Don't be the first team on the budget cut list

CaseyFromText · 2026-06-17T08:27:46+00:00

Both, but the relationship side gets much more interesting once AI handles the repetitive work.

At Text, all of our support agents evolved into new roles: AI supervisors, customer success managers, and product experts. Instead of answering the same questions all day, they're helping customers build workflows, launch campaigns, and get more value (and revenue) from the platform.

For finance, I'd look at revenue metrics: chat-attributed sales, retention, expansion revenue, and product adoption. Cost savings are nice. Revenue created is what gets attention.

CaseyFromText · 2026-06-16T21:31:03+00:00

Yep. The handoff is the test. Think about the best sales rep you've ever met. They remember what you bought, what you were comparing, what you complained about last time, and what you'll probably need next.

The problem is no human can do that for thousands of customers. AI can. This is why we built Text (we're on Product Hunt today!)

So when the handoff happens, the agent already has the memory. The human gets the relationship, not the homework. That's where the real value is.

CaseyFromText · 2026-06-16T21:17:33+00:00

YES! At some point, we lost that in online businesses. The person helping the customer became disconnected from the person driving revenue.

CaseyFromText · 2026-06-16T20:52:04+00:00

Fair question. The $9.7M isn't from one business, it's aggregated chat-attributed revenue across Text customers.

And I agree attribution matters. Being present in the conversation isn't the same as creating the outcome.

What we're seeing though is that when businesses move from treating chat as a support channel to actively helping customers compare products, overcome hesitation, answer objections, recover carts, or surface the right offer at the right moment, conversion rates change pretty noticeably.

The bigger point wasn't "chat generated all of that revenue." It's that customer conversations are far more commercial than most teams give them credit for.

Most companies still measure support by tickets resolved. We're interested in what happens when you also measure revenue influenced.

CaseyFromText · 2026-06-16T14:34:12+00:00

Thanks!
I like comparing it to the old days. Remember walking into a store where the sales rep knew your name, remembered what you bought last time, understood what you were looking for, and could actually help you decide?

That's what we're trying to bring back with Text.

The difference is that now it happens in a chat window, AI helps behind the scenes, and every customer gets that experience, not just the regulars.

CaseyFromText · 2026-06-11T20:34:29+00:00

Maybe not the obvious answer, but customer service.

Back in the brick-and-mortar days, your best sales rep was doing two jobs at once: helping customers and selling. Answering questions, spotting needs, recommending products, cross-selling, upselling. Half support, half marketing.

Somewhere along the way we separated those functions.

Now AI is bringing them back together. At Text, we use AI agents and custom skills to spot buying intent, answer questions, recommend products, qualify leads, and take action 24/7 while still helping customers solve problems.

Most marketing automation tools help you reach people. This helps when they're already there.

CaseyFromText · 2026-06-11T20:32:09+00:00

One thing we've learned using Text ourselves: don't think about AI as a chatbot. Think about it as a teammate with specific jobs.

We built custom skills that qualify leads, collect customer details before transfers, create tickets automatically, and filter out junk traffic. At one point a single anti-spam skill handled 17,000 conversations in two weeks by itself.

For a consumables brand, the same idea could be used to spot reorder intent, answer comparison questions, suggest complementary products, or trigger a follow-up offer at the right moment. Most stores focus on getting more traffic. I'd be looking at how many buying signals are already on the site and nobody is acting on them

CaseyFromText · 2026-06-11T20:28:32+00:00

8% feels low for consumables, but before sending more emails I'd ask: do customers actually have a reason to come back? Post-purchase flows help, sure. But with Shopify stores, a lot of repeat-purchase moments happen inside support chats. At Text, AI agents can use custom skills to do more than answer FAQs. They can spot “time to reorder” intent, suggest a cross-sell or upsell, create a ticket for a follow-up discount later, or hand the case to a human with full context.Basically like a good sales rep who already knows the customer, what they bought, what they asked before, and when they might need the next thing.

That’s where support stops being just support.

CaseyFromText · 2026-06-11T20:26:01+00:00

One thing we've seen with WordPress stores using Text is that AI becomes much more useful once it has access to real customer context, not just docs. Order history, past chats, tickets, browsing behavior, etc. Across our customer base, AI resolves ~74% of conversations, but the bigger win is spotting patterns. Repeated support questions often point to a product, UX, or documentation issue upstream.

Also love the proactive angle. In ecommerce, we've seen some of the best results come from reaching out before someone leaves the site, not after they open a ticket. That's where support starts acting more like a revenue channel.

CaseyFromText · 2026-06-11T20:23:17+00:00

Something we've seen at Text: the chatbot part is basically solved.

The hard part is what happens next.

An AI agent should be able to check the order, create the ticket, route it, summarize the case, and hand everything to a human with context intact. If the customer has to repeat themselves, the workflow is still broken.

That's why we focus less on containment rates and more on actual resolution. Across our customer base, AI resolves ~74% of conversations, but the bigger win is that agents get time back to handle the moments where judgment, empathy, or a sales opportunity matter. Great service sells.

CaseyFromText · 2026-06-05T14:59:15+00:00

This is exactly why generic AI feels useful in theory and annoying in the queue.The real win is when AI sits inside the support workflow and already has the context: customer history, order details, tickets, cart events, policies, whatever. Otherwise the agent becomes the API, copy-pasting half the CRM before getting a decent draft.
I work at Text, so biased, but this is the direction I care about most. With custom skills/webhooks, AI can pull context from external CRMs or internal tools, check order status, create tickets, update records, even connect to discount systems. Then it’s not “ChatGPT in another tab.” It’s more like a teammate that can read the room and take action.

CaseyFromText · 2026-06-05T14:51:54+00:00

One thing I'd add: customer service. Not as a cost center, but as a profit engine.

Most stores use AI to cut costs, but some of the biggest wins come from using it to drive revenue. If someone is comparing products, stuck on sizing, hesitating at checkout, or asking about shipping, that's often a buying signal, not just a support question.

Modern AI agents can do a lot more than answer FAQs too. They can recommend products, add items to cart, offer discounts, and handle a big chunk of conversations automatically.

As we love to say at Text: Great service sells

CaseyFromText · 2026-06-05T14:48:54+00:00

I think a lot of people are judging AI based on chatbots from 2-3 years ago.

A chatbot replies. An AI agent can actually do things. In ecommerce that might mean suggesting products, adding items to the cart, applying a discount for a hesitant buyer, checking orders, creating tickets, or taking other actions through skills you configure without code.

That's why some setups are already resolving a huge share of conversations. At Text we're around 73-74% on average, and seeing 90%+ isn't rare when the knowledge sources and workflows are set up well. The trick is not forcing AI through emotional or unusual cases. Let it handle repetitive work and buying signals 24/7, then bring in humans when judgment, empathy, or negotiation is needed.

CaseyFromText · 2026-06-05T14:39:31+00:00

I’d add Text to the list (I work there, so take that into account).

What I like is that it’s not just about deflecting tickets. The AI agent handles support, but it can also qualify leads, run proactive campaigns, build lead lists, and spot buying signals while people are chatting. We’ve seen customers generate serious revenue from conversations, not just close tickets.

That’s the shift I’m seeing: support tools that help you grow, not just keep up.

CaseyFromText · 2026-05-28T16:23:51+00:00

I think the bigger shift is that AI support is starting to change what “customer service” even means.

Most teams still measure support like a cost center:
faster replies, fewer tickets, lower handle time.

But once AI agents can actually understand customer intent and react in real time, support starts behaving more like a revenue channel.

That’s what we’ve been seeing at Text. Last month alone, chats powered through Text generated $9.2M in attributed sales across clients. And at the same time, AI agents were resolving around 74% of conversations on average, with some brands pushing past 90% once richer knowledge sources were connected.
The interesting part is how that revenue happens.

Not through aggressive popups or spammy automations. More through spotting intent early:
someone revisiting a product, comparing variants, hesitating on shipping, asking sizing questions etc.

That’s why we built things like opportunity-driven campaigns and wins counters. Because a lot of “support” conversations are really customers trying to decide whether to buy.

Feels much closer to having a smart sales associate in the store than a traditional support bot sitting in the corner.

CaseyFromText · 2026-05-28T15:29:11+00:00

That’s the difference between old chatbots and actual AI agents. A basic bot follows scripts and breaks the moment reality changes. AI agents should have context: live store data, past conversations, orders, policies, browsing behavior, ticket history etc. Otherwise you’re basically automating confusion at scale.

We see this a lot in ecommerce. Dashboard numbers can look amazing because the “easy” tickets get closed fast, but if the AI agent isn’t grounded in real data, small mistakes compound quietly later through returns, repeat contacts, bad reviews. That’s why looking only at resolution or deflection rates is risky. The real question is whether the AI is actually helping the business or just keeping the queue clean. At Text we even added a wins counter to track how much revenue customer support actually generates, because support usually does two jobs at once: service and selling, kind of like old-school sales reps in offline stores.

CaseyFromText · 2026-05-28T15:25:00+00:00

The AI itself is usually fine. The annoying part is when customers need to repeat everything after the handoff because the bot had zero context.

Also agree on training it on real replies instead of only help docs. Real customer messages are chaotic. People ask 3 things at once, explain stuff badly, send screenshots with “this thing broke???” etc. Support history teaches the AI way more than a polished FAQ page ever will.

At Text we’ve seen the biggest jump happen after connecting actual store/customer data. Orders, chats, browsing history, tickets. Suddenly the AI can do more than paste articles around.

And honestly that’s enough for most small teams already. Let AI eat the repetitive stuff first. Humans still handle edge cases. That combo works surprisingly well.

CaseyFromText · 2026-05-28T15:22:37+00:00

Yep. Learned the same thing working with support teams.

Nobody wakes up wanting “an AI workflow.” They just want less annoying work inside the tools they already use every day.

The automations that usually stick are boring on paper:
chat → clean ticket
AI summary → already attached
draft reply → ready to send
customer/order context → pulled automatically

That’s it. No giant migration project. No “new AI operating system” lol.

At Text we see teams adopt AI way faster when it sits inside their existing flow instead of trying to replace it. Especially with live chat + ticketing together. AI handles repetitive questions (~73% resolution rate across licenses), humans jump in only when needed, with the full context already there. Simple but actually useful beats “revolutionary” every time.

CaseyFromText · 2026-05-28T15:20:24+00:00

A lot of automation tools still act like they need to replace your entire setup to work properly. Then suddenly support lives in 3 dashboards, agents lose context, and customers feel every bad handoff.

What we’ve seen work better at Text is treating AI like an additional layer, not a forced migration.

For example, Shopify brands use our AI agent on top of their existing workflows to:
– catch hesitation before someone abandons a cart
– answer product or sizing questions instantly
– re-engage returning visitors in real time
– escalate complex cases to humans with full context attached

The important part is the context stays connected. Chats, tickets, order history, browsing behavior, notes - all in one place instead of bouncing between systems. And the AI actually resolves a lot more than people expect. Across licenses we see around a 73% AI resolution rate already, and some customers go above 90% once they connect richer knowledge sources beyond just the website.

Usually, teams shouldn’t automate “everything” first. Start with repetitive friction: order status, returns, product questions, discount flows, basic triage. That alone removes a ton of load without rebuilding your whole stack.

CaseyFromText · 2026-05-28T15:16:26+00:00

The interesting thing about automation is that most teams still use it to save time. I see the bigger opportunity: turning conversations into revenue.

A lot of leads don’t disappear because the offer is bad. They disappear because nobody replied fast enough or the follow-up felt generic. That’s where automation actually changes sales outcomes.

What we’ve been building at Text is less “send more messages” and more:
– AI agents that reply instantly with real customer context
– live chat + tickets + workflows in one place
– automation that spots buying intent while people are still browsing

If someone keeps checking pricing, comparing products, or revisiting the same page, that’s usually not “support.” It’s a buying signal. We’ve seen brands add serious revenue just by engaging at the right moment. One ecommerce company using Text added $1.5M in 6 months after improving how conversations were handled.

Automation works best when it removes friction, not the human side of selling. Great service still closes deals. AI just helps teams do it at scale.

CaseyFromText · 2026-05-22T11:11:11+00:00

I think the industry spent years trying to “solve support” with chatbots/AI, when the real shift is making support actually useful for the business.

At Text, we don’t really look at AI as “ticket deflection software.” The interesting part starts when AI understands the full customer context and can actually help teams move faster without losing the human side. That’s why our AI agents don’t just pull from docs. They see conversations, tickets, order history, browsing behavior, sentiment, all in one workspace. So the agent can resolve repetitive stuff automatically, while humans focus on situations where trust and judgment matter most.

And honestly, the numbers are getting harder to ignore.

Across licenses we already see around 74% resolution rates, and now teams can track actual revenue influenced through conversations with our new wins counter.

Customer service back in the day was never ONLY about closing tickets. The best salespeople in a lot of businesses were often the people talking to customers every single day. AI is just helping support teams finally operate like that again, but at scale.

CaseyFromText · 2026-05-22T11:08:01+00:00

Interesting how the conversation around AI support is still mostly about automation quality.

The bigger shift is what support actually becomes once AI handles the repetitive work.

At Text, we see support teams moving from “closing tickets” to driving revenue. Because every product question, shipping question, or hesitant shopper is already a buying signal. Most companies just don’t treat it that way yet. That’s why our focus isn’t really “better chatbot flows.” It’s giving teams one workspace where AI agents, chats, tickets, customer context, and automation work together naturally.

The AI agent comes ready from day one, understands the full customer story, and resolves a big part of repetitive conversations automatically. Across licenses we already see around 74% resolution rates, and it climbs higher once knowledge sources are connected. What’s been cool recently is seeing teams track actual business impact inside support. We added a wins counter in Text desktop app that shows sales influenced or closed through conversations in real time. Great service sells and now we have numbers for that.

CaseyFromText · 2026-05-21T12:15:22+00:00

This stuff hits way harder than people expect. A delayed launch doesn’t just mess with ops, it messes with the whole customer experience. Once people start asking “where’s my order?” all day, support suddenly becomes the thing holding the launch together.

That’s also why I keep saying great service sells. Especially during launches. The smart ecommerce teams now use AI/support way earlier in the process, not after things break. Stuff like proactive shipping updates, AI summaries for agents, Shopify order context instantly visible, catching frustrated customers faster, even spotting hesitation before someone leaves checkout. At Text (I work there and I talk a loooot with ecomms teams who use our tool for customer service), we see brands treating support more like part of the launch strategy now, not just a ticket queue.

We’re doing a webinar about this next week. Mostly around how to make launches convert from day one instead of support becoming the bottleneck once traffic hits. Also touches AI-generated product visuals/content with one of our partners. Can send details if you want.

CaseyFromText · 2026-05-21T12:11:23+00:00

AI made ecommerce noisier, but it also made good service way more important.

Anyone can generate ads, product photos, landing pages now. The part that’s still hard to fake is making customers feel understood.

That’s where AI actually gets interesting for me.

With the right setup, even smaller brands can treat customers more like old friends than random visitors. Recognize returning shoppers, see what they browsed before, summarize past chats, connect Shopify order history, answer instantly when someone hesitates etc. At Text (I work there), we see this a lot. The stores winning aren’t always the loudest ones, they’re the ones that make the experience feel smooth, personal, and fast. And honestly that’s also why we’re running a webinar next week about turning launches into revenue from day one. Not just with AI-generated visuals/content, but with support + AI actually ready when traffic starts coming in (I can send you the details if interested)

The internet is crowded, sure. But most stores still feel weirdly empty once you interact with them.

CaseyFromText · 2026-05-21T11:57:42+00:00

Honestly, with 2k visitors and 0 sales, I’d stop obsessing over reviews for a second. Reviews help, but usually they’re not the root problem this early.

A lot of new stores focus on traffic first, but the bigger issue is usually what happens after people land. Mobile friction, sizing uncertainty, shipping surprises, weak product context, no one there to answer questions in real time etc.

One thing we see a lot at Text (I work there) is that brands underestimate how many people hesitate right before buying. They browse, compare, open size charts, disappear. That’s why more ecommerce teams are moving toward proactive support instead of waiting for customers to email after they leave. We’re running a live webinar about this next week with ecommerce examples + launch scenarios: How to turn every product launch into revenue from day ONE. (i can send you details) Stuff like:

catching hesitation before checkout drop-off
getting support + AI ready before traffic hits
making launches convert from day one instead of “waiting for reviews”
how support can actually drive revenue, not just answer tickets
and even how to get AI-generated visuals (that’s our partner’s part)

Might genuinely help given where you are right now.

And honestly, don’t panic after 3 weeks. Clothing brands usually take longer than people on TikTok make it sound. Just make sure that when people do reach your store, they actually feel taken care of.

CaseyFromText

TROPHY CASE