AI-Powered Contact Center Transformation with Amazon Connect

traeger · E-commerce · 2024 · source Agentic: yes · promotional bias: med · source-backed · extracted with prompt 0.5.4 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Consumer support contact center

Traeger needed to replace fragmented BPO-controlled contact-center systems, poor visibility into service quality, and agent workflow complexity while integrating the Meater support operation.

Constraints:

The contact center had to support both Traeger and Meater brands
Agents needed to preserve a relationship-oriented customer experience
Unexpected incidents could flood queues faster than manual operations could respond

Architecture

Traeger rebuilt customer experience around Amazon Connect: telephony/routing, QuickSight analytics, sentiment, transcription, chat, video streaming, screen recording, Amazon Connect Cases as a single-pane workspace, AI-generated notes and emails, self-healing queue controls, and IVR/chatbot automation for non-value-added setup work. The design lets agents focus on relationship-heavy support across Traeger and Meater brands.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  amazon-connect-core["amazon-connect-core"]
  quicksight-analytics["quicksight-analytics"]
  video-streaming-support["video-streaming-support"]
  single-pane-cases-workspace["single-pane-cases-workspace"]
  brand-backend-systems["brand-backend-systems"]
  ai-notes-and-email-bot["ai-notes-and-email-bot"]
  self-healing-queue-controller["self-healing-queue-controller"]
  traegy-ivr-chatbot["traegy-ivr-chatbot"]
  amazon-connect-core --> quicksight-analytics
  amazon-connect-core --> video-streaming-support
  amazon-connect-core --> single-pane-cases-workspace
  amazon-connect-core --> self-healing-queue-controller
  single-pane-cases-workspace --> brand-backend-systems
  single-pane-cases-workspace --> ai-notes-and-email-bot
  video-streaming-support --> single-pane-cases-workspace
  traegy-ivr-chatbot --> amazon-connect-core
  traegy-ivr-chatbot --> brand-backend-systems

Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component	Role	Interacts with
`amazon-connect-core`	Core contact-center platform for telephony, routing, chat, text links, video streaming, screen recording, transcription, and AI integrations.	`quicksight-analytics`, `single-pane-cases-workspace`, `video-streaming-support`, `self-healing-queue-controller`
`quicksight-analytics`	Data-visualization and conversational analytics layer for contact-center performance and product/customer-experience insights.	`amazon-connect-core`
`video-streaming-support`	Sends customers a text link that opens the phone camera and streams product troubleshooting video to agent desktops.	`amazon-connect-core`, `single-pane-cases-workspace`
`single-pane-cases-workspace`	Amazon Connect Cases workspace that hides Traeger/Meater backend differences, replaces the old CRM, and gives agents one place to handle case lifecycle work.	`amazon-connect-core`, `brand-backend-systems`, `ai-notes-and-email-bot`
`brand-backend-systems`	Underlying order-management, registration, and product systems for Traeger grills and Meater probes.	`single-pane-cases-workspace`
`ai-notes-and-email-bot`	Generates case summaries, notes, and on-brand follow-up emails inside the cases workflow.	`single-pane-cases-workspace`
`self-healing-queue-controller`	Monitors contact-center overload signals and automatically turns callbacks or chat off and back on to prevent unrecoverable backlog.	`amazon-connect-core`
`traegy-ivr-chatbot`	AI IVR/chatbot automation used for non-value-added tasks such as registration before a human support handoff.	`amazon-connect-core`, `brand-backend-systems`

Data flow

A customer contacts Traeger or Meater through Amazon Connect. The called number identifies the brand, account/product context appears in the single-pane workspace, agents handle live support while backend systems process brand-specific orders or registrations, and AI creates case notes and follow-up emails. Queue telemetry can automatically disable callbacks or chat during overload and restore them when the center recovers.

“Based on the phone number called we know if it's a Traeger interaction or a Meater interaction. The account information pops, the agent is then just”transcript, stated

“The AI that is present within the interactions is doing all of the case summaries and leaving case notes on single pane of glass for the next agent.”transcript, stated

Why this architecture

Traeger used Amazon Connect as the control plane because it gave immediate data ownership, then progressively added capabilities without rebuilding a legacy CRM. Amazon Connect Cases became the fastest way to support two brands with one agent experience, while AI removes documentation and registration work that does not need human empathy.

“when I was able to stand up Amazon Connect in over a weekend, immediately having access to our own data and was able to baseline for the first time”transcript, stated

“We made the decision to move to Amazon Cases within Amazon Connect. We refer to it as single pane of glass because to our agents”transcript, stated

Models used: Amazon Connect AI features · generative AI case-note and email bot · IVR and chatbot AI technologies

Scale

Traeger first discovered its actual baseline was 35% CSAT and 30% first-contact resolution.
“what the reality was, was we were at a 35% CSAT and a 30% first contact resolution.”transcript, stated
Amazon Connect had analyzed 12 billion customer interactions with AI over the prior year.
“12 billions of customer interactions analyzed with AI over the past year has just been an incredible amount of scale.”transcript, stated
The Meater single-pane solution was built in four to five months and deployed in June and July.
“and we were up in, in four to five months with a complete build of this solution on top of our Amazon Connect instance. We deployed in June and July.”transcript, stated
The Amazon Connect journey had been running for over six years at Traeger.
“So working with the Amazon Connect team over the last six years, we started small, right.”transcript, stated

Alternatives considered (roads not taken)

Continuing to customize the legacy CRM for Meater support

Rejected because: Traeger chose to leapfrog into Amazon Connect Cases rather than spend an innovation year rebuilding Meater support in the old CRM.

“do we take the time and the investment to go into our CRM and modify it and rework everything to do with making Meater flow a lot better? Or do we leapfrog”transcript, stated

“to take the time to rebuild Meater. We made the decision to move to Amazon Cases within Amazon Connect.”transcript, stated

Using AI to deflect relationship-heavy customer support

Rejected because: Traeger kept human agents focused on emotional cooking/community connection and used automation for setup or administrative work.

“Grilling, cooking, cooking with Meater probes, cooking with our grills, is really about the emotional connection”transcript, implied

Patterns

`observability-tracing` (contact-center-analytics)

Traeger used Connect data, QuickSight, sentiment analysis, and transcriptions to turn support traffic into operational and product signals.

“analysis transcriptions, allowing us to start standing up a true customer experience ecosystem where now we can analyze our data”transcript, stated

`tool-use-orchestration` (agent-workspace)

Connect Cases became a single workspace where agents could identify brand, reason, orders, devices, and registration actions without switching systems.

“Based on the phone number called we know if it's a Traeger interaction or a Meater interaction. The account information pops, the agent is then just asked to pick a button.”transcript, stated

`structured-output-contract` (agent-assist)

The agentic bot standardized case notes and follow-up emails so different support agents produced consistent, understandable records.

“whether it's Traegy or our Amazon Connect instance, the consistency, the tone, and the way that we can train how to write these is really providing”transcript, stated

`fallback-degradation` (contact-center-resilience)

A self-healing contact center automatically reduces optional channels such as callbacks and chat when queues approach an unrecoverable state.

“we turn off callbacks automatically. We stop offering them. We turn chat off to stop ghost chats from coming in. And we monitor it. And by we, I mean the tool”transcript, stated

`human-in-the-loop-gate` (customer-experience)

Bots remove low-value setup work, but still hand off to human agents for relationship-building technical support.

“Before I pass you to an agent, can I just take a moment and have you text me a picture of your serial number and I'll get that product registered for you?”transcript, stated

Tradeoffs

How to integrate Meater support quickly.

Chose leapfrog to Amazon Connect Cases over reworking the legacy CRM for a second brand.

Rationale: The modern workspace let Traeger build the right flows for both brands without spending a year rebuilding Meater support inside the old system.

“do we take the time and the investment to go into our CRM and modify it and rework everything to do with making Meater flow a lot better? Or do we leapfrog”transcript, stated

Where to apply chat and IVR automation.

Chose remove non-value-added setup work before agent handoff over fully automating the relationship-heavy support interaction.

Rationale: The company wanted customer interactions to remain non-invasive and focused on emotional connection while bots handled serial numbers and registration.

“trying to not be invasive with the customer but also trying to strip away work that is non-value add for an agent to take the time”transcript, stated

Gotchas (what bit them)

Outsourced contact-center reporting masked the true baseline.

Root cause: Traeger relied on BPO-owned IVR, data, and reporting rather than direct telemetry.

Resolution: Standing up Amazon Connect gave Traeger direct data and revealed the actual CSAT/FCR baseline.

“when I was able to stand up Amazon Connect in over a weekend, immediately having access to our own data and was able to baseline for the first time”transcript, stated

Meater integration made the old CRM workflow awkward for agents.

Root cause: The original support system had been built for Traeger processes and had to shoehorn a second brand into different screens and support flows.

Resolution: Traeger chose Amazon Connect Cases as a new single-pane workspace for both brands.

“we were shoehorning Meater into a system that was built for Traeger. And it was enough for us to limp along, but it was a very awkward agent experience”transcript, stated

Manual incident response could worsen overloaded queues.

Root cause: By the time the team detected an outage and changed IVR messaging, customers were already queued or abandoned chats were accumulating.

Resolution: The self-healing contact center monitors volume, staffing, and handle time and temporarily disables callbacks/chat until the queue recovers.

“by the time that you have identified the issue, that you have taken the time to put a message to your customers, they've missed it, they're in queue”transcript, stated

Evaluation

Traeger evaluated the transformation through direct Connect telemetry, QuickSight dashboards, CSAT/FCR baselines, training time, agent satisfaction, and queue resilience during incidents.

Metrics: CSAT · first contact resolution · new-hire training time · agent satisfaction · queue overload recovery

“how much QuickSight has really allowed me and my team to hone in on areas of opportunity and data visualization.”transcript, implied

“the tool is smart enough to know how many agents I have working, what my handle time is, what the volume is coming in”transcript, stated

Outcomes

Direct data revealed a 35% CSAT and 30% first-contact-resolution baseline.
New-hire training fell by 40%.
Agents trained only on the new workspace outperformed tenured agents during nesting.
AI-generated notes and emails reduced agent after-contact work and improved consistency.

The system supports Traeger and Meater support flows across voice, chat, text, video streaming, cases, and bot-assisted registration.

3 supporting quotes

“what the reality was, was we were at a 35% CSAT and a 30% first contact resolution.”transcript, stated

“what that led to was a 40% reduction in training for new hires. Our path to proficiency is light speed now.”transcript, stated

“agents who we brought in and trained only knowing single pane of glass as an experience outperformed tenured agents within hitting the floor”transcript, stated