AI for insights and analytics
The team's first contact resolution is 84 percent while the fourth largest account, ten weeks from renewal, is at 45 percent on a surge of tickets in one category. AI analytics reads the ticket export, the contract file and the revenue file, and answers the head of service: which accounts are in trouble, and which have gone silent.
These are the questions service teams ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| Which accounts are raising far more tickets than usual? | Contact volume against own baseline | Tickets by account against its own baseline, with the category driving the surge. |
| Are we meeting each account's own SLA? | SLA attainment, account's own SLA | Attainment against the response and resolution times in each contract, not a team average. |
| Which accounts have gone silent? | Silence list | Accounts whose contacts fell to nothing against their baseline, by revenue, with the renewal date. |
| Where does effort exceed revenue? | Effort per account against revenue | Tickets and hours per account against revenue, with the ones out of proportion. |
| What keeps coming back? | Recurring causes | Root causes by count and by accounts affected, with the repeat contacts they generate. |
| Which tickets could have been deflected? | Deflectable tickets | Tickets matching a documented answer, by category and account. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
Which accounts are surging?
121 tickets above baseline were raised this month across 3 teams, by accounts more than double their own normal volume. Enterprise team carries 61 of them, 50% of the surge.
| Enterprise team | 61 | 50% of the total |
| Mid-market team | 38 | 31% of the total |
| Small business team | 22 | 18% of the total |
| The 3 lines sum to | 121 | 0 unexplained |
Which enterprise accounts?
3 accounts account for 51 of the 61 excess tickets in the Enterprise team, 84% of it. The largest is ten weeks from renewal, and seventeen of its tickets are in one category.
| Halcyon Systems | 22 | 31 tickets against baseline 9; renewal in 10 weeks |
| Corvid Retail | 17 | SLA met on 71%; contract says four hours |
| Brightline Health | 12 | repeat contact rate 38% |
| These 3 are | 51 | 84% of Enterprise team |
Who needs to know this week?
The account manager for Halcyon, today, with the category and the SLA figures. The team lead takes the recurring cause to engineering. The surge list runs every Monday.
| Account manager, Halcyon | Call the customer with the category breakdown and SLA figures | Today |
| Team lead | Raise the recurring cause with engineering | This week |
| Head of service | Run the surge and silence lists every Monday | Weekly |
Each measure has one formula, one source and one meaning. They are computed per account and team and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Contact volume against own baseline | Tickets in the last 30 days ÷ the account's trailing 12-month monthly average; surges and drops | Ticket export; account list | Accounts in trouble, and accounts gone quiet |
| First contact resolution per account | Tickets resolved without reopen or follow-up ÷ tickets, per account, above a minimum count | Ticket export | Customers who have to ask twice |
| Repeat contact rate | Tickets reopened, or raised in the same category within 14 days of a closure ÷ tickets | Ticket export | Fixes that did not fix |
| SLA attainment, account's own SLA | Tickets meeting the response and resolution times in that account's contract ÷ tickets | Ticket export; contract terms | Breaches where the terms are strictest |
| Effort per account against revenue | Support cost or hours ÷ account revenue | Time records or handling time; revenue | Accounts that cost more than they pay |
| Silence list | Accounts with volume below a stated fraction of own baseline for 6 weeks, by revenue | Ticket export; account list | Usage that has stopped |
| Deflectable tickets | How-to tickets answerable by an article ÷ tickets, by category | Ticket export with category | Articles worth writing |
| Time to resolution by severity | Median and 90th percentile, by severity | Ticket export | Whether serious issues close faster than minor ones |
| Backlog ageing | Open tickets by age band and owner | Ticket export | Where tickets wait |
| Recurring causes | Categories with repeats across many accounts in the period | Ticket export | Product faults, with the account list attached |
Each measure is worked through, with the export it comes from and what to drop, in KPIs for B2B customer service teams.
From your question and the measures declared for customer service, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the account or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Tickets | Tickets = first-contact resolved + repeat + open |
| SLA | Tickets = met + breached + not yet due; every account has its SLA terms recorded |
| Accounts | Every ticket belongs to one account; unmatched tickets are counted, not dropped |
| Backlog | Opening + raised − closed = closing |
The exports service teams already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Surge and silence lists | Head of service with account management | Weekly |
| SLA by account; backlog ageing | Team leads | Weekly |
| First contact resolution and repeats by account; recurring causes | Head of service; product | Monthly |
| Effort against revenue; deflectable tickets | Head of service; finance | Quarterly |
Because a team dashboard at 96 percent SLA and 84 percent resolution hides one account at 45 percent. Each account's normal volume, its own contract terms and its own history are the comparison. A surge is a change for that account, and it appears whether or not the team average moved.
From the contract file: response and resolution times per account. Attainment is computed against those, per account, and the team average is shown beside it. If an account has no contract terms loaded, the tool uses the default and says so.
Accounts whose contacts fell to nothing against their baseline, ranked by revenue, with the renewal date. Silence before a renewal is as much a signal as a surge, and it never shows on a ticket dashboard because there are no tickets.
The ticket export with account, category, times and resolution, the contract file with SLA terms and renewal dates, and revenue by account. Hours per ticket, if logged, turn volume into effort against revenue.
Surge · SLA attainment · Repeat contact · Effort ratio · Deflection list · Baseline
Asset managers · Commercial banking · Compliance · Construction and building materials · Consulting and advisory · Distributors · Education · Finance and FP&A teams · Financial services · FMCG and CPG brands · Foodservice distributors · Freight brokers and 3PLs · Healthcare and med-tech · Hospitality · Industrial distributors · Industrial manufacturers · Insurance brokers · Investment banking · Law firms · Oil and gas services · Pharma · Procurement · Retail banking · SaaS · Sales teams · Shipping and logistics · Sports · Supply chain · Tax and accounting · Telecoms and connectivity · Trading · Wealth managers
Bring a few thousand rows. The data map opens next, every column mapped once, and the first question is answered in minutes. Free, in your browser, no account.