CRM Manager LinkedIn Profile Optimisation
High-impact headline, data-led about section, and ATS-friendly skills focus.
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Target completion score for an All-Star profile
CRM Manager | Salesforce Marketing Cloud · SQL | Retention +15% · LTV +20% · Churn -8%
Lifecycle CRM & Marketing Automation | Segmentation (RFM) · Journey Optimisation · A/B Testing
CRM Manager | Consent-led personalisation (GDPR) | Cohorts & Incrementality
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I’m a CRM Manager with 5 years’ experience driving retention and revenue outcomes in e-commerce at ~£20M annual turnover. I manage a 500K+ customer database and build lifecycle journeys in Salesforce Marketing Cloud to increase engagement, reduce churn, and lift LTV. In recent programmes I delivered retention up +15%, LTV up +20%, and churn down -8% by tightening segmentation, message timing, and channel mix. I also use SQL to audit audience quality, diagnose deliverability patterns, and support incrementality testing across campaigns and cohorts.
My day-to-day work combines CRM strategy with hands-on execution: RFM segmentation, cohort analysis, and data hygiene to keep targeting reliable. I design and optimise around 50+ automation journeys, including win-back flows, browse-abandon recovery, and post-purchase reactivation. I test systematically using A/B and holdout frameworks to validate learning, improve conversion, and protect margin. I’m also strict about consent and governance, ensuring marketing automation adheres to GDPR and preference-centre principles before activating personalisation.
If you want a CRM profile that reads like a measurable case study, let’s connect. I’m particularly interested in roles using modern tooling such as Salesforce Marketing Cloud, Braze, or HubSpot alongside analytics workflows and SQL-driven insights. I bring a KPI-first mindset, clear documentation habits, and a practical approach to marketing technology and customer data. I’m ready to help teams scale lifecycle performance while keeping data compliance at the centre.
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Salesforce Marketing Cloud (journey builder, automation, audience management)
Marketing Automation & Lifecycle CRM
SQL (audience queries, data validation, KPI reporting)
RFM Segmentation & Propensity Targeting
Retention Strategy (churn reduction) & LTV Optimisation
Email & Multi-Channel Campaign Optimisation
A/B Testing, Experiment Design & Holdout Testing
Cohort Analysis & Funnel Diagnostics
GDPR, Consent Management & Preference Centre Best Practice
HubSpot or Braze (CRM execution and lifecycle orchestration)
Deliverability & List Hygiene
Copy and paste directly into your LinkedIn profile
Advanced Optimisations
Replace generic claims with measurable outcomes such as “Retention +15%, LTV +20%, churn -8%” and tie them to the system you used (e.g., Salesforce Marketing Cloud).
Include tools and KPIs in a readable order (e.g., Salesforce Marketing Cloud, SQL, RFM) to pass ATS screening without repeating the same phrase. Add one line that signals scope, like “500K database” or “e-commerce lifecycle”.
In your About section, explicitly reference SQL for audience validation and cohort analysis for measurement, then connect it to journey performance outcomes from automation programmes.
Lifecycle execution with measurable uplift (not activity)
I build lifecycle CRM programmes where every automation has a defined objective, KPI, and measurement plan. For example, I use Salesforce Marketing Cloud journey logic to map behaviours to timed actions, then track impact on retention and reactivation using cohort views. Rather than reporting only open and click rates, I anchor decisions to business KPIs such as churn rate, LTV, and incremental conversion. This approach helps teams prioritise the journeys that produce sustainable revenue, not just engagement spikes.
Segmentation that survives real-world data (RFM + hygiene)
Strong CRM segmentation depends on reliable customer attributes and behavioural history, so I focus on data quality before scaling targeting. I apply RFM segmentation and validate audience membership using SQL queries to remove duplicates, detect inactivity windows, and correct edge cases from event tracking. In e-commerce environments, I also monitor dataset freshness to ensure journeys respond to current buying patterns and not stale signals. The result is fewer wasted sends, more accurate targeting, and cleaner measurement when you compare cohorts across experiments.
Experimentation for incrementality (A/B with holdouts)
To improve performance responsibly, I run experimentation that balances speed with statistical confidence. I use A/B testing frameworks and holdout groups to evaluate whether changes improve KPIs like purchase frequency or LTV, rather than merely shifting behaviour temporarily. In practice, this means setting clear hypotheses, defining success metrics, and documenting results so the winning approach can be rolled into templates and journey versions. By treating testing as a repeatable system, teams can steadily increase retention and reduce churn through evidence-based iteration.
Consent-led personalisation and governance (GDPR by design)
Personalisation only works long-term when consent, preferences, and data handling are designed from the start. I ensure marketing automation respects GDPR requirements, including appropriate consent states and preference-centre controls before triggering journeys. Where available, I implement suppression logic for non-consenting audiences and maintain audit-ready records of consent changes over time. This governance reduces compliance risk while improving customer trust, which supports stronger long-term engagement outcomes.
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