Magic LibraryRead the desk with Magic Analytics
Magic Analytics is your reporting layer — volume, response and resolution times, XLA attainment, sentiment, and AI impact, across the whole desk. It’s where you answer “how are we doing?” with a number instead of a hunch. Spend your first session getting oriented: how dashboards are laid out, how to filter by team and date range, and how to read the trend lines. The getting-started guide walks the fundamentals so you’re not guessing at what a metric means. The questions a service manager brings to it every week:- Are we keeping our promises? XLA response and resolution attainment, and where breaches cluster.
- Where’s the time going? Volume by client, board, and category, and what’s trending up.
- Who’s carrying what? Load and throughput by team and technician, so you can rebalance before someone burns out.
- Is AI pulling its weight? Triage Agent deflection and assistive-AI usage, so you can see automation’s real dent in the queue.
Explore beyond the defaults
Once the standard dashboards are familiar, the topics you can explore shows the fuller range, the dimensions and questions Magic Analytics can answer beyond the out-of-the-box views. Reach for it when a specific question comes up (“which clients drive our after-hours volume?”, “what’s our first-contact resolution by pod?”) that the default dashboards don’t answer head-on.Watch the live picture with View insights
Magic Analytics is the trend over time; View insights are the pulse right now. Every View carries live counts and signals for its slice of the queue, so your operational Views double as a real-time readout. Use the two together:
Keep your unassigned and breaching-soon Views in front of you during the day; lean on Magic Analytics when you plan the week and prep coaching.
Close the loop with CSAT
Internal metrics tell you how the desk ran; CSAT tells you how it felt to the customer. A CSAT survey fires after resolution and captures the client’s rating and comments, feeding satisfaction trends back into your analytics. Set it up early — it’s low-effort and it’s the outcome your clients actually judge you on. Then work it as signal:- Watch the trend, not just the average. A slipping CSAT line is an early warning long before it shows up in churn.
- Read the low scores individually. A detractor comment is the most specific coaching material you’ll get all week.
- Tie it to sentiment. Magic Sentiment flags tone dips mid-ticket; CSAT confirms the outcome. Together they tell you which tickets to review.
CSAT and sentiment are inputs to QA, not a scoreboard to wave at the team. Use a bad score to find the ticket worth reviewing — then coach on what happened in it, not on the number.
Run a repeatable QA loop
Analytics point you at what to look at; QA is how you improve it. Make it a standing weekly habit rather than a reaction to a blow-up:1
Sample the right tickets
Don’t review at random. Pull from where the signal is — reopened tickets, low CSAT, breached XLAs, and a few standard closures for baseline. The Weekly QA Digest assembles this for you.
2
Score against one rubric
Run Ticket QA Review so every ticket is judged on the same criteria (notes, communication, closure quality), instead of your mood that day. Consistency is what makes QA fair.
3
Grade the board's hygiene
Use Queue Hygiene Score to catch the systemic stuff (stale statuses, missing time entries, tickets parked with no next step) that per-ticket review misses.
4
Coach from the data
Bring the QA findings and a Tech Performance Review into each one-on-one. Specific, grounded, same rubric for everyone — that’s coaching that lands and doesn’t feel like gotcha.
Next
You can see the desk and you’re improving it. Last piece: getting Thread fully adopted across the team so all of this compounds.Roll out & drive adoption
Change management, enablement, and the rituals that make adoption stick.