LinkedIn Connection List Comparison: Prepare CSV Snapshots Correctly

Open the Follower & Connection List Checker, choose LinkedIn, and use snapshot mode. Put the earlier connection list in source A and the later list in source B. LinkedIn has no followers versus following mode in this checker. You prepare the CSV and select its URL column; the tool compares the resulting identifiers locally without logging into LinkedIn or fetching any profiles.

Define one professional network and one consistent list scope

A connection snapshot and a list of people you follow are not interchangeable. Decide that both inputs will describe the same account’s first degree connections, or another clearly defined subset that you can reproduce. Do not compare one person’s full network with another person’s event contacts and describe the difference as network growth. Each input needs the same membership rule before its date becomes meaningful.

The three result categories describe the supplied lists: Absent from later list, Added to later list, and Present in both. The first category is not a verified record of who removed a connection. The checker observes identifiers, not account actions or their causes. It cannot tell whether a missing entry reflects a relationship change, an account change, an altered URL, or incomplete preparation of the later source.

Suppose an operations consultant wants to review her network around a procurement workshop. Her main comparison should use the full connection snapshot before the workshop and the full snapshot afterward. An attendee list is a separate reference, not a replacement for either connection snapshot. Attendance does not establish connection status, and a new connection during that period need not have come from the workshop.

Write a scope note beside the two source files: account owner, connection category, any filters, and collection labels. If a filter cannot be recreated, compare the unfiltered sources instead and make the narrower review afterward. This avoids turning a change in your spreadsheet selection into an apparent change in the professional network. Consistent scope is an input requirement, not something a result chart can repair.

Use LinkedIn’s native export as a source, with realistic expectations

LinkedIn offers a native connection export through Settings & Privacy and Data privacy. Its help documentation describes requesting account data and downloading it from the notification sent to your primary email address. Look for the connection data in the available download options or larger archive. This is a way to obtain your own source material, not a task performed by the free checker on your behalf.

LinkedIn describes connection data as including names, public profile URLs, company, position, connection date, and email addresses when the connection has authorized their inclusion. Missing emails are therefore an expected possibility. The export is not a way to reveal hidden addresses, and neither this checker nor a different CSV selection can recover information that LinkedIn did not include. The profile URL column is the relevant field for this comparison.

Extract a downloaded archive on your computer and locate its connection CSV. Keep the original file unchanged as a reference. The checker is not a complete LinkedIn archive extractor and does not import a ZIP or an HTML profile page. Prepare the relevant table before loading it. Files for invitations, imported address book contacts, messages, or followed members answer other questions and should not be substituted for the connection list.

Record when you requested and received the data, without assuming the delivery time is the exact time every connection was observed. A filename can identify a collection date but cannot establish a removal timestamp. If you have only one export, use it as your baseline and collect another later. Two copies of the same download can check your preparation process, but they do not create a historical comparison.

Turn the connection CSV into a clean table before importing

Open a working copy in a spreadsheet or CSV aware editor. Find the real header row, where fields such as First Name, Last Name, URL, Company, Position, and Connected On identify the columns. If explanatory lines or blank rows precede that header, remove them from the working copy. The prepared table should start with one header row, followed by connection records. Keep the original available to check anything you change.

Check a few rows in the URL column before exporting the working copy. The values should be actual personal profile addresses with an /in/ path, not company pages, post addresses, email addresses, or visible link labels. A spreadsheet cell displaying a person’s name may contain a hyperlink underneath; exporting that cell as CSV may preserve only the name. Inspect the resulting CSV text rather than assuming the hidden address survived.

Save the table as CSV with its header intact. Keep quoted fields intact when a company name or job title contains a comma. For example, a position described as Director, Supply Operations belongs in one field, not two. Use the spreadsheet’s CSV export or a structured CSV editor instead of splitting each line at every comma. A malformed neighboring column can shift the profile address away from the header you intend to select.

You can also prepare a single column named URL containing only the profile addresses. This reduces distractions when the other fields are unnecessary for matching. Export the complete intended scope, not just the rows currently selected or visible under a spreadsheet filter. After saving, inspect the first and last records and compare the row total with your scope note. Preparation should make the source easier to understand without silently changing its population.

Select profile URLs and keep descriptive fields out of matching

Load the prepared earlier CSV into A and the prepared later CSV into B, then explicitly choose the URL identity column for each source. Do not assume the column position is identical across files. One working copy may have additional notes or a different field order. The column’s meaning matters more than its letter in the spreadsheet. Confirm that the selected candidates look like personal profile addresses before interpreting any unique counts.

An illustrative address is https://www.linkedin.com/in/ada-lane-example/. The checker uses a canonical LinkedIn /in/ profile URL for matching, not the person’s first and last name. Two connections can share a name; one connection can change their name. Combining name and employer does not create a stable profile identifier either, because employment and the spelling of organization names can change independently of connection membership.

Email addresses are also unsuitable keys here. They can be missing from exports, change over time, or differ between professional and personal contexts. Do not discard every record with a blank email cell: it may still contain a perfectly usable profile URL. The checker does not fuzzy match names or substitute an email when a URL is absent. Keep rows without usable profile identifiers in a separate preparation review rather than inventing a match.

Profile URLs are the supported comparison key, not an immutable person registry. LinkedIn allows custom public profile URLs to change. A renamed path can appear as an old URL absent and a new URL added, even if the person remains a connection. Normalization cannot resolve that alias history. If you independently verify a changed URL, record it as a review finding while preserving what the two original snapshots actually contained.

Reconcile the workshop example from rows to distinct profiles

Return to the operations consultant. Her illustrative earlier CSV, labeled April 6, supplies 438 candidate records. After preparation and parsing, it contains 420 distinct valid profile URLs, 12 duplicate occurrences, and six invalid candidates. Her later CSV, labeled May 4, supplies 474 candidates: 455 distinct valid URLs, 11 duplicate occurrences, and eight invalid candidates. The invalid examples are nonprofile values accidentally left in the selected column, not connections lacking email addresses.

The source arithmetic is 420 plus 12 plus six equals 438, and 455 plus 11 plus eight equals 474. Duplicate occurrences are extra copies, not extra people. If one profile appears three times, it contributes one unique URL and two duplicate occurrences. Resolve invalid entries where possible before accepting the comparison. Otherwise, the results describe the validated URL subsets, with the unresolved candidates remaining a visible qualification.

Assume the validated sources share 397 profile URLs. There are 23 earlier only URLs, because 420 minus 397 equals 23. There are 58 later only URLs, because 455 minus 397 equals 58. Across both snapshots, the union contains 478 distinct URLs: 397 plus 23 plus 58. The later source increased by 35 valid unique URLs, also equal to 58 additions minus 23 absences.

The consultant can report a larger validated connection list with 58 added URLs and 23 absent URLs. She cannot report 58 workshop conversions or 23 confirmed disconnections from these files alone. The event occurred during the comparison period, but timing is not attribution. Preserve the two source totals and cleanup counts alongside the buckets so another reviewer can reproduce the arithmetic and understand exactly which records entered the comparison.

Investigate apparent changes before assigning a business meaning

Start a surprising absence review with the source, not the person’s intentions. Was the later CSV exported with an active filter? Did you select Position rather than URL? Did a worksheet include only the workshop attendees? Were several files from different collections combined? These checks concern how your list was made. Correcting a preparation error is more informative than writing an explanation for a connection change that the inputs never established.

Consider a fictional connection whose saved path changes from /in/ada-lane-operations to /in/ada-lane-example. If both snapshots use those different addresses, exact URL matching places one path in each difference bucket. The checker does not visit either address or follow redirects. A similar name may suggest something worth checking separately, but it is not enough to merge the records automatically or overwrite the original evidence.

Distinguish a descriptive change from a matching change. A new employer, revised title, or newly available email does not affect this comparison when the selected profile URL remains the same. Conversely, an unchanged name cannot rescue an altered or missing URL. This is a practical reason to keep the original descriptive columns in a reference copy, even when your imported working file contains only the profile URL column.

For the workshop review, keep a separate notes table for any independently checked aliases or event context. Do not silently replace the tool’s 23 absences with a smaller business category and present it as the original result. Explain which entries received further review and what evidence supported it. A third snapshot can show later membership, but it still cannot reconstruct every intermediate connection action or establish precisely when a change occurred.

Choose a report format that preserves the evidence you need

The checker displays the three disjoint buckets with source unique counts and a chart. Select a bucket, search it, and move through pages of 50 rows for inspection. The full counts remain available even when only one page is visible. A search result is a way to locate an identifier inside the comparison, not a new estimate of total network size. Keep this distinction clear when describing a small reviewed sample.

Use the CSV download when your next task requires the full categorized identifier results. Use copy when you need the selected list for a focused working note. The PDF is an aggregate report with the chart, exact input and cleanup counts, optional dates, context, and practical next steps. It excludes raw profile identifiers. A colleague cannot reconstruct the individual absent profiles from that PDF alone; provide the CSV when individual review is required.

In the consultant’s handoff, the PDF explains why the validated sources differ by 35 URLs. The CSV supports reviewing the 23 absent and 58 added entries. Neither export adds names, retrieves missing email addresses, confirms current profiles, or joins the list to a customer database. If a business report needs customer status or workshop attendance, those classifications require separate evidence and should be labeled as additional analysis.

Input changes and rejected imports clear the previous result’s export availability. Recompare the corrected files before issuing an updated handoff. Do not pair yesterday’s PDF with today’s revised CSV. Give both files the same account and observation context, and retain the untouched source exports. Results appear as text rather than external profile links, so clicking through to verify a live connection is not part of the checker’s output workflow.

Maintain a connection baseline without confusing it with outreach

For the next review, repeat the preparation steps in the same order: locate connection data, remove any preamble, confirm the header, inspect URL values, export the intended scope, and select the URL column. Keep the earlier and later observations separate. This short preparation record makes a quarterly comparison easier to reproduce, especially when a colleague takes over and does not know which spreadsheet filters were used last time.

Each file must stay within 5 MB, with at most 10 MB and 100,000 candidate rows per source. If you split a prepared table into files, preserve the same column meaning and keep every relevant part in its correct source. A blank input is not evidence of an empty network. Mark intentionally empty input only when that is genuinely the source you mean to compare, not when a CSV import failed.

Turn the result into a bounded followup question. The consultant might separately review which newly added profiles were workshop participants, or use the Social Media Profile Audit to organize her own profile improvements. Commercial evaluation belongs in a separate Social Media ROI Calculator exercise using actual cost and outcome inputs. A connection URL is not automatically a lead, appointment, sale, or proof of interest.

For ongoing account work, SMTasker’s LinkedIn workflows support connection requests, Like, Comment, and Publish. Those capabilities are separate from the free list checker. The comparison does not connect to SMTasker, automatically import its CSV, send requests, or take bulk actions. Use the snapshot review to understand your records; decide any later outreach or publishing task on its own purpose and evidence.

Questions about LinkedIn connection snapshots

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