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:

1. Replaces our ENV variables with the right username and password for our replication (make sure you have enough connections for your replica user)
2. Launches the pghoard daemon.

‍

‍

#!/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:

• Monitoring (are you backups actually done? if not, do you receive a notification?)
• Stats collection.
• Encryption of backups locally and then uploaded to the cloud (this is supported by pghoard).

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.

‍

Docker structure

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:

• gorgias/base - we're using phusion/baseimage as a starting base image.
• gorgias/pgbouncer
• gorgias/rabbitmq
• gorgias/nginx - extends gorgias/base and installs NGINX
• gorgias/python - Installs pip, python3.5 - yes, using it in production.
• gorgias/app - This installs all the system dependencies: libpq, libxml, etc.. and then does pip install -r requirements.txt
• gorgias/web - this sets up uWSGI and runs our flask app
• gorgias/worker - Celery worker

‍

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...

‍

We chose Kubernetes too

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:

• Database (Postgres)
• Message queue (RabbitMQ)
• App servers (uWSGI running Flask)
• Web servers (NGINX proxies uWSGI and serves static files)
• Workers (celery)

‍

Why Kubernetes again?

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:

• A pod is a group of containers (docker, rtk, lxc...) that runs on a Node. It's a group because sometimes you want to run a few containers next to each other. For example we are running uWSGI and NGINX on the same pod (on the same VM and they share the same ip, ports, etc..).
• A Node is a machine (VM or metal) that runs a k8s daemon (minion) that runs the Pods.
• The nodes are managed by the k8s master (which in our case is managed by the container engine from Google).
• Replication Controller or for short rc tells k8s how many pods of a certain type to run. Note that you don't tell k8s where to run them, it's master's job to schedule them. They are also used to do rolling updates, and autoscaling. Pure awesome.
• Services take the exposed ports of your Pods and publishes them (usually to the Public). Now what's cool about a service that it can load-balance the connections to your pods, so you don't need to manage your HAProxy or NGINX. It uses labels to figure out what pods to include in it's pool.
• Labels: The CSS selectors of k8s - use them everywhere!
• ‍

There are more concepts like volumes, claims, secrets, but let's not worry about them for now.

‍

Postgres

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

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

‍

uWSGI & NGINX

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.

‍

Celery

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

‍

‍

Some tips

• Installing some python deps take a long time, for stuff like numpy, scipy, etc.. try to install them in your namespace/app container using pip and then do another pip install in the container that extends it, ex: namespace/web, this way you don't have to rebuild all the deps every time you update one package or just update your app.
• Spend some time playing with gcloud and kubectl. This will be the fastest way to learn of google cloud and k8s.
• Base image choice is important. I tried phusion/baseimage and ubuntu/core. Settled for phusion/baseimage because it seems to handle the init part better than ubuntu core. They still feel too heavy. phusion/baseimage is 188MB.

‍

‍

Conclusion

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!

‍

Share templates inside the extension

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.

‍

New navigation

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

We've been busy, but not deaf!

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:

• The HTML/WYSIWYG editor sucks.
• No support for Outlook.com.

‍

We listened and now we're presenting:

• A brand new editor
• Support for outlook.com
• More on the Rich-Text editor

‍

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:

• Get instant context with Ticket Summaries. Jump into any conversation without digging through past messages or tabs.
• Organize tickets and customers with Ticket and Customer Fields. Attach properties to tickets and customers to see the whole picture, then turn it into reportable data.
• Support global customers with real-time translations. Engage in natural conversations in your customer’s language without paying for another tool.
• Resolve urgent issues with Priority Scoring. Rank tickets by importance, so high-value or sensitive issues don’t get overlooked.
• Onboard quickly with in-house migration. Whether you’re coming from Zendesk or Richpanel, Gorgias handles the move for you.

We recently unveiled the latest upgrades to Gorgias Helpdesk during Moments that Matter: Meet the Modern Helpdesk. 

The event was hosted by Bora Shehu, VP of Product Design, with updates from John Merse (VP of Product), Fraser Bruce (Senior Solutions Consultant), Nicole Simmen (Senior Manager, Customer Implementation), and a customer story from Michael Duran (Operations Manager, Authentic Brands).

From quality of life improvements to brand new features, here’s what’s waiting for you in Gorgias.

Watch the full presentation here:

Support faster with built-in ticket summaries

Agents shouldn’t have to dig for context. Every conversation now comes with Ticket Summaries. Whether an agent has jumped into a ticket mid-conversation or is dealing with a new customer, these AI-generated summaries tell the whole story in no time.

We’ve also given the Customer Timeline a makeover. Now, you can glance at past tickets and order updates in one clean view. Plus, a dedicated Order View lets agents dive into past purchases without leaving the ticket or opening a new tab.

Enrich your data with detailed ticket and customer properties

Agents have always had visibility into customer history, but now that context is easier to act on.

Ticket Fields automatically tags tickets with AI-detected reasons, whether that’s shipping questions or product feedback, to help organize your conversations more effectively.

Then, add in another layer of data using Customer Fields (in beta) to note whether you’re speaking to a longtime, VIP customer or a customer with a history of high returns.

All of this data can be funneled into your ticket reports, making it easier for your team to discover new insights about your products, support quality, and more.

Speak every customer’s language with instant translations

Taking your brand global doesn’t have to mean hiring a whole new team or spending extra on a localization tool. AI-powered translations (in beta) will soon be available on the helpdesk.

Finally, your team will be able to support customers in any language in real-time. Customers write in their native language, agents respond in theirs, and the exchange feels natural on both sides.

Never miss urgent tickets with Priority Scoring

How many times has an urgent ticket been buried at the bottom of your inbox? The new Priority Scoring system prevents that by automatically labeling tickets as Low, Normal, High, or Critical based on your Rules. 

For example, you might label a negative Facebook comment with threatening sentiment as ‘High,’ or bump high-value shoppers to the top with a ‘Critical’ label. This ensures your team always sees the conversations that need the most attention, so no sensitive issue slips through the cracks.

Shape every call journey with the new IVR flow builder

Now in beta, our flow-based IVR (interactive voice response) system lets teams on Gorgias Voice build customized call journeys for every type of conversation. Route customers through interactive menus, segment them based on their data, or direct them to voicemail, and schedule SMS follow-ups and callbacks.

To match agent availability, you can set business hours per phone number and per channel across storefronts. Teams also have more flexibility with ring strategies (ring available agents all at once or one at a time), wrap-up time between calls, and faster availability refreshes.

Stay on top of every goal with custom dashboards

We understand that CX teams need more than surface-level KPIs—they need to know what’s actually driving performance, revenue, and retention. 

With Dashboards, you can build reports focused on CX data you care about, from agent performance to product return trends. Then, filter by store or sub-brand to zoom in on the details each team is responsible for.

We’re also introducing the Human Response Time metric to show how quickly your team responds to escalations from AI Agent. This gives you a clear sign of what issues require human attention, how fast they’re resolved, and whether you need to adjust staffing.

Effortless, in-house migration for new joiners

Leave the moving to us—we now manage migrations in-house. Depending on your plan, our Implementation team will transfer emails, customers, macros, and more for you. Combined with 99.99% uptime, switching platforms is smoother, faster, and more reliable than ever.

For accelerated performance, consider our 50-in-50 implementation program, which aims to resolve 50% of your ticket volume using AI Agent within 50 days.

Enterprise customers receive a dedicated Enterprise CSM, optimization workshops, and 24/7 support to get the most out of Gorgias from day one. 

What’s coming next

Our teams are hard at work changing the landscape of customer experience. Here’s what’s on the Gorgias Product Roadmap:

• Cleaner, minimal interface. We’re giving our UI a new look to reduce clutter and highlight key information, making conversations front and center.
• Detailed order view. Quickly view past purchases and make order updates without opening new tabs or interrupting your workflow.
• Shop right in chat. Soon, product photos, descriptions, and even customer reviews will be shown directly in Gorgias Chat, so shopping experiences are as frictionless as possible.
• Scheduled CSV exports. Prove the value of CX with automated exports, perfect for stakeholders, whether they use Gorgias or not. 
• New integration with Assembled Workforce Management. Our partnership will help you leverage Gorgias ticket data to optimize forecasting and agent scheduling.
• Role-based access control. Decide which dashboards, views, conversations, and settings can be accessed by each user role.
• Okta single sign-on. Let your team sign in to Gorgias using the same authentication service you use for the rest of your tech stack.

The future of support starts with your helpdesk

Our latest helpdesk updates make it easier than before to create memorable customer moments.

As Bora Shehu, our VP of Product Design, said, “We hope that the tools we’re building help you spend less time on robotic work, and more time on impactful human work that grows your businesses through the power of conversations.”

If you’re not on Gorgias yet and want to see what’s possible, book a demo today.

{{lead-magnet-1}}

TL;DR:

• 58% of fashion shoppers “bracket” orders—buying multiple sizes and returning what doesn’t fit. This drives high return rates, increased costs, and customer frustration.
• 70% of returns are due to sizing issues. Nearly half of shoppers abandon carts over inconvenient returns.
• Leading solutions include: AI-powered fit tools, 3D visualizations that show scale and model stats, “fit finder” tools, at-home fitting experiences, and AI-powered customer support.
• Brands that replicate the fitting room online gain higher retention, lower costs, and stronger sustainability.

Sizing has long been a friction point for ecommerce fashion shoppers.

Without the ability to try items on, 58% of shoppers resort to "bracketing"—ordering multiple sizes of the same piece and returning what doesn’t fit. 

While it gives customers a temporary fix, it ultimately creates frustration for them and logistical headaches for brands. 

The result is rising return rates, higher costs, and wasted resources. To break this cycle, ecommerce brands need to rethink how they guide shoppers toward the perfect fit. The good news is that many brands are already showing the way by using AI-powered tools and smarter product experiences to replicate the fitting room from the comfort of home. 

{{lead-magnet-1}}

Returns are getting unsustainable 

Recent data highlights just how severe the return challenge has become for fashion and apparel retailers: 

• 46% of shoppers have abandoned a purchase because return methods were inconvenient. 
• Fashion and apparel brands get twice as many return requests than any other industry.
• According to Gorgias data, 70% of returns are due to sizing confusion.
• “Will this fit me?” is one of the top reasons customers contact support.

In addition, rapidly rising concerns around sustainability and climate change, as well as heightened awareness around over-consumption, are prompting consumers to make changes in their purchasing habits. 

Brands who prioritize well-fitting, long-lasting pieces and reduce carbon footprints and the amount of clothing diverted to landfills by lowering returns can actually benefit from a strategic edge. 

“Those who choose to approach sustainability with a long-term mindset even while battling short-term problems will be rewarded with more efficient business operations and a competitive advantage,” writes McKinsey in its State of Fashion 2025 report.

Effective sizing solutions for ecommerce brands 

Most brands already have size charts, but shoppers don’t want to measure themselves, or find those charts to be inaccurate.  

When shoppers lack confidence in choosing the right fit, they either abandon their carts or rely on bracketing, both of which lower profitability and customer trust. 

Forward-looking fashion and apparel brands are solving sizing issues by using tools for a more intuitive shopping experience. This ultimately helps them build loyalty, increase retention, and reduce returns. 

Implement AI-powered body measurement tools

Rather than purely providing static size charts on your website, opt for AI-generated personalized fit recommendations instead. 

For example, European fashion retailer Zalando reduced size-related returns by 10% using AI-driven advice.

The brand flags whether an item is true to size or not. It also offers the ability for customers to see recommendations based on logged fit-based return reasons,  past purchases, and other clothing items that fit them well. 

Zalando also launched a body measurement feature in 2023 where shoppers can actually scan themselves for more accurate size advice.  

Show size and scale in product photos 

As AI grows in proficiency, there are more tools than ever to help shoppers visualize product scale and fit. 

For example, accessory shop LeSportsac uses Tangiblee, a product experience tool, to help customers understand scale and what fits inside each bag.

Performance hunting gear shop KUIU takes another approach. It uses a photo-based layering guide, so shoppers can see how the size and fit look with multiple layers on a model. Different model stats shown within product photography give contextual sizing cues.

Sleep shop Cozy Earth takes a similar route, stating model height and size on product photos.

Introduce “fit finder” tools 

Some brands are helping shoppers pick the right size with interactive quizzes based on factors like height, weight, and the sizes of other clothing items that fit well. SuitShop is among those brands using a Fit Finder quiz on its website.  

Similarly, Psycho Bunny leverages the AI tool True Fit as a size finder on product pages. 

Bring the fitting room home 

Ergonomic shoe brand Orthofeet eliminates sizing qualms altogether by including customizable inserts inside each box. Fitting spacers ensure a snug fit and arch enhancement for those who need it, helping shoppers get comfortable shoes that fit.

Jonas Paul Eyewear shares the “try it on at home” approach, offering a free or low-cost home try-on kit. 

Leverage AI-powered customer support

Gorgias Shopping Assistant helps brands meet that need by delivering human-like guidance at scale, giving shoppers instant answers that feel personal. 

For example, VESSEL uses Shopping Assistant in chat to provide real-time support on sizing and inventory, helping customers choose with confidence. By addressing fit questions directly, Shopping Assistant reduces returns and builds trust at the point of purchase.

Similarly, outdoor clothing retailer Arc‘teryx provides an “ask me anything” AI chat where shoppers can confirm any questions they have around fit or sizing. 

The future for ecommerce size guides 

Sizing for ecommerce fashion and apparel brands has become a business-critical challenge. With 70% of returns tied to fit issues and nearly half of shoppers abandoning purchases over inconvenient returns, brands that replicate the fitting room online stand to gain a competitive advantage.

From Zalando’s 10% reduction in size-related returns to VESSEL’s use of AI-powered chat, the path is clear: investing in smarter size chart solutions pays off with higher retention, lower costs, and stronger sustainability. 

The brands that provide fitting room-level experiences online now will set themselves apart from the rest.

Book a demo to see how Gorgias, the leading conversational commerce platform, helps fashion brands cut returns, drive sales, and deliver fitting-room level experiences online.

{{lead-magnet-2}}

TL;DR:

• Conversational commerce replaces static support with real-time conversations. Instead of making customers wait or dig through FAQs, brands can respond instantly via chat, messaging apps, and voice assistants.
• The main types are live chat, AI assistants, messaging apps, and voice support. Each helps guide shoppers and answer questions instantly.
• It’s most effective during key moments like cart hesitation, post-purchase anxiety, and peak seasons. Proactive conversations reduce drop-offs and boost conversions.
• Start small and scale. Begin with repetitive questions or cart recovery, then layer in automation and AI as you grow.

While your competitors are still making customers wait days for email replies, the smartest brands are having conversations that close sales in real time.

Instead of forcing customers to search through FAQs or go through an automation loop, conversational commerce lets you have instant chats through live chat, messaging apps, and even AI assistants.

In this guide, we’ll explain conversational commerce, where it delivers the most value, and how to start using it to drive revenue and improve CX without overwhelming your team.

What is conversational commerce?

Conversational commerce means using real-time, two-way conversations as your storefront. Rather than bottling up questions in FAQ pages or forcing customers to wait for your support team to respond, you can instantly connect via:

• Chat
• AI agents
• Messaging apps
• Voice assistants

Maybe someone is on your product page and asks a question like, “Does this jacket run large?”. Through chat, they get an instant answer, increasing the chance of a sale. Or a shopper receives personalized recommendations via WhatsApp and checks out, all without leaving the app.

These channels allow you to meet customers where they already are, effortlessly. When paired with AI chatbots, you can deliver fast, accurate responses 24/7, even while your team is off the clock. That means better experiences for your customers and more sales captured for your brand.

Conversational commerce bridges the gap between shopping and support. It turns your support team (and AI tools) into revenue drivers by helping shoppers feel seen, heard, and ready to buy.

Types of conversational commerce

Conversational commerce means bringing your storefront into the flow of conversation, wherever that happens for your customers. 

Here’s where those conversations typically happen:

1. Live chat
2. AI assistants
3. Messaging apps
4. Voice assistants

1. Live chat

This is a chat widget on your site, often in the bottom right corner, where shoppers can ask questions and receive immediate answers from a human agent or automation.

It’s a quick path to support or purchase, which one agent can manage multiple chats from simultaneously, boosting efficiency and keeping things personal.

2. AI assistants

These smart helpers use Natural Language Processing (NLP) to understand what shoppers mean beyond what they type. They guide customers through questions, offer product suggestions, handle FAQs, and can sometimes complete transactions right in the chat, even handling post‑purchase support like order status or returns.

Natural Language Processing (NLP): The processing of understanding and interpreting natural language using computers. NLP is used in tasks such as sentiment analysis, summarization, speech recognition, and more.

3. Messaging apps

Think WhatsApp, Facebook Messenger, WeChat, and SMS—the apps where customers already spend their time in their day-to-day. Instead of sending them to shop on your website, you bring the shopping to them. Answer their questions, provide recommendations, and win purchases in a channel they already trust.

4. Voice assistants

Voice assistance isn’t limited to smart speakers like Siri and Alexa anymore.

Now, AI voice support lets brands deliver natural conversations over the phone, without needing a massive contact center team. These AI voice agents can:

• Answer common questions using branded knowledge
• Route calls or escalate when needed
• Handle returns, exchanges, or order tracking
• Personalize support based on customer intent and past behavior

AI-powered voice support combines the human feel of a phone call with the speed and accuracy of automation. It's especially useful for high-ticket products, customers who prefer calling, or peak season overflow when your human team is maxed out.

The benefits of conversational commerce for ecommerce brands

Conversational commerce isn’t a CX buzzword. When done right, it directly impacts your bottom line.

Here’s how it pays off for ecommerce brands:

1. Higher conversion rates
2. Faster and more efficient support
3. Bigger carts, fewer drop-offs
4. Stronger customer relationships

1. Higher conversion rates

When customers can ask questions and get answers in real time, whether it's sizing info, shipping details, or help choosing between products, they’re far more likely to hit “buy.”

Success story: Clothing brand Tommy John generated $106K+ in sales in just two months through conversation-led upselling and cross-selling, with a 15% conversion rate.

2. Faster and more efficient support

Conversational commerce tools like AI agents help offload the repetitive support tasks, including answering questions like “Where’s my order?” or “What’s your return policy?”

With that time back, agents get time back to:

• Handle complex or sensitive customer issues
• Follow up with VIP customers
• Collaborate with marketing and sales teams to improve processes
• QA conversations to enhance human and AI agent performance
• Update knowledge docs used by AI tools for more accurate resolutions

Instead of getting buried in basic tickets, your team gets to do the work that really moves the needle for your customers and your business.

Related: Every successful marketing campaign starts with a customer question

3. Bigger carts, fewer drop-offs

The right nudge at the right moment, like a personalized recommendation from an AI shopping assistant, can turn a single item into a full cart. You can also recover more abandoned checkouts by re-engaging customers directly through chat or a messaging app.

Read more: You’re missing out on sales without an AI shopping assistant—here’s why

4. Stronger customer relationships

Conversational commerce lets you meet customers with a human (or human-like) touch. When your brand is helpful, fast, and easy to talk to, shoppers remember and return. 

In the long run, that means better customer retention, higher lifetime value, and more organic growth through word of mouth.

When conversational commerce creates the biggest impact 

Conversational commerce shines brightest when the stakes are high or when the moment is just right.

Here are the critical moments where a real-time conversation can make all the difference:

1. When shoppers have items in their cart but are hesitating to check out
2. Right after customers place an order, and anxiety starts to kick in
3. During peak shopping seasons like Black Friday and Cyber Monday
4. When customers are browsing complex products like skincare, makeup, or apparel

1. When shoppers have items in their cart but are hesitating to check out

A customer’s on your product page, they’ve added an item to their cart, but are hesitating. Maybe they’re unsure about sizing, shipping time, or which variation to choose. This is where a quick, helpful chat, automated or human, comes in and becomes the difference between bounce and conversion.

Pro tip: Use proactive chat prompts based on page behavior to start the conversation before the shopper leaves.

2. Right after customers place an order, and anxiety starts to kick in

After a customer hits “place order,” expect more questions to roll into your inbox. Where’s my order? How do I track it? What’s your return policy? Post-purchase excitement—and anxiety—is normal, and a smart AI agent helps you get ahead of these questions while putting customers at ease.

3. During peak shopping seasons like Black Friday and Cyber Monday

Black Friday. Holiday rush. Product drops. These are prime opportunities to boost revenue—but they also flood your support team. Conversational commerce tools help you scale without sacrificing quality, keeping shoppers happy and sales flowing.

4. When shoppers are browsing complex products like skincare, makeup, or apparel

If you sell skincare, supplements, tech, or anything that requires a bit of education, your customers likely need guidance before they commit. A personalized conversation helps them find the right fit and feel more confident in their purchase.

What to consider before you start

Conversational commerce sounds exciting, and it is. But before you dive in, it’s worth thinking through a few key factors to set your team (and your customers) up for success.

1. Cost vs. ROI: Start small, scale smart
2. Team resources: Who’s managing the conversations?
3. Customer expectations: Meet them where they are

1. Cost vs. ROI: Start small, scale smart

You don’t need a full-blown chatbot army on Day 1. Start with your highest-impact touchpoints, like pre-sale FAQs or WISMO questions, and layer in automation over time. The goal is to generate clear ROI early, then expand once you see traction.

Here’s how to gradually implement automation into your CX process:

• Identify your top repetitive questions. Use your support data to pinpoint your most common tickets. For most brands, these are WISMOs, shipping concerns, and product-specific questions.
• Create macros for your most-asked questions. These macros will be used to answer the top recurring questions. For agents, this means no more copy-pasting the same responses.
• Build out self-service automation flows. Once you’re feeling more comfortable with automation, set up self-service flows to let customers resolve their own needs, like checking order status, starting a return, or finding their size.
• Automate your top channels. Don’t stop at email. Automate responses on live chat, Instagram DMs, and SMS too. Shoppers expect speed everywhere, not just on your site.
• Maintain impact, then introduce conversational AI. If your CSAT is still healthy after these changes, you can expand to using conversational AI for faster support and team efficiency.

The goal isn’t to automate everything, it’s to automate smartly so your team can spend time where it counts: high-touch sales, VIP support, and strategic growth.

Learn more: How Dr. Bronner's saved $100K/year by switching from Salesforce, then automated 50% of interactions with Gorgias

2. Team resources: Who’s managing the conversations?

Do you have in-house agents ready to handle live chat? Or do you need automation to handle the bulk of it? Make sure your setup aligns with your team’s bandwidth.

Pro tip: Tools like Gorgias AI Agent and Shopping Assistant can handle the support and sales heavy lifting, making them perfect for lean CX teams.

3. Customer expectations: Meet them where they are

Your customers aren’t just on your website. They’re messaging on Instagram, browsing via mobile, or checking their texts. To deliver great conversational commerce, you’ll want to show up in the places your shoppers already use.

Pro tip: Don’t spread your efforts too thin. Start with the channel that aligns with your goals and customer behavior, live chat, SMS, or social DMs, and build from there.

How to get started with conversational ecommerce in 2 steps

Ready to make conversational commerce part of your CX strategy? You don’t need to overhaul your tech stack or hire a whole new team. With Gorgias, you can start fast, stay lean, and scale smart.

Here’s how:

1. Start with AI Agent for 24/7 support

Gorgias AI Agent is designed to take repetitive tickets off your team’s plate, from “Where’s my order?” to “How do I make a return?” It understands natural language, pulls in relevant customer data, and responds in seconds—all using your brand’s approved knowledge.

The result is faster responses, fewer tickets, and more time back for your team.

2. Add Shopping Assistant to drive revenue

While AI Agent, covers the support front, Shopping Assistant is your digital salesperson. It engages high-intent shoppers in real time, recommends the right products, and even upsells or cross-sells based on what the customer is browsing.

Whether it’s helping someone choose the perfect shade or nudging them to complete their cart, Shopping Assistant is designed to increase AOV and reduce abandonment.

The future of ecommerce is conversational

Every time a shopper lands on your site, scrolls through Instagram, or replies to a shipping update, they’re opening the door to a conversation. The brands that show up quickly, helpfully, and with the right message, are the ones winning loyalty and revenue.

With AI Agent, you can automate accurate responses to common questions, giving your team time back without sacrificing customer experience. And with Shopping Assistant, you can turn those conversations into conversions, offering personalized recommendations, upsells, and discounts based on shopper intent.

You don’t need a massive team or months of setup to start. Just the right tools, and a strategy built for your customers.

Book a demo and learn how Gorgias helps you turn every conversation into an opportunity to grow.

{{lead-magnet-2}}