

TL;DR:
CSAT surveys are the fastest way to measure customer happiness after any interaction with your brand. Unlike lengthy feedback forms that customers abandon, a well-designed CSAT questionnaire captures satisfaction data in seconds while the experience is still fresh.
This guide covers the exact questions, templates, and distribution strategies that ecommerce brands use to collect actionable feedback and improve their customer experience. You'll learn how to create surveys that customers actually complete and turn those responses into real business improvements.
A CSAT survey questionnaire is a measurement tool that captures how satisfied customers are with a specific interaction, product, or service. This means you ask customers to rate their experience on a scale, usually from one to five, right after they interact with your brand.
The core of any CSAT survey is one simple rating question: "How satisfied were you with [specific interaction]?" Customers respond on a scale from Very Dissatisfied to Very Satisfied. This simplicity drives higher completion rates than complex surveys.
CSAT differs from other feedback tools in timing and focus. While relationship surveys measure long-term loyalty, CSAT captures immediate reactions to specific touchpoints like support conversations, deliveries, or product experiences. The questionnaire becomes a survey instrument when you add follow-up questions to understand the "why" behind ratings.
Different metrics serve different purposes in your customer experience strategy. Using the right tool for the job ensures you get clear, actionable data that helps you make better decisions.
CSAT excels at measuring satisfaction with specific interactions. Use it after support tickets, deliveries, or purchases when you need quick feedback on how well you performed. The 5-point scale makes it easy for customers to respond and for your team to track trends.
Key benefits of CSAT surveys:
Net Promoter Score (NPS) asks customers how likely they are to recommend your brand on a 0-10 scale. This metric predicts customer retention and word-of-mouth growth. Send NPS surveys quarterly or after customers have experienced your full product range.
Customer Effort Score (CES) measures how easy it was for customers to complete a task or resolve an issue. Low effort correlates with higher loyalty. Deploy CES after complex processes like returns, account setup, or multi-step support resolutions.
The format of your questions directly impacts response quality and completion rates. Match your question type to the data you need and make it as easy as possible for customers to respond.
Likert scales offer five to seven response options from one extreme to another, like Strongly Disagree to Strongly Agree. Use them for satisfaction ratings, agreement levels, and frequency measurements. They provide data that's easy to analyze while giving customers enough options to express their true feelings.
Yes/No or thumbs up/down questions work best for simple confirmations. Think: "Was your issue resolved?" or "Would you shop with us again?" Binary choices maximize response rates but sacrifice detail. Use them when you need high participation over deep insights.
Text responses explain the "why" behind ratings. Keep them optional and place them after rating questions with prompts like, "What's the primary reason for your score?" These insights guide improvement priorities, even with lower response rates.
These proven questions address the moments that matter most for online shoppers. Start with these templates and adapt them to your brand's specific touchpoints.
Post-purchase surveys capture satisfaction with the buying and delivery experience:
Support surveys measure how well your team resolved customer issues:
Product surveys help you understand satisfaction with your actual offerings:
Loyalty questions gauge future purchase intent and advocacy:
Building an effective CSAT survey requires strategic planning before you write a single question. Following a structured process ensures your survey delivers reliable, actionable data.
Start with the business decision your survey will influence. Are you measuring support quality to improve agent training? Tracking delivery satisfaction to evaluate shipping carriers? Clear objectives determine which questions to ask and how to analyze results.
Choose the right tool for your goal. Select CSAT for transactional feedback, NPS for relationship health, or CES for process improvement. Match your survey type to your objective: post-interaction surveys for operational metrics, periodic surveys for strategic insights.
Use simple language that customers understand immediately. Avoid leading questions like "How excellent was our service?" which bias results. Instead, use neutral phrasing like, "How would you rate our service?" Test questions internally to catch confusion before launch.
Keep surveys under two minutes. Place rating questions first, followed by optional open-ended questions. Since most customers respond on mobile, use large buttons, minimal scrolling, and responsive design. Preview on multiple devices before sending.
Pilot your survey with a small customer segment first. Monitor completion rates, response quality, and technical issues. After launch, review results weekly to identify question improvements and timing adjustments.
Timing and channel selection determine whether customers engage with your survey or ignore it. The right approach maximizes response rates and data quality.
Send transactional surveys within 24 hours of the interaction while details remain fresh. Post-purchase surveys perform best two to three days after delivery. Support surveys should trigger immediately after ticket resolution.
Avoid survey fatigue by limiting requests to once per customer per month. During peak seasons like Black Friday, increase post-purchase surveys but reduce follow-up questions to maintain completion rates.
Different channels work better for different types of feedback:
Collecting feedback without acting on it damages customer trust. Here's how to close the loop and turn scores into improvements.
Assign clear ownership for each feedback category. Support owns resolution feedback, fulfillment owns delivery ratings, product owns quality scores. Create weekly reviews where owners present trends and action plans to improve your CSAT scores.
Break down scores by customer segment, product line, and support channel. Compare new versus returning customers. Identify which segments drive low scores and prioritize improvements with highest impact.
Key segmentation approaches:
Follow up with dissatisfied customers within 48 hours. Thank satisfied customers and request reviews. Share improvements made based on feedback through email updates or Help Center articles. This shows customers their voice creates change.
Use root cause analysis on negative feedback patterns. Tag responses by theme to identify systemic issues. Create escalation workflows for scores below your threshold. Set service level agreements for follow-up based on score severity.
CSAT surveys give you the customer insights needed to improve your support, products, and overall experience. The templates and strategies in this guide provide your starting framework for collecting valuable feedback data.
Gorgias automates CSAT collection right within your helpdesk. Send surveys automatically after ticket resolution, track scores by agent and topic, and identify improvement areas through AI-powered insights. This turns feedback into a powerful tool for operational excellence and customer retention.
Book a demo to see how Gorgias turns feedback into better customer experiences.
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TL;DR:
The way shoppers buy online has shifted and customers are at the center.
They no longer want to scroll through product pages, dig through FAQs, or wait 24 hours for an email reply. They open a conversation, ask a specific question, and expect a useful answer in seconds. Brands that can’t deliver these experiences at scale are seeing customer hesitation turn into abandoned carts and lost revenue.
This shift has a name: conversational commerce. It's the practice of using real-time, two-way conversations as your primary sales channel, through chat, AI agents, messaging apps, and voice.
What started as an experiment for early adopters has become a key growth lever, with 84% of ecommerce brands treating conversational commerce as a strategic pillar this year vs. last year.

We surveyed 400 ecommerce decision-makers across North America, the U.K., and Europe to understand how conversational commerce and AI are reshaping the ecommerce landscape. These findings are complemented by aggregated and anonymized internal Gorgias platform data from 16,000+ ecommerce brands.
The State of Conversational Commerce in 2026 trends report breaks down all of the findings, including five key trends shaping the ecommerce landscape.
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A few years ago, adding an AI chatbot to your site that could provide tracking links and Help Center article recommendations was a differentiator. Today, it's table stakes. McKinsey found that 71% of shoppers expect personalized experiences, and 76% get frustrated when they don't get them.
Right now, most ecommerce professionals use AI, with 93% having used it for at least 1 year. Enthusiasm is accelerating quickly, with only 30% of ecommerce professionals rating their excitement for AI at 10/10 in April 2025. Similarly, while AI adoption rose steadily year over year, it reached a clear peak in 2026.

The use cases driving this adoption are practical and high-volume:

These are the tickets that flood brands’ inboxes every day. AI agents resolve them instantly, without pulling teams away from conversations that actually require human judgment.
Explore AI adoption and use case data in more depth in the full report.
The traditional ecommerce funnel, visit site, browse products, add to cart, check out, is losing ground. Shoppers now discover products on Instagram, ask questions via direct message, and complete purchases without ever visiting a website.

Conversational AI is actively increasing revenue, with 79% of brands reporting that AI-driven interactions have increased sales and conversion in their business.

The practical implication is that every channel is becoming a storefront. Creating personalized touchpoints with customers earlier in the journey, through proactive engagement, is impacting the bottom line.
Read the full report to explore how AI conversions have increased QoQ by industry.
Pre-purchase hesitation is one of the biggest conversion killers in ecommerce. A shopper lands on your product page, has a question about sizing or compatibility, can't find the answer quickly, and leaves. That's a lost sale that had nothing to do with your product.
Conversational AI changes that dynamic. When a shopper can ask a question and get an accurate, personalized answer in real time, the friction disappears.
Brands using Gorgias saw this play out at scale in 2025. When AI Agent recommended a product, 80% of the resulting purchases happened the same day, and 13% happened the next day.

Brands are further accelerating the buying cycle through proactive engagement. On-site features such as suggested product questions, recommendations triggered by search results, and “Ask Anything” input bars drove 50% of conversation-driven purchases during BFCM 2025.
Explore how AI is collapsing the purchase cycle in Trend 3 of the report.
There's a persistent narrative that AI is making CX teams redundant. The data tells a different story. 62% of ecommerce brands are planning to grow their teams, not cut them. But the scope of those teams is changing.

New roles are emerging around AI configuration and quality assurance. Teams are investing in technical members to write AI Guidance instructions, develop tone-of-voice instructions, and continuously QA results.
CX teams are also bridging the gap between support goals and revenue goals, as the two functions increasingly overlap.

The result is CX teams that are more technical than they were before. Agents who once spent their days answering repetitive tickets are now spending that time on higher-value work: complex escalations, VIP customer relationships, and improving the AI systems and knowledge bases that handle the volume.
Learn more about the evolution of CX roles in Trend #4.
Despite increasing AI adoption, data shows that ecommerce brands shouldn’t strive for 100% automation. Winning brands are building systems in which AI handles repetitive tier-1 tickets, and humans handle complex, sensitive cases.

AI handles speed and scale. It resolves order-tracking requests at 2 a.m., processes return-eligibility checks in seconds, and answers the same shipping question for the thousandth time without compromising quality.
Human agents handle conversations that require context, empathy, or decisions that fall outside the standard playbook. There are several topics where shoppers still prefer human support.

Successful hybrid systems require continuous iteration, meaning reviewing handover topics, Guidance, and reviewing AI tickets on a weekly basis.
Discover how leading brands are balancing human and AI systems in Trend #5.
The 2026 trends are about expansion and standardization. The 2030 predictions are about what comes next.

Voice-based purchasing is the biggest bet on the horizon. Only 7% of brands currently use voice assistants for commerce, but 89% expect it to be standard by 2030. The vision is a customer who can reorder a product, check their subscription status, or manage a return entirely over the phone.
Proactive AI is the other major shift. Rather than waiting for a customer to reach out, AI will anticipate needs based on browsing behavior, purchase history, and where someone is in their relationship with your brand. Think of it as the digital equivalent of a sales associate who remembers what you bought last time and knows what you're likely to need next.
Explore where ecommerce brands are allocating their AI budgets in the full report.
The brands winning in 2026 are creating smart, scalable systems where AIhandles volume and humans handle nuance. They’re treating every conversational channel as an opportunity to serve and sell.
The data is clear: AI adoption is accelerating, customer expectations are rising, and the revenue impact of getting this right is measurable.
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TL;DR:
A year ago, ecommerce brands were still debating whether AI was worth the investment. That debate is over. Today, nearly every ecommerce professional uses AI to do their job.
The shift isn't just about adoption. It's about what AI is used for and how brands measure its impact. Support automation was the entry point. Now, AI is embedded across the full operation, from product recommendations to inventory control to real-time shopping conversations.
In our 2026 State of Conversational Commerce Report, we break down trends on AI usage among 400 ecommerce decision-makers and 16,000+ ecommerce brands using Gorgias.
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If we rewind 12 months ago, the industry was still split on AI. Some ecommerce professionals were excited, but most were still hesitant. In 2024, 69% of ecommerce professionals used AI in their roles. By 2025, that number reached 77%. In 2026, it hit 96%.

The confidence numbers back it up. 71% of brands say they are confident using AI for ecommerce, and 73% are satisfied with its business impact.
In early 2025, only 30% of ecommerce professionals rated their excitement for AI at 10/10. Today, zero percent of respondents describe themselves as hesitant about AI.

Using AI in ecommerce is not new. In fact, it dates back to the 1980s with the invention of algorithms and expert systems. And if you’ve ever leveraged similar product recommendations or chatbots, you’ve already integrated AI into your ecommerce stack.
Modern AI is far more sophisticated.
With the rise of agentic commerce and conversational AI, brands began leveraging AI agents to automate the processing of repetitive support tickets. That’s still happening today, but the scope has expanded beyond the support queue.

Ecommerce brands are deploying AI across every layer of their operation:
When brands were asked which channels contribute most to their AI success, conversational channels dominated. Social media messaging led at 78%, followed by SMS at 70%, and website live chat at 51%. Shoppers want fast, personal conversations, and AI is the best way to deliver that at scale.
Learn more about AI adoption, perception, and use case trends in the full 2026 Conversational Commerce Report.
For decades, customer support success meant fast response times and high satisfaction scores. Those are still important indicators of success, but leading brands are adding revenue-focused metrics to their dashboards.
91% of brands still track CSAT as a measure of AI's impact. But 60% now include AOV as a top indicator, and higher-revenue brands earning $20M+ are focusing on metrics like total operating expenses, cost per resolution, incremental revenue, and one-touch ticket rate.

AI can now start a conversation, ease customer doubts, sell, upsell, and recover abandoned carts in a single conversation. When you’re only measuring CSAT, you’re ignoring the real ROI of conversational AI investment.
Virtual shopping assistants now proactively engage shoppers, adapt to their needs in real time, and offer contextual product recommendations and upsells. When the moment calls for it, they can close the deal with a targeted discount.
Gorgias brands using AI Agent's shopping assistant capabilities nearly doubled their purchase rates and converted 20–50% better than those using AI Agent for support only.
Orthofeet, the largest provider of orthopedic footwear in the US, is a concrete example of this in practice. Using Gorgias, they achieved:
The data tells a clear story: AI has evolved beyond a tool for handling tier 1 support tickets. It’s a core part of your revenue generation strategy.
57% of brands are already using AI for 26–50% of all customer interactions, and 37% expect that share to rise to 51–75% within the next two years. The brands building toward that range now are the ones who will have the operational advantage when it matters most.
The practical question isn't whether to invest in AI. It's where to focus first. Based on where brands are seeing the most impact, three priorities stand out:
Want to go deeper on the full 2026 conversational commerce trends? Read the complete report for data across every major AI use case in ecommerce.
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TL;DR:
Customer education has become a critical factor in converting browsers into buyers. For wellness brands like Cornbread Hemp, where customers need to understand ingredients, dosages, and benefits before making a purchase, education has a direct impact on sales. The challenge is scaling personalized education when support teams are stretched thin, especially during peak sales periods.
Katherine Goodman, Senior Director of Customer Experience, and Stacy Williams, Senior Customer Experience Manager, explain how implementing Gorgias's AI Shopping Assistant transformed their customer education strategy into a conversion powerhouse.
In our second AI in CX episode, we dive into how Cornbread achieved a 30% conversion rate during BFCM, saving their CX team over four days of manual work.
Before diving into tactics, understanding why education matters in the wellness space helps contextualize this approach.
Katherine, Senior Director of Customer Experience at Cornbread Hemp, explains:
"Wellness is a very saturated market right now. Getting to the nitty-gritty and getting to the bottom of what our product actually does for people, making sure they're educated on the differences between products to feel comfortable with what they're putting in their body."
The most common pre-purchase questions Cornbread receives center around three areas: ingredients, dosages, and specific benefits. Customers want to know which product will help with their particular symptoms. They need reassurance that they're making the right choice.
What makes this challenging: These questions require nuanced, personalized responses that consider the customer's specific needs and concerns. Traditionally, this meant every customer had to speak with a human agent, creating a bottleneck that slowed conversions and overwhelmed support teams during peak periods.
Stacy, Senior Customer Experience Manager at Cornbread, identified the game-changing impact of Shopping Assistant:
"It's had a major impact, especially during non-operating hours. Shopping Assistant is able to answer questions when our CX agents aren't available, so it continues the customer order process."
A customer lands on your site at 11 PM, has questions about dosage or ingredients, and instead of abandoning their cart or waiting until morning for a response, they get immediate, accurate answers that move them toward purchase.
The real impact happens in how the tool anticipates customer needs. Cornbread uses suggested product questions that pop up as customers browse product pages. Stacy notes:
"Most of our Shopping Assistant engagement comes from those suggested product features. It almost anticipates what the customer is asking or needing to know."
Actionable takeaway: Don't wait for customers to ask questions. Surface the most common concerns proactively. When you anticipate hesitation and address it immediately, you remove friction from the buying journey.
One of the biggest myths about AI is that implementation is complicated. Stacy explains how Cornbread’s rollout was a straightforward three-step process: audit your knowledge base, flip the switch, then optimize.
"It was literally the flip of a switch and just making sure that our data and information in Gorgias was up to date and accurate."
Here's Cornbread’s three-phase approach:
Actionable takeaway: Block out time for that initial knowledge base audit. Then commit to regular check-ins because your business evolves, and your AI should evolve with it.
Read more: AI in CX Webinar Recap: Turning AI Implementation into Team Alignment
Here's something most brands miss: the way you write your knowledge base articles directly impacts conversion rates.
Before BFCM, Stacy reviewed all of Cornbread's Guidance and rephrased the language to make it easier for AI Agent to understand.
"The language in the Guidance had to be simple, concise, very straightforward so that Shopping Assistant could deliver that information without being confused or getting too complicated," Stacy explains. When your AI can quickly parse and deliver information, customers get faster, more accurate answers. And faster answers mean more conversions.
Katherine adds another crucial element: tone consistency.
"We treat AI as another team member. Making sure that the tone and the language that AI used were very similar to the tone and the language that our human agents use was crucial in creating and maintaining a customer relationship."
As a result, customers often don't realize they're talking to AI. Some even leave reviews saying they loved chatting with "Ally" (Cornbread's AI agent name), not realizing Ally isn't human.
Actionable takeaway: Review your knowledge base with fresh eyes. Can you simplify without losing meaning? Does it sound like your brand? Would a customer be satisfied with this interaction? If not, time for a rewrite.
Read more: How to Write Guidance with the “When, If, Then” Framework
The real test of any CX strategy is how it performs under pressure. For Cornbread, Black Friday Cyber Monday 2025 proved that their conversational commerce strategy wasn't just working, it was thriving.
Over the peak season, Cornbread saw:
Katherine breaks down what made the difference:
"Shopping Assistant popping up, answering those questions with the correct promo information helps customers get from point A to point B before the deal ends."
During high-stakes sales events, customers are in a hurry. They're comparing options, checking out competitors, and making quick decisions. If you can't answer their questions immediately, they're gone. Shopping Assistant kept customers engaged and moving toward purchase, even when human agents were swamped.
Actionable takeaway: Peak periods require a fail-safe CX strategy. The brands that win are the ones that prepare their AI tools in advance.
One of the most transformative impacts of conversational commerce goes beyond conversion rates. What your team can do with their newfound bandwidth matters just as much.
With AI handling straightforward inquiries, Cornbread's CX team has evolved into a strategic problem-solving team. They've expanded into social media support, provided real-time service during a retail pop-up, and have time for the high-value interactions that actually build customer relationships.
Katherine describes phone calls as their highest value touchpoint, where agents can build genuine relationships with customers. “We have an older demographic, especially with CBD. We received a lot of customer calls requesting orders and asking questions. And sometimes we end up just yapping,” Katherine shares. “I was yapping with a customer last week, and we'd been on the call for about 15 minutes. This really helps build those long-term relationships that keep customers coming back."
That's the kind of experience that builds loyalty, and becomes possible only when your team isn't stuck answering repetitive tickets.
Stacy adds that agents now focus on "higher-level tickets or customer issues that they need to resolve. AI handles straightforward things, and our agents now really are more engaged in more complicated, higher-level resolutions."
Actionable takeaway: Stop thinking about AI only as a cost-cutting tool and start seeing it as an impact multiplier. The goal is to free your team to work on conversations that actually move the needle on customer lifetime value.
Cornbread isn't resting on their BFCM success. They're already optimizing for January, traditionally the biggest month for wellness brands as customers commit to New Year's resolutions.
Their focus areas include optimizing their product quiz to provide better data to both AI and human agents, educating customers on realistic expectations with CBD use, and using Shopping Assistant to spotlight new products launching in Q1.
The brands winning at conversational commerce aren't the ones with the biggest budgets or the largest teams. They're the ones who understand that customer education drives conversions, and they've built systems to deliver that education at scale.
Cornbread Hemp's success comes down to three core principles: investing time upfront to train AI properly, maintaining consistent optimization, and treating AI as a team member that deserves the same attention to tone and quality as human agents.
As Katherine puts it:
"The more time that you put into training and optimizing AI, the less time you're going to have to babysit it later. Then, it's actually going to give your customers that really amazing experience."
Watch the replay of the whole conversation with Katherine and Stacy to learn how Gorgias’s Shopping Assistant helps them turn browsers into buyers.
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TL;DR:
In 2025, chat’s growth outpaced email by 2.5x quarter over quarter. Chat has become our most powerful customer experience tool for how shoppers discover products, ask questions, and decide to buy.
We knew it needed an upgrade, so we reimagined the entire experience from the ground up.
The result is 36% more engagement with product recommendations, nearly 2.25x more shoppers add-to-cart, and 7.3% more customer engagement.
In this post, we'll walk you through our thinking, what’s new in Chat, and how brands are already seeing big gains.
Chat has outpaced email support. Today’s shoppers prefer the speed of quick chat conversations over email. And when shoppers make a new move, we watch, listen, and move with them.
This behavioral shift isn’t happening in isolation. It aligns with the rise of conversational commerce and proves a universal move toward real-time conversations in ecommerce.
In fact, the signals were already there. Two years of building AI Agent showed us just how much design shapes behavior. The interface is the experience, and we knew that pushing chat experiences to closely resemble human interactions would transform how shoppers engage.
Our new and updated chat brings that vision to life. We believe that shopping is moving from static pages to conversations. This new update is built for how people actually want to shop.
The new design turns live chat into an interactive shopping surface made for modern shoppers. We've brought together multiple ways for shoppers to jump into chat, added clickable replies instead of typing, browsable product cards right in the conversation, and quick cart access.
Let's walk through what's new.
Chat now comes in a softer color palette that adapts to your store’s branding. We removed message bubbles in favor of an airy design that brings in the familiarity of speaking to your favorite conversational AI assistant. Every interaction now has the breathing room for deeper conversation and personalization.

It’s now easier for shoppers to get an answer with quick reply buttons and suggested questions in Chat. This replaces the tree-based flows of the previous Chat, removing the need to follow a fixed path. Shoppers can find answers faster without typing text-heavy explanations.

Browsing and buying within Chat is now possible. Previously, it only supported product links that would open in a new page. With the upgrade, you can view item details without leaving the conversation. Shoppers can browse, compare products, and add to cart in one place.

We’re keeping the context by removing the external redirects. The new interface lets shoppers browse product recommendations right in chat. View key product details, images, descriptions, variants, and pricing without opening a new tab.

Chat adds clickable questions on product pages — like “Is this true to size?” or “What’s the difference between shades?” — designed to match what a shopper is likely wondering in the moment. These context-aware prompts help remove buying hesitation before shoppers even think to ask.

Chat adds instant access to shopper actions, like a cart button and an orders button for returning customers. Shoppers can jump straight to their cart or check on an existing order without waiting for an agent to give them a status update.

Every update in Chat drives performance. We didn’t simply give it a makeover, we also fine-tuned its underlying mechanics.
When product suggestions are easy to browse, shoppers interact with them more. The new product cards make shopping feel natural, allowing customers to explore items at their own pace. That convenience led to a 36% increase in engagement with recommended products.
Chat keeps the entire shopping journey inside the conversation, from browsing and asking questions, to adding to cart and checking out. This new layout removes the usual tab-switching between chat and the website. Less friction has led to more than double add-to-cart actions than before the redesign.
Chat's cleaner design and contextual entry points make it easier for shoppers to start a conversation. With suggested questions on product pages and quick reply buttons, more visitors are choosing to engage earlier in their journey. This has resulted in a 7.3% lift in chat engagement.
Conversational commerce has moved from concept to reality. Chat makes it part of the everyday shopping experience, letting shoppers browse, ask questions, compare products, and check out in one interaction. It brings the ease of the in-person shopping experience into the digital world.
We built Chat to redefine the shopping experience. We hope you see it reflected in your customers’ journeys.
Book a demo to see what's possible with the new experience.

TLDR: https://github.com/xarg/pghoard-k8s
This is a small tutorial on how to do incremental backups using pghoard for your PostgreSQL (I assume you’re running everything in Kubernetes). This is intended to help people to get started faster and not waste time finding the right dependencies, etc..
pghoard is a PostgreSQL backup daemon that incrementally backups your files on a object storage (S3, Google Cloud Storage, etc..).
For this tutorial what we’re trying to achieve is to upload our PostgreSQL to S3.
First, let’s create our docker image (we’re using the alpine:3.4 image cause it’s small):
FROM alpine:3.4
ENV REPLICA_USER "replica"
ENV REPLICA_PASSWORD "replica"
RUN apk add --no-cache \
bash \
build-base \
python3 \
python3-dev \
ca-certificates \
postgresql \
postgresql-dev \
libffi-dev \
snappy-dev
RUN python3 -m ensurepip && \
rm -r /usr/lib/python*/ensurepip && \
pip3 install --upgrade pip setuptools && \
rm -r /root/.cache && \
pip3 install boto pghoard
COPY pghoard.json /pghoard.json.template
COPY pghoard.sh /
CMD /pghoard.sh
REPLICA_USER and REPLICA_PASSWORD env vars will be replaced later in your Kubernetes conf by whatever your config is in production, I use those values to test locally using docker-compose.
The config pghoard.json which tells where to get your data from and where to upload it and how:
{
"backup_location": "/data",
"backup_sites": {
"default": {
"active_backup_mode": "pg_receivexlog",
"basebackup_count": 2,
"basebackup_interval_hours": 24,
"nodes": [
{
"host": "YOUR-PG-HOST",
"port": 5432,
"user": "replica",
"password": "replica",
"application_name": "pghoard"
}
],
"object_storage": {
"aws_access_key_id": "REPLACE",
"aws_secret_access_key": "REPLACE",
"bucket_name": "REPLACE",
"region": "us-east-1",
"storage_type": "s3"
},
"pg_bin_directory": "/usr/bin"
}
},
"http_address": "127.0.0.1",
"http_port": 16000,
"log_level": "INFO",
"syslog": false,
"syslog_address": "/dev/log",
"syslog_facility": "local2"
}
Obviously replace the values above with your own. And read pghoard docs for more config explanation.
Note: Make sure you have enough space in your /data; use a Google Persistent Volume if you DB is very big.
Launch script which does 2 things:
#!/usr/bin/env bash
set -e
if [ -n "$TESTING" ]; then
echo "Not running backup when testing"
exit 0
fi
cat /pghoard.json.template | sed "s/\"password\": \"replica\"/\"password\": \"${REPLICA_PASSWORD}\"/" | sed "s/\"user\": \"replica\"/\"password\": \"${REPLICA_USER}\"/" > /pghoard.json
pghoard --config /pghoard.json
Once you build and upload your image to gcr.io you’ll need a replication controller to start your pghoard daemon pod:
apiVersion: v1
kind: ReplicationController
metadata:
name: pghoard
spec:
replicas: 1
selector:
app: pghoard
template:
metadata:
labels:
app: pghoard
spec:
containers:
- name: pghoard
env:
- name: REPLICA_USER
value: "replicant"
- name: REPLICA_PASSWORD
value: "The tortoise lays on its back, its belly baking in the hot sun, beating its legs trying to turn itself over. But it can't. Not with out your help. But you're not helping."
image: gcr.io/your-project/pghoard:latest
The reason I use a replication controller is because I want the pod to restart if it fails, if a simple pod is used it will stay dead and you’ll not have backups.
Future to do:
Hope it helps, stay safe and sleep well at night.
Again, repo with the above: https://github.com/xarg/pghoard-k8s

At Gorgias we recently switched our flask & celery apps from Google Cloud VMs provisioned with Fabric to using docker with kubernetes (k8s). This is a post about our experience doing this.
Note: I'm assuming that you're somewhat familiar with Docker.
The killer feature of Docker for us is that it allows us to make layered binary images of our app. What this means is that you can start with a minimal base image, then make a python image on top of that, then an app image on top of the python one, etc..
Here's the hierarchy of our docker images:
Piece of advice: If you used to run your app using supervisord before I would advise to avoid the temptation to do the same with docker, just let your container crash and let k8s handle it.
Now we can run the above images using: docker-compose, docker-swarm, k8s, Mesos, etc...
There is an excellent post about the differences between container deployments which also settles for k8s.
I'll also just assume that you already did your homework and you plan to use k8s. But just to put more data out there:
Main reason: We are using Google Cloud already and it provides a ready to use Kubernetes cluster on their cloud.
This is huge as we don't have to manage the k8s cluster and can focus on deploying our apps to production instead.
Let's begin by making a list of what we need to run our app in production:
We ran the above in a normal VM environment, why would we need k8s? To understand this, let's dig a bit into what k8s offers:
There are more concepts like volumes, claims, secrets, but let's not worry about them for now.
We're using Postgres as our main storage and we are not running it using Kubernetes.
Now we are running postgres in k8s (1 hot standby + pghoard), you can ignore the rest of this paragaph.
The reason here is that we wanted to run Postgres using provisioned SSD + high memory instances. We could have created a cluster just for postgres with these types of machines, but it seemed like an overkill.
The philosophy of k8s is that you should design your cluster with the thought that pods/nodes of your cluster are just gonna die randomly. I haven't figured our how to setup Postgres with this constraint in mind. So we're just running it replicated with a hot-standby and doing backups with wall-e for now. If you want to try it with k8s there is a guide here. And make sure you tell us about it.
RabbitMQ (used as message broker for Celery) is running on k8s as it's easier (than Postgres) to make a cluster. Not gonna dive into the details. It's using a replication controller to run 3 pods containing rabbitmq instances. This guide helped: https://www.rabbitmq.com/clustering.html
As I mentioned before, we're using a replication controller to run 3 pods, each containing uWSGI & NGINX containers duo: gorgias/web & gorgias/nginx. Here's our replication controller web-rc.yaml config:
apiVersion: v1
kind: ReplicationController
metadata:
name: web
spec:
replicas: 3 # how many copies of the template below we need to run
selector:
app: web
template:
metadata:
labels:
app: web
spec:
containers:
- name: web
image: gcr.io/your-project/web:latest # the image that you pushed to Google Container Registry using gcloud docker push
ports: # these are the exposed ports of your Pods that are later used by the k8s Service
- containerPort: 3033
name: "uwsgi"
- containerPort: 9099
name: "stats"
- name: nginx
image: gcr.io/your-project/nginx:latest
ports:
- containerPort: 8000
name: "http"
- containerPort: 4430
name: "https"
volumeMounts: # this holds our SSL keys to be used with nginx. I haven't found a way to use the http load balancer of google with k8s.
- name: "secrets"
mountPath: "/path/to/secrets"
readOnly: true
volumes:
- name: "secrets"
secret:
secretName: "ssl-secret"
And now the web-service.yaml:apiVersion: v1
kind: Service
metadata:
name: web
spec:
ports:
- port: 80
targetPort: 8000
name: "http"
protocol: TCP
- port: 443
targetPort: 4430
name: "https"
protocol: TCP
selector:
app: web
type: LoadBalancer
That type: LoadBalancer at the end is super important because it tells k8s to request a public IP and route the network to the Pods with the selector=app:web.
If you're doing a rolling-update or just restarting your pods, you don't have to change the service. It will look for pods matching those labels.
Also a replication controller that runs 4 pods containing a single container: gorgias/worker, but doesn't need a service as it only consumes stuff. Here's our worker-rc.yaml:
apiVersion: v1
kind: ReplicationController
metadata:
name: worker
spec:
replicas: 2
selector:
app: worker
template:
metadata:
labels:
app: worker
spec:
containers:
- name: worker
image: gcr.io/your-project/worker:latest
With Kubernetes, docker finally started to make sense to me. It's great because it provides great tools out of the box for doing web app deployment. Replication controllers, Services (with LoadBalancer included), Persistent Volumes, internal DNS. It should have all you need to make a resilient web app fast.
At Gorgias we're building a next generation helpdesk that allows responding 2x faster to common customer requests and having a fast and reliable infrastructure is crucial to achieve our goals.
If you're interested in working with this kind of stuff (especially to improve it): we're hiring!

We've released a new version of the Chrome Extension, with sharing features and a new navigation bar. We hope you'll love it!
Before, the only way to share templates with your teammates was to login on Gorgias.io.
If you're on the startup plan, when you create a template, you can choose who has access to it: either only you, specific people, or your entire team.

The account management section is now available in the extension, under settings.
Tags are now available on the left. It's easier to manage hundreds of templates with them.
You can also navigate through your private & shared templates. Shared templates include templates shared with specific people or with everyone.

We hope you'll enjoy this new version of our Chrome Extension. As usual, your feedback & questions are welcome!

Today, we’re thrilled to announce that we’ve raised a $1.5 million Seed round led by Charles River Ventures and Amplify Partners, to help build our new helpdesk.
We’re incredibly grateful to early users, customers, mentors we’ve met both at and Techstars.
We started the journey with Alex at the beginning of 2015 with our Chrome extension, which helps write email faster using templates. We’ve been pleased all along with customers telling us about how helpful it was, especially for customer support.
While building the extension, we’ve realized that a big inefficiency in support lies in the lack of integration between the helpdesk, the payment system, CRM and other tools support is using. As a result, agents need to do a lot of repetitive work to respond to customer requests, especially when the company is big.

That’s why we’ve decided to build a new kind of helpdesk to enable customer support agents to respond 2x faster to customers. You can find out more and sign up for our private beta here.
When a company has a lot of customers, support becomes repetitive. We want to provide support teams with tools to automate the way they treat simple repetitive requests. This way, they have more time for complex customer issues.
We'll now focus on this helpdesk and on growing the team, oh, and if you'd like to join, we're hiring! We're super excited about this new helpdesk product. If you’re using the extension, don’t worry.
Romain & Alex

Last few months we got lots of feedback about our extension and found to our delight that most people are satisfied, but still a few recurrent issues came up:
We listened and now we're presenting:
WYSIWYG editors for the web are notoriously buggy and are just difficult to develop.
I have yet to see one that is bug free. There are few venerable editors that do a good job like TinyMCE, FKEditor or CKEditor.. but they are big and all have edge cases that break the intended formatting and add a lot of garbage html.
There are newer good quality editors in town such as Redactor. The one that got my attention and finally landed in Gorgias is this wonderful editor called which is super lightweight, uses modern content-editable (no i-frames) and 'just works' most of the time. That's not to say it's perfect, but it's good enough and I'm satisfied with it's direction in terms of development.
Enjoy it and as always send us bug-reports or feedback on: support@gorgias.com

TL;DR:
Wholesale accounts, retail partners, and corporate buyers represent some of the highest-revenue relationships your brand manages. So why are so many ecommerce teams still supporting them with the same inbox they use for everything else?
B2B customer service is its own discipline. Your buyers are juggling multi-stakeholder approvals, complex order workflows, and expectations shaped by dedicated account managers. They're not looking for a chatbot. They want a partner who actually knows their business and can keep up with it.
If you're building out a B2B support operation, or fixing one that's been held together with spreadsheets and good intentions, you're in the right place. This guide covers the real differences between B2C and B2B support. You'll also find proven team structures and the tools top brands use to turn their most valuable accounts into long-term partners.
{{lead-magnet-1}}
B2B customer service is support provided by one business to another business that purchases its products or services. This means helping wholesale partners, corporate clients, and other business accounts that buy from your ecommerce store.
B2B service focuses on managing complex, high-value relationships rather than individual transactions. A single B2B account can represent thousands of dollars in recurring revenue, making the quality of support critical for retention and growth.
The nature of B2B service differs from consumer support because the stakes are higher and the needs are more specialized. Your B2B customers have different expectations, longer decision-making processes, and more complex operational requirements.
B2B customer service typically involves:
B2B and B2C customer service aim for customer satisfaction, but their execution differs significantly. The core distinction comes from the nature of the customer: a business with complex operational needs versus an individual consumer.
B2B support inquiries often involve technical troubleshooting, custom configurations, or multi-system dependencies. A client might need help integrating your product's API with their internal software or managing a custom product catalog for their employees.
These issues require agents with deep product knowledge and problem-solving skills. In contrast, B2C issues are typically simpler and more repetitive, such as questions about order status, return policies, or product sizing.
A single B2B support ticket may involve communication with multiple decision-makers within the client's organization. The procurement team might have questions about invoicing, the IT department may need technical specifications, and the end users might require product training.
Support agents must navigate these internal dynamics and provide clear, consistent information to all relevant stakeholders. This level of coordination is rarely seen in B2C, where the agent typically interacts with just one person.
B2B customer service is built on long-term partnerships, not one-time transactions. The goal is to support the client's success over the entire lifecycle of a contract, which can span several years.
This long-term focus encourages proactive support and deep investment in understanding the client's business. It leads to higher retention and opportunities for account expansion.
The complexity of B2B issues and involvement of multiple stakeholders naturally lead to longer resolution times. A simple request might require internal approvals from the client, technical investigation from your engineering team, or coordination with a third-party vendor.
B2B support teams often work with Service Level Agreements (SLAs) that set clear expectations for response and resolution times. These acknowledge that a quick fix is not always possible or desirable.
For ecommerce brands with wholesale or corporate sales channels, excellent B2B customer service is not just a cost center. It's a powerful engine for growth that directly influences revenue, retention, and market position.
Each B2B account represents significant, recurring revenue. That alone should change how you think about support.
When service is exceptional, trust follows. And trust opens doors: upsells, expanded contracts, new product lines your client wouldn't have explored otherwise. A well-supported B2B buyer doesn't stay static. They grow with you, becoming a source of predictable revenue rather than a transaction you have to chase again next quarter.
Acquiring a new B2B client is expensive. Losing one is worse, because you're not just losing a contract. You're losing the compounding value of everything that account could have become.
Proactive, responsive support makes it genuinely difficult for competitors to get a foothold. You're not just solving problems. You're making the relationship too valuable to walk away from.
Business leaders talk to each other, and in B2B, word-of-mouth travels fast and lands hard. A reputation for dependable, personalized service becomes part of your brand identity in ways that marketing spend simply can't replicate.
Done right, those relationships turn into case studies, testimonials, and referrals. The kind that do the selling for you.
When products and pricing are similar across competitors, customer service becomes the key differentiator. A support experience that is personalized, efficient, and proactive creates relationship stickiness.
It raises the switching costs for your clients because they are not just buying a product. They are invested in the partnership and the quality of support they receive.
Delivering exceptional B2B service requires a strategic approach that goes beyond standard support tactics. Leading ecommerce brands build their B2B operations around deep understanding of their clients and commitment to proactive, collaborative support.
You cannot provide great service without deep understanding of your client's business. This means going beyond their order history.
Map out the key contacts in each account, understand their business goals, and document their specific workflows and technical requirements. This information allows your team to provide context-aware, personalized support that anticipates needs.
Start by creating detailed customer profiles that include:
B2B customer service is a team sport. Support agents need to work seamlessly with sales, account management, and even product teams to resolve complex issues.
Break down internal silos by using shared tools, like a unified helpdesk, and establishing clear communication protocols for escalating issues or sharing client feedback. When your sales team knows about a support issue, they can proactively address concerns during their next check-in.
Not all B2B accounts are the same. Create tiered Service Level Agreements (SLAs) that define response time commitments and support channels based on the client's contract value or strategic importance.
This manages expectations and ensures your most valuable accounts receive the priority attention they require. Your enterprise clients should have faster response times and more direct access to senior support staff than smaller accounts.
|
SLA Tier |
Response Time |
Resolution Target |
Support Channels |
|
Enterprise |
Under 1 hour |
24 hours |
Phone, Email, Chat, Dedicated Rep |
|
Mid-Market |
Under 4 hours |
48 hours |
Email, Chat |
|
Standard |
Under 8 hours |
72 hours |
Email, Help Center |
Empower your B2B clients to find answers on their own. A comprehensive knowledge base with technical documentation, API guides, and troubleshooting articles can deflect a significant number of tickets.
For more advanced needs, a customer portal can allow clients to manage their account, track orders, and access exclusive resources without needing to contact an agent. This is especially valuable for B2B customers who often work outside standard business hours.
Self-service options should include:
The best B2B support teams solve problems before the client even knows they exist. Use data to monitor account health, identify potential issues like declining usage, and reach out proactively.
Schedule regular business reviews to discuss their goals, gather feedback, and ensure they are getting the most value from your products. These conversations often reveal opportunities for additional sales or prevent churn before it happens.
Scaling high-quality B2B support is impossible without the right technology stack. Modern tools are designed to manage complexity, provide deep customer context, and automate repetitive tasks.
B2B communication happens across email, phone, chat, and social media. A unified helpdesk brings all these conversations into a single inbox.
This gives your team a complete, chronological view of every interaction with an account, regardless of which contact reached out or which channel they used. No more searching through different systems to understand the full context of a client relationship.
A robust knowledge base is your first line of defense. Use it to host detailed documentation for common B2B needs, such as integration guides, API documentation, and bulk order instructions.
This not only reduces ticket volume but also positions your brand as an expert resource. B2B customers appreciate having access to comprehensive information when they need it, especially outside business hours.
Artificial intelligence and automation are critical for B2B efficiency. Use AI to automatically tag and route incoming tickets to the right agent or department based on topic or client tier.
Automation rules can handle repetitive workflows, such as sending order status updates or assigning tickets from VIP accounts to a dedicated manager. This ensures nothing falls through the cracks while freeing up your team for higher-value work.
To provide effective support, your agents need data. Integrating your helpdesk with your ecommerce platform, Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM) systems is essential.
These integrations pull critical account information directly into the support inbox. Agents can see order history, contract details, and custom pricing without switching tabs, enabling faster and more accurate responses.
The right tools make the difference between a support team that's constantly putting out fires and one that runs like a well-oiled machine. Here are five platforms worth considering for your B2B operation.
Starting price: $10/month
Best for: Ecommerce brands managing B2B and DTC support in one place.
Key features:
May not be the best fit if: You're not running on an ecommerce platform or have no DTC channel at all.
Starting price: $55/agent/month
Best for: Mid-to-large B2B operations that need flexible, highly customizable ticketing workflows.
Key features:
May not be the best fit if: You're a smaller team. The pricing and complexity can be overkill without dedicated admin resources.
Starting price: $15/seat/month
Best for: B2B teams already using HubSpot for sales and marketing who want everything under one roof.
Key features:
May not be the best fit if: You're not in the HubSpot ecosystem. Standalone, it's harder to justify against more specialized tools.
Starting price: $29/seat/month
Best for: B2B teams that prioritize conversational support and proactive account engagement.
Key features:
May not be the best fit if: Your support volume is high and ticket-based. Intercom's conversational model can get expensive and hard to manage at scale.
Starting price: $25/user/month
Best for: Enterprise B2B operations with complex account hierarchies and large support teams.
Key features:
May not be the best fit if: You're a small or mid-sized team. Implementation is heavy and the cost adds up fast without a dedicated Salesforce admin.
B2B customer service isn't just a support function. It's how you protect your highest-value accounts, reduce churn, and turn good clients into long-term partners.
The brands that get this right invest in the right tools, train their teams to think in relationships rather than tickets, and are ready before problems escalate.
Ready to streamline your B2B support operation? See what Gorgias can do for your B2B business.

TL;DR:
Switching customer support software is a nightmare, and everyone on your team knows it. Yet here you are, staring down a tool that can't keep up with your ticket volume, leaves agents toggling between five browser tabs, and offers zero insight into whether any of it is actually driving revenue.
Modern customer support platforms are built to solve exactly that.
The best ones bring every channel into a single workspace: email, chat, social, and SMS. They display customer order data (so agents don't have to dig for it), and handle repetitive tasks automatically so your team can focus on conversations that actually matter.
For ecommerce brands on Shopify or scaling beyond it, the right platform doesn't just reduce handle time. It becomes a revenue channel in its own right.
Here's how the top options stack up.
With dozens of platforms on the market, finding the right fit for your store takes time you don't have. The table below compares the top customer support platforms on the factors ecommerce teams care about most: pricing, core features, Shopify integration, and AI capabilities.
Platform |
Starting price |
Best for |
Key features |
Shopify integration |
AI capabilities |
Gorgias |
$10/month |
Shopify merchants |
AI Agent, revenue tracking, deep Shopify sync |
Premium Partner |
Resolves up to 60% of tickets |
Zendesk |
$55/agent/month |
Enterprise teams |
Omnichannel, robust reporting, large app market |
Yes |
AI-powered routing & bots |
Intercom |
$39/seat/month |
Proactive support |
Live chat, product tours, targeted messages |
Yes |
Custom bots, AI Agent |
Freshdesk |
$0/month (Free plan) |
Small businesses |
Ticketing, knowledge base, multi-channel |
Yes |
AI-powered ticketing |
Help Scout |
$20/user/month |
Simplicity & collaboration |
Shared inbox, knowledge base, live chat |
Yes |
AI-summaries & suggestions |
Zoho Desk |
$14/agent/month |
Zoho ecosystem users |
Context-aware AI, process automation |
Yes |
AI assistant (Zia) |
Front |
$19/seat/month |
Internal collaboration |
Shared inbox, team-based workflows |
Yes |
AI-summaries & templates |
HubSpot Service Hub |
$0/month (Free tools) |
HubSpot CRM users |
Ticketing, live chat, customer feedback |
Yes |
Conversation intelligence |
Salesforce Service Cloud |
$25/user/month |
Salesforce CRM users |
Case management, workflow automation |
Yes |
Einstein AI |
Re:amaze |
$29/month |
Multi-store brands |
Live chat, chatbots, social media integration |
Yes |
Chatbots & Cues |
We evaluated the top platforms on the market based on their ecommerce-specific features, integration depth, automation capabilities, and overall value for growing brands. Here are the best customer support tools for online retailers.
Gorgias is a conversational commerce platform built specifically for ecommerce brands. It combines a powerful helpdesk with an autonomous AI Agent to manage both customer support and revenue-driving interactions in one place.
As Shopify's only Premium CX Partner, its integration is second to none. Teams can view and edit orders directly within a support ticket. The platform's AI Agent can automate up to 60% of common support inquiries like "Where is my order?" and return requests.
The AI also acts as a shopping assistant, providing personalized product recommendations to drive sales. This dual focus on support and revenue makes Gorgias unique for brands that view customer experience as a growth driver, not just a cost center.
Main features:
Ideal for:
Pricing: Starter: $10/month (50 tickets), Basic: $60/month (300 tickets), Pro: Custom pricing based on volume, AI Agent: Additional per-resolution pricing
Zendesk is one of the largest and most established players in the customer service software space. It offers a comprehensive suite of tools that can handle the needs of large, complex enterprise organizations.
Its platform is highly customizable and features robust reporting, a massive app marketplace, and true omnichannel support. For ecommerce brands, Zendesk provides the scale needed for high-volume operations.
However, its generalist approach means the deep, native ecommerce integrations found in platforms like Gorgias often require third-party apps or custom development. This can lead to a higher total cost of ownership and a less streamlined experience for agents.
Pricing: Suite Team: $55/agent/month, Suite Growth: $89/agent/month, Suite Professional: $115/agent/month
Intercom excels at proactive, conversational support, making it a strong choice for brands focused on engagement and sales through chat. Its platform is built around a messenger that can be used for live chat, targeted outbound messages, and product tours.
This makes it effective for converting website visitors and onboarding new customers. While Intercom offers powerful automation and a user-friendly interface, its core strength is in pre-purchase conversations.
Post-purchase support workflows and deep ecommerce backend integrations are less of a focus compared to specialized retail platforms. Pricing can also become expensive as your contact list or seat count grows.
Pricing: Essential: $39/seat/month, Advanced: $99/seat/month, Expert: $139/seat/month
Freshdesk is a versatile helpdesk solution that offers a wide range of features, including a generous free plan for up to ten agents. This makes it an accessible starting point for small businesses and startups.
The platform includes ticketing, a knowledge base, and multi-channel support across email, phone, chat, and social media. As a general-purpose tool, Freshdesk requires integrations to connect with ecommerce platforms like Shopify.
While these integrations exist, they may not provide the same level of real-time data and order management actions as a purpose-built solution. It's a solid, budget-friendly option for brands with basic support needs.
Pricing: Free: $0 (up to 10 agents), Growth: $15/agent/month, Pro: $49/agent/month, Enterprise: $79/agent/month
Help Scout is known for its simplicity and focus on a human-centric customer experience. It provides a clean, shared inbox that feels like a regular email client, making it easy for teams to learn and use.
The platform also includes a knowledge base builder, live chat, and reporting features. Help Scout is a great choice for teams that prioritize straightforward, collaborative support without the complexity of larger enterprise systems.
Its Shopify integration allows agents to view customer and order data, but it lacks the advanced automation and revenue-driving features found in dedicated conversational commerce platforms.
Pricing: Standard: $20/user/month, Plus: $40/user/month, Pro: $65/user/month
Zoho Desk is part of the broader Zoho ecosystem of business applications, which includes CRM, marketing, and finance tools. Its main advantage is its seamless integration with other Zoho products.
The platform offers context-aware AI, advanced process automation, and omnichannel support capabilities. For businesses already invested in the Zoho suite, Zoho Desk is a logical and powerful choice.
However, for ecommerce brands not using other Zoho tools, it functions as another generalist helpdesk. It has a Shopify integration, but its primary value is unlocked when used as part of the larger Zoho platform.
Pricing: Standard: $14/agent/month, Professional: $23/agent/month, Enterprise: $35/agent/month
Front is a customer communication hub that unifies email, social media, chat, and other channels into a single shared inbox. Its core strength is enabling team collaboration around customer conversations.
Teams can assign messages, leave internal comments, and work together to resolve issues without forwarding emails or switching apps. Front is excellent for B2B companies or teams with complex internal workflows.
For high-volume B2C ecommerce, its model can be less efficient for managing individual customer tickets at scale. While it integrates with Shopify, it's designed more for collaborative communication than for high-speed, automated ticket resolution.
Pricing: Starter: $19/seat/month, Growth: $59/seat/month, Scale: $99/seat/month
HubSpot Service Hub is a customer service software that is fully integrated with HubSpot's CRM, Marketing Hub, and Sales Hub. This creates a unified view of the customer across the entire journey.
The platform includes ticketing, a knowledge base, live chat, and customer feedback surveys. If your brand is already using HubSpot's CRM as its central source of truth, Service Hub is a natural fit.
It allows you to connect support interactions with marketing campaigns and sales data. For brands not on the HubSpot platform, it may be more than what's needed, and its ecommerce-specific features are less developed than those of dedicated retail solutions.
Pricing: Free Tools: $0, Starter: $18/month (2 users), Professional: $450/month (5 users)
Salesforce Service Cloud is an enterprise-grade customer service platform built on the world's leading CRM. It offers powerful case management, workflow automation, and AI capabilities through its Einstein AI.
It's designed for large organizations that require deep customization and scalability. For ecommerce brands running on Salesforce Commerce Cloud or those with complex, multi-brand operations, Service Cloud provides unmatched power.
However, for most Shopify merchants, it is overly complex and expensive. Implementation often requires significant resources and specialized expertise.
Pricing: Essentials: $25/user/month, Professional: $75/user/month, Enterprise: $150/user/month
Re:amaze is a customer communication platform designed for small and medium-sized ecommerce businesses. It combines live chat, chatbots, a shared inbox, and a knowledge base in one package.
It's known for its strong support for multi-store brands, allowing you to manage support for several Shopify stores from a single account. The platform offers a good balance of features for growing ecommerce teams, with a particular strength in live chat and proactive engagement.
Its automation capabilities are primarily focused on chatbots rather than the end-to-end ticket resolution offered by more advanced AI agents.
Pricing: Basic: $29/month, Pro: $49/month, Plus: $69/month
Customer support software is a set of tools used to collect, organize, respond to, and report on customer support requests. It acts as a central command center for a brand's customer service operations, ensuring no message is missed and every customer gets a timely, consistent response.
At its core, the software consolidates all communication channels into one place. This prevents agents from having to jump between their email inbox, social media DMs, and live chat dashboards to help customers.
The software transforms every customer inquiry into a trackable ticket that can be assigned, prioritized, and monitored until it's resolved. Most platforms also include automation tools that handle repetitive tasks and answer common questions without human intervention.
For ecommerce brands, implementing the right customer support software goes beyond just organizing tickets. It directly impacts efficiency, customer loyalty, and the bottom line.
When evaluating platforms for an ecommerce business, certain features are non-negotiable. Look for tools built to handle the specific challenges and opportunities of online retail.
Customer support software is not just for frontline agents. It serves as a central hub for multiple roles across an ecommerce organization, each using it for different purposes.
Support agents use the platform daily to respond to tickets, manage conversations, and resolve customer issues. Team leads monitor team performance, manage ticket queues, and use conversation recordings for coaching and quality assurance.
CX managers analyze support metrics, build automation workflows, and develop the overall customer experience strategy. Operations teams use insights from support tickets to identify and fix issues with shipping, fulfillment, or the website.
Marketing teams gather customer feedback and insights to inform product development and marketing campaigns. This cross-functional usage makes customer support software a valuable investment that impacts the entire organization.
Customer support software pricing typically follows one of two models. Here's how they compare:
|
Per-agent pricing |
Usage-based pricing |
|
How it works |
Flat monthly fee per user |
|
Based on ticket volume handled |
Common with |
|
Zendesk, Help Scout, Front |
Gorgias |
|
Predictability |
High, easy to forecast |
|
Varies with customer demand |
Scales with team growth |
|
Gets expensive as you add agents |
Cost stays tied to demand, not headcount |
|
Best for |
Stable teams with consistent volume |
|
Growing brands with fluctuating demand |
Whichever model you're evaluating, look beyond the sticker price. Factor in onboarding fees, essential integrations, and premium features like AI, which are often sold as add-ons.
The right platform will shape your team's efficiency and your customers' experience for years, so it's worth evaluating carefully. For Shopify brands, that evaluation should center on a few key factors:
Choosing the right customer support software is a strategic decision that enables your brand to scale efficiently while delivering a memorable customer experience. The best platforms for ecommerce don't just solve problems; they create opportunities to build loyalty and drive revenue.
By unifying your channels, automating repetitive work, and empowering your team with data, you can transform your support center from a cost center into a growth engine. Ready to see how a platform built for ecommerce can make a difference?
Book a demo to learn how you can turn every conversation into an opportunity to grow.
{{lead-magnet-2}}

TL;DR:
CSAT surveys are the fastest way to measure customer happiness after any interaction with your brand. Unlike lengthy feedback forms that customers abandon, a well-designed CSAT questionnaire captures satisfaction data in seconds while the experience is still fresh.
This guide covers the exact questions, templates, and distribution strategies that ecommerce brands use to collect actionable feedback and improve their customer experience. You'll learn how to create surveys that customers actually complete and turn those responses into real business improvements.
A CSAT survey questionnaire is a measurement tool that captures how satisfied customers are with a specific interaction, product, or service. This means you ask customers to rate their experience on a scale, usually from one to five, right after they interact with your brand.
The core of any CSAT survey is one simple rating question: "How satisfied were you with [specific interaction]?" Customers respond on a scale from Very Dissatisfied to Very Satisfied. This simplicity drives higher completion rates than complex surveys.
CSAT differs from other feedback tools in timing and focus. While relationship surveys measure long-term loyalty, CSAT captures immediate reactions to specific touchpoints like support conversations, deliveries, or product experiences. The questionnaire becomes a survey instrument when you add follow-up questions to understand the "why" behind ratings.
Different metrics serve different purposes in your customer experience strategy. Using the right tool for the job ensures you get clear, actionable data that helps you make better decisions.
CSAT excels at measuring satisfaction with specific interactions. Use it after support tickets, deliveries, or purchases when you need quick feedback on how well you performed. The 5-point scale makes it easy for customers to respond and for your team to track trends.
Key benefits of CSAT surveys:
Net Promoter Score (NPS) asks customers how likely they are to recommend your brand on a 0-10 scale. This metric predicts customer retention and word-of-mouth growth. Send NPS surveys quarterly or after customers have experienced your full product range.
Customer Effort Score (CES) measures how easy it was for customers to complete a task or resolve an issue. Low effort correlates with higher loyalty. Deploy CES after complex processes like returns, account setup, or multi-step support resolutions.
The format of your questions directly impacts response quality and completion rates. Match your question type to the data you need and make it as easy as possible for customers to respond.
Likert scales offer five to seven response options from one extreme to another, like Strongly Disagree to Strongly Agree. Use them for satisfaction ratings, agreement levels, and frequency measurements. They provide data that's easy to analyze while giving customers enough options to express their true feelings.
Yes/No or thumbs up/down questions work best for simple confirmations. Think: "Was your issue resolved?" or "Would you shop with us again?" Binary choices maximize response rates but sacrifice detail. Use them when you need high participation over deep insights.
Text responses explain the "why" behind ratings. Keep them optional and place them after rating questions with prompts like, "What's the primary reason for your score?" These insights guide improvement priorities, even with lower response rates.
These proven questions address the moments that matter most for online shoppers. Start with these templates and adapt them to your brand's specific touchpoints.
Post-purchase surveys capture satisfaction with the buying and delivery experience:
Support surveys measure how well your team resolved customer issues:
Product surveys help you understand satisfaction with your actual offerings:
Loyalty questions gauge future purchase intent and advocacy:
Building an effective CSAT survey requires strategic planning before you write a single question. Following a structured process ensures your survey delivers reliable, actionable data.
Start with the business decision your survey will influence. Are you measuring support quality to improve agent training? Tracking delivery satisfaction to evaluate shipping carriers? Clear objectives determine which questions to ask and how to analyze results.
Choose the right tool for your goal. Select CSAT for transactional feedback, NPS for relationship health, or CES for process improvement. Match your survey type to your objective: post-interaction surveys for operational metrics, periodic surveys for strategic insights.
Use simple language that customers understand immediately. Avoid leading questions like "How excellent was our service?" which bias results. Instead, use neutral phrasing like, "How would you rate our service?" Test questions internally to catch confusion before launch.
Keep surveys under two minutes. Place rating questions first, followed by optional open-ended questions. Since most customers respond on mobile, use large buttons, minimal scrolling, and responsive design. Preview on multiple devices before sending.
Pilot your survey with a small customer segment first. Monitor completion rates, response quality, and technical issues. After launch, review results weekly to identify question improvements and timing adjustments.
Timing and channel selection determine whether customers engage with your survey or ignore it. The right approach maximizes response rates and data quality.
Send transactional surveys within 24 hours of the interaction while details remain fresh. Post-purchase surveys perform best two to three days after delivery. Support surveys should trigger immediately after ticket resolution.
Avoid survey fatigue by limiting requests to once per customer per month. During peak seasons like Black Friday, increase post-purchase surveys but reduce follow-up questions to maintain completion rates.
Different channels work better for different types of feedback:
Collecting feedback without acting on it damages customer trust. Here's how to close the loop and turn scores into improvements.
Assign clear ownership for each feedback category. Support owns resolution feedback, fulfillment owns delivery ratings, product owns quality scores. Create weekly reviews where owners present trends and action plans to improve your CSAT scores.
Break down scores by customer segment, product line, and support channel. Compare new versus returning customers. Identify which segments drive low scores and prioritize improvements with highest impact.
Key segmentation approaches:
Follow up with dissatisfied customers within 48 hours. Thank satisfied customers and request reviews. Share improvements made based on feedback through email updates or Help Center articles. This shows customers their voice creates change.
Use root cause analysis on negative feedback patterns. Tag responses by theme to identify systemic issues. Create escalation workflows for scores below your threshold. Set service level agreements for follow-up based on score severity.
CSAT surveys give you the customer insights needed to improve your support, products, and overall experience. The templates and strategies in this guide provide your starting framework for collecting valuable feedback data.
Gorgias automates CSAT collection right within your helpdesk. Send surveys automatically after ticket resolution, track scores by agent and topic, and identify improvement areas through AI-powered insights. This turns feedback into a powerful tool for operational excellence and customer retention.
Book a demo to see how Gorgias turns feedback into better customer experiences.
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