A prospect CSV can open successfully and still be unsuitable for the job you bought it for. The columns may be consistent while the companies miss your brief. The companies may fit while the role information is too weak to support a useful conversation. These are different problems, and the sample should help you distinguish them.
Before paying for a larger list, inspect a small actual sample and write down what it establishes. A screenshot of a polished table is not the same evidence as the file you will need to use.
Decide what the file must support
Start with the next action you genuinely intend to take. A consultant reviewing professional profiles needs a different file from a team carrying out permissioned email communication. A LinkedIn URL may be central to the first task and insufficient for the second.
Fictional example: a consulting firm called Alder Advisory is investigating a project-management offer for specialist agencies. It asks a supplier for ten sample records so it can check company fit and current role relevance. Ten is an illustrative batch size, not a statistically representative sample or an industry standard. Alder has not bought anything, and the example reports no commercial results.
Alder’s minimum usable record is a profile reference, enough role information to inspect the hypothesis and a clear way to relate the person to the candidate business. It is not commissioning verified email addresses. If a different job requires verified email, the supplier must explicitly describe that separate field, method and scope; Alder cannot infer it from a name or headline.
Open the file without changing the evidence
Keep the original download intact and work on a copy. Record the delivery date, filename and the supplier’s description of the sample. This gives you something concrete to compare if you later discuss missing columns or changed values.
Import the copy into your spreadsheet application and check the separator before judging the contents. Google Sheets’ official import instructions describe importing a file and choosing a separator. A row appearing in one cell may be an import setting, rather than evidence that the researcher delivered one giant field.
CSV quoting also matters. RFC 4180 describes a common format in which commas, line breaks or quotes inside a field require quoting, with an internal double quote represented by two double quotes. The RFC is informational; implementations can vary. Inspect parsed cells before assuming that a comma in a job title represents an extra column.
You do not need to become a file-format specialist. Check that the header matches the cells beneath it, records stay on their intended rows and links remain usable. If the import is unclear, ask for the expected delimiter and encoding before manually repairing the sample and masking the original issue.
Check fields, fit and meaning separately
Use three passes. First, inspect the structure: expected columns, readable characters, row alignment and repeated identifiers. Second, inspect coverage: which required cells are present or blank? Third, inspect relevance: does the business and observed role meet the written brief?
The published Leadsforlinked header-only sample provides an example of the first pass. Its eleven actual columns are:
full_name, first_name, last_name, linkedin_url, headline, company, position, location, avatar_url, created_at, civility.
It contains no personal rows and therefore cannot demonstrate field coverage, freshness, company fit or deliverability. A header called company does not promise a populated company cell in every exported record. There is no email or phone column in this header sample, and “profile export” does not mean “verified contact address”.
For an actual sample, a review table could distinguish these observations:
| Observation | What it means for the review |
|---|---|
| Blank company cell | A coverage gap to assess against this task; not automatically proof of a bad profile. |
| Repeated profile URL | Inspect whether it is a duplicate rather than counting it as another distinct person. |
| A current role differs from the brief | A relevance issue to discuss using the source, not a formatting defect. |
| Clear headline but uncertain purchasing role | The buying hypothesis remains unproven. |
| Company matches an exclusion | Reject against the agreed criterion even if every cell is complete. |
Check the source references manually for the sample you are reviewing. Record when you checked them. A plausible title is not proof of current employment, and a recent export date is not necessarily the date every underlying profile detail changed.
Make the sample decision explicit
Return to Alder’s fictional ten rows. Suppose two repeat a profile URL, one business is outside the agreed segment and two records lack information Alder needs to review the role. These are invented observations for this example, not claims about any supplier or product.
Alder should not describe the result as “five bad leads” without explanation. A duplicate, an excluded business and an unresolved role have different remedies. Ask whether deduplication happens before delivery, whether the exclusion was in the brief and whether the missing information can actually be obtained.
Write a decision such as: “Pause the larger order until we agree how unique profiles are counted and review a corrected pilot against the stated exclusions.” Alternatively, the sample may support a limited research task even with some blanks. Make the required next action determine the decision, rather than using a universal completeness score.
A small sample can reveal obvious misunderstandings. It does not prove that a much larger file will have the same mix of records, and it cannot predict replies or sales. Ask how the larger delivery is produced and checked rather than treating the sample as a forecast.
Ask for terms before more volume
Agree what counts as one record, the required fields, how unknowns appear, the scope of duplicate checks and what happens when a row conflicts with an explicit criterion. Distinguish correction of a delivery error from discovering that your own targeting hypothesis was weak.
If you want to create your own small profile sample, start with a focused LinkedIn People search, collect selected profiles through the Leadsforlinked Chrome extension, review the audience in the app and export the CSV. The first paid decision should follow your inspection of actual records and the current offer terms. A larger number of rows is useful only when those rows support work you can explain.