TL;DR:

• AI agent ROI is about business impact, not just ticket deflection, so your report should connect automation to cost savings, team capacity, customer experience, and revenue.
• Executives care about five core metrics: cost savings, automation success rate, customer experience impact, team capacity, and revenue impact.
• Quality matters as much as speed, because faster replies only prove ROI when AI resolves issues accurately and avoids repeat contacts or unnecessary handoffs.
• A strong AI ROI report explains the math, including AI-handled interactions, time saved, cost per human-handled ticket, AI tool costs, and revenue attribution rules.
• AI ROI reporting should become a regular workflow, with daily performance checks, weekly handover reviews, and a monthly business summary for leadership.

Your AI agent is answering tickets, but leadership wants proof that it’s paying off.

That proof can’t stop at ticket deflection or faster replies. To show real AI agent ROI, you need to connect automation performance to cost savings, team capacity, customer experience, and revenue impact.

This guide breaks down the metrics that matter, how to calculate them, and how to turn AI reporting into a business case executives can understand.

Why proving AI agent ROI is harder than it sounds

AI agent ROI is hard to prove because most teams measure activity, not impact.

Ticket deflection doesn’t always mean resolution: A deflected ticket is not always a solved problem. A customer may abandon the conversation, ask the same question later, or contact your team through another channel.

Automation rate needs context: A high automation rate can look impressive in a report. But it needs to be paired with metrics like CSAT, handover rate, repeat contact rate, and resolution time to show whether AI is handling the right tickets well.

Speed can hide quality issues: AI can reduce FRT and resolution time quickly. But fast answers only prove ROI when they’re accurate, helpful, and complete.

Cost savings need a clear calculation: Leadership needs to know how your team calculated savings. That means connecting automated interactions to agent time saved, average handle time, cost per ticket, and AI tool costs.

Revenue impact is easy to miss: AI agents can influence purchases, recommend products, or recover carts. Those results are harder to prove when AI reporting, support data, and ecommerce data live in separate tools.

ROI needs a complete view: No single metric proves AI agent ROI. The strongest reports connect efficiency, customer experience, team capacity, and revenue impact.

The 5 metrics executives actually care about

To prove AI agent ROI, focus on metrics that connect AI performance to business outcomes.

Leadership does not need every AI stat in your dashboard. They need to know whether AI is lowering costs, helping the team scale, protecting customer experience, and contributing to revenue.

1. Cost savings

Executives care about whether AI is reducing the cost of support without creating more work somewhere else.

Track:

• automated interactions
• time saved by agents
• cost per human-handled ticket
• cost per AI-handled interaction

Cost savings show how much money your AI agent saves by handling customer interactions instead of a human agent.

Show how many interactions AI handled, what those interactions would have cost your team, and what it costs for AI to handle them instead.

AI ROI is not just about cutting costs. It’s about helping the business handle more volume without increasing support costs at the same pace.

2. Automation success rate

Is your conversational AI actually giving customers correct, high-quality answers?

Track:

• AI automation rate
• success rate
• handover interactions
• repeat contact rate 

Automation success rate shows whether your AI agent is actually resolving customer interactions without human help.

A high automation rate with high escalations may indicate poor AI quality.

A lower automation rate with strong CSAT and fewer repeat contacts may show that AI is handling the right tickets well.

The best AI programs optimize for successful resolution, not maximum automation.

3. Customer experience impact

AI should improve efficiency without hurting the customer experience.

Track:

• customer satisfaction score (CSAT)
• first response time (FRT)
• resolution time
• human response time after AI handoff
• repeat contact rate

Customer experience metrics show whether customers are getting faster, helpful support from AI.

Speed is not the same as quality.

AI can reduce FRT and resolution time, but those gains only matter when customers still get accurate, complete answers.

The strongest AI reports show that customers got help faster and still had a good experience.

4. Team capacity

Executives care about whether AI helps the team scale.

Track:

• time saved by agents
• automated interactions
• closed tickets
• coverage rate
• workload handled by AI

Team capacity shows how much repetitive work AI removes from the queue.

This matters because human agents can spend more time on complex issues, high-value customers, retention risks, and revenue-generating conversations.

Team capacity is not the same as headcount reduction.

A stronger story is that AI helps the same team handle more customer demand without adding the same amount of cost or pressure.

5. Revenue impact

Executives care about whether AI contributes to revenue, not just cost savings.

Track:

• revenue influenced
• orders influenced
• revenue per interaction
• AOV
• purchase rate from AI-assisted conversations

Revenue impact shows whether AI helps shoppers choose products, get answers before purchase, use discounts, or recover carts.

Revenue attribution needs a clear window.

Explain how your team defines an AI-influenced purchase, such as an order placed within a set number of days after an AI-assisted conversation.

AI agents are becoming part of the shopping experience, not just a way to reduce support tickets.

How to build an executive AI ROI report

An executive AI ROI report should show what changed because of your AI agent.

Start with the outcome leadership cares about most, then add the proof underneath.

Step 1: Lead with the headline result

Start with the clearest business impact. That might be cost saved, time saved, revenue influenced, or tickets resolved without human help.

For example: “Our AI agent resolved 8,000 interactions this month and saved the team 420 hours.”

This gives leadership the answer before they have to interpret the data.

Step 2: Show how you calculated it

Explain the math behind the headline result.

If you’re reporting cost savings, show the number of AI-handled interactions, average cost per human-handled ticket, and cost per AI-handled interaction.

This makes the number easier to trust.

Step 3: Add quality checks

Next, prove the AI agent is not just handling volume.

Add success rate, handover interactions, CSAT, and repeat contact rate if available.

This shows whether AI is solving issues well, not just removing tickets from the queue.

Step 4: Connect the result to team capacity

Then show how AI changed the team’s workload.

Use time saved, automated interactions, and queue impact to show whether agents had more time for complex issues, retention risks, or sales-focused conversations.

This turns AI reporting into an operations story.

Step 5: Add revenue impact if AI supports shopping

If your AI agent answers pre-purchase questions, include revenue metrics.

Show revenue influenced, orders influenced, revenue per interaction, and AOV.

Make the attribution window clear, such as purchases made within three days of an AI-assisted conversation.

Step 6: End with what improves next

Close the report with the actions your team will take next.

That might include updating AI instructions, improving handoff rules, filling help center gaps, or reviewing low-CSAT conversations.

This shows leadership that AI performance is being actively managed, not passively monitored.

Common mistakes teams make when reporting AI ROI

AI ROI reporting falls flat when the numbers look impressive but don’t answer the real business question.

Avoid these common mistakes when you’re building your report.

1. Leading with ticket deflection

Ticket deflection is useful, but it doesn’t prove ROI on its own.

A ticket can be deflected without being resolved. Pair deflection with success rate, CSAT, handovers, and repeat contact rate to show whether AI actually solved the issue.

2. Treating automation rate like the goal

A higher automation rate is not always better.

The goal is to automate the right conversations well. If automation rate rises while handovers, repeat contacts, or poor CSAT scores also rise, your AI agent may be creating hidden work.

3. Ignoring the handoff experience

A handoff is not a failure when it gets the customer to the right person faster.

But leadership should know what happens after AI escalates a ticket. Track human response time after AI handoff so you can spot delays, routing gaps, or tickets that need clearer escalation rules.

4. Reporting cost savings without the math

Cost savings need context.

Show how you calculated the number, including AI-handled interactions, average cost per human-handled ticket, agent time saved, and AI tool cost. This makes the ROI story more credible.

5. Leaving revenue out of the report

AI agents can do more than reduce support volume.

If your AI agent helps shoppers choose products, answers pre-purchase questions, recommends SKUs, or offers discounts, include revenue impact. Executives need to see where AI supports both efficiency and sales.

6. Measuring AI in a separate tool

AI reporting gets harder when support data, automation data, CSAT, and revenue live in different systems.

Disconnected reporting makes it harder to prove what changed because of AI. A stronger setup gives your team one place to track AI performance across support, customer experience, and revenue.

How to build AI ROI reporting into your day-to-day workflow

AI ROI reporting works best when it becomes a regular operating habit.

A monthly report can show leadership the results, but your team needs a daily and weekly rhythm to understand what’s improving, what’s breaking, and where AI needs coaching.

• Check performance daily: Review automated interactions, handovers, success rate, CSAT, and any unusual spikes in ticket volume. This helps your team catch issues early, before they show up in an executive report.
• Review handovers weekly: Look at the most common reasons AI escalated tickets to a human agent. Then decide whether the fix is better AI instructions, clearer help center content, more accurate product data, or a new handoff rule.
• Connect AI performance to team workload: Track whether AI is actually giving agents more capacity. Look at time saved, queue volume, AHT, and the types of tickets agents are handling.
• Watch quality, not just automation: Pair automation rate with CSAT, repeat contact rate, handovers, and resolution time. This helps your team avoid chasing a higher automation rate at the expense of customer experience.
• Share a monthly business summary: Turn the daily and weekly signals into a simple leadership update. Show what AI handled, what it saved, how it affected customers, how it affected the team, and what your team is improving next.

See AI agent ROI in one place

Proving AI ROI gets harder when your support, automation, and revenue data live in separate tools.

Gorgias’s AI Agent brings AI-specific reporting into the same helpdesk your team uses every day, so you can track what AI handled, what it saved, and how it contributed to the customer experience.

Book a demo to see how AI Agent helps ecommerce teams measure and improve AI support from one customer experience platform.

{{lead-magnet-1}}

TL;DR:

• Ticket volume is your support workload: It counts every customer inquiry across every channel in a given time period.
• High volume signals friction in your business: Spikes usually point to unclear policies, product issues, or gaps in your website experience.
• Every ticket has a real cost: Agent time, tooling, and overhead add up fast — and they compound during peak seasons.
• Automation reduces volume without reducing quality: AI tools and self-service deflect repetitive questions while keeping customers satisfied.
• Measurement drives improvement: Tracking volume by channel, category, and time period reveals exactly where to focus your efforts.

Your ticket volume number is probably wrong. If customers are reaching you through email forwards, Slack DMs, or channels that bypass your helpdesk, those tickets aren't being counted, and your SLA reporting is built on incomplete data. This guide covers how to get an accurate count, break it down by channel and category, and use your vertical benchmark to figure out whether your volume is actually a problem or just normal for your industry.

What is ticket volume?

Ticket volume is the total number of customer inquiries your support team receives across all channels — email, live chat, phone, social media, and contact forms — within a specific time period. It is the most direct measure of your team's workload.

Do not confuse it with contact rate. Contact rate = tickets ÷ orders (or customers). That normalized number is more useful for benchmarking and planning because it accounts for business growth. Raw ticket volume tells you how busy your team is. Contact rate tells you whether support demand is outpacing your business.

How to calculate your ticket volume

Start by looking at the last 30 days of customer conversations, no matter where they currently live.

Pull these four numbers:

• Total customer questions received across all channels
• Breakdown by channel (email, chat, social DMs, phone, contact forms, etc.)
• Breakdown by category (shipping, returns, product questions, account issues)
• Tickets or conversations per order during the same period — this gives you your contact rate baseline

Here’s how to pull that data depending on your setup:

Gmail or Outlook

Open your inbox or Sent folder and filter by the last 30 days. Count how many customer conversations came in during that period. You can also copy subject lines into ChatGPT or Claude to group conversations by topic.

Shopify Inbox

Go to Inbox > Conversations and review your recent conversations. Count how many messages you received and look for repeated themes or questions.

Any helpdesk (Gorgias, Zendesk, Freshdesk, Help Scout, etc.)

Most helpdesks have ticket reporting or exports built in. Search “export tickets” or “ticket report” in your platform’s help center. From there, you can pull:

• Total tickets
• Channel breakdown
• Top ticket categories
• Tickets over time

If a large portion of customer questions are still happening in untracked places like Slack DMs, personal inboxes, or Instagram comments, your reporting is incomplete. Before optimizing support operations, route customer conversations into one shared system so you can accurately measure volume, response times, and recurring issues.

Why your volume breakdown matters more than the total

A raw ticket count tells you how busy your team is. The breakdown tells you what to fix.

Run this categorization for your last 30 days. Your top two or three categories are your highest-leverage targets.

Track volume alongside these KPIs

Ticket volume only tells part of the story. Track it alongside:

• Contact rate (tickets ÷ orders) — so you know if volume is growing faster than your business
• First response time (FRT) — volume spikes show up here first
• Average handle time (AHT) — high AHT + high volume = a capacity problem
• Cost per ticket — total support costs ÷ total tickets, the clearest financial measure
• Backlog size — a growing backlog is the earliest warning sign that volume is outpacing capacity
• Deflection rate — tickets resolved through self-service or automation without agent involvement

How to reduce ticket volume without reducing quality

Once you know what is driving your volume, address each category at the source. The goal is to eliminate unnecessary tickets.

Automate the highest-volume, lowest-complexity tickets first. WISMO inquiries, order status checks, and basic return initiations require no agent judgment. An AI Agent connected to your ecommerce platform can handle these end-to-end without a human stepping in. When a question is too complex, the AI escalates it with full context attached.

Build self-service content around your top categories. A Help Center that directly addresses your most common ticket types is the highest-leverage tool for sustained volume reduction. Start with your top five categories. Write one article per category. Surface those articles on relevant product pages, in checkout, and in post-purchase emails — before customers need to search.

Send proactive messages at the moments that generate the most tickets. Post-purchase is the single highest-value touchpoint: an order confirmation that includes a tracking link, estimated delivery window, and a clear link to your return policy eliminates a large share of inbound questions before they are ever submitted.

Measure deflection, not just volume. Deflection rate, the percentage of issues resolved through self-service or automation, is the metric that tells you whether your volume reduction efforts are actually working. Track it weekly alongside CSAT for automated interactions to make sure quality is holding.

Ticket volume benchmarks

The all-industry average is not your benchmark. Ticket volume per 100 orders varies 2.4x across verticals, so comparing yourself to a cross-industry number will either make you complacent or create false urgency.

According to Gorgias platform data from March 2026 across 14 verticals at the $10M GMV band, here is what tickets per 100 orders actually looks like by vertical:

Source: Gorgias Ecom Lab, March 2026

High ticket volume is not always a sign of poor CX — it often reflects product complexity. Electronics brands generate nearly one ticket per two orders because customers have more pre- and post-purchase questions about technical products. Food and Beverage brands generate about one in five. That gap is not a performance difference; it is a category difference.

The right question is not "are we below 10 tickets per 100 orders?" It is "are we above or below our vertical peers?" Find your row. That is your baseline. Then use the reduction tactics above to move below it.

How to predict ticket volume if your tool charges per ticket

If your ticketing tool uses usage-based pricing, where your bill scales with ticket volume rather than agent headcount, forecasting volume directly affects your budget.

The core formula is simple:

Projected tickets = projected orders × (tickets per 100 orders ÷ 100)

So if you expect 2,000 orders next month and your vertical median is 22 tickets per 100 orders, your forecast is approximately 440 tickets.

But a flat monthly estimate misses the real risk: peak seasons. A volume spike during BFCM that triples your order volume will also triple your ticket count — and your bill — unless you have guardrails in place.

To build a more accurate forecast:

• Use your contact rate, not raw volume. Divide your tickets by orders for each of the last 12 months. This gives you a stable ratio that accounts for business growth and seasonal swings.
• Apply that ratio to your order forecast. If your marketing team has a sales projection for November, multiply it by your contact rate to estimate support volume.
• Separate your AI-handled tickets from agent-handled tickets. Some platforms bill differently for automated resolutions versus human ones. If you're using an AI Agent to deflect WISMO and returns, those deflected tickets may not count toward your billable volume at all — which changes the math significantly.
• Build in a buffer for peak periods. Your contact rate tends to rise during high-demand periods, not just your order volume. First-time customers generate more tickets than repeat buyers, and BFCM brings a disproportionate share of first-timers.

Before signing any usage-based contract, ask two questions: What counts as a billable ticket? And is there a hard cap on monthly charges? Variable billing only works in your favor if you have clear definitions of what triggers a charge and a ceiling on how high costs can go during an unexpected spike.

If your platform bills per ticket resolved by a human agent (not AI), your deflection rate becomes a financial metric, not just an operational one. Every percentage point of additional deflection directly reduces your bill.

Start reducing ticket volume today

Begin by identifying your top ticket categories, then work backward to find the root cause of each one.

From there, layer in self-service content, automation, and proactive messaging to address those root causes directly. The result is a support operation that handles more customers and a team that spends its time on the work that actually requires human judgment.

Book a demo to see how Gorgias helps ecommerce brands reduce ticket volume and improve customer experience at the same time.

{{lead-magnet-2}}

TL;DR

• Eight metrics define support performance: CSAT, NPS, CES, FCR, FRT, AHT, TTR, and SLA adherence are the core benchmarks to track.
• First response time varies 5.5x across ecommerce verticals — your vertical benchmark matters more than any industry average.
• Benchmarking is a six-step process: Define goals, select metrics, source benchmarks, capture data, analyze gaps, and set targets.
• Benchmarks connect support to revenue: Faster response times and higher resolution rates directly affect repeat purchases and customer retention.
• Brands at 30%+ automation respond 10x faster than brands at zero — with the same or better CSAT.

The benchmarks in this article are drawn from the Gorgias Ecom Lab, a research hub that publishes platform-level behavioral data from thousands of ecommerce brands. Where we cite a specific figure, it comes from that data, not generic industry surveys.

What is customer service benchmarking?

Customer service benchmarking is the practice of comparing your support performance to industry standards or peer data to find gaps and set improvement targets.

It means measuring how your team performs on metrics like response time and satisfaction scores — then checking those numbers against what similar businesses achieve. The goal is not just to measure. It's to create a clear picture of where you stand and what to fix first.

Benchmarking has four core components:

• Baseline measurement: Recording your current performance across all support channels before making any changes.
• Peer comparison: Looking at how brands of similar size and type perform on the same metrics.
• Gap analysis: Pinpointing where your numbers fall short of industry targets.
• Target setting: Using benchmark data to define realistic, time-bound goals for your team.

Why customer service benchmarks matter for ecommerce brands

Benchmarks turn "we need to improve support" into a specific, actionable goal. Instead of a vague directive, you get a clear target: reduce email response time from 48 hours to 24, or lift CSAT from 72 to 82 percent.

Here's what most benchmarking guides won't tell you: the all-industry average is often the least useful number in the room.

Ecom Lab data across 14 ecommerce verticals shows that first response time varies 5.5x at the same $10M GMV band — from 1.6 hours in Hardware to 8.8 hours in Apparel. A brand sitting in the middle of that range looks fast against one peer and slow against another. Without vertical context, the comparison tells you nothing.

For ecommerce brands, support performance also connects directly to revenue. Customers who get fast, accurate answers are more likely to complete a purchase and come back again. That shows up across the whole business — from support team efficiency to operations, finance, and marketing.

Core customer service benchmarks to track

These eight metrics are the foundation of support performance measurement. Each one captures a different dimension of the customer experience, from speed to ease to loyalty.

First response time

First response time (FRT) is the time between a customer sending a message and receiving your team's first reply.

This is the metric customers feel most immediately — and the one with the most variation across ecommerce brands. According to the Ecom Lab report Stop Benchmarking Against the Average, FRT varies 5.5x across 14 ecommerce verticals at the same revenue band. CSAT, by contrast, varies by just 0.2 points across those same verticals. If you want to know whether your operation is ahead of your peers, FRT is the metric that will tell you.

General targets by channel:

• Email: Under 24 hours (excellent: under 12 hours)
• Live chat: Under one minute (excellent: under 30 seconds)
• Social media: Under two hours (excellent: under one hour)
• Phone: Under three minutes hold time

AI automation changes what's achievable here. Brands automating close to zero percent of tickets average 736-minute response times. At 30% automation, that drops to 80 minutes. At 40%, 12 minutes.

The gains don't scale evenly — they accelerate. If your team has deployed any AI, your FRT benchmark should reflect your automation rate, not just your channel.

First contact resolution

First contact resolution (FCR) measures the percentage of tickets resolved in a single interaction, without the customer needing to follow up.

A high FCR means your team has the right information and authority to solve problems on the first attempt. The industry target is 70 to 75 percent across all channels.

For teams running AI, there's a more actionable metric to track alongside FCR: AI Resolution Rate — the share of AI-touched tickets that close end-to-end without any human involvement.

Ecom Lab data shows the median ecommerce brand resolves 45% of AI-touched tickets end-to-end. The top quartile reaches 65%. Every point of improvement removes a 10-hour median wait and a one-in-three abandonment risk from a customer's experience.

Read more: First contact resolution rate: Your guide to understanding the metric

Customer satisfaction score

Customer satisfaction score (CSAT) measures how satisfied a customer was with a specific support interaction, typically through a short post-conversation survey.

The standard benchmark is 80 to 85 percent. Ecommerce brands with proactive, personalized support often reach 85 to 90 percent. For a deeper look at moving that number, see How to Improve CSAT: 8 Fixes That Make a Real Difference.

Two things stand out from Ecom Lab data. First, CSAT is remarkably stable across verticals — it varies by only 0.2 points at the same revenue band, so your category matters far less here than it does for FRT.

Second, there is a modest tradeoff early in AI adoption: brands at 20% automation average 87.9% CSAT, versus 90.3% at zero. This reflects AI encountering more diverse ticket types as coverage expands. Brands that move past 30% automation and properly configure their AI bring CSAT back up while keeping response times fast.

Net promoter score

Net promoter score (NPS) asks customers how likely they are to recommend your brand to someone else, scored on a scale of zero to 10.

It reflects the overall customer experience — not just a single interaction. The ecommerce benchmark is between 30 and 50, with anything above 50 considered strong. See How To Calculate Net Promoter Score for the full methodology.

Customer effort score

Customer effort score (CES) measures how easy it was for a customer to get their issue resolved, typically on a seven-point scale.

Lower effort correlates with higher repeat purchase rates. The industry benchmark is 5.5 or higher.

The single biggest driver of high-effort experiences is the handoff wait. According to the Ecom Lab report The Cheapest Ticket Is the One a Human Never Touches, the median wait between an AI handing off and a human responding is 10 hours. At the 90th percentile, that wait hits 71 hours — three full days. And a third of handed-off tickets never receive a human response at all.

That experience is what drives CES scores down. Reducing handoffs, not just handling them faster, is the most direct path to a better effort score.

Average handle time

Average handle time (AHT) is the total time an agent spends on a live interaction, including hold time and wrap-up work.

It measures efficiency without accounting for quality, so it works best alongside CSAT and FCR.

Targets by channel:

• Phone: 6 to 8 minutes
• Live chat: 5 to 7 minutes

As automation rate rises, AHT on human-handled tickets typically drops — AI absorbs the simple volume and leaves agents with a shorter, more focused queue. Ecom Lab data shows that at 50%+ automation, AI does the equivalent work of 6.3 full-time agents while the human team at that tier averages just 3 people. Those agents handle 29% more tickets per month and spend more time on the complex issues that actually require judgment.

Time to resolution

Time to resolution (TTR) is the total time from when a ticket opens to when it fully closes.

Unlike FRT, which measures only the first reply, TTR captures the entire support interaction. For a closer look at this metric and how to reduce it, see Resolution Time: What It Is and How to Reduce It.

General targets by complexity:

• Simple inquiries: Under 24 hours
• Complex issues: 24 to 72 hours
• Technical problems: 3 to 5 business days

For brands with AI in the mix, Ecom Lab data gives channel-level baselines for tickets that require human involvement. Contact form handoffs resolve in a median of 36 hours with a 42% abandonment rate. Email handoffs resolve in 32 hours and abandon 30% of the time. Chat resolves in 8 hours and abandons 13%, because real-time pressure forces faster responses.

These aren't just benchmarks to optimize — they're the cost of every ticket that doesn't resolve end-to-end.

Service level agreement adherence

A service level agreement (SLA) is a defined commitment to respond to or resolve tickets within a set timeframe.

SLA adherence measures the percentage of tickets where your team meets that commitment. The industry benchmark is 90 to 95 percent compliance. For the tactics that make hitting those commitments repeatable, see SLA Best Practices for Effective Support Ticket Management.

How to benchmark customer service performance

Benchmarking works best as a structured process, not a one-time audit. These six steps take you from identifying what to measure to building a plan for improvement.

Step 1: Define your goals

Start by deciding what you want to improve and why.

Are customers complaining about slow responses? Are agents spending too long on simple tickets? Tying your benchmarking effort to a specific business problem keeps the process focused and the results actionable. Limit your initial scope to three to five metrics — tracking everything at once makes it harder to act on what you find.

Step 2: Choose the right metrics

Pick metrics that match the problem you identified in step one.

If customers are frustrated by how long it takes to get help, FRT and TTR are your starting points. If satisfaction scores are slipping, CSAT and CES will tell you more. Match the metric to the pain point.

If you have any automation running, add AI Resolution Rate to your list. The median brand sits at 45%; the top quartile is at 65%. A gap between your rate and the top quartile almost always comes down to one of four things: limited intent coverage, insufficient action authority (AI can't issue refunds or apply discounts), missing system integrations, or an escalation policy that's routing too much to humans by default.

Step 3: Source your benchmarks

You need external data to compare against. Reliable sources include:

• Industry reports: Published by customer service associations and research firms.
• Helpdesk platforms: Many publish benchmark data from their customer base, including Ecom Lab.
• Peer networks: Non-competing brands in adjacent markets often share performance data.
• Customer surveys: Direct feedback on what customers expect from your support.

One critical caveat: filter by your specific vertical, not just "ecommerce." Ticket volume per 100 orders varies nearly as much as response time. Electronics brands generate about 46 support tickets per 100 orders. Food & Beverages brands generate about 20. If you're in a high-ticket-volume vertical, that's your baseline — not a problem to fix.

Step 4: Measure your current performance

Pull at least three months of data from your helpdesk to establish a reliable baseline.

Shorter windows get skewed by seasonal spikes or one-off events. Make sure you're measuring each metric the same way across all channels so the data is consistent. How to Evaluate the Effectiveness & Impact of Your Customer Service Team is a good companion resource for this step.

Step 5: Identify and diagnose gaps

Compare your numbers to the benchmarks you sourced.

Look for patterns. Are certain channels consistently slower? Do specific ticket types take longer to resolve? The goal is to understand why the gap exists, not just that it does.

Step 6: Set targets and build an action plan

Use your gap analysis to set incremental targets.

If your email FRT is 48 hours and the benchmark is 24, aim for 36 hours first. Assign ownership, set a timeline, and schedule a review date. Benchmarking only drives improvement when it leads to a concrete next step.

What good benchmarking looks like in practice

Benchmarking changes how your team operates day to day. Agents know what "good" looks like and can measure their own progress against it. Managers can identify coaching opportunities using real data rather than observation alone.

For ecommerce brands specifically, the operational benefits compound over time:

• Lower cost per ticket: Efficient workflows reduce the time and resources needed to resolve each issue.
• Higher repeat purchase rates: Customers who get fast, easy support are more likely to buy again.
• Reduced cart abandonment: Quick answers to pre-purchase questions keep shoppers moving toward checkout. See Reduce Cart Abandonment: Proven, Data-Backed Strategies.
• Stronger team accountability: Clear targets give agents and managers a shared definition of success.

The financial picture from the Ecom Lab report Most Brands Are Overpaying for Support is concrete. Even at the lowest automation tier, brands net $73K per year after platform costs.

Nearly 1 in 4 brands (23.5%) reduced their support team after enabling Gorgias AI Agent. Of those, 51% achieved all three outcomes at once: fewer people, same ticket volume, same or more revenue. Brands that reduced by at least one person saw each remaining agent handle 29% more tickets per month while revenue grew 22%.

The adoption gap matters too. Only about 1 in 5 ecommerce brands has deployed AI in customer-facing support today. Brands at near-zero automation average 736-minute response times. Brands at 30%+ automation average 80 minutes.

That's not an incremental improvement. It's a structural shift.

Improve your benchmarks with Gorgias

Benchmarks tell you where to focus. The right tools help you get there.

Gorgias gives ecommerce support teams a unified view of every customer conversation, with built-in reporting that tracks the metrics that matter most. AI Agent resolves routine tickets automatically — across email, chat, and SMS — so your team spends less time on repetitive requests and more time on work that requires a human touch.

Book a demo to see how Gorgias helps ecommerce brands hit and exceed their customer service benchmarks.

{{lead-magnet-2}}

TL;DR:

• AI Agent only answers from sources you connect. Help Center articles, your website, uploaded documents, and Shopify data. Nothing outside of that.
• Every behavior is configurable. What AI Agent says, how it sounds, which topics it handles, and which ones it always passes to a human are all settings you control in plain language.
• You decide what AI Agent never touches. Handover topics and exclusions let you route specific ticket types directly to your human team, every time, regardless of what AI Agent knows.
• Configuration is ongoing, not a one-time setup. A monthly review of flagged tickets and Guidance Opportunities keeps AI Agent calibrated as your policies and products change.

How much of AI Agent can you actually control? The answer is more than most people expect.

AI Agent has several distinct control layers — what it knows, how it speaks, which topics it handles, which ones it passes to your team, and what actions it can take on a customer's behalf. Each layer is configurable in plain language, directly inside your Gorgias settings.

This article walks through each control, what it does, and what good configuration looks like in practice. 

{{lead-magnet-1}}

How to tell AI Agent how to behave

AI Agent allows you to configure four inputs: Guidance, tone of voice and language, handover topics and exclusions, and knowledge sources.

Each one controls a different dimension of AI Agent's behavior. Together, they determine what AI Agent knows, how it communicates, what it will and won't handle, and where it draws its answers from.

Guidance: your policies and instructions, in plain language

Guidance is the highest-priority input in AI Agent's knowledge hierarchy. When information exists in both a Guidance and another source — like a Help Center article or a connected URL — AI Agent always follows the Guidance first.

It's designed for rules and behavioral instructions, not just answers. Good examples of what belongs in a Guidance:

• "Do not offer a discount without a human agent approving it first."
• "If a customer mentions a legal dispute or uses the word 'lawyer,' hand over immediately."
• "Always confirm the order number and email address before processing a return."
• "Do not provide medical advice or interpret how our products may affect specific health conditions."

You can add up to 100 Guidance, and each one can be as specific as your policies require.

Be specific. Vague instructions produce vague behavior. "Handle returns politely" leaves AI Agent to interpret what that means. A more useful Guidance looks like this: 

"For return requests: confirm the order number, check whether the item is within the 30-day return window, and if eligible, send the prepaid return label link. If outside the window, explain the policy and hand over to a human agent."

Nothing left to chance. When writing Guidance, focus on the situation and the desired outcome.

Keep it current with Guidance Opportunities. Over time, AI Agent detects recurring questions it could not confidently answer and surfaces them as suggested new Guidance in your dashboard. Review, edit to match your policies, then approve or dismiss. Nothing is added without your sign-off.

Read more: How to write Guidance with the “when, if, then” framework

Tone of voice: how AI Agent sounds

Tone of voice is a separate setting from Guidance and knowledge. It controls how AI Agent communicates, the register, the warmth, the vocabulary, rather than what it says or what it does.

You have four options:

• Friendly. Warm, approachable, and casual. Empathetic and encouraging.
• Professional. Formal, respectful, and focused. Business-like without being cold.
• Sophisticated. Refined and considered. Complex vocabulary, subtle nuance.
• Custom. Your instructions, in plain language, up to 5,000 characters.

Custom is where brands with specific voice guidelines should spend time. Describe your communication style the same way you'd brief a copywriter: words and phrases that fit your brand, what to avoid, how to handle emotionally charged conversations. Emoji usage and phrases your team always or never uses can all go here.

Two things to keep separate as you configure this:

• Tone vs. Guidance: Tone of voice tells AI Agent how to write. Instructions about specific situations, like "when a customer asks about returns, do X," belong in Guidance, not here. Mixing the two produces inconsistent results.
• Tone vs. Language: AI Agent supports over 80 languages and automatically replies in the language a customer writes in. Language is its own dedicated setting. If you've been using the tone of voice field to control language, switch, as it can conflict with how responses are generated.

Tone usually takes a few iterations to get right. Use the test conversation feature, found under AI Agent > Test, to simulate customer conversations and verify how AI Agent sounds before it goes live.

Learn more: Customize AI Agent's tone of voice

Handover topics and exclusions: what AI Agent should never touch

Handover topics and exclusions give you explicit control over which conversations AI Agent handles and which go straight to your human team. Both live in the Handover and Exclusion section of your AI Agent settings.

Handover topics are subjects AI Agent is always instructed to pass to a human, even if it has relevant information. You add them in plain language. Some examples worth considering for most stores:

• Legal disputes or any mention of legal action
• Medical questions or references to health conditions
• Fraud-related language or chargeback mentions
• Any request that requires a judgment call outside of written policy

Exclusions work differently. Using the "Prevent AI Agent from answering" Rule, you can tell AI Agent to ignore certain tickets entirely, based on a tag, a sender's email address, or specific words in the message. These tickets stay in the queue for your human agents without AI Agent touching them.

You can also toggle whether AI Agent tells the customer it is handing them over, or does so silently. Both options are available in the same section.

Note that AI Agent hands over automatically in some situations regardless of configuration, including when it cannot find a relevant answer, when a response does not pass its internal quality check, and when a customer asks to speak to a human.

Read more: Customize how AI Agent hands over to your team

Knowledge sources: what AI Agent draws from

AI Agent's answers are grounded entirely in the sources you connect. It won't speculate beyond them, and if it cannot find a relevant answer, it hands over instead of guessing.

The sources you can connect are:

• Help Center articles. Your existing documentation and policy pages.
• Your store website. Crawled automatically when you connect it.
• Specific public URLs. Individual pages you want AI Agent to reference.
• Uploaded documents. PDFs, Word docs, or spreadsheets with product or policy information.
• Shopify data. Order history, customer profiles, product catalog, and fulfilment information.

Start by connecting what you already have. Gaps will surface quickly through handovers in the inbox or through Guidance Opportunities flagging unanswered questions. Adding a URL or uploading a document is usually faster than writing a Guidance for every scenario.

Learn more: Onboard AI Agent with knowledge sources

Which channels should AI Agent be active on?

AI Agent runs on email, chat, and SMS. None are on by default. You enable each one manually. Turning a channel off doesn’t affect your configuration.

Email is where most brands start. AI Agent handles incoming tickets, filters spam, and pulls from your knowledge sources and Shopify data to reply with context. Anything it cannot resolve gets handed over with the full conversation attached.

Chat is faster and more transactional. Customers tend to ask shorter, more immediate questions — order status, return eligibility, quick policy checks. AI Agent adapts automatically, writing shorter and more conversational replies on chat than it would on email.

SMS requires a separate add-on subscription. It is the most tone-sensitive channel, so configure your tone of voice carefully and test thoroughly before enabling AI Agent here.

There is no required order for activation. Most brands pick the channel with the highest ticket volume and clearest policies, then expand. Switching a channel off is a simple toggle. Nothing gets deleted.

How to test AI Agent before it goes live

Before enabling AI Agent on any channel, test it first. Gorgias has a built-in test conversation feature that lets you simulate customer interactions without affecting real tickets, reporting, or customers. Go to AI Agent > Test.

Step 1: Start with your hardest tickets. Skip the generic questions. Test the scenarios that made you hesitant in the first place — complex product questions, return edge cases, sensitive topics on your handover list. If AI Agent handles these well, you can activate with confidence.

Step 2: Test each channel separately. AI Agent adapts its response style by channel. Shorter on chat, more detailed on email. A response that reads well on email may feel too long on chat, so configure the channel in the test settings to match the one you are evaluating.

Step 3: Check your handovers. Send a message containing the language or scenario you want escalated. Confirm AI Agent passes it to your team rather than attempting a response. Do this for every topic on your handover and exclusion list.

Step 4: Test your tone. Run several conversations before settling on your tone configuration. Try emotionally charged messages, not just neutral ones. Tone usually takes a few rounds to get right.

Test conversations do not count toward your automated interaction billing.

What should AI Agent escalate to a human?

Some tickets should never be handled by AI. Not because AI Agent cannot generate a response, but because the situation calls for a human regardless.

There are two types of escalation to understand: the ones AI Agent does automatically, and the ones you configure yourself.

Automatic escalations. These are built in and can’t be turned off. AI Agent hands over automatically when it lacks confidence in an answer, when it can’t find relevant content in its knowledge sources, when it detects customer anger or frustration, and when a customer explicitly asks to speak to a human. 

Every response also passes through an internal QA step — a second AI model measures confidence, and if the response does not meet the threshold, it is not sent.

Configured escalations. These are yours to define. Two tools handle this.

Handover topics tell AI Agent to always pass a conversation to a human on a specific subject, even if it has relevant information. Add them in plain language in your AI Agent settings. Good candidates for most stores:

• Legal disputes or any mention of legal action
• Medical questions or references to health conditions
• Fraud-related language or chargeback mentions
• Any complaint requiring a judgment call outside of written policy

Exclusions go further. Using the "Prevent AI Agent from answering" Rule, you can tell AI Agent to ignore certain tickets entirely, based on tags, sender email addresses, or specific words in the message. These tickets never get touched by AI Agent at all.

Route escalations to the right place. When AI Agent hands over, configure which tag, team, or queue the ticket routes to. A return escalation goes to fulfilment. A billing dispute goes to a senior agent. Escalated tickets should never land in a generic inbox.

Learn more: Security and privacy FAQ for Gorgias AI Agent

How to review what AI Agent did and improve it over time

Configuration is not a one-time task. The brands who get the most out of AI Agent check in regularly, flag what is not working, and update its knowledge as policies and products change. Here is a simple review checklist to run through on a monthly basis, or after any major policy or product update.

• Read flagged tickets. Open your inbox and filter for AI Agent conversations labeled "Automated." Check the full thread and which knowledge sources were used. If a response looks off, the source is visible.
• Rate what you see. Mark tickets as good, ok, or bad. For negative ratings, select a reason — wrong answer, wrong tone, should have escalated. This feeds directly into Guidance Opportunities.
• Check the Intents page. Go to AI Agent > Intents to see which topics AI Agent is handling and where handovers are clustering. Recurring handovers on the same topic mean a Guidance is missing.
• Review Guidance Opportunities. Open your dashboard and work through pending suggestions. Edit each one to match your policies, then approve or dismiss. Nothing is added without your sign-off.
• Update your Guidance. Any time a policy changes, a new product launches, or a seasonal shift affects your return or shipping terms, review relevant Guidance and update accordingly.

Learn more: Continuously improve AI Agent with Opportunities (Beta)

Considerations for sensitive and regulated categories: what to configure differently

Most of the configuration covered in this article applies to every store. This section is for brands in categories where the stakes of an incorrect or out-of-scope response are higher. Skim to your vertical and take what applies.

Health and wellness

• Block medical advice. Write a Guidance that explicitly instructs AI Agent not to interpret how products may affect specific health conditions, and to hand over if a customer asks.
• Add a second layer. Add medical questions as a handover topic in addition to your Guidance.
• Upload usage documentation. For products with complex instructions, upload those documents as a knowledge source so AI Agent answers accurately without improvising.

Food and beverage

• Keep allergen content current. Make sure Help Center articles on ingredients and allergens are accurate, up to date, and connected as a knowledge source.
• Set clear boundaries in Guidance. Specify exactly what AI Agent can and cannot say about allergens and ingredients.
• Use exclusions for ambiguous formulations. Where product ingredients change frequently, exclusions are safer than handovers. AI Agent will not touch those tickets at all.

Subscriptions

• Gate sensitive actions with conditions. Only allow cancellations or pauses after a minimum subscription duration.
• Require customer confirmation. Enable confirmation for any action that modifies a subscription before it executes.
• Add billing topics to your handover list. Billing disputes and chargeback mentions should always go to a human.

Now, let's set it up for your store

You now know exactly what you can control, and there is more of it than most brands expect. The next step is seeing it configured for your store specifically.

Book a demo →

{{lead-magnet-2}}

TL;DR:

• Intercom's pricing structure jumps sharply between tiers, making it expensive for growing ecommerce brands
• Gorgias is built for Shopify with native order management, AI automation, and proactive sales tools in one platform
• Budget-friendly options like Crisp and Tidio offer free tiers for brands just getting started
• Enterprise platforms like Zendesk and Salesforce Service Cloud offer scale but require significant setup and investment
• The right choice depends on your platform, ticket volume, and whether you need support, sales, or both

Intercom built its reputation as a customer messaging tool for SaaS companies. As it shifted toward enterprise, many ecommerce brands found themselves paying more for features they didn't need — and missing the ones they did. This guide covers the 15 best Intercom alternatives, evaluated specifically for ecommerce brands on Shopify and beyond.

Why brands switch from Intercom

Intercom is a customer messaging platform that combines live chat, email, and automation for support and sales. It works well for SaaS companies, but ecommerce brands often run into friction fast.

The biggest issue is pricing. The jump from the Essential plan ($74 USD per month) to the Advanced plan ($395 per month) is steep, and many features — like custom bots and product tours — cost extra on top of that. For a growing brand managing hundreds of tickets a week, the total cost adds up quickly.

Beyond price, Intercom was not built with ecommerce in mind. There is no native Shopify integration, no order management inside conversations, and limited automation for common requests like "Where is my order?" That gap pushes many brands to look for tools designed around how online retail actually works.

Best Intercom alternatives at a glance

The 15 best Intercom alternatives for 2026

1. Gorgias

Gorgias is a customer experience (CX) platform built specifically for ecommerce brands. It connects your helpdesk directly to Shopify, so agents can view orders, issue refunds, and update subscriptions without leaving the conversation.

The AI Agent handles up to 60% of incoming tickets automatically — shipping questions, return requests, order status — across email, chat, and SMS. The Shopping Assistant goes further, proactively engaging shoppers on product pages to help them find the right item and complete their purchase.

Gorgias is the strongest Intercom alternative for Shopify brands that want support and sales in one place.

Key features:

• Native Shopify integration with real-time order data
• AI Agent that resolves tickets autonomously
• Shopping Assistant for proactive product recommendations
• 100+ ecommerce app integrations
• Unified inbox for email, chat, SMS, voice, and social

Pricing:

• Starter: $10/month (50 tickets)
• Basic: $60/month (300 tickets)
• Pro: $360/month (2,000 tickets)
• Advanced: $900/month (5,000 tickets)

{{lead-magnet-1}}

2. Zendesk

Zendesk is an enterprise helpdesk platform that handles high ticket volumes across email, chat, voice, and social. Its reporting tools are among the most advanced available, and its marketplace includes hundreds of integrations.

It is a strong fit for large teams with complex workflows. For smaller ecommerce brands, the setup time and per-agent pricing can be a barrier.

Pricing: Suite Team starts at $55/agent/month

3. Freshdesk

Freshdesk is a helpdesk platform with a free tier for up to 10 agents, making it one of the most accessible Intercom alternatives for teams on a tight budget. It includes ticketing, automation, and a knowledge base across its plans.

Its ecommerce integrations are less deep than purpose-built tools, but it covers the basics well for brands managing moderate ticket volume.

Pricing: Free for up to 10 agents; paid plans from $15/agent/month

4. Help Scout

Help Scout is a shared inbox tool that makes email support feel personal and organized. Agents work in a clean interface that looks like a regular email client, which reduces the learning curve significantly.

When comparing Help Scout vs Intercom, Help Scout wins on simplicity. It suits brands that rely heavily on email and want a tool their team can use on day one.

Pricing: Standard plan starts at $25/user/month

5. Kustomer

Kustomer is a CRM-first platform that organizes every customer interaction into a single timeline. Agents see the full history of a shopper's orders, conversations, and behavior in one view.

It is best suited for enterprise brands that need deep personalization and are willing to invest in a more complex setup. Pricing reflects that positioning.

Pricing: Enterprise plan starts at $89/user/month

6. Drift

Drift is a conversational marketing platform built for B2B sales teams. Its chatbots qualify leads, book meetings, and route prospects to sales reps automatically.

When comparing Drift vs Intercom, both target sales use cases — but neither is built for ecommerce. Drift's pricing starts at $2,500/month, making it one of the most expensive options on this list.

Pricing: Premium starts at $2,500/month; advanced plans are custom

7. Tidio

Tidio is a live chat and chatbot tool aimed at small ecommerce businesses. Its free plan covers up to 50 conversations per month, and its paid plans are affordable for brands just scaling up.

The chatbot builder is easy to use without technical knowledge. It lacks the deep integrations and automation power of more advanced platforms, but it is a solid starting point.

Pricing: Free up to 50 conversations; Starter from $29/month

8. Crisp

Crisp is a multichannel messaging platform with a generous free plan that includes two seats. It brings together live chat, email, and social messaging in one shared inbox.

It is a practical, cheaper alternative to Intercom for startups and small teams. Paid plans unlock chatbots and more advanced automation.

Pricing: Free for two seats; Pro from $25/month

9. Zoho Desk

Zoho Desk is the support module within the Zoho software suite. Its biggest advantage is how tightly it connects with Zoho CRM, giving sales and support teams a shared view of every shopper.

If your brand already uses Zoho products, Desk is a natural fit. If you don't, the value of the integration is less compelling.

Pricing: Standard plan starts at $14/agent/month

10. Front

Front is a collaborative inbox tool that brings email, SMS, and social channels into one shared workspace. Teams can assign conversations, leave internal comments, and track response times without switching tools.

Front is strong for collaboration but lighter on traditional helpdesk features like ticket routing and automation rules. It suits teams that manage a high volume of email and need better internal coordination.

Pricing: Starter plan starts at $19/seat/month

11. Gladly

Gladly organizes all customer communication into a single, ongoing conversation thread — no ticket numbers, no channel silos. Agents always see the full picture, regardless of where the shopper reached out.

This model works well for premium brands that prioritize a personal, high-touch experience. The price point reflects that focus.

Pricing: Hero plan starts at $150/agent/month

12. LiveAgent

LiveAgent is an all-in-one helpdesk that includes live chat, email, a call center, and a knowledge base in a single platform. It is one of the most affordable options with a broad feature set.

The interface feels dated compared to newer tools, but the functionality is solid for brands that want everything in one place without a high price tag.

Pricing: Small plan starts at $9/agent/month

13. HubSpot Service Hub

HubSpot Service Hub is the customer service layer of the HubSpot platform. It connects directly to HubSpot CRM, giving support teams full visibility into a shopper's marketing and sales history.

For brands already running on HubSpot, it is a logical extension. For those who aren't, adopting the full suite just for support is a significant commitment.

Pricing: Starter from $20/month for two users

14. Salesforce Service Cloud

Salesforce Service Cloud is one of the most powerful and customizable support platforms available. It handles complex workflows, advanced automation, and deep reporting at enterprise scale.

The tradeoff is complexity. Implementation takes time, requires technical resources, and the cost scales quickly. It is best suited for large brands with dedicated operations teams.

Pricing: Starter from $25/user/month; Enterprise from $165/user/month

15. Groove

Groove is a simple helpdesk for small teams that have outgrown shared Gmail inboxes. It includes a shared inbox, knowledge base, and basic reporting without the overhead of a larger platform.

It is a practical, no-frills option for brands with low ticket volume and straightforward support needs.

Pricing: Standard plan starts at $16/user/month

How to choose an Intercom alternative for ecommerce

The right platform depends on three things: your ecommerce stack, your ticket volume, and what you need the tool to do beyond answering questions.

Start with your platform. If you run on Shopify, you need a tool that connects natively — not through a workaround. Native integration means agents can see order details, edit shipments, and process returns without switching tabs.

Then think about scale. Per-agent pricing works well for small teams but gets expensive fast. Ticket-based pricing, like Gorgias uses, scales more predictably as your volume grows.

Finally, decide whether you need support only or support and sales. Some platforms on this list handle tickets well. Others — like Gorgias — are built to drive revenue through conversations, not just resolve them.

Key questions to ask any vendor:

• Does it integrate natively with your ecommerce platform
• What is included in the base plan versus add-ons
• How long does onboarding typically take
• How does pricing change as your ticket volume grows
• What automation is available without custom development

Next steps to evaluate Intercom alternatives

Most platforms offer a free trial or starter plan. Use it. A week of real usage tells you more than any feature comparison chart.

For Shopify brands that need more than Intercom offers, Gorgias is worth a closer look. You get native order management, 60% automation coverage, and no per-agent pricing. Start a free trial to see it on your actual workflows.

TL;DR:

• AI Agent is priced per resolved interaction, not per seat or per message. You only pay when the AI fully resolves a conversation on its own.
• Most plans are $0.90 per resolved interaction. Starter plans begin at $1. Plans include 90 to 2,500+ automated interactions per month.
• If you go over your plan, overage fees apply per additional interaction. Rates vary by tier and are lower on annual plans.
• Your automation rate emerges from usage over time. Start by estimating your ticket volume and pick an interaction allotment that fits.
• AI Agent runs on email, chat, and SMS, and includes tone of voice customization, Actions, multi-language support, vision, and performance reporting.

If you're wondering what it costs to add AI Agent to your Helpdesk, you're in the right place. This article walks through how pricing works, what counts as a billable interaction, and how to think about the investment before talking to anyone on our team.

The good news: there are no seat fees, no per-message charges, and no token-based billing. You pay for conversations your AI actually resolves. If you've looked into other AI tools for customer support and found the pricing models confusing or hard to predict, Gorgias AI Agent works differently.

What is a billable interaction?

A billable interaction is counted when the AI resolves a customer conversation entirely on its own. The customer asks something, the AI handles it, the conversation closes. That's one interaction.

If the AI can't fully resolve a conversation and hands it to a human agent, that ticket shifts over to your regular Helpdesk plan. It becomes a standard resolved ticket. You're not charged for both.

A few things that don't count as billable interactions:

• Emails that come in but no one replies to
• Spam or filtered messages
• Conversations resolved by a human agent

This matters most for brands coming from seat-based tools. With Gorgias, your whole team can work in the platform. Agent seats are unlimited. Pricing scales with what your AI is actually doing, not with how many people have access.

Understand the difference between seat-based vs. usage-based pricing.

How AI Agent plans work

AI Agent is an add-on to your Gorgias Helpdesk plan. The two are priced separately but work together. Your Helpdesk plan covers all the conversations your human agents resolve. Your AI Agent plan covers the interactions the AI resolves on its own.

When you choose a plan, you select how many automated interactions you want included per month. Depending on your plan, that ranges from 90 to 2,500+ interactions, with custom interaction numbers available for enterprise. You can see the full breakdown on the Gorgias pricing page.

Each resolved conversation costs $0.90 on most plans. Starter plans begin at $1 per resolved conversation. You only pay for fully automated interactions, meaning conversations the AI handles from start to finish without a human stepping in.

Choosing the right plan

The main input is your average monthly ticket volume. From there, you estimate how many of those conversations AI could realistically handle on its own.

Order status updates, return requests, and shipping questions tend to be the highest-volume ticket types AI resolves well. AI Agent actions shows the full range of what it can handle, which makes it easier to estimate your starting number.

Your actual automation rate, meaning the share of total tickets the AI ends up resolving, emerges from usage over time. Most brands start with their most repetitive ticket types and expand from there as they see results.

Related: Which Gorgias plan should you choose?

What happens if you go over your plan

You're charged an overage fee for each additional automated interaction if you exceed your plan's baseline in a given month. The exact rate depends on your plan tier and whether you're on a monthly or annual subscription.

Generally, the higher your plan tier, the lower your overage rate. Annual plans also carry lower overage rates than monthly plans. So if you're regularly going over, upgrading to a higher tier or switching to annual often works out cheaper than paying overage fees month after month.

If you're on a Support + Shopping Assistant plan, the overage rate is $1.50 per interaction across all paid tiers. If you're on a Support-only plan, rates range from $1.00 to $2.00 per interaction on monthly plans, and $0.83 to $1.67 on annual plans, depending on your tier.

For seasonal businesses, forecasting your customer service volume before peak periods is the best way to choose the right plan size and avoid unexpected fees.

How to think about the cost

At $0.90 per resolved interaction on most plans, each AI resolution costs less than a human agent handling the same ticket. Once you know what a human-resolved ticket costs your business, the comparison becomes straightforward.

For brands building an internal case for the investment, how to pitch AI Agent to your boss covers the ROI framing in detail. 

To see what results look like in practice, how 10 brands transformed customer support into revenue has real ecommerce examples.

What's included with AI Agent

AI Agent comes with everything you need to set it up, customize it, and improve it over time:

• Knowledge training — AI Agent learns from your Shopify data, store website, Help Center articles, URLs, documents, and custom guidance. The more content it has, the more accurately it responds.
• Tone of voice — set instructions for how AI Agent sounds, whether that's professional, friendly, or something else, and it stays consistent across every conversation.
• Actions — connect AI Agent to your other tools so it can complete tasks like cancelling an order, processing a return, or modifying a subscription without a human stepping in. See what AI Agent can do.
• Multi-language support — AI Agent detects the language a customer writes in and replies in the same language automatically.
• Vision — AI Agent can read and understand images, so it can handle tickets where customers share photos of damaged items or order issues.
• Performance reporting — track automation rate, CSAT, first-response time, and ticket topics directly in the dashboard.
• Testing — preview how AI Agent responds to real customer questions before going live or after making changes.
• Handover to humans — AI Agent automatically passes conversations to your team when it lacks confidence, detects frustration, or encounters a topic you've marked for human handling.

Learn more: Gorgias AI Agent guardrails: What they are and how to configure them

Curious what AI Agent would automate for your store?

The best way to get a sense of what AI Agent will cost is to look at your own ticket volume and the types of questions your customers ask most. From there, the right plan becomes much clearer.

If you want to talk through the numbers with someone from our team, book a demo and we'll walk through it with you.

If you'd rather keep exploring first, here are a few good next reads:

• AI Agent keeps getting smarter — the latest performance data across the Gorgias customer base
• 10 must-know AI Agent use cases — the ticket types brands are automating today
• How 10 brands transformed customer support into revenue — real ecommerce results
• How to customize AI Agent with 7 brand voice examples — what the experience looks like for customers

{{lead-magnet-2}}

TL;DR:

• Gorgias is the strongest choice for Shopify brands that want automation and in-ticket actions. It’s built for ecommerce workflows and can handle tasks like returns, refunds, and order edits without switching tools.
• Zendesk is best for large, complex support operations. It offers deep customization and scalability, but requires more setup and often additional integrations for ecommerce use cases.
• Intercom fits teams that combine support with sales or marketing. Its strength is proactive messaging and engagement, not deep order management.
• Help Scout and Freshdesk are simpler, lower-cost options. They work well for smaller teams that need solid ticketing, but offer limited automation and ecommerce-specific functionality.
• Kustomer and Tidio serve more niche needs. Kustomer is ideal for CRM-first support with rich customer context, while Tidio works best for small teams focused on pre-sale chat and basic automation.

You've already decided Richpanel isn't the answer. The question is what to replace it with.

If you're hitting Richpanel's ceiling, it usually looks like one of three things: your AI deflects tickets but can't resolve them, your agents view order data but still switch tabs to act on it, or your automation coverage stalls as ticket volume grows. The platform was built for self-service — not for teams that need to actually do things inside conversations at scale.

This guide compares seven alternatives built for ecommerce brands, with honest assessments of where each one wins, where it doesn't, and who it's actually right for.

Top 7 Richpanel alternatives: feature and pricing comparison

One thing to flag before reading this table: "Shopify integration" means different things on different platforms. Viewing order data inside a ticket is not the same as editing an order, issuing a refund, or applying a discount code without leaving the helpdesk. The table distinguishes between the two.

Understanding helpdesk pricing before you compare tools

Before you evaluate features, learn about pricing models because the model determines whether your costs stay predictable.

Per-agent pricing charges a monthly fee per user on your team. Zendesk, Freshdesk, and Help Scout use this model. It's predictable until your team grows, then adds up fast.

Per-ticket pricing charges based on how many tickets you resolve each month. It rewards teams that automate well: the more you automate, the lower your effective cost per ticket.

Flat-rate pricing charges a single monthly fee regardless of agents or volume. It works at a very small scale but typically comes with feature or usage caps that create problems as you grow.

The reason this matters before you look at any comparison table: a per-agent tool at $55/agent looks cheaper than a per-ticket tool at $360/month — until you have six agents and 2,000 tickets, and the math flips. Know your current ticket volume and team size before you read any pricing number in this article.

{{lead-magnet-1}}

The 7 best Richpanel alternatives for ecommerce brands

Not all helpdesks understand ecommerce. Here are seven alternatives to Richpanel, each with distinct strengths for different types of brands.

1. Gorgias

Best for: Shopify and Shopify Plus brands processing hundreds of orders per day who want automation that resolves tickets, not just deflects them.

Gorgias is built specifically for ecommerce — not adapted for it. That distinction matters when you're evaluating what the platform can actually do inside a ticket versus what it can only display.

The core difference from Richpanel is in how automation works. Gorgias's AI Agent is designed for conversational commerce, meaning it doesn't run on pre-configured messages — it understands context and responds naturally. It can even execute actions, such as initiating a return, applying a discount code, and closing a ticket, without a human agent touching it. That's how it reaches 60% automation coverage for most ecommerce brands.

For support managers, the operational impact is straightforward: your team handles the conversations that actually require judgment, and the platform handles everything else.

Pricing

• Starter: $10/month (50 tickets)
• Basic: $60/month (300 tickets)
• Pro: $360/month (2,000 tickets)
• Advanced: $900/month (5,000 tickets)
• Enterprise: Custom

Read more: Which Gorgias plan should you choose? (Pricing breakdown)

AI and automation

Where Richpanel's AI tells a customer how to start a return, Gorgias's AI Agent actually starts it. Key capabilities:

• Intent detection across WISMO, returns, exchanges, and product questions
• Autonomous actions — refunds, cancellations, discount codes, without human escalation
• Generative responses trained on your store data, macros, and brand voice
• 60% automation coverage for most ecommerce brands out of the box

Shopify integration

Native, not app-based — agents can edit orders, issue refunds, and create discount codes directly inside a ticket. Supports multiple Shopify stores from one dashboard, with automation rules triggered by Shopify data like order value, customer tags, and VIP status. For Shopify Plus brands, Gorgias includes multi-store management, custom automation at scale, and dedicated onboarding — most Plus merchants go live in under two weeks.

Read more: The Gorgias & Shopify integration: 8 features your support team will love

Channels

• Email, live chat, voice, SMS
• Instagram DM, Facebook Messenger, WhatsApp
• Social comment management

Integrations

100+ native ecommerce integrations covering the tools Shopify brands actually use: Klaviyo, Attentive, Recharge, Loop, ShipStation, and Yotpo among them. Agents get full customer context — purchase history, active subscriptions, recent campaigns — without leaving the ticket.

2. Zendesk

Best for: Large, multi-channel retailers with dedicated support ops teams, complex escalation workflows, or multi-brand operations that need enterprise-grade configurability.

Zendesk is the default choice for enterprise support operations — and that's both its strength and its limitation for ecommerce brands. It can handle almost any support configuration at any scale, but that flexibility comes with complexity and cost that many Shopify teams don't need.

For support managers coming from Richpanel, the biggest adjustment is setup time. Zendesk is highly customizable, but out of the box it doesn't understand ecommerce workflows. Shopify integration requires a third-party app, and AI features are add-ons rather than native to the core product.

Pricing

• Suite Team: $55/agent/month
• Suite Growth: $89/agent/month
• Suite Professional: $115/agent/month
• Enterprise: Custom

Read more: Zendesk pricing: Plans, add-ons, and if it’s worth it

AI and automation

Useful at higher tiers, but not included in base plans — expect to pay extra. What you get:

• Intelligent triage and routing based on ticket content and history
• Ticket summarization and macro suggestions to speed up agent responses
• Advanced bots for deflection at scale
• Limitation: none of it connects to Shopify data natively, so the AI works with conversation context only — not order history

Shopify integration

App-based via the Zendesk marketplace. Agents can view order data inside tickets but can't act on it — refunds, edits, and cancellations still require switching to Shopify admin. For high-volume teams, that tab-switching adds up. Zendesk can scale to Shopify Plus volume, but expect to involve a solutions partner — the configuration required to make it work well for ecommerce isn't something most teams can stand up on their own.

Channels

• Email, live chat, voice, SMS
• Facebook, Instagram, WhatsApp, X
• Help center and community forums

Integrations

1,000+ integrations across CRM, ERP, marketing, and ecommerce. The caveat: "integration available" doesn't always mean seamless — many require configuration or a solutions partner to work properly.

3. Freshdesk

Best for: SMBs and mid-market teams that need a broad feature set at a lower per-agent cost and aren't primarily Shopify-focused.

Freshdesk is one of the most widely used helpdesks in the market, and its appeal is straightforward: it covers a lot of ground at a price point that's hard to argue with. For teams coming from Richpanel, it's a credible step up in ticketing sophistication and reporting depth — without the enterprise price tag of Zendesk.

The trade-off is that Freshdesk is a general-purpose tool. It doesn't speak ecommerce natively, and its Shopify integration has the same limitation as Zendesk's: agents can see order data, but acting on it requires leaving the helpdesk.

Pricing

• Free: $0 (up to 10 agents)
• Growth: $15/agent/month
• Pro: $49/agent/month
• Enterprise: $79/agent/month

Read more: Freshdesk pricing guide: What you actually pay

AI and automation

Freshdesk's AI features, branded as Freddy AI, are functional but uneven depending on which plan you're on. What you get:

• Freddy Answer Bot for ticket deflection via self-service, similar in approach to Richpanel
• Ticket categorization and routing based on content and priority
• Suggested responses to help agents reply faster
• Limitation: the most useful AI features sit behind the Pro and Enterprise tiers, so the $15/agent entry point doesn't reflect what you'd actually need to make automation work

Shopify integration

App-based, with the same view-only limitation as Zendesk. Agents can pull up order details inside a ticket but can't take action without switching tabs. There's no native understanding of ecommerce workflows — things like return automations or order-triggered rules require custom configuration or third-party apps.

Channels

• Email, live chat, phone
• Facebook, Instagram, WhatsApp, X
• Help center and community forums

Integrations

1,000+ integrations via the Freshworks marketplace. Ecommerce coverage exists but is slimmer than Zendesk's. Expect to do more legwork connecting the tools your Shopify stack relies on.

4. Help Scout

Best for: Small to mid-sized teams that prioritize a clean agent experience and handle lower ticket volumes without complex automation needs.

Help Scout occupies a specific niche: it's the helpdesk for teams that find tools like Zendesk and Freshdesk overwhelming and want something that feels closer to email. Setup is fast, the interface is clean, and agents can get productive quickly without a long onboarding process.

For support managers evaluating it as a Richpanel alternative, the honest assessment is this: Help Scout solves the inbox and collaboration problem well, but it won't solve the automation problem. If limited automation is what's pushing you off Richpanel, Help Scout isn't the answer.

Pricing

• Standard: $20/user/month
• Plus: $40/user/month
• Pro: $65/user/month

AI and automation

Help Scout's AI features are the most recent addition to the platform and are still maturing. What's available:

• AI Summarize to condense long threads for faster agent context
• AI Assist for drafting and improving replies
• Basic automation rules for routing and tagging
• Limitation: there's no AI Agent capable of resolving tickets autonomously — the AI assists humans, it doesn't replace the human step

Shopify integration

App-based and view-only. Agents can see customer order history inside conversations, but order actions require going to Shopify directly. No native ecommerce automation triggers.

Channels

• Email, live chat
• Facebook and Instagram messages
• Help center

Integrations

Lighter integration library than Zendesk or Freshdesk with around 50+ native integrations. Covers the basics but thins out quickly for teams with more complex Shopify tech stacks. A Zapier connection extends this, but adds another layer to maintain.

Read more: Help scout review: Pros, cons, and alternatives

5. Kustomer

Best for: Mid-market to enterprise brands that want a CRM-first approach to support, where full customer history matters more than the number of tickets resolved.

Kustomer's core idea is different from every other tool on this list. Instead of organizing support around tickets, it organizes it around customers. Every interaction, from purchases and conversations to returns and complaints, lives on a single customer timeline that agents see before they respond to anything.

For support managers coming from Richpanel, the appeal is depth of context. Where Richpanel shows you the ticket, Kustomer shows you the person. The trade-off is complexity and cost. This is not a platform you stand up in a week, and the pricing reflects that.

Pricing

• Enterprise: ~$89/agent/month
• Ultimate: ~$139/agent/month
• Custom enterprise pricing available

Read more: Kustomer pricing guide: What you’ll actually pay

AI and automation

Kustomer's AI is built around its CRM data model, which gives it more customer context to work with than most platforms. What you get:

• Intelligent routing that assigns tickets based on customer history, sentiment, and agent skill
• AI-suggested responses drawn from full customer timeline, not just the current ticket
• Workflow automation triggered by CRM data — lifetime value, churn risk, order frequency
• Limitation: like Zendesk, the AI doesn't connect to Shopify natively, so ecommerce-specific automation still requires custom configuration

Shopify integration

App-based and view-only at the ticket level. Where Kustomer differentiates is in pulling Shopify purchase data into the customer timeline. Agents get a richer picture of buying behavior than they would in most helpdesks. Acting on orders still requires leaving the platform.

Channels

• Email, live chat, voice, SMS
• Facebook, Instagram, WhatsApp, Twitter
• Self-service portal

Integrations

Solid coverage across CRM, marketing, and ecommerce tools. Klaviyo, Recharge, and Shopify are all supported. The integration depth varies — some connections are richer than others and may require development resources to configure properly.

6. Intercom

Best for: Teams that run support and sales or marketing from the same platform — where proactive engagement and reactive support need to live together.

Intercom sits at the intersection of customer support and customer engagement. It's known for its messenger-first experience and its AI agent, Fin, which is one of the more capable AI support tools on the market. For brands where support is closely tied to conversion — think high-consideration products, subscription businesses, or brands with active sales chat — Intercom makes that connection explicit.

For support managers evaluating it as a Richpanel alternative, the key question is whether you need that sales and marketing layer. If you just need a better helpdesk, Intercom may be more than you need and priced accordingly. If your support team is also expected to drive revenue, it's worth a serious look.

Pricing

• Essential: $29/seat/month
• Advanced: $85/seat/month
• Expert: $132/seat/month
• Enterprise: Custom

AI and automation

Intercom's Fin AI agent is one of the strongest on this list for conversational resolution. What you get:

• Fin AI agent that resolves tickets across chat, email, and social using your knowledge base and help content
• Proactive messaging triggered by customer behavior — cart abandonment, browsing patterns, order events
• Workflow builder for multi-step automations across the customer lifecycle
• Limitation: Fin's resolution rate depends heavily on the quality of your help content — teams with thin knowledge bases will see lower automation coverage than the headline numbers suggest

Shopify integration

App-based and view-only for order data. Intercom's strength is in behavioral data rather than transactional data from Shopify. For brands where understanding browsing and purchase intent matters as much as order status, that's a meaningful difference. For teams that primarily need to act on orders, it's still a tab-switching situation.

Channels

• Live chat, email, SMS
• Instagram, Facebook, WhatsApp
• In-app messaging and push notifications

Integrations

Strong integration library with good coverage of ecommerce and marketing tools. Klaviyo, Stripe, Salesforce, and Shopify are all supported. The platform is built with product and growth teams in mind as much as support teams, so the integrations reflect that breadth.

7. Tidio

Best for: Small ecommerce businesses or early-stage brands that want AI-powered chat for pre-sale engagement and basic support at a low entry price.

Tidio is the most limited tool on this list in terms of helpdesk depth, and it's worth being upfront about that. It's not trying to be a full support operations platform — it's a chatbot-first tool that happens to include a shared inbox. For teams coming from Richpanel with growing ticket volumes and complex post-purchase support needs, Tidio is likely a step sideways, not forward.

Where it does earn its place on this list: if your primary support challenge is pre-sale engagement and basic FAQ deflection, and your team is small enough that per-agent pricing on other tools becomes disproportionately expensive, Tidio's flat-rate model makes it genuinely cost-effective.

Pricing

• Free: $0 (up to 50 conversations)
• Starter: $29/month
• Growth: $59/month
• Plus: $749/month

AI and automation

Tidio's AI product is Lyro, a conversational AI chatbot trained on your help content. What you get:

• Lyro AI for handling common questions automatically across chat
• Pre-built automation flows for cart abandonment, welcome messages, and basic FAQ responses
• Basic ticket routing to assign conversations to the right agent
• Limitation: Lyro operates primarily in chat — it doesn't extend across email or social the way a full AI agent would, and it can't take actions on orders

Shopify integration

Basic. Tidio connects to Shopify to display order status information in chat, which is enough for simple WISMO queries. It doesn't support order editing, refunds, or any transactional actions from within the platform. For Shopify Plus brands, this integration depth will feel immediately insufficient.

Channels

• Live chat, email
• Facebook Messenger, Instagram
• SMS via third-party integration

Integrations

Covers the basics — Shopify, Klaviyo, Mailchimp, and a handful of other common tools. The integration library is noticeably thinner than every other platform on this list, which becomes a constraint as your tech stack grows.

Which Richpanel alternative fits your situation

The honest answer is that most teams narrow this down to two or three tools quickly once they're clear on two things: how much of their ticket volume they want to automate, and how deeply they need to work inside Shopify without leaving the platform.

Here's how the shortlist typically looks by situation:

You're on Shopify and support is your biggest operational bottleneck

Gorgias is the obvious choice. The native Shopify integration and AI automation coverage are meaningfully ahead of everything else on this list for ecommerce-specific workflows. If your team is spending time on WISMO, returns, and order edits, those are exactly the tickets Gorgias automates.

You're a large retailer with complex multi-team support operations

Zendesk. The configurability, SLA management, and reporting depth justify the cost and setup investment at enterprise scale. Just factor in a solutions partner and don't expect it to work out of the box for ecommerce.

Your support team also owns sales and proactive engagement

Intercom. If the line between support and revenue generation is blurry on your team — subscription brands, high-consideration products, anything with an active sales chat motion — Intercom's combination of Fin AI and proactive messaging tools is worth the premium.

You need something simpler and cheaper while you're still finding your footing

Help Scout for inbox management, Freshdesk if you need more ticketing structure. Both are easier to set up than Zendesk and cheaper than Gorgias at low ticket volumes. Understand that you're trading automation capability for simplicity.

Your primary challenge is pre-sale engagement, not post-purchase support

Tidio. It's not a full helpdesk, but if chatbot-driven lead capture and basic FAQ deflection is the actual problem, it's cost-effective and fast to set up.

Customer history and lifetime context matter more than ticket speed

Kustomer. If your support philosophy is built around knowing the customer rather than closing the ticket, the CRM-first data model is genuinely differentiated. Be prepared for the implementation investment.

Explore more before you decide

If you're still in research mode, these might help:

• Best AI helpdesk tools: 10 platforms compared — broader view of the AI helpdesk landscape
• Best customer service chatbots for ecommerce in 2026 — if AI-first deflection is your priority
• 10 best ecommerce helpdesk platforms in 2026 — how Gorgias stacks up across the full ecommerce helpdesk category
• Gorgias vs. Richpanel — side-by-side on the features that matter most for Shopify brands

If Gorgias is already at the top of your shortlist, start a free trial and see how it handles your actual workflows.

TL;DR:

• Built directly from agent feedback, Helpdesk 2.0 fixes real workflow pain points. The redesign focuses on reducing friction and helping agents handle more context-heavy tickets.
• A chat-style interface replaces the old email layout. Conversations are easier to follow and resolve in one view.
• Customer context is shown beside the conversation in a right-side panel. Agents can view history, orders, and details without leaving the ticket.
• AI handoffs come with clear summaries. Agents instantly see what happened, what was tried, and what to do next.‍
• Navigation is simpler and faster across teams. Clean menus, structured queues, and multi-store access keep agents moving efficiently.

Helpdesk 2.0 starts with the people who use it most: the agents. 

We spent time understanding customer support from the agent's seat. What do they reach for constantly? What slows them down? What does a better workday look like? 

Everything we found is in this brand-new update.

Why we redesigned Helpdesk

Conversational commerce is the new standard. 

In customer support, this means customers expect context to remain intact wherever they reach out, whether a conversation starts on social, moves to email, or ends on a call.

This new approach to support has also changed the agent's role. Recurring tickets, like order status checks, shipping updates, and returns, are now handled by AI. What lands in the agent inbox are edge cases that require human judgment and troubleshooting, or tickets that require the full picture.

However, the original Helpdesk was built for a different era of support.

Context was separated across views rather than built into the conversation itself. It's something one in five Gorgias customers flagged, through support tickets, NPS surveys, and conversations with our team. So, we got to work. 

Helpdesk 2.0 is the result.

What's new in Helpdesk 2.0

Here's a look at everything that changed.

Read conversations the way they're meant to be read

Conversations have a natural rhythm, one that’s already found in every messaging tool we use. We brought that same layout into the helpdesk. 

Say goodbye to the 2000s email interface and hello to chat bubbles. This updated design changes how quickly you can orient yourself and resolve the ticket in one go.

Chats with customers now look like real conversations, using the speech bubble style you’re familiar with on popular messaging apps.

Check customer history without losing your place

Checking a customer's history used to mean leaving the conversation, an extra step that interrupted what should have been a smooth workflow.

Now, past conversations open in a sidebar next to the active conversation. You can view a customer’s full history, search through their timeline, and open prior tickets without going to a new page.

Check past conversations, orders, and customer details in the brand-new Customer Timeline.

See order details the moment you open a ticket

Order information is easier to reference than ever. Open a ticket, and you instantly see the customer's recent orders, marked with product images and invoice details at a glance. Need to dig deeper? Click on an order, and the expanded information appears in the same panel.

For teams using custom integrations, apps are fixed in a quick-access integration menu on the right.

See order details, product images, and totals at a glance on the right panel, without leaving the conversation.

Pick up where AI left off

You shouldn't have to dig through a thread to figure out what AI already tried. Now you don't have to.

When AI Agent escalates a conversation, it includes a concise handover summary that mentions the issue, what actions were taken, and why it was passed to your team.

Escalated tickets include a brief AI-generated handover summary, marked in yellow, for quick reference.

Move faster across every store and team

We restructured and simplified the navigation. The left sidebar organizes everything into clear categories: Inbox, AI Agent, Marketing, and Analytics, so anyone on your team knows exactly where to go.

To quickly update your knowledge base or adjust a workflow, both now live right in the sidebar. For teams managing multiple stores, switching between them is just as straightforward, accessible from the sidebar, so agents can move between inboxes without breaking their flow.

Agents can switch between stores and their corresponding inboxes directly from the left menu.

A workspace that works the way agents do

Support comes down to the person on the other end of the conversation. We built Helpdesk 2.0 is to make sure they have everything they need to show up for that moment.

The best way to see the difference is to work in it. Start a free trial today.