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ThreadCraft is a mid-size apparel brand on Shopify doing roughly $1.8 million in annual revenue. They sell direct-to-consumer through their Shopify storefront and process an average of 120 orders per day, peaking at 300+ during seasonal sales. When they approached us, their support operation was stretched thin: 4 full-time agents handling approximately 200 tickets per day across email, live chat, and Instagram DMs. The average first-response time was 6 hours and 14 minutes — a number their founder, Sarah, knew was costing them repeat business but couldn't afford to fix by hiring more people.
The breakdown of those 200 daily tickets told a familiar story. Roughly 34% were order status inquiries — customers asking where their package was, when it would arrive, or why tracking hadn't updated. Another 28% were return and exchange requests, mostly straightforward size-swap situations. Product questions (sizing, materials, fit comparisons) accounted for 22%. The remaining 16% were a mix of discount code issues, wholesale inquiries, damaged item reports, and the occasional billing dispute.
The cost side was equally telling. Four agents at an average fully loaded cost of $4,800 per month meant $19,200 in monthly support spend. Add in their Zendesk subscription at $480 per month, phone system at $120 per month, and management overhead from Sarah spending roughly 8 hours per week on support operations, and the true monthly cost was closer to $22,000. For a business with 32% gross margins, that support expense consumed nearly 15% of gross profit.
We deployed AiKon on ThreadCraft's store on a Monday morning. The initial setup took 45 minutes: connecting the Shopify store (automatic sync of 2,400 active products, 18 months of order history, and 31,000 customer records), uploading their knowledge base (return policy, shipping FAQ, sizing guides for all 6 product categories, and a 40-page brand voice document), and installing the chat widget on their storefront with ThreadCraft's brand colors and a welcome message that matched their tone.
The first week was what we call the shadow period. The AI handled incoming chat conversations but flagged every response for human review before sending. This let the ThreadCraft team catch any issues with tone, accuracy, or policy interpretation before customers saw them. During this week, we identified 14 knowledge base gaps — questions the AI couldn't confidently answer because the information wasn't in the uploaded documents. The most common gap was around their loyalty program rules, which existed only in Sarah's head. We documented those rules and added them to the knowledge base in a 30-minute session.
In week two, we switched to live mode with automatic escalation. The AI responded to customers directly but escalated to a human agent whenever confidence was below 85%, the customer expressed frustration, or the issue involved a refund over $100. We also set up a daily review process: every morning, one agent spent 30 minutes reviewing the previous day's AI-handled conversations, flagging any that needed correction or follow-up. This review process caught 3-4 issues per day in the first week and dropped to 1-2 by the end of week two.
After 30 days of full deployment, the results exceeded what we projected. The AI fully resolved 82% of all incoming support conversations without any human involvement. Average response time dropped from 6 hours and 14 minutes to 12 seconds for AI-handled conversations. Even human-handled conversations got faster, dropping to an average of 23 minutes, because agents were no longer buried under a queue of 200 tickets — they were handling 36 tickets per day instead, with full conversation context from the AI's initial interaction.
The staffing impact was significant. ThreadCraft went from 4 full-time support agents to 1 senior agent who handled escalations, reviewed AI conversations, and managed the knowledge base. Two agents were redeployed to other roles in the business (one to marketing, one to operations), and one left through natural attrition. Monthly support costs dropped from $22,000 to approximately $7,500 — the remaining agent ($5,200), AiKon subscription ($199), and reduced tooling costs ($2,100 after downgrading their Zendesk plan). That is a 66% reduction in support spend, saving ThreadCraft $174,000 annually.
Customer satisfaction actually improved. Their CSAT score went from 3.8 out of 5 to 4.4 out of 5. The biggest driver was speed: customers who got an answer in 12 seconds rated the experience higher than those who waited 6 hours, regardless of whether the answer came from a human or AI. The Net Promoter Score moved from 31 to 47 over the same period. Support-related negative reviews on their Shopify store dropped from an average of 4.2 per month to 0.8 per month.
The revenue impact caught everyone off guard. ThreadCraft's AI agent was available 24/7, which meant customers shopping at 11 PM on a Tuesday could get instant answers to sizing questions, shipping timelines, and product comparisons. Before AI, those late-night shoppers either abandoned their carts or emailed a question and forgot about it by the next day. In the first 30 days, ThreadCraft tracked $23,400 in revenue from orders placed within 10 minutes of an AI conversation outside business hours — sales that almost certainly would not have happened without instant support. Their cart abandonment rate dropped from 71% to 58%, a 13-percentage-point improvement that translated to roughly $41,000 in recovered revenue per month.
Another unexpected benefit was cart recovery through support interactions. When a customer reached out about a product question, the AI would answer the question and then naturally mention relevant products or current promotions. This wasn't aggressive upselling — it was contextual helpfulness. For example, when a customer asked about the fit of a specific jacket, the AI would answer the sizing question and mention that the matching scarf was currently 20% off. These organic product suggestions generated an additional $8,700 in attributable revenue during the first month.
The biggest lesson learned was about knowledge base maintenance. The stores that get the best results from AI support treat their knowledge base like a product, not a document. ThreadCraft now has a weekly 15-minute review where their remaining agent identifies the top 5 questions the AI struggled with and updates the knowledge base accordingly. They also learned that overly formal language in the knowledge base led to overly formal AI responses — rewriting their docs in a conversational tone made the AI feel more natural and on-brand. Finally, setting the right escalation thresholds was critical. Too aggressive and the AI escalated too often, defeating the purpose. Too lenient and occasional edge cases slipped through. They found their sweet spot at an 85% confidence threshold after two weeks of tuning.
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