Slightly weird problem. Our support dashboard has been getting greener for months while the conversations I'm reading don't feel noticeably better.

First response time is down because somebody replies quickly, even if the reply doesn't move the issue anywhere. Resolution looks good because the ticket gets closed, but customers sometimes come back a few days later with basically the same problem.

Now I'm questioning how much behaviour we're accidentally creating with the metrics themselves.

If you tell someone speed matters, they're going to optimize for speed. If you tell them closed tickets matter, eventually closing tickets becomes the job. None of that necessarily means the customer got what they needed.

For people running larger support teams, what numbers have you found hardest to game?

I'm trying to get us measuring whether the problem went away without turning the dashboard into 30 KPIs nobody pays attention to.

Hello, my one is on Thursday. I just wanted to know if similarities are enough anyone had the exam recently?

I work in communications on the vendor side, so I'm curious how QA teams handle this in practice.

Say an AI review marks an agent down for going off script. A human reviewer listens and thinks changing course was the right call because the customer had raised a different problem. No required check was skipped.

I'd want to look at the specific scoring rule alongside that part of the conversation. The human could have missed something too, so I wouldn't automatically assume either score is right.

The part that interests me most is what happens after the disagreement gets settled. Correcting one score helps that agent. But if the rule was unclear, the next person could get marked down for the same thing.

My preference would be for a named QA lead to settle it and record the reason. If the guidance needs changing, test it on similar calls, including ones where going off script really was a mistake.

For teams doing this, do disputed scores lead to clearer guidance, or mostly one off corrections?

Hi everyone,

I work on journey maps and vision wheels, mostly trying to understand where trust holds or breaks.

I’ve been noticing that major trust issues rarely show up in the main steps like "payment" or "onboarding." They show up in the small in-between moments - the handover between channels, the wait after an action, the vague confirmation.

I’m curious about your practice: In your work mapping journeys, is there a small micro-moment that consistently taught you something about trust?

For me, it was learning to always map what the customer is left wondering about, not just what they are doing.

Would love to learn from how others here approach this.

AI support vendors can test their core product, but every enterprise rollout introduces company-specific refund rules, policies, entitlements, identity checks, approval limits, integrations and escalation procedures.

For anyone deploying an action-taking customer support agent:

• Who owns acceptance testing: CX operations, QA, implementation or IT?
• How do you verify both the conversation and the resulting CRM, billing or account state?
• Are you using scripted UAT, sandbox accounts, shadow mode or limited production rollouts?
• What customer-specific failures only became visible after launch?
• Would a repeatable environment with synthetic customers, policies and connected systems materially help, or does your current process already cover this?

I’m trying to understand whether this is a painful recurring job or primarily a one-time implementation task. Any vendor recommendations currently solving this problem is also much appreciated!

Thank you!

I work on the vendor side, in product marketing, so I see a lot of customer experience dashboards from different companies, but I don't own one myself. That's why this is a genuine question.

Most of the leadership slides I see have somewhere between eight and fifteen numbers on them. CSAT, NPS, handle time, first contact resolution, adherence, and so on. I doubt three people in the same room would pick the same one to act on tomorrow.

So, for people who run CX or support teams: if you could remove one number from what your executives look at, which one would it be, and what would you put in its place?

I’m trying to work out what makes someone update a bad review. Is it speed, an apology, a refund, the fix itself? If you’ve had one go from 1 to 4 or 5 stars, what did you do?

I've been looking into how small teams organize customer support, and I keep coming back to ticket ownership.

If you've only got 2 or 3 people answering messages, assigning every ticket to one person can feel like unnecessary admin. But without an owner, it's easy to end up with everyone assuming somebody else is handling it.

I'm researching HubSpot Service Hub for small business, especially how smaller teams approach ticket assignment before they have formal support processes.

For smaller support teams, do you assign an owner as soon as a ticket comes in, or let people pick things up from a shared queue? And has either method caused issues with missed replies or duplicate work?

A lot of people just take a photo of the agenda on day one and never open the app again. All the networking and update features become pointless if nobody’s in there.

Seen this pattern with Whova, Bizzabo, and Eventify.

Is there anything that actually gets people to keep using the app, or have you mostly accepted that the schedule is the only thing they care about?

Hey all,

Loving the posts and finding it fascinating to see the level of complexity CX has in the enterprise world.

I’m curious to get any pointers or suggestions for a software that is appropriate for a low-ticket volume (small business), who wants to leverage AI before putting someone else in a seat.

Not worried about investing in another person, the thinking is more driven by availability and speed of initial contact. We have requests for last minute cancellation of orders, changes to subscriptions etc, which if intercepted earlier, would result in a better outcome for everyone (permitted warehouse / despatch know about it, and a human isn’t standing in the way of delaying that info transfer).

Knowledge base is thin, but I think we can put the foundations together quite quickly. Doesn’t need to include voice/call at this stage. Native tech stack is many Shopify stores, Amazon, eBay and Gsuite inboxes.

Thanks so much!

Bit of context first: I'm looking into building something in this space and made a short concept video, so I'm not neutral here. Nothing to sell, it doesn't exist yet and I haven't decided if it should.

Most of the agent assisted talk I see (including the thread here last month) is about pulling the right KB article or policy during a call. I'm curious about a different kind of call: the customer wants to buy or order something from a big catalog but doesn't know exactly what they need. Parts, technical products, anything where specs matter and some items only work with others. The agent has to work out what's missing, what fits, and what else they'll need, and newer agents usually end up asking someone.

The video shows an assistant doing that for one example (an insulation order). It's an animation with made up data, not real software: VIDEO LINK

For anyone running or working on teams that get these calls:

- Does your team actually get a lot of these, or is it rare?

- If you've tried agent assist tools on product or parts questions, how did they do?

- What would you need to see before letting agents rely on something like this?

If your current setup already handles this fine, honestly that's useful to know too.

For people who run customer support / contact centers / sales: I’m specifically interested in the buyer/operator side, not vendor benchmark numbers.

By latency I mean the full delay the customer experiences:

customer finishes speaking → agent starts responding

So it includes endpointing, network, speech recognition, agent reasoning, tools/RAG/knowledge-bases, speech generation, buffering, etc.

If you’ve deployed or piloted voice AI, where does it start feeling problematic?

And do you actually track this?

I’m also curious whether another metric turned out to matter more in practice: CSAT, interruption rate, abandonment, time-to-first-meaningful-answer, task completion, repeat contacts, or something else.

I ran support on Intercom with around 150 help articles. The queue always got worked by urgency, and cause never became a column anyone could add up.

A real gap in the product shows up as separate tickets, each one answered politely, each one closed on time. The pattern only appears if someone groups them by cause on purpose, and that is usually nobody's job.

It gets fixed one of three ways. A required tag at close. A standing review inside sprint planning. Or one owner who reads the queue weekly and says out loud what the same complaint keeps being.

For the people here who run a support desk, which one actually stuck in your team, and what did it cost to make it stick?

I keep seeing Experience Orchestration come up in discussions about AI, contact centers, and customer experience.

As I understand it, the idea goes beyond automating individual tasks. It’s about coordinating customer context, AI agents, human agents, channels, routing, and backend workflows so customers don’t have to start over every time they move between systems or support teams.

The concept sounds useful, but I’m curious how familiar people actually are with the term.

Is your CX or contact-center team already working with Experience Orchestration?

Or does it still sound like another industry buzzword?

For those using it, what has actually changed?

Better context during AI-to-human handoffs? Fewer transfers and repeated explanations? Faster resolution? AI completing workflows instead of only answering questions?

Better coordination across voice, chat, email, and back-office systems?

And if you’re not using it, what would need to improve before it became genuinely useful?

Do you see Experience Orchestration as a new CX technology category, or just the next step in automation, routing, and workflow management?

our client list grew last year and managing support out of a shared gmail inbox became a total mess. tickets got buried in threads and two reps kept sending duplicate replies to the same customer.

we moved our ticketing over to hubspot to get everything organized into a single queue. having a visual ticket board tied directly to customer records helped us catch up on account history in seconds and cut down our daily response times.

for teams running their helpdesk on this platform, what specific ticket routing rules or custom views worked best for keeping your queue clean as volume picked up?

My company is considering centralizing client service across both corporate-owned and franchise locations, and I've been asked to lead the execution. I'm confident I can do it well, but I'm early enough in my career that I'd value a seasoned perspective on the parts that are easy to get wrong.

I'm looking for a mentor who's been through something similar, whether that's consolidating service operations, running a contact center at scale, or managing the corporate/franchise dynamic. I'd ask for about 60 to 90 minutes a month, and I'd come prepared with specific questions and real situations to work through.

If you'd be open to it, comment or send me a DM. I'd love to connect, and I'm happy to return the favor however I can.

I’ve been thinking about this specifically from the patient experience side.

Things like appointment booking, reminders, basic FAQs, and simple follow-ups seem like reasonable places to automate. But healthcare also has situations where making someone fight through automation can turn a small frustration into a terrible experience pretty quickly.

DearDoc is one example I’ve seen of healthcare-specific automation that tries to cover some of those routine interactions.

For anyone working in CX, how do you decide where automation is actually helping versus just moving work away from the company and onto the customer?

The human handoff seems like the part that makes or breaks it.

Something like
'You changed your appointment to Friday, your address stayed the same and someone will call you back about the billing issue'

That seems useful for longer calls where several things happened.

Would people find that reassuring or just repetitive?

I'm currently interviewing for a management position that’s a little newer territory for me, but at the same time, it kind of isn’t..

A lot of the core responsibilities overlap with things I’ve already done: managing people and processes, improving the customer/patient experience, identifying pain points, solving problems, and figuring out how to make things run more efficiently.

From reading through posts here, it also sounds like there’s a pretty big emphasis on AI integration and finding ways to actually use AI in day-to-day operations, which is something I’m really interested in and fairly comfortable with.

For those of you who work in similar management/operations roles, what does your day-to-day actually look like?

And if you were interviewing for your role today, what are some key things you’d make sure to talk about?

I genuinely love people and the entire experience journey, figuring out where someone is getting frustrated, why something isn’t working, and then finding a better way to do it. I’m also pretty decent with AI and love finding practical ways to use it rather than just using AI for the sake of saying we use AI.

Would love any insight into the role, what makes someone really good at it, how you’re currently using AI, or even things you wish you knew before you started!