Prepare contact CSV imports carefully

Start with a concrete task

Clean column headings and review a small sample before importing. Map fields deliberately and inspect validation results before confirming the batch.

Inspect the source file before mapping columns

A CSV import begins with the meaning of the data, not the upload button. Identify whether the file contains contacts or companies, what each row represents and which columns are intended to match existing fields. A spreadsheet that looks neat can still contain inconsistent information.

Review a small sample from the beginning, middle and end of the file. Look for unexpected blank rows, repeated headings and values that have shifted into the wrong columns. These issues are easier to correct before import than after they have become application records.

Keep an unchanged copy of the source file for your own review process. Work on a separate prepared copy so you can explain what was corrected. This is a practical recordkeeping suggestion, not a claim that the CRM automatically manages versions of your local files.

Make column meanings explicit

Column names should describe the value they contain. A field labeled contact might mean a person's name, an email address or a phone number depending on the source. Resolve that ambiguity before mapping it to a destination field.

Check how the file handles optional information. Empty values, text such as unknown and placeholder symbols do not all mean the same thing. Choose a consistent representation that matches the import requirements rather than allowing placeholders to become misleading customer details.

Pay attention to formatting that spreadsheet programs may alter. Phone numbers and other identifiers should retain their intended characters. Review the actual CSV output after export, because a visually correct workbook can produce a file that differs from what you expected.

Use a fictional contact-list example

Imagine a team receives a list with columns for person, organization and contact detail. Some rows use contact detail for email, while others use it for a phone number. Mapping the whole column to a single destination would misrepresent part of the list.

The useful preparation step is to separate the different kinds of information and review ambiguous rows. Do not guess an email address or company relationship simply to make every row pass validation. A missing value is preferable to an invented one.

If two rows share a name but have different organizations, investigate before treating them as duplicates. The example illustrates why import preparation requires understanding the records, not just adjusting a file until the upload accepts it.

Respect the batch limit and preview the result

The documented import limit is up to 200 contact or company rows per file. Divide a larger dataset into reviewed batches that stay within that boundary. Keep a simple record of which batch was imported so the same file is not accidentally submitted twice.

Use the mapping and validation preview to inspect the intended result before confirming. A successful file read does not establish that each column has been assigned correctly. Check representative rows and pay particular attention to values that look plausible even when mapped to the wrong field.

The workflow supports skipping duplicates and an atomic confirmed import. Understand what the preview identifies and what will be skipped. Do not assume that every original row will become a new record simply because the import finishes successfully.

Investigate validation errors rather than bypassing them

An error can reveal a malformed value, an unsupported field or a misunderstanding of the expected structure. Read the message and correct the underlying issue. Replacing problematic values with arbitrary placeholders may make the file easier to import while reducing the quality of the resulting records.

If several rows fail in the same way, inspect the source convention rather than editing each row independently. The cause may be an export setting or a column that mixes different types of information. Fixing the pattern is usually more reliable than repeating a manual patch.

Keep uncertain rows separate until the team can resolve them. An import does not need to include every available row in one attempt. A smaller, trustworthy batch is more useful than a larger collection containing guesses that colleagues may later treat as facts.

Review the imported records in their working context

After confirmation, inspect a sample of records in the CRM. Check names, company relationships and the details the team will use for contact. This confirms that the imported information is useful in the actual interface, not only that the file passed a technical check.

Compare the expected and actual batch outcome, including skipped rows. Investigate a discrepancy before starting the next batch. Repeated imports without understanding the first result can make reconciliation more difficult.

Finally, agree how the team will maintain the records after import. A clean initial dataset can become inconsistent if colleagues use different naming or note conventions. Import preparation and everyday recordkeeping should support the same understanding of what each field means.

What the project provides

Import up to 200 contact or company rows per file. Map columns, preview validation and skip duplicates before an atomic confirmed import.

Review the result before moving on

The documented import limit is 200 rows per file. Split larger datasets into reviewed batches and investigate skipped duplicates instead of assuming every row was added.

Access and setup

Use admin / admin to explore an isolated one-hour demo with fictional records. For persistent business data, create your own email/password account. Owners can generate seven-day invitation links for teammates with matching account emails. Email recovery and verification, online payment links and AI writing require dedicated providers to be configured later.

Common questions

How should I handle a row with uncertain information?

Keep it out of the confirmed batch until the uncertainty is resolved, or use an allowed empty value when that accurately represents what is known. Do not create a plausible phone number, company relationship or email address simply to satisfy a field. Record the question in your preparation process so the row can be reviewed later.

This is especially important when several source files use inconsistent conventions. A smaller trustworthy import is easier to maintain than a larger dataset containing assumptions that later colleagues treat as verified facts. Validation should support data quality, not become a game of making every row pass.

Where can I find the current project details?

Read the CoreBiz IT Services & CRM project page for its documented features, access notes and live application link.

Does this guide describe an automated workflow?

Only the capabilities explicitly described in the project section are product features. Planning and review suggestions may need to be performed manually.

Your next step

To explore a similar experience for your own audience, review our related service. Start the discussion with a concrete user task and the information needed to complete it.