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.