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A call-center case study should let a reader trace the headline result to real records. This guide is a method for preparing such a study. It does not report a QuickVoice customer deployment, quote a customer, or present a composite story as measured evidence.
Start by defining the decision the study should support: continuing a pilot, changing coverage, or expanding an approved task. Choose a baseline and observation period before examining outcomes. Preserve the call-routing and cost assumptions that make those periods comparable.
Define the population before calculating savings
Describe the lines, hours, locations, call intents, languages, and eligibility rules included. Count excluded calls separately, with reasons. If the AI handles only straightforward status questions while people handle disputes, comparing their raw average costs will reflect the different work as well as the technology.
Create a record for every eligible interaction with the attempt identifier, task category, result, staff follow-up, and any repeat contact linked to the same issue. Verify results in the receiving system. A call marked “completed” by a carrier does not mean the caller's task was resolved.
Reconcile the complete cost ledger
| Cost category | Evidence to gather |
|---|---|
| Staff handling and follow-up | Actual time records or a documented sample method |
| Platform and model usage | Invoices tied to the observation period |
| Telephony and numbers | Carrier charges, including failed attempts where billed |
| Integration and rollout | Recorded implementation and testing effort |
| Ongoing operation | Monitoring, incident work, review, and maintenance |
| Correction and repeat work | Time spent repairing failed or incorrect outcomes |
Use the same accounting treatment for both periods. Distinguish one-time rollout costs from recurring costs, and explain whether they are included or amortized. Retaining staff while changing their assignments is capacity redeployment; it does not automatically create cash savings.
Calculate cost per verified outcome = included period costs ÷ verified completed outcomes. State the denominator and show unresolved outcomes alongside it. If there are no verified completions, report that fact instead of manufacturing a cost-per-success figure.
Check quality before attributing a result
Inspect factual accuracy, receiving-system errors, repeat contacts, complaints, and requests for a person. Report the sampling method and missing data. A lower apparent cost that shifts work into uncounted callbacks is not a complete result.
Compare call mix and coverage across periods. Document seasonal demand, staffing changes, pricing changes, promotions, or system incidents that could affect the outcome. A before/after observation can show a change without proving that AI alone caused it. If the study cannot isolate causes, state that limitation beside the headline.
Publish only approved evidence
Obtain permission for identifiable customer details and quotations. Preserve the underlying calculations and have the responsible finance and operations reviewers approve the exact public claims. Anonymization does not make invented people or unsupported averages acceptable.
NIST's AI Risk Management Framework offers a broader voluntary framework for evaluating and managing AI risks. It is not a benchmark for QuickVoice savings. The study design above is an original operational worksheet, with results to be filled only from an actual evaluation.
QuickVoice's repository can support implementation inspection, but source code is not customer-outcome evidence. Use the receptionist cost worksheet to structure inputs, then publish a case study only when the records and approvals support it.
Rahul Agarwal
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