Excel Fuzzy Lookup Add-in Alternative for Windows and Mac

For many years, you reached for the Microsoft Fuzzy Lookup Add-in for Excel to match data. It allowed users to find "fuzzy" matches between datasets, even when text was not identical.

However, as data has become more complex and workflows have moved to the cloud, the Excel add-in has begun to show its age.
It is limited to the Windows version of Excel, can be slow with large datasets and lacks the advanced features required for modern data cleaning.

Enter Flookup Data Wrangler, which matches inside Google Sheets on any platform, so the workbook is imported once and the review sheet lives next to the results.

Why Seek an Alternative to the Excel Fuzzy Lookup Add-in?

While the Excel add-in was a pioneer, it comes with several significant drawbacks that can hinder your productivity:

• Platform Restrictions: It only works on the Windows version of Microsoft Excel. Mac users and those using Excel Online are left without a solution.
• Performance Issues: It can be slow and resource-intensive with large datasets, often leading to Excel freezing or crashing.
• Complex User Interface: The interface is somewhat dated and can be difficult to navigate for new users.
• Lack of Automation: It is a manual tool. There is no easy way to automate your matching workflows or integrate them with other applications.
• Limited Matching Algorithms: It primarily uses a single matching method, which may not be suitable for all types of data.

The platform restriction is the one that affects Mac users most directly. Our Fuzzy Matching in Excel for Mac covers the install problem on macOS, and our overview of data cleaning on Mac sets out what Excel for Mac and Power Query can and cannot do.

Fuzzy Match in Excel Without the Add-in

If you cannot install the Fuzzy Lookup add-in or you simply want a formula-based approach, you can approximate fuzzy matching in Excel with built-in functions.
These methods are useful for predictable variations, though they struggle with true similarity scoring.

• Normalise first: wrap your lookup and reference columns in LOWER(TRIM()) and use SUBSTITUTE or CLEAN to strip punctuation and stray characters, so both sides share the same base text.
• Token-based matching: split values into words and use COUNTIF with wildcards to check that the key word, such as the surname or the street name, appears in both cells.
• Levenshtein-style logic with VBA: a small User-Defined Function can implement edit distance and return a similarity score for each pair. This is the closest formula-level equivalent to the add-in, but it requires writing and maintaining VBA code.
• Power Query fuzzy merge: Excel's Power Query offers a built-in fuzzy matching option when merging two queries, which handles many approximate-match cases without VBA. See our Power Query vs Flookup comparison for how it stacks up as a full data cleaning workflow.

Each of these approaches can work for a narrow, well-understood problem.
The trade-off is maintenance. When your data changes, your formulas and VBA need to change with it and none of these methods deduplicate records or profile your columns.

For a one-off match, any of them is fine. For a recurring workflow, a dedicated tool removes the maintenance burden, which is where Flookup comes in.

Introducing Flookup Data Wrangler

Flookup Data Wrangler was designed from the ground up to address these limitations and provide a superior data matching experience.

What Sets Flookup Apart Beyond the Table

Five capabilities set Flookup apart from the Excel add-in in practice.

• Semantic versus lexical: Jaccard finds overlapping words, while Flookup semantic models understand that car and automobile mean the same thing
• Multi-column composite keys: match on name and company together, so Jen Walsh at Acme Corp is compared with Jennifer Walsh at Acme Corporation as one record
• Confidence badges and threshold slider: every match returns a percentage and badge from 0.75 to 1.0 for quick review
• One-click Column Profiling: scan any column for distinct values and duplicate hints before you match
• Learn from Examples versus code: teach a transformation with two pairs instead of GREL or M and schedule it

The two scores below are the numbers each method writes beside a pair. Drag the threshold to see how many true pairs each one keeps.

The most important difference is the core technology. The Excel add-in uses a lexical method like the Jaccard index, which compares the simple overlap of words. That is why a pair like "Pepsi Co" and "Pepsico" scores zero in the table: the two values share no whole words at all, even though a reader can see they are the same brand.

Flookup, however, uses semantic matching. It understands that "car" and "automobile" are synonyms and that the two sentences have the same meaning.
This allows it to find much more intelligent and accurate matches, which is why the same pair is scored rather than dismissed.

The algorithms comparison covers the other methods, including where Jaccard and semantic matching each fit.

A Smarter Workflow with Flookup

Unlike the Excel add-in's rigid, pop-up-based workflow, Flookup integrates directly into your spreadsheet with powerful formulas. This allows for a more flexible and repeatable process.

Consider reconciling a list of new customer names in Column A against a master customer list in another sheet e.g. "MasterList!A:B".

1. Clean Your Data: Use the Text Standardisation function to clean the new customer names. This removes punctuation and standardises text to improve match accuracy.
2. Perform the Fuzzy Lookup: Use the Fuzzy Match function. Select your cleaned data as the source, specify your master list as the lookup range and set the similarity threshold to 80 per cent.
3. Apply to All Rows: Process your entire list in one pass, providing consistent and accurate results across your dataset.

Introduction to Semantic Matching

Semantic matching is one of Flookup's powerful capabilities. Instead of just comparing spelling, this feature analyses the actual meaning and context of your text.

This allows you to find matches between phrases that are conceptually similar, even if they use completely different words.

For example, a traditional fuzzy match would see no similarity between these two phrases:

• "I absolutely love very large dogs" Vs "I truly adore very large canines"

Because they share no keywords, other tools would miss the connection.
Flookup, however, understands the semantic meaning and can identify them as a very strong match. This is made possible through two key features:

• Compare Text by Semantic Similarity: This allows you to compare two columns of text and get a similarity score based on their meaning.
• Fuzzy Match by Semantic Similarity: This allows you to look up a value in another table and find the best match based on meaning, not just keywords.

This technology allows you to uncover deep relationships in your data that are impossible to find with older tools.

Choosing a Modern Data Matching Workflow

The Microsoft Fuzzy Lookup Add-in for Excel was a valuable tool, but for modern data challenges, a more robust solution is required.

Flookup Data Wrangler offers a superior alternative by combining advanced, meaning-based matching and phonetic matching with the flexibility of a Google Sheets add-on that runs on Mac and Windows.

If you are ready to move beyond the limitations of the old desktop add-in and embrace a faster, smarter workflow, get started with Flookup today.

Fuzzy Lookup for Excel vs Flookup Data Wrangler

Microsoft Fuzzy Lookup is Windows-only, needs a local install and scores token overlap only. Flookup Data Wrangler runs as a Google Sheets add-on in the browser on Mac and Windows. The add-on combines Levenshtein, semantic and phonetic matching with an adjustable threshold and writes a review sheet with match score and confidence. Import the Excel range into Sheets, then review borderline pairs before merging. Get started in Sheets or see the one-click templates.

Frequently Asked Questions