Article summary
A complete WhatsApp number-checking workflow: normalize a TXT list, select the right WS/WA task, understand Excel fields, reconcile results and run QA.
A reliable WhatsApp number-checking job has three separate layers: the phone-number list you are authorized to process, the WS/WA task that defines what will be checked, and the Excel schema used to return the result. Most costly mistakes occur when one of those layers is left implicit.
This walkthrough uses the AIPUSH workflow—TXT input, task selection and Excel output—and shows how to preserve a clean join back to the source system. It is intended for list cleaning and segmentation, not for account registration, ban recovery or proving marketing consent.
Map the workflow before touching the file
The operational path is: source system → authorized-number extract → normalization → one-number-per-line TXT → selected WhatsApp task → Excel result → staging-table reconciliation → approved business segment. Keep the source extract and the returned workbook separate; neither should directly overwrite production CRM data.
Write down the decision first. “Remove records that are not registered” points to a registration-status task. “Create activity bands” points to an activity task. “Review a richer set of profile-related fields” may require another task and tighter data-minimization controls.
Normalize the TXT input
Export only the phone-number column for submission. In the local source file, retain a stable customer or lead ID, country, acquisition source, permission state and last internal event. Normalize each phone number to the country code plus subscriber number, without display punctuation.
| Raw source value | Country context | TXT-ready pattern | Action |
|---|---|---|---|
| +1 (415) 555-0123 | United States | 14155550123 | Remove symbols; retain country code |
| 020 7946 0123 | United Kingdom | 442079460123 | Replace domestic trunk prefix with country code |
| Duplicate normalized value | Any | One retained line | Deduplicate and keep source-to-master mapping locally |
| Missing country context | Unknown | Do not guess | Move to an exception queue |
The sample numbers above are illustrative. The normalization rule should be implemented consistently across upload preparation and result reconciliation. A number that is changed in only one direction will fail to join even if the check itself succeeds.
Choose the WS/WA task from the output you need
| AIPUSH WhatsApp task | Excel fields returned | Typical use |
|---|---|---|
| WS registration check | Phone number, registered or not | Separate records by registration status |
| WS fast registration check | Phone number, registered or not | Registration-focused processing where the fast service fits the project |
| WS high-precision registration check | Phone number, business account, WhatsApp-mapped phone number | Projects requiring the high-precision task’s specific schema |
| WS activity check | Phone number, activity time, active days, WhatsApp-mapped phone number | Build activity-based review cohorts |
| WS gender and age check | Phone number, age, gender, avatar, WhatsApp-mapped phone number | Permitted segmentation using the listed profile-related fields |
| WS avatar check | Phone number, business account, avatar, WhatsApp-mapped phone number | Review avatar and business-account fields |
| WS all-format check | Phone number, activity time, active days, gender, age, avatar, skin tone, avatar type, business account, WhatsApp-mapped phone number | A broader field requirement that has been justified in advance |
Do not use “all format” by default. Broader output creates more review work and more data-handling responsibility. The narrowest task that answers the stated question is usually easier to validate.
Run a pilot and write down expected counts
Before a large job, submit a small sample drawn from different countries, sources and formatting histories. Record four numbers: source rows, nonblank phone rows, normalized unique numbers and TXT lines. These counts form the input side of the audit trail.
The pilot should include several known examples and several intentional exceptions. Confirm that the selected task produces the advertised columns and that the result can be matched back to the normalized source. Fix the transformation once; do not manually edit hundreds of output rows.
Import the Excel workbook into a staging table
Keep an untouched copy of the downloaded result. Load a working copy into a staging table, preserve all original headers and add derived business columns to the right. Never rename a source field in place if its definition needs to remain auditable.
Join the returned phone number—or the mapped WhatsApp number where the task provides it—using documented rules. A mapped number should not be silently substituted for the submitted number; keep both so analysts can see which key was used.
Use three QA equations
Simple ratios expose most processing errors:
- Input uniqueness rate = normalized unique numbers ÷ nonblank source phone rows.
- Return coverage = unique returned submitted numbers ÷ TXT lines.
- Join success rate = result rows matched to source IDs ÷ result rows.
Investigate a weak value rather than forcing all rows into a segment. Common causes include missing country codes, inconsistent trunk-prefix handling, duplicates, blank lines and spreadsheet conversion of long phone numbers.
Keep “not registered,” “not returned” and “invalid” separate
These states answer different questions. “Not registered” is a task result. “Not returned” describes a reconciliation condition. “Invalid” describes an input-quality problem. Combining them into a single “bad number” label destroys information and can cause the team to discard records for the wrong reason.
Create an exception sheet with the normalized phone number, source ID, issue type, transformation applied and analyst disposition. Reuse the issue taxonomy in future batches so quality trends can be compared.
Turn results into business segments carefully
Join the checked fields with internal customer context only after reconciliation passes. Registration status can support channel planning; activity fields can support cohort testing; profile-related fields may support a permitted analysis. None of them proves that outreach is wanted.
Apply suppression lists and consent rules after the join and before activation. Measure complaints, blocks, replies and conversions by acquisition source, not just by checking result. A technically clean list can still perform poorly if its source or message is inappropriate.
Troubleshoot the most common workflow failures
The Excel count is lower than the TXT count
Check blank lines, duplicate normalized values, rejected formats and whether the service represents exceptions separately.
Returned phone numbers do not match the CRM
Compare country-code and trunk-prefix transformations. Confirm whether the selected task returned a WhatsApp-mapped number in addition to the submitted number.
Analysts ordered the wrong task
Return to the decision statement and field matrix. Do not infer activity, age, gender or business status from a registration-only export.
Spreadsheet software changed phone numbers
Import the column as text. Scientific notation and stripped leading digits can break the join even when the original Excel cell looked acceptable.
The final handoff package
A completed WhatsApp number-checking job should leave six artifacts: the decision statement, a protected source extract, the normalized TXT, the untouched Excel output, a reconciled staging workbook and a short QA note. The operational team should receive only the approved segment and the definitions it needs.
That package turns a one-off upload into a repeatable process. AIPUSH provides the task-specific checking and Excel fields; the customer team remains responsible for source authorization, interpretation, permissions and downstream use. Keeping those responsibilities separate is what makes the WS/WA workflow scalable without turning it into a black box.
