

TL;DR:
You’ve chosen your AI tool and turned it on, hoping you won’t have to answer another WISMO question. But now you’re here. Why is AI going in circles? Why isn’t it answering simple questions? Why does it hand off every conversation to a human agent?
Conversational AI and chatbots thrive on proper training and data. Like any other team member on your customer support team, AI needs guidance. This includes knowledge documents, policies, brand voice guidelines, and escalation rules. So, if your AI has gone rogue, you may have skipped a step.
In this article, we’ll show you the top seven AI issues, why they happen, how to fix them, and the best practices for AI setup.
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AI can only be as accurate as the information you feed it. If your AI is confidently giving customers incorrect answers, it likely has a gap in its knowledge or a lack of guardrails.
Insufficient knowledge can cause AI to pull context from similar topics to create an answer, while the lack of guardrails gives it the green light to compose an answer, correct or not.
How to fix it:
This is one of the most frustrating customer service issues out there. Left unfixed, you risk losing 29% of customers.
If your AI is putting customers through a never-ending loop, it’s time to review your knowledge docs and escalation rules.
How to fix it:
It can be frustrating when AI can’t do the bare minimum, like automate WISMO tickets. This issue is likely due to missing knowledge or overly broad escalation rules.
How to fix it:
One in two customers still prefer talking to a human to an AI, according to Katana. Limiting them to AI-only support could risk a sale or their relationship.
The top live chat apps clearly display options to speak with AI or a human agent. If your tool doesn’t have this, refine your AI-to-human escalation rules.
How to fix it:
If your agents are asking customers to repeat themselves, you’ve already lost momentum. One of the fastest ways to break trust is by making someone explain their issue twice. This happens when AI escalates without passing the conversation history, customer profile, or even a summary of what’s already been attempted.
How to fix it:
Sure, conversational AI has near-perfect grammar, but if its tone is entirely different from your agents’, customers can be put off.
This mismatch usually comes from not settling on an official customer support tone of voice. AI might be pulling from marketing copy. Agents might be winging it. Either way, inconsistency breaks the flow.
How to fix it:
When AI is underperforming, the problem isn’t always the tool. Many teams launch AI without ever mapping out what it's actually supposed to do. So it tries to do everything (and fails), or it does nothing at all.
It’s important to remember that support automation isn’t “set it and forget it.” It needs to know its playing field and boundaries.
How to fix it:
AI should handle |
AI should escalate to a human |
|---|---|
Order tracking (“Where’s my package?”) |
Upset, frustrated, or emotional customers |
Return and refund policy questions |
Billing problems or refund exceptions |
Store hours, shipping rates, and FAQs |
Technical product or troubleshooting issues |
Simple product questions |
Complex or edge‑case product questions |
Password resets |
Multi‑part or multi‑issue requests |
Pre‑sale questions with clear, binary answers |
Anything where a wrong answer risks churn |
Once you’ve addressed the obvious issues, it’s important to build a setup that works reliably. These best practices will help your AI deliver consistently helpful support.
Start by deciding what AI should and shouldn’t handle. Let it take care of repetitive tasks like order tracking, return policies, and product questions. Anything complex or emotionally sensitive should go straight to your team.
Use examples from actual tickets and messages your team handles every day. Help center articles are a good start, but real interactions are what help AI learn how customers actually ask questions.
Create rules that tell your AI when to escalate. These might include customer frustration, low confidence in the answer, or specific phrases like “talk to a person.” The goal is to avoid infinite loops and to hand things off before the experience breaks down.
When a handoff happens, your agents should see everything the AI did. That includes the full conversation, relevant customer data, and any actions it has already attempted. This helps your team respond quickly and avoid repeating what the customer just went through.
An easy way to keep order history, customer data, and conversation history in one place is by using a conversational commerce tool like Gorgias.
A jarring shift in tone between AI and agent makes the experience feel disconnected. Align aspects such as formality, punctuation, and language style so the transition from AI to human feels natural.
Look at recent escalations each week. Identify where the AI struggled or handed off too early or too late. Use those insights to improve training, adjust boundaries, and strengthen your automation flows.
If your AI chatbot isn’t working the way you expected, it’s probably not because the technology is broken. It’s because it hasn’t been given the right rules.
When you set AI up with clear responsibilities, it becomes a powerful extension of your team.
Want to see what it looks like when AI is set up the right way?
Try Gorgias AI Agent. It’s conversational AI built with smart automation, clean escalations, and ecommerce data in its core — so your customers get faster answers and your agents stay focused.
TL;DR:
While most ecommerce brands debate whether to implement AI support, customers already rate AI assistance nearly as highly as human support. The future isn't coming. It's being built in real-time by brands paying attention.
As a conversational commerce platform processing millions of support tickets across thousands of brands, we see what's working before it becomes common knowledge. Three major shifts are converging faster than most founders realize, and this article breaks down what's already happening rather than what might happen someday.
By the end of 2026, we predict that the performance gap between ecommerce brands won't be determined by who adopted AI first. It will be determined by who built the content foundation that makes AI actually work.
Right now, we're watching this split happen in real time. AI can only be as good as the knowledge base it draws from. When we analyze why AI escalates tickets to human agents, the pattern is unmistakable.
The five topics triggering the most AI escalations are:
These aren’t complicated questions — they're routine questions every ecommerce brand faces daily. Yet some brands automate these at 60%+ rates while others plateau at 20%. The difference isn't better AI. It's better documentation.
Take SuitShop, a formalwear brand that reached 30% automation with a lean CX team. Their Director of Customer Experience, Katy Eriks, treats AI like a team member who needs coaching, not a plug-and-play tool.
When Katy first turned on AI in August 2023, the results were underwhelming. So she paused during their slow season and rebuilt their Help Center from the ground up. "I went back to the tickets I had to answer myself, checked what people were searching in the Help Center, and filled in the gaps," she explained.
The brands achieving high automation rates share Katie's approach:
AI echoes whatever foundation you provide. Clear documentation becomes instant, accurate support. Vague policies become confused AI that defaults to human escalation.
Read more: Coach AI Agent in one hour a week: SuitShop’s guide
Two distinct groups will emerge next year. Brands that invest in documentation quality now will deliver consistently better experiences at lower costs. Those who try to deploy AI on top of messy operations will hit automation plateaus and rising support costs. Every brand will eventually have access to similar AI technology. The competitive advantage will belong to those who did the unexciting work first.
Something shifted in July 2025. Gorgias’s AI accuracy jumped significantly after the GPT-5 release. For the first time, CX teams stopped second-guessing every AI response. We watched brand confidence in AI-generated responses rise from 57% to 85% in just a few months.
What this means in practice is that AI now outperforms human agents:
For the first time, AI isn't just faster than humans. It's more consistent, more accurate, and even more empathetic at scale.
This isn't about replacing humans. It's about what becomes possible when you free your team from repetitive work. Customer expectations are being reset by whoever responds fastest and most completely, and the brands crossing this threshold first are creating a competitive moat.
At Gorgias, the most telling signal was AI CSAT on chat improved 40% faster than on email this year. In other words, customers are beginning to prefer AI for certain interactions because it's immediate and complete.
Within the next year, we expect the satisfaction gap to hit zero for transactional support. The question isn't whether AI can match humans. It's what you'll do with your human agents once it does.
The brands that have always known support should drive revenue will finally have the infrastructure to make it happen on a bigger scale. AI removes the constraint that's held this strategy back: human bandwidth.
Most ecommerce leaders already understand that support conversations are sales opportunities. Product questions, sizing concerns, and “just browsing” chats are all chances to recommend, upsell, and convert. The problem wasn't awareness but execution at volume.
We analyzed revenue impact across brands using AI-powered product recommendations in support conversations. The results speak for themselves:
It's clear that conversations that weave in product recommendations convert at higher rates and result in larger order values. It’s time to treat support conversations as active buying conversations.
If you're already training support teams on product knowledge and tracking revenue per conversation, keep doing exactly what you're doing. You've been ahead of the curve. Now AI gives you the infrastructure to scale those same practices without the cost increase.
If you've been treating support purely as a cost center, start measuring revenue influence now. Track which conversations lead to purchases, which agents naturally upsell, and where customers ask for product guidance.
We are now past the point where response time is a brand's key differentiator. It is now the use of conversational commerce or systems that share details and context across every touchpoint.
Today, a typical customer journey looks something like this: see product on Instagram, ask a question via DM, complete purchase on mobile, track order via email. At each step, customers expect you to remember everything from the last interaction.
The most successful ecommerce tech stacks treat the helpdesk as the foundation that connects everything else. When your support platform connects to your ecommerce platform, shipping providers, returns portal, and every customer communication channel, context flows automatically.
A modern integration approach looks like this. Your ecommerce platform (like Shopify) feeds order data into a helpdesk like Gorgias, which becomes the hub for all customer conversations across email, chat, SMS, and social DMs. From there, connections branch out to payment providers, shipping carriers, and marketing automation tools.
As Dr. Bronner’s Senior CX Manager noted, “While Salesforce needed heavy development, Gorgias connected to our entire stack with just a few clicks. Our team can now manage workflows without needing custom development — we save $100k/year by switching."
As new channels emerge, brands with flexible tech stacks will adapt quickly while those with static systems will need months of development work to support new touchpoints. The winners will be brands that invest in their tools before adding new channels, not after customer complaints force their hand.
Start auditing your current integrations now. Where does customer data get stuck? Which systems don’t connect to each other? These gaps are costing you more than you realize, and in the future, they'll be the key to scaling or staying stagnant.
Post-purchase support quality will be a stronger predictor of customer lifetime value than any email campaign. Brands that treat support as a retention investment rather than a cost center will outperform in repeat purchase rates.
Returns and exchanges are make-or-break moments for customer lifetime value. How you handle problems, delays, and disappointments determines whether customers come back or shop elsewhere next time. According to Narvar, 96% of customers say they won’t repurchase from a brand after a poor return experience.
What customers expect reflects this reality. They want proactive shipping updates without having to ask, one-click returns with instant label generation, and notifications about problems before they have to reach out. When something goes wrong, they expect you to tell them first, not make them track you down for answers.
The quality of your response when things go wrong matters more than getting everything right the first time. Exchange suggestions during the return flow can keep the sale alive, turning a potential loss into loyalty.
Brands that treat post-purchase as a retention strategy rather than a task to cross off will see much higher repeat purchase rates. Those still relying purely on email marketing for retention will wonder why their customer lifetime value plateaus.
Start measuring post-return CSAT scores and repeat purchase rates by support interaction quality. These metrics will tell you whether your post-purchase experience is building loyalty or quietly eroding it.
After absorbing these predictions about AI accuracy, content infrastructure, revenue-centric support, context, and post-purchase tactics, here's your roadmap for the next 24 months.
Now (in 90 days):
Next (in 6-12 months):
Watch (in 12-24 months):
The patterns we've shared, from AI crossing the accuracy threshold to documentation quality, are happening right now across thousands of brands. Over the next 24 months, teams will be separated by operational maturity.
Book a demo to see how leading brands are already there.
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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:
Rising customer expectations, shoppers willing to pay a premium for convenience, and a growing lack of trust in social media channels to make purchase decisions are making it more challenging to turn a profit.
In this emerging era, AI’s role is becoming not only more pronounced, but a necessity for brands who want to stay ahead. Tools like Gorgias Shopping Assistant can help drive measurable revenue while reducing support costs.
For example, a brand that specializes in premium outdoor apparel implemented Shopping Assistant and saw a 2.25% uplift in GMV and 29% uplift in average order volume (AOV).
But how, among competing priorities and expenses, do you convince leadership to implement it? We’ll show you.
Shoppers want on-demand help in real time that’s personalized across devices.
Shopping Assistant recalls a shopper’s browsing history, like what they have clicked, viewed, and added to their cart. This allows it to make more relevant suggestions that feel personal to each customer.
The AI ecommerce tools market was valued at $7.25 billion in 2024 and is expected to reach $21.55 billion by 2030.
Your competitors are using conversational AI to support, sell, and retain. Shopping Assistant satisfies that need, providing upsells and recommendations rooted in real shopper behavior.
Conversational AI has real revenue implications, impacting customer retention, average order value (AOV), conversion rates, and gross market value (GMV).
For example, a leading nutrition brand saw a GMV uplift of over 1%, an increase in AOV of over 16%, and a chat conversion rate of over 15% after implementing Shopping Assistant.
Overall, Shopping Assistant drives higher engagement and more revenue per visitor, sometimes surpassing 50% and 20%, respectively.

Shopping Assistant engages, personalizes, recommends, and converts. It provides proactive recommendations, smart upsells, dynamic discounts, and is highly personalized, all helping to guide shoppers to checkout.
After implementing Shopping Assistant, leading ecommerce brands saw real results:
Industry |
Primary Use Case |
GMV Uplift (%) |
AOV Uplift (%) |
Chat CVR (%) |
|---|---|---|---|---|
Home & interior decor 🖼️ |
Help shoppers coordinate furniture with existing pieces and color schemes. |
+1.17 |
+97.15 |
10.30 |
Outdoor apparel 🎿 |
In-depth explanations of technical features and confidence when purchasing premium, performance-driven products. |
+2.25 |
+29.41 |
6.88 |
Nutrition 🍎 |
Personalized guidance on supplement selection based on age, goals, and optimal timing. |
+1.09 |
+16.40 |
15.15 |
Health & wellness 💊 |
Comparing similar products and understanding functional differences to choose the best option. |
+1.08 |
+11.27 |
8.55 |
Home furnishings 🛋️ |
Help choose furniture sizes and styles appropriate for children and safety needs. |
+12.26 |
+10.19 |
1.12 |
Stuffed toys 🧸 |
Clear care instructions and support finding replacements after accidental product damage. |
+4.43 |
+9.87 |
3.62 |
Face & body care 💆♀️ |
Assistance finding the correct shade online, especially when previously purchased products are no longer available. |
+6.55 |
+1.02 |
5.29 |
Shopping Assistant drives uplift in chat conversion rate and makes successful upsell recommendations.
“It’s been awesome to see Shopping Assistant guide customers through our technical product range without any human input. It’s a much smoother journey for the shopper,” says Nathan Larner, Customer Experience Advisor for Arc’teryx.
For Arc’teryx, that smoother customer journey translated into sales. The brand saw a 75% increase in conversion rate (from 4% to 7%) and 3.7% of overall revenue influenced by Shopping Assistant.

Because it follows shoppers’ live journey during each session on your website, Shopping Assistant catches shoppers in the moment. It answers questions or concerns that might normally halt a purchase, gets strategic with discounting (based on rules you set), and upsells.
The overall ROI can be significant. For example, bareMinerals saw an 8.83x return on investment.
"The real-time Shopify integration was essential as we needed to ensure that product recommendations were relevant and displayed accurate inventory,” says Katia Komar, Sr. Manager of Ecommerce and Customer Service Operations, UK at bareMinerals.
“Avoiding customer frustration from out-of-stock recommendations was non-negotiable, especially in beauty, where shade availability is crucial to customer trust and satisfaction. This approach has led to increased CSAT on AI converted tickets."

Shopping Assistant can impact CSAT scores, response times, resolution rates, AOV, and GMV.
For Caitlyn Minimalist, those metrics were an 11.3% uplift in AOV, an 18% click through rate for product recommendations, and a 50% sales lift versus human-only chats.
"Shopping Assistant has become an intuitive extension of our team, offering product guidance that feels personal and intentional,” says Anthony Ponce, its Head of Customer Experience.

Support agents have limited time to assist customers as it is, so taking advantage of sales opportunities can be difficult. Shopping Assistant takes over that role, removing obstacles for purchase or clearing up the right choice among a stacked product catalog.
With a product that’s not yet mainstream in the US, TUSHY leverages Shopping Assistant for product education and clarification.
"Shopping Assistant has been a game-changer for our team, especially with the launch of our latest bidet models,” says Ren Fuller-Wasserman, Sr. Director of Customer Experience at TUSHY.
“Expanding our product catalog has given customers more choices than ever, which can overwhelm first-time buyers. Now, they’re increasingly looking to us for guidance on finding the right fit for their home and personal hygiene needs.”
The bidet brand saw 13x return on investment after implementation, a 15% increase in chat conversion rate, and a 2x higher conversion rate for AI conversations versus human ones.

Customer support metrics include:
Revenue metrics to track include:
Shopping Assistant connects to your ecommerce platform (like Shopify), and streamlines information between your helpdesk and order data. It’s also trained on your catalog and support history.
Allow your agents to focus on support and sell more by tackling questions that are getting in the way of sales.
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TL;DR:
Most shoppers arrive with questions. Is this the right size? Will this match my skin tone? What’s the difference between these models? The faster you can guide them, the faster they decide.
As CX teams take on a bigger role in driving revenue, these moments of hesitation are now some of the most important parts of the buying journey.
That’s why more brands are leaning on conversational AI to support these high-intent questions and remove the friction that slows shoppers down. The impact speaks for itself. Brands can expect higher AOV, stronger chat conversion rates, and smoother paths to purchase, all without adding extra work to your team.
Below, we’re sharing real use cases from 11 ecommerce brands across beauty, apparel, home, body care, and more, along with the exact results they saw after introducing guided shopping experiences.
When you’re shopping for shoes similar to an old but discontinued favorite, every detail counts, down to the color of the bottom of the shoe. But legacy brands with large catalogs can be overwhelming to browse.
For shoppers, it’s a double-edged sword: they want to feel confident that they checked your entire collection, but they also don’t want to spend time looking for it.
How Shopping Assistant helps:
Shopping Assistant accelerates the process, turning hazy details into clear, friendly guidance.
It describes shoe details, from colorways to logo placement, compares products side by side, and recommends the best option based on the shopper’s preferences and conditions.
The result is shoppers who feel satisfied and more connected with your brand.

Results:
Big events call for great outfits, but putting one together online isn’t always easy. With thousands of options to scroll through, shoppers often want a bit of styling direction.
How Shopping Assistant helps:
Shoppers get to chat with a virtual stylist who recommends full outfits based on the occasion, suggests accessories to complete the look, and removes the guesswork of pairing pieces together.
The result is a fun, confidence-building shopping experience that feels like getting advice from a stylist who actually understands their plans.

Results:
Shade matching is hard enough in-store, but doing it online can feel impossible. Plus, when a longtime favorite gets discontinued, shoppers are left guessing which new shade will come closest. That uncertainty often leads to hesitation, abandoned carts, or ordering multiple shades “just in case.”
How Shopping Assistant helps:
Shoppers find their perfect match without any of the guesswork. The assistant asks a few quick questions, recommends the closest shade or formula, and offers smart alternatives when a product is unavailable.
The experience feels like chatting with a knowledgeable beauty advisor — someone who makes the decision easy and leaves shoppers feeling confident in what they’re buying.
Katia Komar, Sr. Manager of Ecommerce and Customer Service Operations at bareMinerals UK says, “What impressed me the most is the AI’s ability to upsell with a conversational tone that feels genuinely helpful and doesn't sound too pushy or transactional. It sounds remarkably human, identifying correct follow-up questions to determine the correct product recommendation, resulting in improved AOV. It’s exactly how I train our human agents and BPO partners.”

Results:
When shoppers are buying gifts, especially for someone else, they often know who they’re shopping for but not what to buy. A vague product name or a half-remembered scent can quickly make the experience feel overwhelming without someone to guide them.
How Shopping Assistant helps:
Thoughtful guidance goes a long way. By asking clarifying questions and recognizing likely mix-ups, Shopping Assistant helps shoppers figure out what the recipient was probably referring to, then recommends the right product along with complementary gift options that make the choice feel intentional.
It brings the reassurance of an in-store associate to the online experience, helping shoppers move forward with confidence.

Results:
Finding the right bra size online is notoriously tricky. Shoppers often second-guess their band or cup size, and even small uncertainties can lead to returns — or abandoning the purchase altogether.
Many customers just want someone to walk them through what a proper fit should actually feel like.
How Shopping Assistant helps:
Searching for products is no longer a time-consuming process. Shopping Assistant detects a shopper’s search terms and sends relevant products in chat. Like an in-store associate, it uses context to deliver what shoppers are looking for, so they can skip the search and head right to checkout.

Results:
For shoppers buying personalized jewelry, the details directly affect the final result. That’s why customization questions come up constantly, and why uncertainty can quickly stall the path to purchase.
How Shopping Assistant helps:
Shopping Assistant asks about the shopper’s style preferences and customization needs, then recommends the right product and options so they can feel confident the final piece is exactly their style. The experience feels quick, helpful, and designed to guide shoppers toward a high investment purchase.

Results:
Decorating a home is personal, and shoppers often want reassurance that a new piece will blend with what they already own. Questions about color palettes, textures, and proportions come up constantly. And without guidance, it’s easy for shoppers to feel unsure about hitting “add to cart.”
How Shopping Assistant helps:
Giving shoppers personalized styling support helps them visualize how pieces will work in their home.
Shoppers receive styling suggestions based on their existing space as well as recommendations on pieces that complement their color palette.
It even guides them toward a 60-minute virtual styling consultation when they need deeper help. The experience feels thoughtful and high-touch, which is why shoppers often spend more once they feel confident in their choices.

Results:
When shoppers discover a new drink mix, they’re bound to have questions before committing. How strong will it taste? How much should they use? Will it work with their preferred drink or routine? Uncertainty at this stage can stall the purchase or lead to disappointment later.
How Shopping Assistant helps:
Clear, friendly guidance in chat helps shoppers understand exactly how to use the product. Shopping Assistant answers questions about serving size, flavor strength, and pairing options, and suggests the best way to prepare the mix based on the shopper’s preferences.

Results:
Shopping for health supplements can feel confusing fast. Customers often have questions about which formulas fit their age, health goals, or daily routine. Without clear guidance, most will hesitate or pick the wrong product.
How Shopping Assistant helps:
Shopping Assistant detects hesitation when shoppers linger on a search results page. It proactively asks a few clarifying questions, narrows down product options, and points shoppers to the best product or bundle for their needs.
The entire experience feels supportive and gives shoppers confidence they’ve picked the right option.

Results:
Shopping for kids’ furniture comes with a lot of “Is this the right one?” moments. Parents want something safe, sturdy, and sized correctly for their child’s age. With so many options, it’s easy to feel unsure about what will actually work in their space.
How Shopping Assistant helps:
Shopping Assistant guides parents toward the best fit right away. It asks about their child’s age, room layout, and safety considerations, then recommends the most appropriate bed or furniture setup. The experience feels like chatting with a knowledgeable salesperson who understands what families actually need as kids grow.

Results:
Even something as simple as choosing a toothbrush can feel complicated when multiple models come with different speeds, materials, and features. Shoppers want to understand what matters so they can pick the one that fits their routine and budget.
How Shopping Assistant helps:
Choosing between toothbrush models shouldn’t feel like decoding tech specs. When shoppers can see the key differences in plain language, including what’s unique, how each model works, and who it’s best for, they can make a decision with ease.
Suddenly, the whole process feels simple instead of overwhelming.

Results:
Across all 11 brands, one theme is clear. When shoppers get the guidance they need at the right moment, they convert more confidently and often spend more.
Here’s what stands out:
What this means for you:
Look closely at your most common pre-purchase questions. Anywhere shoppers hesitate from fit, shade, technical specs, styling, bundles is a place where Shopping Assistant can step in, boost confidence, and unlock more sales.
If you notice the same patterns in your own store, such as shoppers hesitating over sizing, shade matching, product comparisons, or technical details, guided shopping can make an immediate impact. These moments are often your biggest opportunities to increase revenue and improve the buying experience.
Many of the brands in this post started by identifying their most common pre-purchase questions and letting AI handle them at scale. You can do the same.
If you want to boost conversions, lift AOV, and create a smoother path to purchase, now is a great time to explore guided shopping for your team.
Book a demo or activate Shopping Assistant to get started.
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When your company decides to launch a new support channel — usually for efficiency and customer convenience — setting it up is only half the battle. The other half is driving customers toward the new channel (and away from your old ones). Without a concerted effort for customer adoption, you risk paying for a support channel that nobody uses.
Berkey Filters, a world leader in water purification and seller of water filter systems, wanted to add SMS as a support channel for their shoppers. SMS is more convenient for on-the-go shoppers and allows agents to provide service to multiple shoppers more efficiently than other channels.
Berkey Filters launched SMS with Klaviyo, and wanted wanted to add the Klaviyo SMS integration to Gorgias to unify customer conversations in one platform.
The launch was one of the most successful we’ve seen to date, both in terms of ticket efficiency and customer adoption. Within a month of launching SMS, Berkey Filters:
We sat down with Jessica, the Gorgias account owner and Customer Experience Analyst for Berkey Filters, to ask how Berkey Filters achieved such suburb support stats so quickly. Jessica was generously willing to share her strategies to drive customer adoption of the new support channel.
In this Playbook, learn about the six tactics Berkey Filters used to launch SMS, increase the number of customers using this channel, and decrease ticket volume on older channels.
SMS is one of the fastest-growing support channels today. It’s one of five channels consumers expect from brands, alongside email, website, voice, and chat.
Consumers love SMS because it’s fast, convenient, and always with them (even on the go). They don’t need to block off time in their day to sit by their laptop or on the phone to deal with a support situation. They can carry about their day and effortlessly reply to texts whenever they have a moment – something most people already do.
Support managers love direct messaging channels because conversations are typically shorter and resolved faster. And as long as SMS tickets are managed in the same places as other channels, it’s easy for agents to manage.

Jessica was specifically interested in using the SMS channel in Gorgias for Berkey Filters to achieve the following goals:
Jessica’s team also views SMS as a modern support channel. More and more brands want to offer a customer service experience that’s seamlessly integrated into the shopper’s day, and Berkey wanted to be an early adopter.
While some of these benefits are pretty applicable to any store, make sure you’re clear on your “why” before adding a new support channels. This will help you know how to prioritize it compared to other channels and justify the work that goes into adding a new method of communication with your customers.
For the purposes of this playbook, we’ll assume you’ve already created your Gorgias helpdesk. If you haven’t, get started with a free trial or schedule a call with our team for a personalized demo.
Gorgias SMS allows you to send and receive 1:1 SMS and MMS messages with your customers. To add it, go to Settings > Integrations > SMS.
You’ll need a Gorgias phone number to get started. If you have one already (likely because you use Gorgias voice support), you can add the SMS integration without changing numbers. If you do not have a number yet, it’ll prompt you to create one first.
If you already have a phone number but it isn’t owned by Gorgias, you’ll need to port it. Learn how in this help doc.
If you’ve just added SMS (or any new channel), there are a few administrative tasks we recommend before following the steps outlined this playbook:
Now, we’ll share exactly how Jessica promoted SMS for Berkey Filters customers.
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Jessica knew they would eventually add their SMS number directly on the Berkey Filters website, but she also knew she’d have to wait for her developer to do so. In the meantime, she started with the tools available to her in Gorgias.
Here are six tactics Jessica used to drive adoption of the newly launched support channel:
Let’s break each of these down.
Even though Jessica would need to wait for her developer to update the actual page content, she knew she could launch a Gorgias Chat Campaign on the “Contact us” page to announce they now offer support via SMS. (If you don’t know, a Chat Campaign is a live chat session that automatically and proactively triggers for targeted website visitors, often to announce special promotions.)
Here’s what their campaign looked like:

Jessica’s campaign automatically opens a live chat box announcing the launch of SMS for anyone who stays on the Berkey Filters contact page for longer than 30 seconds. That time frame is a good way to target anyone who’s clearly trying to identify the best contact method, and not someone who accidentally clicked onto the page (and would likely bounce before 30 seconds).
To create a Chat Campaign in Gorgias, go to Settings > Integrations > Chat and select the chat widget you want to use. Click the “Create Campaign” button in the top right.
From here, you can enter the URL(s) the campaign should appear on, set a required time spent on the page, and customize the message that displays.
Read this help doc to learn more about chat campaigns.
One of the best ways to tell customers about a new support channel is to promote it on one of your existing channels — especially to customers who are already accustomed to those existing channels and may never visit the contact page again.
For the segment of customers who already use email to contact support, Jessica leveraged the initial auto-reply that Berkey Filters sends when a customer emails them to announce the new, faster channel.
In addition to the standard, “Thanks for contacting us! An agent will reply back shortly,” Jessica added, “We are currently experiencing high contact volumes and will be responding as quickly as possible. Our chat and text response times are typically faster. We are now accepting text messages at 1-800-350-4170.”

By customizing the auto-reply to promote the new channel, Jessica met Berkey Filters’ customers where they were to make sure they knew about the latest and greatest way to get support.
At this point, Jessica got developer support to add the support phone number to the website. The contact page is a natural location to add any new support channels, because you know new customers will go there looking for contact information.
Here’s what the Berkey Filters “Contact us” page looks like:

When building this page, Jessica made many intentional decisions to funnel visitors toward the new channel. Specifically, she:
The lesson? When releasing a new support channel, don’t be afraid to give extra context around it to help your shoppers understand when they should use one over the other.
The banner at the top of the website is a high-visibility location that’s especially great for getting in front of returning customers (since they may not need to visit your contact page anymore).
Brands usually use the top banner for promotions or sales, but Berkey Filters uses it for a mix of sales and support to cater to the entire customer experience. If you refresh their website a few times, you’ll see it rotate through three messages:

You might’ve picked up on this already, but Jessica was doing something really strategic with her messaging about SMS: She was promoting their first response time of 2 minutes.
That’s fast! And therefore, a pretty compelling reason for shoppers to use it over other, slower channels like email or voice.
Now, obviously this only works if your team is achieving a fast response time like that and willing to maintain it. (More on that in the next point.)
What’s important is that Gorgias gives you insights into your support team’s performance. While that’s useful for internal planning (staffing, budgeting, etc.), we also highly recommend leveraging these data points with your own customers to show the value of the support you provide.
Support stats that are great to leverage when promoting support via SMS:
We don’t currently include SMS CSAT score in Gorgias reporting. If you’d like to measure and promote your SMS CSAT score, share that product feedback here!
To help her team keep those impressive first response and resolution times, Jessica knew she needed to improve (and not just measure) those times. She set up a service-level agreement (SLA) view in Gorgias that shows SMS tickets that are open and were created more than one minute ago.
Here’s what that looks like:

This view sits at the top of their sidebar along with a few other SLA-based channel views, so agents can quickly prioritize what tickets they should solve next.
In addition to the view, Jessica created an Auto-Reply Rule that sends the first message to an SMS ticket.
This message thanks the customer for texting support, and states the business hours for Berkey Filters. We love how this helps set expectations right from the start, especially for customers who might text in outside of these hours. (So they don’t text again waiting for a reply!)
Here’s what that Rule looks like:

Last but not least, it’s worth mentioning that Jessica was also incredibly intentional about rolling all of this out to the Berkey Filters agents. Specifically, she involved them in the decision to launch the new channel, trained them on the new system, and made sure they were prepared before launch.
None of this would be possible if agents were unsure how to handle incoming SMS tickets or use the SLA view.
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We’ve already teased some of the impact that Berkey Filters has seen since adding SMS support, but how does it all add up?
In their first 30 days using Gorgias SMS, Berkey Filters:
That’s remarkable! And while those stats certainly speak to the high quality of their support team, they first needed to make customers aware and excited about the new channel. If you decide to launch a new support channel, we recommend following Berkey’s lead and creating an intentional adoption campaign to accompany the launch.
Prioritizing SMS shifts customer service conversations to a “live” channel where agents can help multiple customers at once, giving everyone a better experience.
And even if you’re strained for resources (like waiting for your developer to be able to update your store’s site) you can follow Berkey Filters’ lead and use other features and channels in Gorgias to start promoting your new channel.
Gorgias also integrates with SMS marketing platforms like Klaviyo to make texting a seamless part of your customer journey (and easy for agents to manage).
Specifically, if customers reply to an SMS sent with Klaviyo, Gorgias will create a ticket so your agents can respond right away. Plus, Klaviyo and Gorgias share customer data in real time, so you have as much information about your customers as possible in both tools:
“Having the Gorgias + Klaviyo integration has helped provide a service to our customers that we did not have before. Our customer service department is now able to provide a near-instant response via text message without having to exit Gorgias. This feature has made the entire process of getting to these tickets so effortless and much more efficient.”
— Jessica Robles, Customer Experience Analyst at Berkey Filters
To get started with Gorgias SMS, log into your helpdesk or click here to sign up for free.

Every month, our product team holds a casual, conversational event with our customers to demo new features, receive real-time feedback, and host live Q&As.
Watch the video below or read on for a recap of our latest product updates.
While Gorgias does a lot to keep your data secure, one of the best ways to add an extra layer of security is to encourage agents to use secure passwords and two-factor authentication (2FA).
And with our latest update, you can do more than just encourage. Admins can now require agents to set up two-factor authentication.
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Once an admin toggles the option, all users in your account will have 14 days to set up 2FA. After 14 days, users will need to set it up to access your helpdesk.
It can be hard to follow up with chat tickets that were left during off-business hours. Sometimes customers don’t include enough details in their message, making it harder to follow up on the next day.
Using a contact form in Gorgias Chat, you can capture a customer's email and message in a short conversational way. The contact form is designed to collect more information without disrupting the conversational experience, so you can easily follow up and help via email when you log back into Gorgias.
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The contact form prompts your web visitors to select a subject (to help you triage tickets faster), and then provide more details about their issue so your agents know what they’re trying to solve. Last, it collects the shopper’s email address so you know who to follow-up with and where to reach them. All of this information will be collected in a single ticket in your helpdesk.
Read this help center article to learn how to enable the contact form in your Gorgias chat.
Create multiple levels of categories to help your shoppers navigate to improve help center organization and find help content for related issues more easily.
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These new categories also give your team more options when creating a Help Center, so you can organize FAQs in whatever way makes sense for your brand.
Rules are a powerful feature that let Gorgias users automatically organize, tag, and reply to tickets. Thousands of Gorgias customers have adopted rules in their customer support workflow to save time and allow themselves to provide faster and higher quality service. Focusing on the common inquiries like WISMO, we’ve built Managed Rules to optimize time for Automate subscribers.
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Managed Rules are pre-built automations developed by the Gorgias team and include some of the most common and helpful automations. They need no code, no setup. Install them from the Rule Library and you’re good to go! If we improve the Rule, it will automatically update in your helpdesk, no action from you required.
Tune into the above timestamp if you want the full 25 minutes of customer questions and answers from our product team. Here were a few of the highlights!
Some features listed in Q2 of our public roadmap will indeed be released in Q2, while others will spill into Q3 (or later). We’re proud to provide transparency with our public roadmap, but please understand that it’s subject to change throughout the quarter. We do our best to update our roadmap frequently but can’t always do so right away.
Although we can’t promise to release any of these features in Q2, a few features we plan to release sooner than later include:
Our team is actively prioritizing the roadmap for Q3 right now. Check back soon to see the latest plan!
This is a limitation we’re definitely aware of, and are exploring options. The long-term solution is to build better integrations with Loop and other top returns platforms. If this is a feature you’d like to see, please submit the request here.
We’re hoping to release more features around that at the start of 2023. Today if you receive a phone call, you can always reply to that ticket via SMS. In the future, we’ll focus on helping you deflect the phone call entirely and prioritize SMS instead.
Thanks for checking out the recap of our June customer product event. We hold these events once as a month as a way to share the latest releases and connect with our customers in real-time. It’s a favorite – from both customers, and the Gorgias team.
If you’d like to sign up for the next one to attend live, you can register here. We’d love to have you join us!

There are now over 85 incredible integrations in the Gorgias App Store with the tools that power your ecommerce store. While each app is unique, together these integrations can help your agents work more efficiently to provide excellent service to your customers.
Take a look at the newest additions so far from 2022.
In the first half of the year, we’ve launched 15 new integrations for your Gorgias helpdesk:
Read on to learn how you can use these tools to help manage your store, and visit the Gorgias App Store to activate them today!

Klaviyo is an email and SMS marketing automation platform built for ecommerce. Gorgias was the first helpdesk to connect to Klaviyo SMS, allowing your brand to create seamless conversations between your marketing campaigns, shoppers, and support team.
With the updated Klaviyo integration, you can:
This integration helps you streamline customer interactions and create higher-converting marketing campaigns. To learn more, go to the Gorgias App Store.

We recently released Gorgias SMS, an easy way for your brand to offer this convenient and conversational communication channel. It’s one of the fastest-growing support channels for ecommerce brands, and one of the most reliable for customers to contact you on (since it’s not dependent on internet access).
With Gorgias SMS, you can:
Click here to learn more about Gorgias SMS, available with all plans.

Thankful AI is a platform dedicated to helping you deliver better support for the post-purchase needs of your customers. The AI is tailored specifically for retail and ecommerce businesses, so you don’t have to worry about a disjointed experience.
With this integration, the Thankful AI agent can:
This frees up your agents to focus on more meaningful conversations with customers. Visit the Gorgias App Store to learn more about the Thankful integration.

NetSuite is a cloud ERP including financials, CRM, and ecommerce. It helps brand work more efficiently, take control of inventory and fulfillment, and bring all your tools together in a unified business management suite.
Sync NetSuite data into Gorgias to give your agents important customer & order information in a single tab.
With this integration, you can:
This helps your agents have all the context they need next to every conversation they have. Visit the Gorgias App Store to learn more.

Okendo is a customer marketing platform and an Official Google Reviews partner that helps brands capture and showcase high-impact social proof such as product ratings & reviews, customer photos & videos, and Q&A messageboards.
With this integration, you can:
Visit the Gorgias App Store to learn more about our Okendo integration.

Link Narvar Return & Exchanges for Shopify with Gorgias to automate returns management and get rich insights that help you save costs and improve operations.
With this integration, you can:
To learn more about our Narvar integration, visit the Gorgias App Store.

Skio helps brands on Shopify sell subscriptions. With this integration, you can add a Skio widget to your Customer Sidebar in Gorgias. This gives your agents insights into customer subscriptions right in the helpdesk without having to switch tabs.
With this integration, you can:
To learn more about our Skio integration, visit the Gorgias App Store.

Via is a mobile commerce (SMS marketing) platform for ecommerce businesses. Send personalized messages to your customers for increased revenue and customer satisfaction.
With this integration, you can:
Visit the Gorgias App Store to learn more.

With Clyde and Gorgias working together, you can create a seamless and positive support experience by syncing all warranty data inside your Gorgias account. Stay focused and close tickets faster by viewing Clyde contracts and claims information in the same window you use to talk to customers.
With this integration, you can:
Manage warranty requests & find claims information in one tool. Head to the Gorgias App Store to learn more.

Smartrr is a seamless, full-service subscription solution. Paired with Gorgias, you can equip your team with the best customer service tools in one convenient location to increase customer satisfaction and drive customer loyalty.
With this integration, you can:
To learn more about our Smartrr integration, go to the Gorgias App Store.

ShipMonk is an order fulfillment platform for eCommerce businesses ready to scale. They offer technology-driven fulfillment solutions that enable business founders to devote more time to the things that matter most in their businesses.
With this integration you'll be able to:
Learm more about the ShipMonk integration in the Gorgias App Store.

Annex Cloud is a cloud-based customer loyalty platform for enterprises. They provide integrated loyalty, engagement, and retention solutions across a range of program types like paid memberships, incentives, and more.
With this integration, you can:
Click here to learn more about our Annex Cloud integration.

Daton can replicate Gorgias data to your data warehouse in minutes, freeing up your analysts to focus on generating important business insights instead of extracting data.
With this integration, you can sync information from Gorgias to your data warehouse like:
To learn more about Daton, visit the listing in the Gorgias App Store.

Shogun is a headless ecommerce platform built for merchants. Convert more with richer merchandising and sub-second store speed. The Gorgias integration allows merchants to add chat capabilities to their Shogun-powered shops.
With Gorgias chat on your Shogun Frontend, you can:
Click here to learn more about our integration with Shogun Frontend.

Gobot helps fast-growing Shopify stores convert more shoppers and reduce support burden with beautiful guided selling quizzes and AI-powered support chatbots.
With this integration, you can:
Visit the Gobot listing in the Gorgias App Store to learn more.

Shop2app is a mobile app builder. It’s designed for local delivery, national delivery, and in-store pickup, and also makes it easy to manage subscriptions and send push notifications to customers.
With this integration, you can:
Visit the Shop2app listing in the Gorgias App Store to learn more.
The Gorgias App Store features 85+ high-quality integrations with other leading ecommerce tools. By connecting the apps that power your store, you can give your agents the context they need to provide remarkable customer service from a single workspace. (No more switching tabs!)
To add any of these apps to your helpdesk, go to Settings > Integrations or visit the Gorgias App Store.

Each month, our product team holds a casual, conversational event with our customers to demo new features, receive real-time feedback, and answer live Q&As.
Watch the video recap here, or read on for a recap of the latest releases.
With this new channel, you can receive and respond to SMS and MMS messages within Gorgias. This makes it easy for your customers to communicate with your store while they’re on the go, and easy for your agents to provide fast, conversational support.

We’re releasing SMS this quarter as a free trial for every customer on every plan. Conversations will count toward your plan’s ticket count, but there are no additional charges for minutes, usage, phone numbers, etc. In the coming months, we’ll be assessing the best way to provide Voice and SMS so we can continue to innovate and build powerful new features for these channels.
If you want customers to consent to receive SMS messages before your agents actually reply, you can do this with a simple Rule in Gorgias. Here’s what it would look like:

Read this article for four more Gorgias Rules to help automate SMS.

This is especially great for anyone who gets tickets assigned to them, but may not be looking at Gorgias throughout their entire workday. (Think managers, social media collaborators, etc.)
To see these notifications, you may need to adjust your browser and/or computer settings. You can see an example for Chrome + Mac in our official Product Update.
Quick response flows bring in a critical component to self-service, creating more ways to engage with shoppers who visit your store online. We designed quick response flows with the guidance that 60% of the time, customers use chat to ask pre-purchase questions. Most successful merchants leverage their FAQ content to prompt conversation with quick response flows that result in generating revenue, trust and loyalty.
If you haven’t yet activated quick response flows, you’re in for a treat. With this revamp, you can now easily manipulate every step of the experience for quick response flows from self-service settings. Immediately under the Quick Response Flows tab, you can write in any question and answer you prefer and hit save. There is no other place or screen you’d need to navigate. Using the preview on the right, you can reassure the quality of the experience you want to create for your customers.

If customers click on a quick response flow and find the information they need, this will not count towards your monthly ticket volume.
If they click on a quick response flow and select “No, I need more help” option, it will create a ticket for an agent to address.
It’s amazing when our merchants start using a feature and take it to the next level. We’ve seen some of the best practices to include creating unique tags for each quick response flow created (e.g. Quick_Response_Flow_1), then adding a corresponding view in Tickets. This way, you can track closely the conversations prompted by quick response flows and dedicate a select group of agents who are trained to expand on the subject and help your customers become fans. For more on this subject, check out Quick Response Flows help doc here.
Tune into that timestamp if you want the full 25 minutes of customer-led questions and answers from our product team. Here were a few of the highlights!
Gorgias phone is an easy way to add a basic phone line to your store. If you’re looking for advanced, full call center features, our partners like Aircall or RingCentral may be a better solution for you.
For example, their phone-specific statistics are more in-depth than ours, but the ability to create a phone number and answer it in the Gorgias helpdesk is naturally easier with Gorgias.
Our long-term vision for Gorgias Phone is not to fully compete with apps like Aircall, but rather to invest in ecommerce-specific solutions so you can provide the best voice support to your shoppers.
It’s our next new channel, coming Q3! We have access to the API and are ready to start building at the end of the quarter. (Just need to polish up a few existing channel bugs first.)
Not yet, but we’d love to hear more feedback about this if it’s something you’re interested in! Submit this idea on our Product Roadmap to help us prioritize it.
That completes our recap of our May customer product event. We hold these events once as a month as a way to review the latest releases and connect with our customers in real-time. It’s a favorite – from both customers, and the Gorgias team.
If you’d like to sign up for the next one to attend live, you can register here. We’d love to have you join us!

Wondering if your team should add voice support to your ecommerce channels this year? You’re not alone.
Over 15% of our customers currently have a phone integration added to their account, thanks to the Gorgias Voice integration and partners like Aircall and RingCentral.
While voice support may feel like an “outdated” channel in the age of live chat and social media, this tells us that ecommerce support teams are increasingly finding value in offering it to their clients.
Here are 4 benefits of adding voice support to your ecommerce store:
Phones are an immediate communication channel, so it’s not surprising that adding voice support can boost your first response time. What we weren’t expecting, however, was by how much:
Our customers with phones have a first response time that’s 7x faster than merchants that don’t offer voice support. (30 minutes compared to 4 hours.)
What’s even more important to note, however, is that adding voice support doesn’t decrease resolution time (like many support managers fear). In fact, it makes quite a positive impact:
Our merchants using phones have an average resolution time that’s 34% faster than customers who don’t.
So not only does this channel help you respond to customers faster, but it helps you resolve their issues faster. That means your team can work more efficiently and spend up to 66% less time resolving each ticket. (Imagine how that could help increase your store’s revenue!)
Talking (literally) to shoppers and hearing their tone of voice is the best way your agents can adjust their responses to create a great customer experience.
While you can do your best to read clues in email and chat, it’s always going to be easier to match the customer’s tone when actually listening to them on the phone.
And when your agents can express empathy and solve the problem accordingly, you’ve got a better chance at getting that 5-star review and positive customer feedback.
Our customers using phones have an average Satisfaction score of 4.56 out of 5.
While that score also depends a lot on your support agents and their personal approach to customer service, there’s no denying that actually speaking to clients is helpful for both parties in those moments.
Especially if you sell high-end products or have VIP customers (like wholesalers buying in bulk), having a phone number adds a level of legitimacy to your business.
Since most online stores don’t immediately add phones as a support channel, it will stand out to customers when your shop does offer voice support.
Phones add a sense of maturity to your business (and especially if you’re using an integrated solution like Gorgias Voice), there’s not much cost involved to elevate the status of your store like this.
While the internet has come a long way over the years in terms of accessibility, the truth remains that phone support may be an easier and more comfortable contact method for some of your customers than digital channels.
Test your live chat experience with a screen reader, for example. What’s the experience like? (And how does it compare to dialing a phone number and talking verbally to someone?)
If there’s a chance that voice support is more approachable for a part of your customer demographic, you’ll create a better shopping experience for them by adding a phone line.
The first thing you’ll need to decide is who on your team will actually be answering the phones.
A few options to explore:
Next, you’ll need to choose a phone platform.
If you’re adding our built-in voice channel to your Gorgias helpdesk, all you have to do to get started is log into your Gorgias helpdesk and create a new number (or forward or port an existing one, if you happen to have one already).

Our phone integration is included in all Gorgias plans, and unlike other providers, there’s no annual contract fee and no minimum seat requirement.
This makes it a great option for teams looking to add phones for the first time or who want to manage all communication channels in one place.
Plus, our ecommerce integrations save your agents time by displaying callers’ shopping history right in the helpdesk, so they don’t have to go searching for the last order, for example.

For more tips on how to create efficient phone processes and increase resolution time by 34%, check out this article.
Finally, once you’ve set up your team and chosen your provider, all that’s left to do is make your number visible.
If you’re offering voice support for all your customers, you might place it in the footer of your website or all transactional emails.
If you’re piloting voice support or using it exclusively for a segment of shoppers, you might save it for smaller email segments or place it only on dedicated landing pages just for them.
Wherever you decide to put your number, just make sure it's easily accessible and clearly visible so your shoppers can start calling, and your support team can start delivering even better customer experiences!

SMS is a convenient way for customers to contact your brand and receive fast support. It’s no wonder it’s one of the top five channels that consumers expect to engage with brands, alongside email, voice, website, and in-person.
Every Gorgias plan now includes two-way SMS at no additional cost, making it easy for your brand to start offering this conversational channel.
There are many reasons to offer customer service messaging, but here are the top four:
SMS is a conversational, real-time channel. The benefit of this is that customers tend to keep the conversation short and reply quickly to follow-up questions, meaning your agents can resolve the situation quickly, too.
Most people keep their phone with them everywhere they go. With SMS, it’s easy for customers to start the conversation and follow-up as they move throughout their day, instead of feeling stuck to a chat conversation on their laptop.
Sending text messages feels like you’re texting a friend, even if it’s actually between customers and your brand. Younger clientele will feel natural using this support channel, and it can even help you build that friendly-feeling into your brand perception.
Does your refund or return policy require photo evidence to kick off the process? If your customers ever need to send pictures of damaged items or wrong products, SMS is the perfect channel because they’re probably taking those photos on their phone anyway.
Still not sure if SMS is a support channel your brand should prioritize? Try it for 2 weeks. Because SMS is included in every Gorgias plan, it’s easy to turn off if you decide it isn’t right.
Recommended reading: Our list of 60+ fascinating customer service statistics.
You’ll need two things to get started with Gorgias SMS. (Don’t worry, they’re both quick!)
If you’re new here, get started on the Gorgias helpdesk. It only takes a few minutes to create an account, and you can always book a call with our sales team if you have questions.

The second is a Gorgias-owned phone number, meaning you either created it in Gorgias or ported it from your previous phone provider. You can do both of these actions in Settings > Phone Numbers.
Note: SMS is currently only available for US, UK, and Canadian numbers.
Once your phone number is ready in Gorgias, you can add the SMS integration to it. You can do this from Settings > Integrations > SMS.
Once the integration is active, you’re ready to start replying to SMS conversations from your customers.

To tell your customers they can now text your brand, we recommend adding “Text us,” plus your phone number, in some or all of these places:
Below are four top automation rules to take full advantage of SMS customer service. We also have a full guide on customer service messaging that includes templates and macros to upgrade your SMS support.
SMS is an official channel in Gorgias, meaning you can see SMS-specific stats or create SMS-specific Views out of the box. There may be times when you also want to Tag tickets with “SMS” however, in which case you can do so with a Rule like this:

SMS is a fast, conversational channel, so you’ll want to assign these tickets to agents that can keep up with the pace. If you have a dedicated chat team, they’ll be naturals at answering questions via SMS, as well. Here’s a Rule that will automatically assign SMS tickets to a specific team.

When customers text your brand, they’ll expect a fast response. In order to buy your agents some time, we recommend sending an auto-response to let the customer know their message has been received and an agent will be with them shortly. This will also give them confidence that the text message did in fact go through, so they don’t follow-up right away.

Whenever you add a new communication channel for your customers, you should consider how you’ll respond to WISMO (“Where is my order?”) questions on it. With SMS, you’ll want to keep the length of your reply in mind so you’re not sending an insanely long text message back to customers. We recommend creating a Rule that can A) make sure the reply follows the best format for SMS and B) save your agents from having to answer these WISMO questions manually.

Gorgias SMS empowers your brand to keep the conversation going on SMS, even when your customers are on the go.
We also integrate with SMS marketing apps, making it easier for agents to answer promotion replies from one workspace. They can work more efficiently while turning SMS questions into opportunities for better customer value.
In the Gorgias App Store, you’ll find some of the top ecommerce integration partners like Klaviyo, Attentive, Postscript, and more.
If your brand is using any of these apps to drive sales via SMS, we highly recommend integrating with Gorgias so your team can work more efficiently toward your revenue goals. When SMS marketing and SMS customer service work in tandem, they are far more powerful.
Want to see an example of a brand that successfully launched SMS customer support and effectively drove customers to use the new channel? Check out our playbook of Berkey Filters, an ecommerce merchant that did just that.
Ready to get started with this conversational support channel? Add SMS to your Gorgias helpdesk today or book a call with our team to learn more.

As we all locked down in March 2020 and changed our shopping habits, many brick-and-mortar retailers started their first online storefronts.
Gorgias has benefitted from the resulting ecommerce growth over the past two years, and we have grown the team to accommodate these trends. From 30 employees at the start of 2020, we are now more than 200 on our journey to delivering better customer service.
Our engineering team contributed to much of this hiring, which created some challenges and growing pains. What worked at the beginning with our team of three did not hold up when the team grew to 20 people. And the systems that scaled the team to 20 needed updates to support a team of 50. To continue to grow, we needed to build something more sustainable.
Continuous deployment — and the changes required to support it — presented a major opportunity for reaching toward the scale we aspired to. In this article I’ll explore how we automated and streamlined our process to make our developers’ lives easier and empower faster iteration.
Throughout the last two years of accelerated growth, we’ve identified a few things that we could do to better support our team expansion.
Before optimizing the feature release process, here’s how things went for our earlier, smaller team when deploying new additions:
This wasn’t perfect, but it was an effective solution for a small team. However, the accelerated growth in the engineering team led to a sharp increase in the number of projects and also collaborators on each project. We began to notice several points of friction:
It was clear that things needed to change.
On the Site Reliability Engineering (SRE) team, we are fans of the GitOps approach, where Git is the single source of truth. So when the previously mentioned points of friction became more critical, we felt that all the tooling involved in GitOps practices could help us find practical solutions.
Additionally, these solutions would often rely on tooling we already had in place (like Kubernetes, or Helm for example).
GitOps is an operational framework. It takes application-development best practices and applies them to infrastructure automation.
The main takeaway is that in a GitOps setting, everything from code to infrastructure configuration is versioned in Git. It is then possible to create automation by leveraging the workflows associated with Git.
One such class of that automation could be “operations by pull requests”. In that case, pull requests and associated events could trigger various operations.
Here are some examples:
ArgoCD is a continuous deployment tool that relies on GitOps practices. It helps synchronize live environments and services to version-controlled declarative service definitions and configurations, which ArgoCD calls Applications.
In simpler terms, an Application resource tells ArgoCD to look at a Git repository and to make sure the deployed service’s configuration matches the one stored in Git.
The goal wasn’t to reinvent the wheel when implementing continuous deployment. We instead wanted to approach it in a progressive manner. This would help build developer buy-in, lay the groundwork for a smoother transition, and reduce the risk of breaking deploys. ArgoCD was an excellent step toward those goals, given how flexible it is with customizable Config Management Plugins (CMP).
ArgoCD can track a branch to keep everything up to date with the last commit, but can also make sure a particular revision is used. We decided to use the latter approach as an intermediate step, because we weren’t quite ready to deploy off the HEAD of our repositories.
The only difference from a pipeline perspective is that it now updates the tracked revision in ArgoCD instead of running our complex deployment scripts. ArgoCD has a Command Line Interface (CLI) that allows us to simply do that. Our deployment jobs only need to run the following command:
The developers’ workflow is left untouched at this point. Now comes the fun part.
Our biggest requirement for continuous deployment was to have some sort of safeguard in case things went wrong. No matter how much we trust our tests, it is always possible that a bug makes its way to our production environments.
Before implementing Argo Rollouts, we still kept an eye on the system to make sure everything was fine during deployment and took quick action when issues were discovered. But up to that point, this process was carried out manually.
It was time to automate that process, toward the goal of raising our team’s confidence levels when deploying new changes. By providing a safety net, of sorts, we could be sure that things would go according to plan without manually checking it all.
Argo Rollouts is a progressive delivery controller. It relies on a Kubernetes controller and set of custom resource definitions (CRD) to provide us with advanced deployment capabilities on top of the ones natively offered by Kubernetes. These include features like:

We were especially interested in the canary and canary analysis features. By shifting only a small portion of traffic to the new version of an application, we can limit the blast radius in case anything is wrong. Performing an analysis allows us to automatically, and periodically, check that our service’s new version is behaving as expected before promoting this canary.
Argo Rollouts is compatible with multiple metric providers including Datadog, which is the tool we use. This allows us to run a Datadog query (or multiple) every few minutes and compare the results with a threshold value we specify.
We can then configure Argo Rollouts to automatically take action, should the threshold(s) be exceeded too often during the analysis. In those cases, Argo Rollouts scales down the canary and scales the previous stable version of our software back to its initial number of replicas.

Each service has its own metrics to monitor, but for starters we added an error rate check for all of our services.
Remember when I mentioned replacing complex, project-specific deployment scripts with a single, simple command? That’s not entirely accurate, and requires some additional nuance for a full understanding.
Not only did we need to deploy software on different kinds of environments (staging and production), but also in multiple Kubernetes clusters per environment. For example, the applications composing the Gorgias core platform are deployed across multiple cloud regions all around the world.
ArgoCD and Argo Rollouts might seem to be magic tools, we actually still need some “glue” to make things stick together. Now because of ArgoCD’s application-based mechanisms, we were able to get rid of custom scripts and use this common tool across all projects. This in-house tool was named deployment conductor.
We even went a step further and implemented this tool in a way that accepts simple YAML configuration files. Such files allow us to declare various environments and clusters in which we want each individual project to be deployed.
When deploying a service to an environment, our tool will then go through all clusters listed for that environment.
For each of these, it will look for dedicated values.yaml files in the service’s chart’s directory. This allows developers to change a service’s configuration based on the environment and cluster in which it’s deployed. Typically, they would want to edit the number of replicas for each service depending on the geographical region.
This makes it much easier for developers than having to manage configuration and maintain deployment scripts.
This leads us to the end of our journey’s first leg: our first encounter with continuous deployment.
After we migrated all our Kubernetes Deployments to Argo Rollouts, we let our developers get acclimated for the next few weeks.
Our new setup still wasn’t fully optimized, but we felt like it was a big improvement compared to the previous one. And while we could think of many improvements to make things even more reliable before enabling continuous deployment, we decided to get feedback from the team during this period, to iterate more effectively.
Some projects introduced additional technicalities to overcome, but we easily identified a small first batch of projects where we could enable CD. Before deployment, we asked the development team if we were missing anything they needed to be comfortable with automatic deployment of their code in production environments.
With everyone feeling good about where we were at, we removed the manual step in our CI system (GitLab) for jobs deploying to production environments.
We’re still monitoring this closely, but so far we haven’t had any issues. We still plan on enabling continuous deployment on all our projects in the near future, but it will be a work in progress for now.
Here are some ideas for future improvements that anticipate potential roadblocks:
We’re excited to explore these challenges. And, overall, our developers have welcomed these changes with open arms. It helps that our systems have been successful at stopping bad deployments from creating big incidents so far.
While we haven’t reached the end of our journey yet, we are confident that we are on the right path, moving at the right pace for our team.

As you work with SQLAlchemy, over time, you might have a performance nightmare brewing in the background that you aren’t even aware of.
In this lesser-known issue, which strikes primarily in larger projects, normal usage leads to an ever-growing number of idle-in-transaction database connections. These open connections can kill the overall performance of the application.
While you can fix this issue down the line, when it begins to take a toll on your performance, it takes much less work to mitigate the problem from the start.
At Gorgias, we learned this lesson the hard way. After testing different approaches, we solved the problem by extending the high-level SQLAlchemy classes (namely sessions and transactions) with functionality that allows working with "live" DB (database) objects for limited periods of time, expunging them after they are no longer needed.
This analysis covers everything you need to know to close those unnecessary open DB connections and keep your application humming along.
Leading Python web frameworks such as Django come with an integrated ORM (object-relational mapping) that handles all database access, separating most of the low-level database concerns from the actual user code. The developer can write their code focusing on the actual logic around models, rather than thinking of the DB engine, transaction management or isolation level.
While this scenario seems enticing, big frameworks like Django may not always be suitable for our projects. What happens if we want to build our own starting from a microframework (instead of a full-stack framework) and augment it only with the components that we need?
In Python, the extra packages we would use to build ourselves a full-fledged framework are fairly standard: They will most likely include Jinja2 for template rendering, Marshmallow for dealing with schemas and SQLAlchemy as ORM.
Not all projects are web applications (following a request-response pattern) and among web applications, most of them deal with background tasks that have nothing to do with requests or responses.
This is important to understand because in request-response paradigms, we usually open a DB transaction upon receiving a request and we close it when responding to it. This allows us to associate the number of concurrent DB transactions with the number of parallel HTTP requests handled. A transaction stays open for as long as a request is being processed, and that must happen relatively quickly — users don't appreciate long loading times.
Transactions opened and closed by background tasks are a totally different story: There's no clear and simple rule on how DB transactions are managed at a code level, there's no easy way to tell how long tasks (should) last, and there usually isn't any upper limit to the execution time.
This could lead to potentially long transaction times, during which the process effectively holds a DB connection open without actually using it for the majority of the time period. This state is known as an idle-in-transaction connection state and should be avoided as much as possible, because it blocks DB resources without actively using them.
To fully understand how database access transpires in a SQLAlchemy-based app, one needs to understand the layers responsible for the execution.

At the highest level, we code our DB interaction using high-level SQLAlchemy queries on our defined models. The query is then transformed into one or more SQL statements by SQLAlchemy's ORM which is passed on to a database engine (driver) through a common Python DB API defined by PEP-249. (PEP-249 is a Python Enhancement Proposal dedicated to standardizing Python DB server access.) The database engine communicates with the actual database server.
At first glance, everything looks good in this stack. However there's one tiny problem: The DB API (defined by PEP-249) does not provide an explicit way of managing transactions. In fact, it mandates the use of a default transaction regardless of the operations you're executing, so even the simplest select will open a transaction if none are open on the current connection.
SQLAlchemy builds on top of PEP-249, doing its best to stay out of driver implementation details. That way, any Python DB driver claiming PEP-249 compatibility could work well with it.
While this is generally a good idea, SQLAlchemy has no choice but to inherit the limitations and design choices made at the PEP-249 level. More precisely (and importantly), it will automatically open a transaction for you upon the very first query, regardless whether it’s needed. And that's the root of the issue we set out to solve: In production, you'll probably end up with a lot of unwanted transactions, locking up on DB resources for longer than desired.
Also, SQLAlchemy uses sessions (in-memory caches of models) that rely on transactions. And the whole SQLAlchemy world is built around sessions. While you could technically ditch them to avoid the idle-in-transactions problem with a “lower-level” interface to the DB, all of the examples and documentation you’ll find online uses the “higher-level” interface (i.e. sessions). It’s likely that you will feel like you are trying to swim against the tide to get that workaround up and running.
Some DB servers, most notably Postgres, default to an autocommit mode. This mode implies atomicity at the SQL statement level — something developers are likely to expect. But they prefer to explicitly open a transaction block when needed and operate outside of one by default.
If you're reading this, you have probably already Googled for "sqlalchemy autocommit" and may have found their official documentation on the (now deprecated) autocommit mode. Unfortunately this functionality is a "soft" autocommit and is implemented purely in SQLAlchemy, on top of the PEP-249 driver; it doesn't have anything to do with DB's native autocommit mode.
This version works by simply committing the opened transaction as soon as SQLAlchemy detects an SQL statement that modifies data. Unfortunately, that doesn't fix our problem; the pointless, underlying DB transaction opened by non-modifying queries still remains open.
When using Postgres, we could in theory play with the new AUTOCOMMIT isolation level option introduced in psycopg2 to make use of the DB-level autocommit mode. However this is far from ideal as it would require hooking into SQLAlchemy's transaction management and adjusting the isolation level each time as needed. Additionally, "autocommit" isn't really an isolation level and it’s not desirable to change the connection's isolation level all the time, from various parts of the code. You can find more details on this matter, along with a possible implementation of this idea in Carl Meyer's article “PostgreSQL Transactions and SQLAlchemy.”
At Gorgias, we always prefer explicit solutions to implicit assumptions. By including all details, even common ones that most developers would assume by default, we can be more clear and leave less guesswork later on. This is why we didn't want to hack together a solution behind the scenes, just to get rid of our idle-in-transactions problem. We decided to dig deeper and come up with a proper, explicit, and (almost) hack-free method to fix it.
The following chart shows the profile of an idle-in-transaction case over a period of two weeks, before and after fixing the problem.

As you can see, we’re talking about tens of seconds during which connections are being held in an unusable state. In the context of a user waiting for a page to load, that is an excruciatingly long period of time.
SQLAlchemy works with sessions that are, simply put, in-memory caches of model instances. The code behind these sessions is quite complex, but usage boils down to either explicit session reference...
...or implicit usage.
Both of these approaches will ensure a transaction is opened and will not close it until a later ***session.commit()***or session.rollback(). There's actually nothing wrong with calling session.commit() when you need to explicitly close a transaction that you know is opened and you’re done with using the DB, in that particular scope.
To address the idle-in-transaction problem generated by such a line, we must keep the code between the query and the commit relatively short and fast (i.e. avoid blocking calls or CPU-intensive operations).
It sounds simple enough, but what happens if we access an attribute of a DB model after session.commit()? It will open another transaction and leave it hanging, even though it might not need to hit the DB at all.
While we can't foresee what a developer will do with the DB object afterward, we can prevent usage that would hit the DB (and open a new transaction) by expunging it from the session. An expunged object will raise an exception if any unloaded (or expired) attributes are accessed. And that’s what we actually want here: to make it crash if misused, rather than leaving idle-in-transaction connections behind to block DB resources.
When working with multiple objects and complex queries, it’s easy to overlook the necessary expunging of those objects. It only takes one un-expunged object to trigger the idle-in-transaction problem, so you need to be consistent.
Objects can't be used for any kind of DB interaction after being expunged. So how do we make it clear and obvious that certain objects are to be used in within a limited scope? The answer is a Python context manager to handle SQLAlchemy transactions and connections. Not only does it allow us to visually limit object usage to a block, but it will also ensure everything is prepared for us and cleaned up afterwards.
The construct above normally opens a transaction block associated to a new SQLAlchemy session, but we've added a new expunge keyword to the begin method, instructing SQLAlchemy to automatically expunge objects associated with block's session (the tx.session). To get this kind of behavior from a session, we need to override the begin method (and friends) in a subclass of SQLAlchemy's Session.
We want to keep the default behavior and use a new ExpungingTransaction instead of SQLAlchemy's SessionTransaction, but only when explicitly instructed to by the expunge=True argument.
You can use the class_ argument of sessionmaker to instruct it to build am ExpungingSession instead of a regular Session.
The last piece of the puzzle is the ExpungingTransaction code, which is responsible for two important things: committing the session so the underlying transaction gets closed and expunging objects so that we don't accidentally reopen the transaction.
By following these steps, you get a useful context manager that forces you to group your DB interaction into a block and notifies you if you mistakenly use (unloaded) objects outside of it.
What if we really need to access DB models outside of an expunging context?
Simply passing models to functions as arguments helps in achieving a great goal: the decoupling of models retrieval from their actual usage. However, such functions are no longer in control of what happens to those models afterwards
We don't want to forbid all usage of models outside of this context, but we need to somehow inform the user that the model object comes “as is,” with whatever loaded attributes it has. It's disconnected from the DB and shouldn't be modified.
In SQLAlchemy, when we modify a live model object, we expect the change to be pushed to the DB as soon as commit or flush is called on the owning session. With expunged objects this is not the case, because they don't belong to a session. So how does the user of such an object know what to expect from a certain model object? The user needs to ensure that she:
To safely and explicitly pass along these kind of model objects, we introduced frozen objects. Frozen objects are basically proxies to expunged models that won't allow any modification.
To work with these frozen objects, we added a freeze method to our ExpungingSession:
So now our code would look something like this:
Now, what if we want to modify the object outside of this context, later on, (e.g. after a long-lasting HTTP request)? As our frozen object is completely disconnected from any session (and from the DB), we need to fetch a warm instance associated to it from the DB and make our changes to that instance. This is done by adding a helper fetch_warm_instance method to our session...
...and then our code that modifies the object would say something like this.
When the second context manager exits, it will call commit on tx.session, and changes to my_model will be committed to the DB right away.
We now have a way of safely dealing with models without generating idle-in-transaction problems, but the code quickly becomes a mess if we have to deal with relationships: We need to freeze them separately and pass them along as if they aren’t related. This could be overcome by telling the freeze method to freeze all related objects, recursively walking the relationships.
We'll have to make some adjustments to our frozen proxy class as well.
Now, we can fetch, freeze, and use frozen objects with any preloaded relationships.
While the code to access the DB with SQLAlchemy may look simple and straightforward, one should always pay close attention to transaction management and the subtleties that arise from the various layers of the persistence stack.
We learned this the hard way, when our services eventually started to exhaust the DB resources many years into development.
If you recently decided to use a software stack similar to ours, you should consider writing your DB access code in such a way that it avoids idle-in-transaction issues, even from the first days of your project. The problem may not be obvious at the beginning, but it becomes painfully apparent as you scale.
If your project is mature and has been in development for years, you should consider planning changes to your code to avoid or to minimize idle-in-transaction issues, while the situation is still under control. You can start writing new idle-in-transaction-proof code while planning to gradually update existing code, according to the capacity of your development team.


