I feel like people can come up with a million reasons why someone would churn but in my experience it's really just three
Two of which are in your companies control and one less so but am I missing anything?
We’re looking at tools that can take a decent chunk of routine calls without sounding robotic or falling apart the second a customer goes off script.
Big things for us are low latency good human handoffs and something that can work with the systems we already have. Also interested in how much work it took to get live because every vendor demo makes deployment look way cleaner than it usually is
I started Seamless.ai back in May 2026 since they promised me the whole world with their lead generation services. I made an appointment to get subscribed with them and waited for the Zoom call.
On the Zoom call back with one of their salesman in May I said "I want to try this out for a few months before fully committing to this and if anything changes I'll just call to let you know if I'll continue with Seamless in a few months". The salesman on that call even said he's not going anywhere and here to help me with anything I needed.
He made no mention of the annual "contract" I made with Seamless even after I made the comment: I'll just try it for a few months.
In mid-July I decided NOT to continue with Seamless.ai and thus my nightmare negan
Can anyone here help?
Is there a way to cancel and delete my account immediately?
What can I do to stop them from charging me now?
I keep seeing CX teams pushed toward more automation and I’m not convinced automation alone means a better customer experience. Bots are fine for simple stuff. The problem starts when a customer has an issue that doesn’t fit the script. They spend five minutes fighting the bot then finally reach an agent and have to explain everything again. Meanwhile human agents have their own mess. They’re expected to know every policy and process while searching through knowledge bases during live calls. Then QA reviews a small sample of those conversations days later and tells them what they should have done differently. By then the customer already had the bad experience. I think the more interesting use of AI is during the actual conversation. Surface the right answer when an agent needs it.
Flag a missed step before the call ends. Carry context from an AI agent to a human. Learn what the best reps are doing and help the rest of the team do the same. We’re looking into AI tools for our customer support operation right now and one thing I really don’t want is to use AI as another wall between customers and an actual person. I’ve dealt with enough support bots myself to know how annoying that gets lol. It’s more like “how much repetitive work can we remove so the support team has more time for the conversations where a human actually matters?”
I’ve spent a lot of time around customer service and contact-center operations, and one problem kept coming up:
The CRM knows the customer.
The ticketing system knows the issue.
The contact-center platform knows the conversation.
QA listens to a small sample of interactions.
And management often discovers recurring customer problems only after they become big enough to appear in reports.
That was the thinking behind Quantara Flow AI, which I’m currently bringing to market.
Instead of treating CRM, customer service and AI as separate tools, the idea is to connect them into one operational layer:
The part I find most interesting isn't actually the chatbot.
It's what happens after the conversation.
For example:
Customer contacts you → AI understands the intent → case is created/updated → interaction is QA'd automatically → recurring issue is detected across other customers → probable root cause is identified → the relevant team can be alerted or a workflow triggered.
Essentially:
Detect → Understand → Act.
I'm curious what people here think.
When you're choosing a CRM/customer-service platform today, do you still prefer separate best-of-breed tools connected through integrations, or would you rather have CRM + service operations + AI orchestration in one platform?
Disclosure: I'm the founder of Quantara Flow AI, so obviously I have a point of view here. But I'm genuinely interested in how CRM users see this evolving.
Happy to offer free perks in exchange of building case studies, connect with me!
Just as US-EU trade tensions seemed to be cooling, a new flashpoint has arrived.
Trump has threatened a 100% tariff on any country imposing a digital services tax on American companies, and made clear it would supersede any existing trade agreements. This comes less than two weeks after the EU approved a deal designed to cut tariffs on US goods.
The tactic has worked before. Canada repealed its 3% digital services tax after a similar ultimatum to keep trade negotiations alive.
But the EU is a different beast. France already has a DST in place and has previously said it won't bow to US pressure. Germany and Belgium are planning their own versions. The core disagreement, whether large American tech companies pay enough tax on European revenue, has been running for years with no resolution in sight.
For ecommerce sellers operating across borders, this isn't abstract. A 100% tariff on goods from major EU trading partners means higher sourcing costs, more expensive imports, and consumers on both sides paying more for everything.
A few things worth discussing:
Do you think EU countries will back down the way Canada did, or is this a different situation entirely? If these tariffs do go into effect, which product categories do you think get hit hardest?
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There's a real tension in AI customer service deployment right now: transparency is increasingly required by law and expected by customers, but the most-cited research on the subject shows disclosure can collapse conversion rates by nearly 80%. Here's a careful look at what the data actually shows — and why the headline finding is less alarming than it appears once you understand the mechanism.
The sharpest evidence comes from a 2019 Marketing Science study ("Machines vs. Humans" by Luo, Tong, Fang, and Qu). Researchers ran a field experiment with 6,255 customers of a financial-services firm making outbound calls about loan renewals, randomizing when — or whether — the chatbot disclosed it was AI.
The results by condition:
Same bot. Same knowledge. Same measured empathy. One variable: when the disclosure happened.
The mechanism is the most important part of this finding. A voice-mining analysis in the study found the disclosed and undisclosed bots were objectively equivalent in knowledge and empathy. What changed was customer perception: once told they were dealing with a machine, people rated the same agent as less knowledgeable and less empathetic — even though nothing about the actual performance changed.
This is algorithm aversion — a well-documented psychological bias against machine decision-making that persists even when the machine objectively performs as well as or better than humans. It's not rational (in this case), but it's real and it affects behavior.
The important nuance: algorithm aversion isn't fixed. The study found customers with prior AI experience showed a significantly smaller disclosure penalty. As agentic AI becomes more familiar in everyday interactions, the aversion weakens — and the cost of honest disclosure falls with it. The 79.7% penalty figure from 2019 is almost certainly an overestimate of the penalty teams would face today, and will continue to shrink.
For support operations (as opposed to sales conversion), the relevant metric is resolution rate, and disclosure affects it indirectly through abandonment.
A meaningful share of customers disengage when they discover they're talking to AI. Survey data puts this in the range of a third of customers in some contexts, pushing abandonment on disclosed-AI interactions toward 25–30% versus 3–5% for human-fronted ones. An abandoned contact is an unresolved one, which quietly drags first-contact resolution down.
But — and this is the critical point — resolution rate is fundamentally a capability problem, not a labeling problem. An AI that resolves 80%+ of cases end-to-end does so regardless of what badge is on it. The abandonment effect is real but bounded: customers who stay experience the capability. The goal is to minimize abandonment through good disclosure design while maximizing resolution through actual capability.
These aren't in conflict. They reinforce each other: an AI that demonstrably resolves issues earns tolerance for the disclosure. An AI that stalls and deflects earns resentment of it.
The picture here actually flips in disclosure's favor.
CSAT tracks whether the issue got solved, not who solved it. Around 74% of users report higher satisfaction when a chatbot fully resolves their problem without a human handoff, and 87% report positive experiences with AI chatbots overall. Transparency helps CSAT because customers who know they're talking to an AI calibrate their expectations appropriately and judge the interaction more fairly — rather than measuring it against an implicit human standard.
What tanks CSAT isn't disclosure. It's an AI that lacks the context to resolve the issue, can't escalate cleanly, or forces the customer to repeat themselves when they do reach a human. Those are capability and handoff problems, not disclosure problems.
The practical implication: if your AI is genuinely good, disclosure protects your CSAT by setting appropriate expectations. If your AI isn't good, disclosure reveals the problem — which is useful information even if it's uncomfortable.
This is where hiding AI creates the most serious risk.
Around 75–85% of consumers say they want to know when they're interacting with AI. 81% consider AI passing as human to be an ethical problem. These aren't fringe positions — they reflect a broad baseline expectation of transparency.
Concealing AI doesn't protect loyalty; it defers the damage. When customers discover — through a slip, a capability boundary, or external reporting — that they were interacting with AI they weren't told about, they experience it as deception. The discovery-after-the-fact effect on NPS and trust is substantially worse than honest upfront disclosure would have been.
Salesforce research adds a specific finding: 44% of consumers are more likely to use an AI agent when its logic is explained, and 45% when there's a clear escalation path to a human. Transparency and control aren't just ethical requirements — they're conversion drivers in the right context.
This has moved from optional to required in significant markets. The EU AI Act's Article 50 transparency obligations — requiring that people be informed when they're interacting with an AI system — took effect on 2 August 2026. Other jurisdictions are moving in the same direction.
For any team serving EU customers, undisclosed AI isn't a strategy choice — it's a compliance risk. And beyond the legal requirement, the broader trajectory is clear: disclosure is becoming a baseline expectation globally, and building it in now is less costly than retrofitting it later.
The research points toward a specific approach that preserves outcomes:
Let competence lead, not the disclaimer. Front-loading "Hi, I'm an AI" before the customer has seen any value primes algorithm aversion before the interaction has a chance to earn trust. Open with substance — address the customer's situation — and identify the AI clearly but without making the disclosure the first thing they process.
Make human escalation obvious and instant. The most important trust signal in disclosed AI interactions is that the customer can reach a human easily and quickly. "I'm in control" — I can escalate if I want to — converts the disclosure from "I'm stuck with a bot" to "I'm choosing to continue with the AI." That reframe changes how customers experience the interaction.
Invest in actual resolution. Every satisfaction and trust gain in the research data is downstream of the problem actually getting solved. Disclosure is most costly when the AI isn't capable. It's cheapest — potentially costless — when the AI resolves the issue competently. The leverage is in capability, not in disclosure timing.
Test rather than guess. The optimal disclosure wording, placement, and timing vary by channel, customer segment, and use case. Treating disclosure as something to optimize against real resolution and CSAT data — rather than a fixed script — lets you find the approach that works for your specific context.
The 79.7% conversion penalty from the 2019 study represents a specific condition: early, unearned disclosure of AI in a sales context before any value demonstration, among customers with limited prior AI experience, in 2019. That condition is increasingly rare as agentic AI becomes familiar and disclosure design improves.
The broader data supports a different conclusion: honesty about AI, paired with genuine capability and easy escalation, is an NPS and trust asset — not a liability. The penalty belongs to badly-designed disclosure, not to transparency itself.
What's been your experience with disclosure in practice — have you tested timing or framing variations, and did it move the metrics the way the research predicts?
I’ve made an app that has a growing and active user base. And I’d really like to talk to my customers and ask for their feedback.
I tried writing some personal emails to a select few active users, addressing them by name, introducing myself and asking them a question. And offering them a bonus.
None have responded…
If you were a customer of an app, what would make you respond to an email from the owner/founder asking for feedback?
I’m researching how businesses handle incoming customer calls and where the current process breaks down.
I’d love to hear from business owners, managers, receptionists, salespeople, or anyone who regularly handles customer calls.
How often do you miss calls?
What happens when you can’t answer?
What types of calls take up most of your time?
Do you have someone dedicated to answering calls?
What’s the most frustrating part of managing incoming calls?
Are there tasks you wish could be handled automatically?
What would you never want an automated/AI system to handle?
I’m exploring an AI voice receptionist/voice agent and trying to understand the real problems people face before deciding exactly what to build.
Not selling anything here just looking for honest experiences and opinions
A friend and I are building a customer support/helpdesk product and are trying to make sure we’re solving a real problem before going too far.
We’ve spoken to a few support professionals and have started seeing some interesting patterns, but we’d really value perspectives from people who’ve built or worked in this space.
If this is your space and you’d be willing to share some guidance, please reach out.
Would genuinely value your perspective.
Our company records thousands of customer calls every week and most of those recordings are rarely ever being played back. QA team reviews a handful from each agent and managers listen to calls here and there but there are still thousands of calls nobody attends to.
There could be customers bringing up the same issue hundreds of times and we'd have no idea unless enough of those calls happen to make it into the ones we review.
We've basically built up years of customer conversations that nobody has the time to go through. There has to be useful stuff buried in there!
Kind of crazy that we go through the trouble of recording all of it when hardly anyone ever hears it again.
Just curious if industry standard is still Zendesk for many enterprise companies or if there are other players on the market that new, emerging or used as widely
Is anyone else sick of dealing with chat bots. You can hardly resolve any customer service related issues anymore. You cannot ever get ahold of a human being on the phone.
Hi everyone, I’m dealing with an absolute nightmare with Galaxus and Swiss Post right now and could really use some advice on how to claim compensation.
Here is the situation in a nutshell:
I find this ridiculous. My contract is with Galaxus, not their third-party service center. According to the Swiss Code of Obligations, shouldn't Galaxus be fully liable for the negligence of the subcontractors they use? I have been writing with their customer support for more than one month now, they are being very rude and refuse to take any liability and keep repeating the same bullshit.
Has anyone experienced this kind of situatioin with Galaxus or Swiss Post? What is the best way to force them to refund the money? Should I go through FRC (not sure how much they can help), or is there a better way to escalate this?
Any advice would be hugely appreciated! Thank you for the time to read:)
TL;DR: Sent a 1.5k CHF camera to Galaxus for repair. It was shipped back via cheap Eco post (no signature required) and no notification was sent to me. Swiss Post left it at a public entrance and it was lost. Galaxus refuses to refund/provide replacement, claiming their "service center" shipped it, not them, so they took no responsibility. Swiss Post says it's "delivered". How do I get my money back?
So, I need to send a 10-ish question survey. I've looked at some tools but all seem more apt for huge teams, which require me to get on a call before I can even figure out their pricing. I'm thinking between Youform and SurveyMonkey. Youform seems to cover what I need without paying, while SurveyMonkey's free plan is limited to 10 questions, meaning I'd need a paid plan for what I need done. Anyone used these specifically for surveys? I just want to send out the link asap and start getting answers.
Following up on my last post, a few responses made me think that maybe the biggest problem isn’t the number of support tickets, but the effort that goes into resolving them.
For support teams, what actually takes up most of your time?
If you could get rid of one frustrating part of your day-to-day support work, what would it be?
Would genuinely love to hear some real examples from people doing this every day.
We handle enough customer conversations that managers can only review a small sample. By the time QA spots a bad pattern or coaching issue it may have been happening for days. Then the fix is another training session or a reminder in Slack. The frustrating part is we already have plenty of conversation data. We just don't seem to use it while it can still change the outcome.
We have been looking more at tools that analyze every conversation and can give reps help during the actual call. Things like pulling up the right answer when someone gets stuck or flagging a missed step before the call ends. The idea makes sense to me. Post call analytics alone feels a bit like watching game tape when you're already 0-5.
Did agents find the live guidance useful or did it just become another thing on their screen? And did it change anything meaningful like AHT first contact resolution or CSAT?
if you're early stage, this might save you some wasted months.
we spent forever shipping "general" improvements. stuff we assumed every user wanted. trials would come in, click around, churn. we kept treating it like a volume problem and building broader.
then one store owner messaged with a really specific problem. their setup needed a specific integration we didn't have yet, so we wired it into the app for them.
our standard setup just didn't cover it. would've been easy to say "not on the roadmap" and move on.
instead we built the integration to solve their exact thing. bit of a scramble, but it did the job.
they moved to a paid plan basically right after.
point is, generic problem solving only gets you so far when you're small. the one customer in front of you telling you exactly what's broken is worth more than ten guesses about what "the market" wants. solve their real problem and you usually find a bunch of other people had the same one quietly.
curious if anyone else has had a one-off custom fix turn into a feature you now sell.
Been wondering about this as more SaaS companies are adding AI to support. Vendors always talk about deflection rate / resolution rate, but I don’t fully know what that means in practice.
Like if a ticket is “deflected”, it could mean: customer got the right answer, customer just gave up, customer came back later through another channel, bot gave a wrong answer confidently, bot should have escalated but didn’t.
For teams using AI in support or CS, how are you checking this? Do you sample AI handled conversations manually? Compare against QA reviews? Look at reopen rate / CSAT / escalation rate? Or are most teams just trusting whatever the vendor dashboard says?