users can simply speak naturally in your own language, and the phone gets things done for the phone including basic things and even complicated things such as booking things and more

what do y‘all think??

I’m a CSM at a native AI company in an industry that is booming. Unicorn company that is selling like crazy. I have 5 years total experience in this field, 3 in AI with it.

Just started the role above 5 weeks ago after leaving company I worked 5 years at getting promoted into a csm for 3 years.

Comp is great at new job and I am enjoying working. There are a lot of roles opening and movement that will be made at the company for sure.

Maybe in 1 - 1 1/2 year I could move around. I was think solutions engineer? I would move into that role and stay as long as I could with the company and role until I get enough experience to move to top companies in my field as an SE.

How is the transition to the SE field? How is being an SE with native AI? Is it more viable in the long run than being an CSM? I want to stay in my industry as long as I can because I will become a subject matter expert. I do have a salesy mindset without being pushy and more consultant about expansion / upsell.

AI is one of the first enterprise software categories where vendors can’t fully define what “it works” means before a customer ever logs in.
This isn’t malicious or deceptive. It’s simply a consequence of how generative AI operates.
The problem is that the traditional enterprise software sales motion hasn’t adapted to this new reality. When you buy traditional software, expectations are relatively straightforward: clicking button X produces Y and Z every time. The behavior is documented, the feature either exists or it doesn’t, and implementation is largely about configuration, integrations, and user enablement.

AI fundamentally changes that equation.

An AI system’s performance depends on the environment it operates in, the context it has access to, the quality of the underlying data, the prompts users provide, and the workflows it becomes a part of. Vendors don’t fully understand any of those variables until after the customer begins using the product.

Despite these dependencies, AI is still frequently sold in deterministic language:

“Generate meeting notes.”
“Automate your outbound.”
“Draft emails.”

The expectation becomes that the product will simply *work*. When reality proves more nuanced, customer-facing teams end up absorbing the gap between what was promised and what AI can reliably deliver in each customer’s unique environment.
To be clear, traditional software has never been perfectly deterministic. Integrations fail, configurations drift, and users make mistakes. But generative AI introduces an entirely new category of variability because the software itself is probabilistic.
There are countless reasons why AI breaks the traditional software model, but three matter more than the rest:

1. Probabilistic Generation
Traditional software is deterministic. Given the same inputs, it produces the same outputs. Generative AI doesn’t work that way.
Even with the exact same prompt, an LLM can produce multiple valid responses. One answer might be better structured. Another might capture nuance. A third might emphasize completely different details. None are necessarily “wrong.”
The core question shifts from “Did it work?” to “Was this output useful enough for this user in this context?”
That is a fundamentally different success criterion than enterprise software has historically been built around.

2. Context Dependence
An AI model doesn’t operate in isolation. Its output depends entirely on the information available to it: customer data, permissions, connected systems, conversation history, prompt quality, and workflow design.
Two companies can purchase the exact same AI product and have completely different experiences because their environments are different.
The model isn’t necessarily better or worse. The context is.

3. The Limits of Evaluations
Model evaluations (evals) are incredibly valuable, but they answer a different question than customers are asking. Evals measure how a model performs in controlled scenarios against predefined benchmarks.
Customers care whether the product helps them do their job inside their own messy environment. Those are not the same thing.
A model can score exceptionally well on internal evaluations while still producing outputs that fail a customer’s expectations—because those expectations are shaped by company-specific context and subjective definitions of quality.

Closing the GTM Gap
The biggest challenge in enterprise AI isn’t getting the model to work. It’s getting the customer to agree that it’s working.
That requires more than a better model. It requires a Go-To-Market (GTM) motion built for probabilistic software.
Here is how customer-facing teams need to adapt:

1. Shift discovery from features to context.
Sales teams must stop selling AI as a magic button and start selling it as a system. Discovery can no longer just be about "what features do you need?" It must become: "What data does this workflow rely on, and is that data clean enough for an AI to read?"

2. Redefine "Success" during Kickoff.
Customer Success cannot run traditional onboarding. They must explicitly educate the customer on the probabilistic nature of the tool. Set the expectation on Day 1 that tuning prompts, building context, and refining outputs is not a bug in the software—it is the reality of deploying AI.

3. Measure adoption through acceptance, not just execution.
If a user generates a draft but deletes the whole thing and rewrites it, the software successfully "executed," but it provided zero value. Product and CS teams must build telemetry and health scores around acceptance rates and usefulness, not just API calls or clicks.

Vendors who win this era of software won't just be the ones who build the smartest models. They will be the ones whose GTM teams actually help organizations integrate AI into how they already work, instead of expecting customers to adapt to how the model works. 

I've been testing both for the past week and truly can't decide. My agency sends about 50k emails per month for SaaS clients.

Snov seems to have been around longer but their data feels stale sometimes? Like I'll find emails that bounce even though they say verified. Prospeo's emails seem fresher but I'm wondering if that's just my small sample size.

What I really need to know:
- Which one has better mobile number data? We're doing more multi-channel outreach now.
- How's the API speed if I'm enriching 10k contacts at once?
- Anyone used both for EU contacts specifically?
- Real accuracy rates? Both claim 95%+ but that seems optimistic.

Price wise Prospeo looks cheaper but snov has that email warmup feature built in which could save me on a separate tool. Though I already use Instantly for that so maybe doesn't matter.

I also briefly looked at Apollo but their credit system confused me and I didn't want to deal with another learning curve. Just want a solid email finder that actually delivers clean data.

Would really love to hear from people who've used both extensively, not just messed with free trials.