Segmentation & Targeting Optimisation

A Life Sciences organisation faced limitations in how healthcare professionals (HCPs) were segmented and prioritised for field engagement. Target lists were built using static datasets that were manually refreshed at periodic intervals. These lists were primarily based on historical prescribing volumes and broad territory assignments, without incorporating dynamic engagement signals or market access shifts.

As prescribing behaviours evolved and access conditions changed, segmentation models failed to adapt quickly. Field representatives continued to allocate effort toward low-potential HCPs while emerging growth opportunities were not systematically prioritised. This led to inefficient coverage, uneven territory performance and suboptimal commercial returns.

Additionally, there was limited visibility into how engagement patterns influenced prescribing trends. Commercial teams lacked predictive insights to guide next-best actions or recommend optimal focus areas. The organisation required a dynamic, data-driven segmentation framework capable of continuously adjusting to real-time commercial signals.

A multi-dimensional segmentation and targeting model was implemented using Salesforce Data Cloud (Data 360) as the central data foundation.

The new segmentation framework incorporated prescribing data, engagement history, digital interaction signals and market access indicators. Instead of relying on static tiers, HCP segments were dynamically refreshed using real-time data inputs. This ensured that targeting reflected current performance trends and growth potential.

Agentforce 360 was deployed to operationalise insights across commercial workflows. Predictive analytics models were introduced to identify HCPs with rising potential, declining engagement or competitive risk exposure. These insights were translated into prioritisation recommendations at the territory level.

Agentforce Sales (Salesforce Sales Cloud) embedded these recommendations directly into field workflows. Representatives received guidance on next-best actions, optimal call frequency and focus alignment based on capacity and territory objectives.

Territory-level planning was recalibrated to balance sales capacity with segment potential. Low-value engagement efforts were reduced, while high-potential accounts received structured and consistent focus.

The solution shifted targeting from periodic review cycles to continuous optimisation.

Targeting accuracy improved significantly as dynamic segmentation replaced static lists. Field effort was redistributed toward high-growth opportunities, improving overall commercial effectiveness.

Campaign performance improved due to better alignment between segmentation logic and engagement strategy. Predictive insights reduced wasted effort on low-potential HCPs and strengthened coverage consistency within priority segments.

Sales leadership gained clearer visibility into territory performance drivers and segment movement trends. Instead of reacting to historical reports, teams were able to proactively adjust engagement strategies based on forward-looking signals.

The organisation established a scalable targeting framework capable of supporting advanced AI-driven optimisation and continuous commercial refinement.

Business Impact

• 25% ↑ in target accuracy
• 20% ↑ in campaign effectiveness
• 30% ↓ in effort on low-potential HCPs

Technology Stack

• Data 360 (Salesforce Data Cloud)
• Agentforce 360 (Salesforce AI Capabilities)
• Agentforce Sales (Salesforce Sales Cloud)