How a Fortune 500 manufacturer unified buying signals for 11 million customers and lifted win rates by 40%.
This client operated one of the world's largest field sales organisations — thousands of representatives selling across 1.6 million SKUs to over 11 million customers in 150+ countries. Their data lived in multiple regional CRM instances that could not talk to each other.
Sales leaders had no systematic way to identify which customers were ready to buy, which deals were at risk, or which reps needed coaching. Cross-sell opportunities across product lines went undetected. Renewal patterns were invisible. Each expensive CRM migration promised globalisation but delivered only incremental improvement.
The Chief Commercial Officer commissioned a new direction: a data analytics and intelligence platform that would make further migrations unnecessary — delivering global commercial insight without touching the underlying CRM architecture.
Fragmented data meant every insight required manual extraction. There was no live view of pipeline health, customer risk, or cross-sell opportunity — at any scale.
Millions of cross-sell opportunities undetected annually. Win rates below potential. New rep ramp taking 12+ months. Forecast accuracy insufficient for leadership confidence.
We consolidated sales transaction history, CRM engagement data, contract records, and firmographic attributes from across global regions into a cloud data warehouse on Snowflake — creating the single analytical foundation the organisation had never had.
We trained gradient-boosted models on historical purchase patterns to score each of the 11 million customers on likelihood to buy and cross-buy across product categories — refreshed daily via Azure ML pipelines and partitioned across three cloud regions for compliance with global AI data residency requirements.
Analysis of 1.7 billion historical records revealed a critical insight: the anticipated opportunity lifecycle at deal inception had the strongest single influence on win rate. We built models that flagged deals deviating from winning lifecycle patterns — enabling early manager intervention.
We segmented rep activity patterns and built a coaching recommendation engine that generated personalised, behaviour-specific suggestions for junior reps — grounded in the patterns of proven performers in the same market segment and deal type.
Daily ML scores, guided playbooks, and coaching recommendations were delivered through Power BI dashboards with embedded Canvas Apps and Microsoft Teams — integrated with Salesforce and accessible to all 7,000 sales leaders globally without changing their core workflow.
The lifecycle discovery proved the most transformative. Regional leaders initially contested the finding — so we hosted workshops and daily briefings reviewing thousands of won and lost opportunities side by side. The data built the consensus. Sales practices changed. Win rates followed.
The analytics platform removed the business case for further expensive CRM consolidation — delivering global insight without touching the underlying architecture.
Personalised coaching recommendations reduced the time for new representatives to reach full performance — measurably improving ramp velocity across global markets.
To meet global AI data residency regulations, pipelines were partitioned across three separate cloud regions — ensuring GDPR and cross-region data transfer compliance from day one.
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