How to turn support tickets into a weekly issues report with AI

Decide what the weekly report should help someone do

A useful weekly support report helps a team decide what to investigate, which customer problem needs clearer guidance and what evidence is still missing. It does not need a confident story about every change. Start with the people who will use it and the decisions they can actually make.

For this guide, the report's audience is a small operations team reviewing incoming questions and unresolved work. The output is a one-page summary plus an evidence table. It describes ticket volume, issue categories, the supplied closing backlog and a few proposed follow-ups. It does not rate employees or claim to measure all customers' experience.

The download contains twelve invented ticket records. Their summaries, identifiers, statuses and reopen counts were authored for the exercise. The fictional reporting window extends from 26 October to the start of 2 November 2026. It is a teaching snapshot with an invented cutoff, not an actual completed-week performance report.

Keep the first task narrow: reproduce the known counts and make the uncertainty visible. If an assistant cannot distinguish a ticket created this week from an older ticket still open this week, additional charts and polished executive language will make the error harder to notice.

Define the window, cutoff and export coverage

Use an inclusive start and an exclusive end: 2026-10-26 at 00:00 through, but not including, 2026-11-02 at 00:00, both in Africa/Lagos time, UTC+01:00. This rule includes every instant on the final reporting day without relying on a last-second timestamp.

CreatedAt determines whether a ticket belongs to the new-ticket cohort. StatusAtCutoff describes the state at the fixed exercise cutoff. ReopensDuringWindow is a supplied event count for the stated window. These fields have different time meanings even though they appear in the same row.

The twelve-row export is the complete fictional ticket universe for this exercise. That explicit coverage permits a closing-backlog count across those rows. An operational export filtered to tickets created during the week would omit older unsolved tickets and could not establish the team's whole closing backlog.

Write down included queues, channels and exclusions for real reporting. If a source omits phone contacts or a queue is unavailable, keep that limitation in the report. An assistant should not quietly interpret the available extract as a census of every support interaction.

Choose the unit before asking for totals

One row represents one ticket's state at the cutoff, not one message and not necessarily one distinct customer issue. TicketID is the countable identifier for ticket volume. KnownIssueID provides a reviewed grouping in this practice pack. AccountID and OrderID provide context for the repeat-contact example.

T001 and T002 are separate tickets from account A01 about order O01. Their KnownIssueID is D01, and T002 explicitly describes a follow-up on the same delivery. Both belong in ticket volume, but together they represent one known issue in the exercise.

Do not remove T002 to make the dataset look clean. Repeated contact is operational work and may be useful evidence that someone needed another answer. At the same time, do not call the two rows two separate delivery failures. Preserve both the original ticket count and the reviewed grouping.

A real issue grouping may be less certain. Similar wording from different accounts can refer to separate incidents, and one ticket can mention several concerns. Require explicit linkage or a human-reviewed grouping rule. The sample's KnownIssueID is supplied evidence, not permission to infer equivalent certainty in an unrelated export.

Inspect identifiers and timestamps before coding the text

Check that all twelve TicketIDs are present and unique. If an export repeats a TicketID, determine whether it contains multiple events, multiple tags or duplicate extracts before deciding how to count it. A many-row event log needs a different treatment from this one-row-per-ticket snapshot.

Parse CreatedAt with its timezone offset, then apply the reporting boundaries. T012 was created on 25 October, so it falls outside the new-ticket cohort even though it remains open at cutoff. Keep it in the source and the closing-backlog view rather than discarding it as an irrelevant row.

Inspect required fields and blank values. A blank OrderID is allowed for a billing or sign-in question in this exercise. A blank TicketID would undermine the identity-based count. Missing status evidence would undermine a backlog classification. Do not replace every blank with a single generic unknown category.

If a real export is an event log, preserve event timestamps and define which event determines each state. Sorting by a display date and keeping the last row may fail when timestamps share a time, exports arrive late or updates have different meanings. Ask for a documented snapshot or a validated event-to-state method.

Use a small category guide and keep an unclassified option

The starter categories are Delivery, Billing, Access and Product information. Assign one primary category per ticket for this particular frequency table. Record secondary concerns separately if the real workflow needs them, but do not mix a multi-label count with percentages that imply mutually exclusive categories.

Delivery covers requests about shipment or delivery status. Billing covers invoice and billing-record questions. Access covers signing in and reset links. Product information covers a pre-purchase question about an item. These are exercise definitions, not a universal customer-support taxonomy.

T011 describes misaligned columns in an exported CSV. It does not fit the starter categories. Mark it Unclassified and retain its summary for review. Forcing it into Billing because spreadsheets sometimes contain invoices would manufacture a category relationship absent from the source.

After inspecting several weeks of real records, a team might add an export or data-formatting category. Record the taxonomy version when doing so. Reclassifying this week with a new definition while leaving last week unchanged can create an apparent trend that comes from the reporting method rather than customer behavior.

Ask the assistant to classify without inventing causes

Provide the category definitions and instruct the assistant to return a row-level classification table before writing the summary. Require TicketID, proposed primary category, the source phrase supporting it, issue-group reference and an uncertainty note when the text is insufficient.

The supplied summaries can establish the topic of a question, but not its technical root cause. Tracking not updated does not prove a carrier integration failed. Cannot sign in does not prove a password bug. Keep the issue wording close to the evidence until a separate investigation establishes an explanation.

For the repeat-contact pair, require the assistant to preserve the provided D01 group and both ticket references. For T011, require the unmatched category instead of a best-fit guess. Those two cases reveal whether the tool is respecting the classification contract or just producing a complete-looking table.

Save the first classifications before opening the answer key. Review every ambiguous item and a sample of straightforward ones. In this twelve-row exercise, inspect all rows. At larger volumes, a documented quality-review approach is still necessary before the categories become the basis for operational claims.

Classify the fictional support export before drafting a report. Use primary categories Delivery, Billing, Access and Product information with these meanings: delivery or tracking status; invoices or billing records; sign-in or reset access; pre-purchase product questions. Use Unclassified when none fits. Preserve one row per TicketID and the supplied KnownIssueID; do not deduplicate repeated contacts. Return TicketID, category, a short evidence phrase and uncertainty. Do not infer root cause, sentiment, severity, product failure or agent performance from the brief summaries.

Verify the eleven-ticket cohort and its category totals

The new-ticket cohort contains T001 through T011: eleven tickets. T012 is excluded from this particular measure because of its creation date. Among the eleven new tickets, Delivery has four, Billing three, Access two, Product information one and Unclassified one.

Those mutually exclusive counts sum to eleven. The shares are approximately 36.4%, 27.3%, 18.2%, 9.1% and 9.1%. Rounded to one decimal place, they add to 100.1%. Explain the rounding rather than changing a category count or quietly editing a displayed percentage to force an exact sum.

Delivery's four tickets represent three known issues because T001 and T002 share D01. Across the whole new cohort, eleven tickets represent ten known issues. A report can show both views, but each chart or sentence needs to state which denominator it uses.

A defensible statement is that delivery questions account for four of eleven new tickets in this fictional export. Calling 36.4% of all customers affected would require a customer population and a different measure. Neither the total customer base nor a product-wide incident denominator is supplied here.

Report closing backlog from the right population

Among the eleven new tickets, five are solved at cutoff, four are open and two are pending. That leaves six unsolved records in the new cohort under the exercise's rule that open and pending both count as unsolved.

The older T012 is also open. Because the practice export explicitly covers its complete fictional ticket universe, the total closing backlog is seven unsolved tickets. A report that counted only new-ticket rows would understate that supplied backlog by one.

Zendesk's backlog-history documentation distinguishes a historical snapshot of unsolved tickets from current ticket state and update history. The broader lesson is to select data that represents the point in time you claim to report. A current export retrieved later may no longer reproduce last week's ending backlog.

Keep opening backlog and backlog change unavailable unless a valid opening snapshot or sufficient event history is supplied. The present file gives closing statuses and a limited reopen-count field. It does not establish every ticket's state at the start of the window, so a complete opening-to-closing reconciliation would require additional evidence.

Sources: Zendesk: backlog snapshots, current ticket state and update history

Count reopen events separately from final status

T003 and T009 each have one supplied reopen event during the window. Together they contribute two events. T003 is solved again at cutoff, while T009 is open. Counting only currently open tickets would miss T003's earlier reopen.

The field in this pack is an event count. A value of two on a single ticket would mean two events, while a count of tickets with at least one reopen would still count that ticket once. Here both measures happen to equal two, but their definitions remain different.

Do not create a reopen rate by dividing these two events by the five tickets currently solved. That denominator is a status snapshot, not necessarily the population eligible for reopening during the week. A real rate requires a clearly defined event population and consistent source definitions.

Also avoid interpreting every reopen as a confirmed failed resolution. A follow-up can have several explanations, and the short summaries do not establish which applies. The report can flag T003 and T009 for review while leaving the operational interpretation to the team with the full records.

Write the summary after the evidence table is stable

Begin with scope and the checked figures, then name the limited actions the evidence supports. Keep descriptive findings separate from proposals. An owner reading the first paragraph should know what was counted and whether the information is a complete snapshot or a partial extract.

For this exercise, delivery questions form the largest primary category in the new-ticket cohort. That is a within-week comparison. There is no prior-week file, so the report cannot say delivery problems increased, performance improved or a recent change caused the observed mix.

Keep the summary compact enough that its limitations stay visible. Link each action to the relevant TicketIDs and the next question. Proposed ownership and dates are planning suggestions until someone confirms them. Do not describe a drafted action as completed or a generated explanation as an established root cause.

The example below shows the intended level of specificity. It deliberately contains no response-time, satisfaction or service-level claim, because the input includes no event timings, customer survey results or applicable service-level policy.

Fictional window: 26 October through 1 November 2026, UTC+01:00. Eleven new tickets represent ten known issues. Delivery accounts for 4/11 new tickets, including two contacts about D01. The complete supplied snapshot contains seven unsolved tickets at cutoff, including older T012. Two reopen events are recorded. Review the repeated D01 contact, inspect the two reopened records, and classify the CSV-export question. No previous-period or response-time data is supplied.

Turn findings into questions with traceable next steps

For D01, the evidence is the paired T001 and T002 contact about the same order. A useful next step is to inspect whether the first response left a delivery-status question unresolved. The proposed owner might be the delivery-support lead, but the sample provides no actual assignment or proof of the cause.

For the two reopened records, ask what prompted the reopen and whether the later outcome is documented. T003 and T009 should not receive identical narrative conclusions merely because their event counts match. Their final statuses differ, and the full conversation is unavailable.

For T011, ask the reporting owner whether the taxonomy needs an export-related category and whether the product team should investigate the column-formatting symptom. Keep the classification decision separate from the technical investigation. A new category does not resolve the underlying customer issue.

Use a small action table with finding, ticket evidence, proposed next step, proposed owner, review date and confirmed outcome. Leave fields unanswered when the source does not establish them. The point is to make follow-up possible without dressing uncertainty as a completed decision.

Keep a reproducible reporting routine

Save the source snapshot, window definition, category guide, classifications, checked totals and final report together. Record the prompt or analysis method used. This allows another reviewer to explain the figures without depending on an assistant's inaccessible conversation history.

Before the next report, confirm that the export has the same population and fields. A newly added channel, changed status definition or altered taxonomy can break comparability. If a definition changes, either restate the earlier period under the new method or label the break rather than describing it as an operational trend.

For automation, keep deterministic counts and joins inspectable. AI can help classify ambiguous wording and draft a readable summary, while a spreadsheet or reporting query can compute the defined totals. Preserve an unclassified queue so new topics are reviewed instead of absorbed into whichever category looks closest.

When the report produces a useful follow-up, record the actual outcome separately. A later change in ticket mix may justify another investigation, but it does not prove that the report's recommendation caused it. The recurring value comes from clear definitions, evidence-linked decisions and a method another person can repeat.

Using the reviewed classification table and the reporting rules stated in this article, draft a weekly report for [2026-10-26T00:00:00+01:00, 2026-11-02T00:00:00+01:00). Count unique new TicketIDs, distinct supplied KnownIssueIDs within that cohort, category counts and their 11-ticket denominator. Report closing backlog from all supplied records and reopen events from the defined window field, separately. Include an action table with evidence IDs and proposed next steps. Label absent opening-backlog, prior-period, customer-population, response-time and CSAT evidence as unavailable. Avoid unsupported trend, SLA, root-cause or employee-performance claims.