Enterprise CRM systems accumulate years of historically grown data, incomplete records, unvalidated contacts, missing fields, and inconsistent segmentation that nobody wants to touch. The result is that campaigns cannot be filtered properly, personalisation falls flat, and marketing automation fires on the wrong targets. Frenus works with Marketing Operations, Performance Marketing, and CMO teams to rebuild CRM data foundations from the outcome backwards, starting not from what is technically possible but from how the data will actually be used, and delivering a clean, enriched, campaign-ready CRM within weeks.
Enterprise CRM data does not become a problem overnight, it accumulates over years of system migrations, manual entries, incomplete imports, and organisational changes that nobody had time to clean up. The result is a data landscape that marketing teams work around rather than with: campaigns filtered on the fields that happen to be populated rather than the ones that matter, personalisation that defaults to generic because the required attributes are missing, and AI-driven initiatives that promise precision but deliver noise because the underlying data cannot support them. The painful reality is that most enterprise CRMs contain large volumes of records that cannot be validated, contacts whose relevance is unknown, and critical fields, job function, company size, technology stack, buying stage, that are empty across the majority of the database. No campaign strategy, marketing automation platform, or AI tool can compensate for a broken data foundation.
We approach CRM data quality from the outcome backwards, starting with how the marketing team needs to use the data, then identifying and filling only the fields that will actually drive campaign filtering, personalisation, and performance measurement. We audit the existing CRM state, define the target data model in close collaboration with Marketing Operations and the CMO team, and execute enrichment using AI-assisted research validated at the individual record level. Where records can be identified and enriched we do so completely. Where they cannot be validated we flag them clearly rather than leaving the team to discover gaps mid-campaign. The entire process is CRM-native, outputs integrate directly into Salesforce, HubSpot, or Microsoft Dynamics without additional processing.
Marketing teams gain a CRM they can actually use, with the fields populated, the segments filterable, and the data complete enough to support personalisation, automation, and AI-driven campaign execution. Campaigns that were previously launched on best-available data are now launched on purpose-built data. Personalisation that previously defaulted to generic now reaches the right person with the right message. AI initiatives that previously failed on data quality now have the foundation they require. And the CRM stops being the thing nobody wants to touch and starts being the commercial asset it was always supposed to be.
We begin with the end in mind, conducting structured sessions with Marketing Operations, Performance Marketing, and CMO stakeholders to define exactly how the CRM data will be used. Which campaigns need to run? Which filters need to work? Which personalisation fields are required? Which segments need to be identifiable? This outcome-first approach ensures we enrich only the data that will actually be used, avoiding the trap of comprehensive data projects that deliver everything except what the marketing team needs on Monday morning.
We conduct a structured audit of the existing CRM, mapping field completion rates, identifying validation gaps, flagging duplicate and unvalidatable records, and assessing the delta between the current data state and the target data model defined in Step I. The audit produces a clear picture of what exists, what is missing, what can be enriched, and what needs to be flagged for review. This is typically where the real scale of the problem becomes visible - and where the enrichment prioritisation decisions are made.
We execute data enrichment using AI-assisted research - combining automated data sourcing with human validation at the record level. AI accelerates the identification and matching of missing data fields across job function, seniority, company attributes, technology stack, and buying stage signals. Every enriched field is validated before it enters the CRM - because an 80/20 AI-only approach produces errors that corrupt campaign performance and personalisation logic in ways that are expensive to diagnose after the fact. Enrichment is scoped to the fields defined in the target data model, nothing more, nothing less.
Once enriched, records are tagged and segmented according to the campaign filter structure defined at kick-off. ICP scoring, persona tagging, buying stage classification, and regional or vertical segmentation are applied consistently across the database - giving the marketing team a CRM that is not just clean but immediately usable for campaign execution. Filter logic is validated against real campaign scenarios before handover to ensure the segments perform as expected.
All enriched and tagged data is delivered directly into the client's CRM - Salesforce, HubSpot, or Microsoft Dynamics - in the native field structure, without requiring internal processing or reformatting. We conduct a handover session with Marketing Operations to walk through the enrichment methodology, validate key segments, and ensure the team understands how to maintain data quality going forward. Where relevant, we recommend lightweight governance processes to prevent the historically grown data problem from recurring.
We’ll get back to you shortly.
We start with the outcome, not the data. Before touching a single record we define exactly how the marketing team needs to use the CRM, which campaigns, which filters, which personalisation fields. This tells us which data matters and which does not. The audit then maps the current state against that target. This approach means we never spend time enriching data that will never be used, which is the most common failure mode of CRM data quality projects.
We flag them clearly rather than leaving them in the database as apparently complete records that will fail mid-campaign. Depending on the client's preference, unvalidatable records are either quarantined, archived, or marked with a data confidence score - giving the marketing team full visibility of where the data is solid and where it is not. Transparency about data quality is more valuable than a superficially clean-looking CRM.
We work entirely within your existing CRM - Salesforce, HubSpot, or Microsoft Dynamics. We do not require system changes, migrations, or additional platform investments. The output is delivered directly into your existing field structure, ready for immediate use by your marketing and sales teams.
AI accelerates data enrichment significantly, and we use it throughout the process. But unvalidated AI enrichment produces a category of errors that are particularly damaging: records that look complete and correct but contain subtle inaccuracies that corrupt campaign segmentation and personalisation logic silently. An email sent to the wrong persona because AI misclassified a job title costs more than the time saved by skipping validation. We use AI at the speed it enables and human validation at the quality it ensures.
We’ll get back to you shortly.
How we solve real problems for real clients
We work with leaders who refuse to accept the status quo. Our teams bring deep industry knowledge and a relentless focus on results.
