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Roche Diagnostics automates CRM record deduplication

  • Deduplication
  • Automation
When we compared Plauti to other tools, the key advantage was granularity

What data quality challenge did Roche Diagnostics face before Plauti?

Every interaction with Roche Diagnostics' CRM could create its own record, so a single customer built up several records over time. That fragmented histories and produced duplicate communications, according to Ana Marquez Salazar, a global CRM manager on Roche Diagnostics' global CRM and Digital Sales team.

Merging duplicates had been a fully manual task handled by Global IT: one record at a time, or in small batches limited by how much data could be processed at once. Local business teams across different countries ran their own manual checks after every large data import. Marquez Salazar doesn't have exact figures for the time this cost, since other teams managed the work directly, but colleagues describe it as a significant, frustrating workload.

How do Roche's merge rules decide which record wins?

Roche's rules key off how each record was created and set a different winner for each combination. If both records were created manually, the merge keeps the most recent email, phone number, and consent status. If one record is digital and the other manual, the digital record wins, unless a field it needs is blank, in which case the manual record's value is kept. If both records are digital, Roche skips the merge entirely, so it never disrupts a customer logged into a portal or placing an order.

That level of field-by-field control wasn't available in the other tools Roche compared, according to Marquez Salazar.

What changed for admins and business teams?

Admins who manage the merge rules can now run bulk merges with a single action instead of reviewing records one at a time, freeing them for more strategic work. Business colleagues no longer send duplicate communications or update the wrong record, and customer care sees fewer issues caused by duplicate accounts.

Marquez Salazar also points to a compliance angle: when the same person exists as multiple, disconnected records, marketing activity gets misallocated, digital services become harder to deliver consistently, and data-compliance requirements go unmet. Merging those records into one closes that gap.

How did Roche get teams to adopt automated merging?

Roche runs pilots in selected countries first, using each one to refine merge rules and build a track record before expanding further. Local colleagues recognized as CRM experts are involved in testing from the start. When one of them says the rules work, other teams trust the process.

What concerns did teams raise about automating merges at scale?

The biggest worry was data loss: what happens to related records like assets or orders, and what if the wrong record gets picked as the master. Roche's rules always merge related data onto the surviving master record, so nothing tied to a customer disappears.

A second concern was losing control over the process. Roche addresses that by walking teams through the rules themselves, so it's clear the automation follows logic a person would apply manually, just applied faster and consistently.

Why did Roche trust automation over manual review?

Manual merging means a person reviews each pair of records and decides what to keep. Auto-merge applies the same rules automatically, which removes repetitive work and the mistakes that come with it. Once teams saw that consistent rules produced fewer errors than manual review, confidence followed, Marquez Salazar says.

Which merge rules made the biggest difference?

The rule with the most impact compares extra fields beyond the default winner logic, so the merge always keeps the most current consent and contact data rather than defaulting to whichever record wins overall. That distinction has mattered most for accuracy and for meeting data-compliance requirements.

Where does the rollout stand today?

Roche Diagnostics is live in three countries, with three to five more planned in the coming months. Marquez Salazar describes Plauti's support through the rollout as collaborative and expects more concrete numbers to share as it expands.

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Frequently asked questions

What was Roche Diagnostics' biggest data quality challenge before Plauti?

Repeated customer interactions created multiple records per person, fragmenting histories and often causing duplicate communications. Merging duplicates was a fully manual task handled by Global IT, done one record at a time or in small batches.

What makes Plauti's merge rules different from the other tools Roche evaluated?

Plauti lets teams set specific merge criteria per field, including comparison thresholds and rules for handling blank fields, then run those rules automatically at scale. The other tools Roche compared required every merge to be done manually, one by one.

How does Roche Diagnostics decide which record wins when merging manual and digital records?

If both records were created manually, the most recent contact and consent data wins. If one record is digital and the other manual, the digital record wins unless a needed field is blank. Two digital records are left unmerged to avoid disrupting a customer's active portal or order account.

How many countries is Roche Diagnostics running automated merging in today?

Roche is live in three countries, with three to five more planned in the coming months.

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