Overcoming Platform Limitations in Data Cleaning

Struggles with fuzzy matching inside spreadsheets can often be platform-dependent. For example, Microsoft Excel for Mac lacks the built-in “Fuzzy Merge” option available on Windows, leaving users searching for a viable Excel fuzzy lookup alternative.

Similarly, professionals in fields like library science and archival management rely on tools like OpenRefine for critical normalisation but often need similar power within a spreadsheet environment.

For teams facing these challenges, whether in CRM management or metadata-heavy fields, this case study shows how a sheet-based SaaS tool like Flookup Data Wrangler bridges the gap.

It delivers fuzzy matching, similarity scoring and workflow automation inside Google Sheets, which is compatible across all platforms.

CASE STUDY: CRM DUPLICATE CLEAN-UP BY ACME CORP

The Organisation and the Challenge

ACME Corp is a mid-sized tech company whose sales and marketing teams use Google Sheets for their export pipelines, running on macOS. Their CRM was full of inconsistent account and contact records, such as “Acme Tech”, “Acme Technologies Ltd.” and “Acme Corp”.

These duplicate leads and variant names caused major inefficiencies. Sales teams reached out to the same account under different names and marketing sent repetitive messages. To make matters worse, reports were inflated by these duplicate accounts.

Because the team used macOS, they lacked native fuzzy lookup support in Excel or Power Query and were forced into manual methods or relying on Windows users for advanced matching tasks.

Why Standard Tools Failed

Standard duplicate detection and exact matches could not handle the misspellings, punctuation variants or near-duplicates in ACME's data.

The absence of a fuzzy merge function in Excel for Mac is a well-known limitation for data deduplication on that platform.

This mirrors challenges in metadata management, where variant spellings and abbreviations hamper reliable merging—a problem often addressed with tools like OpenRefine for its clustering features.

Why Flookup Data Wrangler Was Chosen

• Platform Independent:
  It works on Google Sheets, so users are not constrained by Windows-only add-ins.
• Powerful Matching:
  It supports advanced fuzzy matching, similarity thresholds, phonetic modes and custom functions for approximate string matching.
• Accessible Workflow:
  It provides a simpler alternative to tools such as OpenRefine for users who need fuzzy match workflows inside a familiar sheet environment.
• Integrated Automation:
  It offers scheduled automation inside Sheets, making it ideal for maintaining CRM data hygiene.

STEP 1: EXPORT AND NORMALISE

The ACME Ops team exported two Google Sheets:

• Old_Accounts_Master > Account Name | Account ID | Industry | Region
• New_Leads_Import > Lead Company | Lead ID | Source | Date

Using Flookup functions, they performed data normalisation on company names by removing punctuation, stripping common endings (“Inc”, “LLC”) and standardising case. For example:

=NORMALIZE(A2, {"Inc","LLC"}, , "text")

STEP 2: FUZZY-MATCH NEW LEADS TO EXISTING ACCOUNTS

In New_Leads_Import they added a column “Matched_AccountID” with formula:

=FLOOKUP(B2, Old_Accounts_Master!A2:D37000, 1, 2, 0.85, "score")

This formula matches a new lead company against the master account list and returns the corresponding Account ID if the similarity score is 85% or higher.

Matches scoring ≥0.90 were automatically linked. Those between 0.80 and 0.90 were flagged for manual review. Any match below 0.80 was treated as a new account.

STEP 3: DEDUPLICATE THE MASTER ACCOUNT LIST

In Old_Accounts_Master the team used the menu path: Extensions > Flookup Data Wrangler > Remove Duplicates > By percentage, threshold set at 0.88, phonetic matching enabled for non-US names. The list shrank from 37,000 to 34,800.

STEP 4: SET UP AUTOMATED DATA CLEANING

To automate the process, they set up a scheduled daily job to fuzzy match new leads using the menu path: Extensions > Flookup Data Wrangler > Schedule data cleaning functions. This ensured each new import on Google Sheets was cleansed automatically without manual workarounds.

BENEFITS REALISED

• Reduced Duplicate Outreach: 4,300 new leads were matched to existing accounts (score ≥0.90).
• Lowered Risk of False Merges: 1,100 leads were flagged for manual review.
• Identified New Opportunities: 500 leads were confirmed as genuinely new accounts.
• Cleaner Master Data:
  The master list was cleaned, resulting in fewer duplicate contacts and better marketing targeting.
• Improved Reporting Accuracy:
  Reports now reflect true account counts with consolidated duplicates.
• Cross-Platform Workflow:
  The entire process was completed on Mac/Google Sheets without needing Windows-only software.
• Versatile Features:
  The tool provided metadata-cleaning features helpful for cataloguing tasks outside of CRM.

KEY INSIGHTS FOR CROSS-PLATFORM AND METADATA-HEAVY WORKFLOWS

• Platform Independence is Key: Many powerful data tools, like Excel's Power Query fuzzy merge, are Windows-only. A cloud-based solution in Google Sheets provides a consistent, powerful alternative for all users, including those on macOS.
• Bridging Tool Gaps is Powerful: For those in metadata-intensive fields like library science, OpenRefine is a standard. Flookup offers a complementary tool, bringing powerful fuzzy matching and clustering directly into a familiar spreadsheet environment.
• The Human Element Matters: Approximate matching is not a perfect science and can produce false positives. Success depends on tuning similarity thresholds, implementing a manual review process for ambiguous matches and establishing clear data governance rules.

CHECKLIST FOR CRM AND METADATA CLEANING WORKFLOWS

1. Export your dataset into Google Sheets, e.g. Accounts/Leads or metadata records.
2. Use normalisation functions to standardise names and remove stop-words.
3. Run fuzzy matching (via Flookup) with a similarity threshold appropriate to your data, e.g. 0.85 to 0.90.
4. Filter automatic matches vs. manual review zone vs. new entries.
5. Deduplicate master lists using similarity/phonetic modes.
6. Set up scheduled automation to maintain data hygiene.
7. Monitor metrics: duplicate rate, match scores, manual review volume, new account/record rate.
8. Document your rules: thresholds, “master record” logic, review process, audit trail.

CONCLUSION

For any team struggling with platform-specific tool limitations or managing messy metadata, Flookup Data Wrangler offers a robust solution within Google Sheets.

It serves as a powerful, cross-platform alternative to Windows-only fuzzy tools and a user-friendly complement to specialised software such as OpenRefine.

By combining fuzzy matching, similarity thresholds, phonetic processing and scheduling, Flookup addresses major data quality gaps.

As demonstrated by ACME Corp, applying these steps leads to cleaner CRM data and better reporting, all without needing to switch operating systems or adopt complex new software.