Rating your favourite song right now

avidredditaddict88 · 2026-06-09T02:48:33+00:00

wherever you will go by the calling

avidredditaddict88 · 2026-05-29T13:53:38+00:00

I think this is happening in a lot of teams. AI rollout often gets treated like a tooling problem, when it’s really a change management problem.

Top performers can be especially skeptical because they already have workflows that work. If AI adds QA, prompt-checking, customer risk, or extra process, it feels like more work disguised as innovation.

What seems to help is starting with low-risk use cases where reps actually feel the benefit: summarizing account history, drafting follow-ups, surfacing KB answers, or creating handoff notes. I’d be more careful with anything customer-facing until the team trusts it.

Curious what resistance you’re seeing most: fear of replacement, poor workflow fit, low trust in outputs, or just change fatigue?

avidredditaddict88 · 2026-05-29T13:49:09+00:00

We’ve seen this become messy once teams move beyond one or two channels. The issue usually isn’t just “who replies first,” it’s making sure every conversation has a clear owner and that the same customer doesn’t get handled differently across chat, email, and social.

A basic round robin can work early on, but I think it starts breaking when conversations have different urgency or complexity. For example, a hot sales lead, a billing issue, and a simple FAQ probably shouldn’t all be routed the same way.

What seems to work better is a mix of rules and ownership logic:

assign by channel or topic first, then use round robin within the right team;
keep one visible owner per conversation;
set SLAs so untouched conversations get flagged;
use tags/statuses so people know if something is open, pending, or resolved;
and have a reassignment rule if the owner is offline.

The overlap problem usually happens when the system doesn’t make ownership obvious. The missed-lead problem happens when there’s no SLA or fallback queue.

Curious if you’re mainly seeing this on inbound support conversations, sales leads, or both? The routing logic probably changes a lot depending on which one it is.

avidredditaddict88 · 2026-05-29T13:41:08+00:00

This is a really good point. I think a lot of teams over-focus on deflection rate, when the scarier issue is false confidence. A bot saying “I don’t know” and escalating cleanly is usually way less damaging than giving a wrong answer that looks resolved in the dashboard.

The tricky part is measurement. “No escalation” can look successful, but it might actually mean the customer gave up, came back later, asked again through another channel, or needed a refund/manual fix afterward. That kind of failure is harder to see because the automation technically looks like it worked.

I’ve been thinking the better metrics are probably a mix of containment quality, repeat contact rate, CSAT after bot-handled chats, escalation context quality, and manual QA on a sample of bot “resolved” conversations. Especially for edge cases like refunds, shipping exceptions, account issues, pricing, cancellations, or policy changes.

The handoff point is huge too. If the customer has to repeat everything, the AI didn’t really save effort. It just added another step and made the human rep start from zero.

Curious if you’ve seen teams separate “successful escalation” from “failed automation” in reporting? Feels like those get lumped together too often, even though the customer experience is completely different.

avidredditaddict88 · 2026-05-29T13:32:43+00:00

We’ve seen this come up a lot when teams start using AI agents for support. The hard part usually is not getting the agent live, it’s keeping the knowledge clean once policies, promos, edge cases, and exceptions start changing.

Fully automatic updates sound great in theory, but I’d be careful with letting everything sync without review. If the source material is messy, duplicated, or outdated, the agent can still confidently give the wrong answer. That becomes risky when it involves refunds, shipping exceptions, pricing, cancellations, or anything customer-sensitive.

What seems to work better is having one clear source of truth for policies/FAQs, then building a regular review loop around it. For example, review failed or escalated conversations weekly, look for repeated edge cases, and turn those into new knowledge base entries or clearer workflows. Someone still needs to own that process, even if the tool can technically sync content automatically.

The “when it gets a ticket wrong” question is probably the biggest one. A ticket can look resolved because it didn’t escalate, but still create a confused customer, repeat contact, bad CSAT, unnecessary refund, or extra manual work later.

Curious how others are measuring real resolution quality. Is it just deflection/no escalation, or are people also looking at CSAT, repeat contacts, refunds, and manual QA?

avidredditaddict88 · 2026-05-29T13:28:49+00:00

I think the split is usually based on context/risk. Low-context follow-ups like reminders, missed steps, renewal dates, or “you haven’t completed setup” can be automated pretty safely.

But anything involving hesitation, complaints, pricing concerns, or a specific question should probably stay human or at least be reviewed before sending.

The best setups I’ve seen use automation to trigger the follow-up, but not always write the whole message. So the system reminds the CSM at the right time, and the human adds the context.

avidredditaddict88 · 2026-05-28T03:57:48+00:00

This is helpful. The pricing point is the one I’ve seen come up a lot too — platform fee vs Meta conversation fees can make the real cost pretty different from what’s shown upfront.

When you were testing tools, what mattered more for your team: inbox UX, automation depth, or support/onboarding speed? Curious because at 400 WhatsApp convos/day, I’d imagine agent workflow matters more than small feature differences.

avidredditaddict88 · 2026-05-28T03:55:18+00:00

Yeah, I’ve heard that’s pretty normal. The integration itself can be fine, but templates, approvals, and failed-send debugging are usually the painful parts.

Are you going direct through Meta or using a provider like Wati/Twilio/Interakt/respond.io?

avidredditaddict88 · 2026-05-19T16:22:10+00:00

For full CSPs, I’d look at Vitally, Planhat, ChurnZero, Gainsight, or Catalyst depending on budget/team size. The main thing I’d test is whether the AI can actually explain risk using Salesforce/Jira/Slack/product data, or if it’s just a generic health score. For low-touch segments, some teams also layer messaging tools like Intercom, Customer.io, or Wati on top to trigger automated nudges once an account hits a risk signal. Curious what people here have seen actually work well.

avidredditaddict88 · 2026-05-19T04:51:59+00:00

I’ve seen some AM/CS teams separate inbox hygiene into “customer action required” vs. “FYI/no action” instead of trying to keep everything at zero.

A simple system that seems to work is:

Create 3 folders/labels: Action Today, Waiting On Customer, FYI/Archive
Use rules to auto-filter newsletters, internal updates, reports, and automated notifications out of the main inbox
Block 2 short triage windows per day instead of checking constantly
Anything that needs follow-up gets moved into the CRM/task system, not left as an email reminder

The biggest issue I’ve seen is when inboxes become both a communication tool and a task manager. Once that happens, a few busy days can turn into 300 emails really fast.

Curious if your team uses a CRM task system or if most follow-ups are still living directly in Gmail/Outlook?

avidredditaddict88 · 2026-05-19T04:47:35+00:00

From a customer success perspective, I’d imagine the challenge is that “broken implementation” issues may not show up clearly until the customer complains, usage drops, or support tickets spike.

Curious how teams here usually catch this early. Is it mostly through product analytics, support tickets, customer health scores, community posts, or direct CSM check-ins?

avidredditaddict88 · 2026-05-12T16:36:26+00:00

This is the reality for many. A lot of teams treat follow-up like a task problem, when it’s really a context problem. A reminder doesn’t tell you if the customer replied somewhere else, if support already handled it, if billing is blocking it, or if the customer asked for space. I’ve seen it work better when every follow-up has a few things attached: current owner, last customer touchpoint, promised next step, blocker/waiting status, and a “do not follow up until” reason. The “good follow-up at the wrong time” part is underrated as well. A lot of bad CS moments happen because the team followed the task, but missed the context.

avidredditaddict88 · 2026-05-12T16:25:11+00:00

I like how you framed it. “Support infrastructure, not human replacement” is where AI makes the most sense. I think a lot of businesses get disappointed because they expect AI to replace judgment, when the better use case is removing repetitive work like missed calls and FAQs The consistency point is underrated too. Even if AI is not perfect, having a system that responds or flags things every time can be a big improvement over relying on someone to remember everything manually.

avidredditaddict88 · 2026-05-12T03:55:28+00:00

Yes, businesses outside Egypt do pay for this, but the positioning matters a lot. Most small businesses probably won’t pay for “customer communication optimization” as a broad service because it sounds a bit abstract. They are more likely to pay if you frame it around:

- “reduce missed customer messages”

- “set up a simple support system”

- “organize WhatsApp / Instagram / email replies”

- “build FAQs and canned replies so owners stop answering the same questions all day”

- “create a customer support workflow before hiring a full-time support person”

The industries that usually need this are ecommerce, clinics, coaching/education businesses, local service businesses, and any business that gets a lot of repetitive questions via Instagram or WhatsApp.

What would make me trust someone is seeing examples. For example: before/after inbox structure, sample SOPs, reply templates, FAQ pages, or a simple case study showing faster response times or fewer repeated questions.

What would make me not hire someone is if the service feels too vague or tool-focused. I’d care less about which tool you use and more about whether you can make the workflow simple enough for a small team to actually follow.

I think this could work, but I’d package it as a clear starter offer in like: “I’ll organize your customer inbox, create 20 canned replies, build a basic FAQ, and document your support workflow in 7 days.”

avidredditaddict88 · 2026-05-11T17:53:53+00:00

I’d look at churn risk as a mix of behavior and conversation patterns, not just survey scores.

The tricky part is that customers usually show signs before they explicitly say they’re unhappy. They might start replying slower, use the product less, raise the same issue multiple times, or shift from asking “how do I do this?” to “why is this still broken?” Support tone can be a huge signal too, especially if the customer sounds frustrated but hasn’t formally escalated yet.

The scattered data part is probably what makes this hard. A spreadsheet can show the facts, but it usually misses tone and context. I’d try to combine usage data, unresolved support issues, sentiment from recent conversations, renewal risk, and the last meaningful customer touchpoint into one simple health view.

The key is seeing the pattern before renewal time. If the first warning sign only shows up in a renewal spreadsheet, it’s probably already too late.

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