How Pet Brands Are Quietly Slashing Support Costs With AI

Issue #189

May 14th, 2025

Quick Hits:

👯 German researchers are studying the uncanny resemblance of dog owners and their dogs in an effort to reduce “returns” of dogs.

🏆 Zoomark 2025 breaks attendance record with 31K visitors

🐊 Beagle scares off an alligator lurking on the back porch

🛫 A lesser-known travel hack to fly with your dog’s in cabin

Customer service is an essential but often hidden cost for pet brands.

Whether it’s helping a pet parent track a late order or troubleshoot a smart pet feeder, support requests can quickly pile up as a company grows.

Traditionally, handling this volume meant hiring more agents or outsourcing to call centers, driving up expenses and sometimes eroding margins.

Today, however, AI-powered automation is stepping in to streamline customer experience (CX).

In fact, Gartner predicts that by the end of this year, about 80% of customer service/support orgs will be using generative AI in some form to improve productivity and overall CX.

The reason is simple: early adopters report that AI can handle a large share of routine inquiries, leading to roughly 30% lower customer service operating costs on average.

In the pet industry where customer loyalty and trust are paramount - brands are beginning to quietly deploy AI solutions that reduce support costs, speed up response times, and improve service quality without compromising the warm, empathetic tone that pet parents expect (💡think replicating Chewy’s support system at scale without the need for overly complex infrastructure).

The Tech Stack Shift

Not long ago, a pet supply startup’s CX “stack” consisted of a helpdesk (email, phone, maybe live chat) and human agents or an outsourced team.

Now, there’s a rapid shift toward integrated AI tools that augment or automate much of the frontline support.

Common components include:

• Gorgias (AI Helpdesk) – A popular helpdesk platform for e-commerce that now offers an AI agent capable of learning a brand’s policies and tone. It can autonomously resolve up to 60% of support inquiries while adhering to the brand’s voice. For a pet brand, this might mean an AI agent that can answer FAQ questions (“How do I adjust the pet feeder schedule?”) or process simple requests like order cancellations, all inside the helpdesk.

• Intercom (AI Chatbot “Fin”) – Intercom’s customer messaging platform introduced Fin, an AI chatbot powered by OpenAI’s GPT-4. Fin can deliver detailed, conversational answers by drawing on a company’s knowledge base. A pet care app using Intercom, for example, can have Fin answer policy questions or guide users through claim forms with human-like understanding, deflecting tickets that would otherwise require an agent.

• Yuma AI (E-commerce Automation) – A newer tool leveraging generative AI to automate Shopify store customer service. Yuma’s “AutoPilot” agents can handle common e-commerce queries end-to-end. For instance, Yuma can autonomously respond to “Where is my order?” questions by checking shipment status, or cancel a customer’s subscription for pet water fountain filters – tasks that used to need human intervention. Yuma reports that its top merchants often automate 40–70% of their support tickets using AI, drastically cutting work load.

• Observe.AI (AI Call QA and Analytics) – Rather than a customer-facing bot, Observe.AI uses AI to listen to and analyze 100% of customer interactions (calls and chats) for quality and insights. This was traditionally done by spot-checking just a few percent of calls. For pet companies with phone support (e.g. pet insurance or veterinary telehealth), Observe.AI’s Auto QA can evaluate every call against quality criteria. This not only improves consistency but also saves substantial overhead by reducing the need for large QA teams.

• ChatGPT and Generative AI Tools – Many firms also experiment with OpenAI’s ChatGPT or similar large language models directly. These can be used to draft personalized responses, translate customer emails, or even generate help center content.

  Generative AI can understand natural language queries and context, making it ideal for handling the complex narratives pet owners might share about their issues. By integrating these models via APIs (and now MCPs - more on that in the vid below), companies can create custom AI assistants or plug them into existing platforms. The result is a support tech stack that is smarter and more automated than ever before.

Case Studies

Real-world examples in the pet sector show what’s now possible.

Petlibro (Pet Products E-commerce): 

Petlibro, a maker of smart pet feeders and fountains, faced rising support volume as its product line expanded.

In early 2024 they deployed Yuma AI to assist their support team.

The results were striking: 49% of all support tickets are now fully handled by the AI without human intervention. Routine queries about product troubleshooting, product inquiries, and “where is my order” are resolved instantly by Yuma’s AutoPilot.

This automation helped Petlibro avoid hiring multiple new agents, translating to a 20% reduction in support costs within six months.

Moreover, by offloading tedious back-and-forth tasks to AI, Petlibro saw full resolution times drop by about ⬇30%, keeping customers happier with faster answers. “We don’t worry about subscription management anymore,” notes Petlibro’s support supervisor, since the AI can even handle canceling or updating subscriptions autonomously.

Figo Pet Insurance: 

In the insurance side of the pet industry, Figo provides a case of AI improving efficiency behind the scenes.

Figo’s contact center receives numerous calls from pet parents filing claims or asking about coverage.

Instead of scaling up a large QA department to monitor these interactions, Figo turned to Observe.AI’s automated quality monitoring.

This AI system now evaluates 100% of Figo’s support calls, providing managers with complete visibility into customer interactions.

The impact on cost is substantial – Figo estimates it would cost about $700,000 per year in salaries to manually achieve that level of QA coverage.

In addition, Figo is saving an estimated 27,000 agent hours per year that would have been spent on call evaluation.

Those hours are now redirected to coaching agents and improving service. The AI has helped Figo spot trends (like why customers cancel policies) and ensure consistent service quality across their team, without adding headcount.

According to Figo’s CX director, decisions that once relied on 2–3% sample of calls can now be made confidently with data from the entire customer base.

ROI and Risks

The return on investment from AI in customer service can be very attractive. The ROI drivers include direct cost savings, productivity gains, and scalability:

• 📉 Lower Support Costs: By deflecting a large portion of tickets to self-service or AI agents, companies can scale back the number of live agents needed (or redeploy them to higher-value tasks).

  Saving 20–30% on support costs in a matter of months is achievable (as Petlibro’s 20% cut showed), and some AI vendors even tout up to 60% cost reduction in support operations in best-case scenarios. These savings directly improve profit margins – a significant win in the pet retail space where margins can be thin.

• 🏃‍♂️💨 Faster Response and 24/7 Service: AI doesn’t sleep. Automated systems can answer customer questions instantly at any hour, which improves first-response times dramatically.

  This is critical when a pet parent has an urgent question at 10 PM about their pet’s prescription food or a malfunctioning device. Faster responses drive higher customer satisfaction and prevent issues from escalating. In trials, generative AI chatbots have cut average response times by well over 80%, keeping anxious customers at ease.

• 👌 Consistent Quality @ Scale: AI can ensure every customer gets a consistent, policy-compliant answer. No more worrying that an inexperienced agent might give the wrong dosage info for a pet medication or an incorrect return policy.

  With tools like Auto QA analyzing every interaction, companies like Figo gain quality assurance without scaling up management overhead. This consistency builds customer trust over time. And as volume grows (think holiday surge in pet gift orders or a viral product on TikTok), AI can handle the spike in inquiries seamlessly, scaling up without a linear increase in cost.

That said, AI is not a silver bullet, and implementing it comes with challenges that operators must manage to protect their brand’s trust and voice:

• 🫶 Maintaining Empathy and Tone: Pet owners often have an emotional connection to their inquiries (“My dog’s toy arrived damaged and it was his favorite”). A robotic or unhelpful response can erode trust.

  AI must be carefully trained to use the brand’s friendly, compassionate tone. The good news is that modern generative models can be surprisingly empathetic – the insurance company, Allstate, found an AI model showed more consistent empathy in customer chats than human reps.

  Even so, companies should fine-tune AI responses and possibly use scripts for sensitive scenarios (like a pet’s passing - which I think should at some point in the convo be flipped over to a human in this particluar instance) to ensure the tone is just right.

• 😰 Handling Edge Cases and Escalation: No matter how smart an AI is, there will be unusual or complex queries it can’t resolve – a medical question about a pet’s condition, or an angry customer with a multi-faceted complaint.

  It’s critical to set up clear escalation paths so that the AI seamlessly hands off to a human agent when it hits its limits. A common strategy is a hybrid approach: let AI handle the simple stuff, but have humans ready for anything tricky or high-stakes.

  This preserves customer satisfaction. Remember, 75% of consumers still prefer a human touch for complex issues, so AI should augment rather than completely replace human support in scenarios that demand real empathy or decision-making.

• 🎯 Training and Accuracy: AI models need relevant data (product info, policy details, past tickets) to perform well. Setting up an AI support agent isn’t entirely plug-and-play; there’s an upfront effort to integrate systems (so the AI can, say, pull order statuses) and to train it on your knowledge base.

  During the learning phase, close monitoring is needed. Operators must review AI responses, correct mistakes, and continually update the AI as the business changes (new products, new policies, etc.).

  The risk of hallucination (the AI confidently giving a wrong answer) means you shouldn’t deploy it unattended until you’re comfortable with its accuracy. Many brands start with AI assisting agents (suggesting answers) before they turn on full automation. This cautious approach ensures that brand trust isn’t compromised by a rogue AI reply.

Actionable Framework (3 Steps for a Pet Brand)

For any ambitious pet brands with resources looking to implement AI in customer support, here’s a simple framework to get started:

1. Map Your Top Support Drivers: Begin with an audit of your customer service logs. Identify the recurring issues that eat up yours or your team’s time. In pet e-commerce, common ones are “Where is my order?” inquiries, product usage questions (e.g. how to set up a pet camera), subscription changes, and basic troubleshooting.

   These high-volume, low-complexity tickets are prime candidates for automation. Prioritize 2–3 such categories that collectively account for a large chunk of your ticket volume.

2. Choose the Right AI Tools and Integrate: With your target use cases in mind, pick tools that fit your business (much easier said than done, but don’t rush into just any service!). If you run a Shopify-based store, a solution like Gorgias or Yuma AI can plug into your platform and CRM data.

   Ensure the AI can access your knowledge base, past tickets, and order system – this context is what enables accurate, personalized answers. Start small and deploy the AI in a helper capacity initially. For instance, enable an AI chatbot on your website FAQ page, or use AI to draft email responses for agent approval.

   This allows you to gauge its accuracy. Work closely with the vendor on training the AI: feed it your product manuals, policy docs, and even recordings of great customer calls.

   The goal is to teach it your brand voice and typical solutions. Many AI platforms now offer quick-start templates (so you don’t need a PhD in machine learning to do this). Set up integration tests: e.g. does the AI successfully pull a tracking number when asked about an order? Tweak as necessary before fully rolling it out.

3. Monitor, Measure, and Iterate: Once the AI is live handling customer inquiries, closely monitor its performance. Track metrics like automation rate (what percentage of tickets it resolves), customer satisfaction scores (surveys for CSAT scores for AI-handled cases vs human-handled), and handle time.

   Tools like Observe.AI or built-in dashboards will help you review transcripts of AI interactions. Regularly spot-check a few conversations to ensure quality and compliance. Gather feedback: if customers are dissatisfied with an AI interaction, why?

   Use this data to continually refine the AI’s responses. Also, keep your team in the loop—train your human agents on how to work with the AI (for example, how to quickly take over a live chat from the bot if needed). As confidence grows, you can expand the AI’s scope (maybe move from just answering FAQs to also processing simple returns or appointment bookings via chatbot).

   It’s an iterative process. Done right, you’ll see the AI’s handling capacity climb over time.

   Benchmark the ROI: after a quarter, calculate the hours of human workload saved and the improvement in response times.

   For instance, if your AI agent handles 50% of tickets, that could easily equate to savings on a few full-time salaries, which for a small to midsize business might be the difference that funds your next growth initiative.

WINNER 🏆️ : BITCOIN

 Proudly introducing the DoggyDex™, an index comprised of 10 publicly traded companies whose primary focus is the dog/pet industry.

List of tickers used can be found below.

The yellow line-plot in the chart represents these companies above (DoggyDex™) and their combined performance against both the S&P 500 and Bitcoin on a weekly basis.

Pawformance is measured by % gains & losses.

• $CHWY - Chewy: E-commerce platform for pet supplies

• $IDXX - Idexx Labs: Vet point of care instruments and vet software

• $FRPT - Fresh Pet: Pet food company

• $ELAN - Elanco: Manufactures pet disease prevention products

• $PETS - PetMeds: Online pet pharmacy

• $ZTS - Zoetis: World's largest producer of meds and vaccines for pets and livestock

• $TRUP - Trupanion: Pet insurance company

• $WOOF - Petco Health & Wellness co.: Pet health & wellness company

• $BARK - BarkBox: Subscription service providing dog products, services, and experiences

• $PET - Wag! Group Co.: Tech platform that allows pet owners to connect with industry professionals for services such as walking, training, etc.

It’s official - AI agents aren’t just tolerated, they’re actually preferred in a lot of customer interactions.

According to Zendesk’s 2024 CX Trends Report, over half of consumers say AI helps them better understand and discover products, and 51% would rather talk to a bot than a human when they need help fast.

For pet brands, this should be a wake-up call: speed and clarity often beat human warmth when your customer just wants to know if the supplement ships overnight or what size collar fits their corgi.

Even more interesting?

Nearly half of consumers think AI can be empathetic. That’s huge for an industry rooted in emotional decisions.

Especially for pet parents dealing with anxiety, grief, or complex care questions. If your support experience still relies on clunky email threads or outsourced scripts, you're likely leaving loyalty (and margin) on the table.

Smart AI implementation isn't just a tech upgrade, it's a CX moat.

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