Bring CRM, campaign, email, advertising, analytics and spreadsheet data into a cleaner, consistent structure. Rudrriv helps you remove avoidable data friction, standardize fields and taxonomies, document rules, and prepare marketing data for targeting, reporting and ongoing operations.
Fixed-scope plans start at $49. Larger datasets, direct platform work and multi-system requirements are scoped separately.
Start with focused cleanup or build a stronger data operating foundation. Record limits refer to the primary working dataset and are confirmed during scope review.
For a small marketing list, CRM export or campaign dataset that needs a reliable cleanup pass and clear issue summary.
For growing teams that need cleanup plus a documented structure for fields, campaigns and reusable marketing operations.
For multi-source marketing operations that need deeper control design, standardization and a repeatable data-quality handoff.
Complex CRM changes, enrichment purchases, paid third-party validation, large-volume databases, custom integrations and ongoing managed operations require a separate scope.
For multiple business units, large databases, platform-to-platform mapping, direct CRM work, recurring hygiene or custom governance requirements, we can define a tailored Marketing Data Management scope.
The workflow separates discovery, transformation, review and handoff so important changes can be checked before they are treated as final.
Review sources, fields, record volumes, formats and known quality problems.
Define source-to-target fields, keys, naming and transformation requirements.
Normalize values, identify duplicates and handle agreed data-quality issues.
Apply field rules, campaign taxonomy, formats and documented conventions.
Run QA checks, create exception lists and stage material changes for review.
Deliver clean files, rules, issues and next-step recommendations for operations.
Marketing data becomes useful when the underlying records, identifiers, campaign structures and handoffs are consistent enough for teams and systems to rely on.
Identify where marketing data originates, how it is exported or transferred, and which sources feed CRM, reporting, email and campaign workflows.
Review duplicates, blank or malformed values, inconsistent formats, stale structures and avoidable issues that weaken segmentation or reporting.
Clarify the fields used to identify people, accounts, campaigns or transactions and document practical match and merge assumptions.
Standardize campaign names, UTM values, channel labels and other dimensions so acquisition and performance data can be grouped consistently.
Create source-to-target field maps, naming standards, transformation notes and data dictionaries for important marketing attributes.
Document validation rules, review points, exception handling and ownership recommendations that support more repeatable marketing data operations.
Instead of treating cleanup as an occasional spreadsheet exercise, the service can document reusable checks that show what should pass, what should be corrected and what needs business review.
Illustrative control categories used to structure review and handoff.
We can work from exports and agreed source files across common marketing data environments. Direct platform changes are scoped according to access and approval requirements.
Standardize and review customer or lead data before a new email, outbound or paid-media activation.
Replace inconsistent campaign and UTM values with a taxonomy that can be reused across teams and channels.
Map and standardize fields before combining CRM, advertising, email and analytics extracts for reporting.
Create checks, exception categories and ownership notes so recurring marketing data work is easier to control.
Inputs, rules, target fields and outputs are defined so the work stays understandable.
Potential merges, deletions or ambiguous fixes can be separated for approval rather than assumed.
Naming, field and QA conventions can be documented so teams can continue them after delivery.
Data is structured with downstream segmentation, campaign operations and reporting needs in mind.
One defined dataset, agreed rules and a clear handoff for an immediate hygiene need.
Cleanup plus mapping, campaign taxonomy and documentation for a stronger operating baseline.
Periodic hygiene, QA, list management and taxonomy support under a separately agreed cadence.
Broader source mapping, governance design or platform work for complex marketing environments.
It is the structured process of organizing, cleaning, standardizing, documenting and maintaining the data marketing teams use across CRM, campaigns, email, advertising, analytics and spreadsheets.
Typical inputs include lead and contact records, campaign and UTM data, CRM exports, email lists, advertising exports, web analytics extracts, event lists, spreadsheet trackers and reporting datasets.
Yes. Depending on scope, we can review duplicates, normalize formats, check field consistency, handle agreed null or exception rules and provide a cleanup summary.
Yes. Field mapping, source-to-target definitions, important field descriptions and naming conventions can be included in the relevant plan or custom scope.
Yes. We can define and apply campaign and UTM conventions so future source, medium, campaign and related dimensions are easier to use consistently.
Direct platform work can be considered after permissions, backups, change approval and access constraints are agreed. Export-based or staging workflows are also available.
We scope access to the information required for the agreed work and recommend approval gates for merges, deletions and material changes. Please do not send highly sensitive or unnecessary data in the first enquiry.
The standard listed-plan delivery window is 5–7 working days. Large volumes, multiple systems, integrations or complex approval requirements may need a separately confirmed timeline.
Depending on scope: cleaned datasets, field maps, duplicate or exception reports, campaign taxonomy, data dictionary items, QA checklists and handoff documentation.
Yes. Where the source supports it, we can standardize fields and campaign dimensions so downstream segmentation, analysis and dashboard work is more consistent.
Yes. Recurring hygiene, QA and taxonomy maintenance can be separately scoped after the initial data structure and operating requirements are understood.
Useful inputs include the sources in scope, approximate record counts, sample exports or field lists, target systems, known issues, required outputs and any internal access or approval constraints.
Share the high-level requirement only. We will review the scope, data volume, source complexity and desired output before confirming the package or a custom engagement.
Start with a focused cleanup, build a structured data foundation, or discuss a broader operating model for recurring marketing data quality.
