AI-Assisted Sales Messaging
Coordinator conversion rate improved by 38%; time-to-first-response reduced by 64%; new coordinator training time halved.
8-week build. Coordinator pilot with 3 team members in weeks 6–7. Full team deployment in week 9. Performance review at 30 and 90 days.
The challenge
Conversion performance varied 2x across the coordinator team with no systematic way to close the gap.
The group's patient coordinator team of 8 people was handling approximately 400 inbound enquiries per week. Top-performing coordinators were converting 34–38% of qualified leads to consultation booking. Lower-performing coordinators were converting 16–19%. The gap wasn't attitude or effort — it was craft: knowing how to frame treatment value, how to respond to price objections, how to create urgency without pressure, and how to personalise a response to the specific concern a patient had expressed. This knowledge lived in the heads of two or three experienced coordinators and wasn't systematically shared. New starters took 6 weeks to reach competent conversion rates, and turnover meant that training investment was regularly written off.
What we did
The approach
We built an AI-assisted sales messaging agent that generated high-quality, contextualised response options for coordinators based on the enquiry content, treatment category, and patient communication history. The agent didn't replace coordinator judgment — it gave every coordinator access to message quality calibrated to the standard of the best performers, with options they could select, adapt, and send.
Key findings & actions
Enquiry classification layer
automatic identification of enquiry type (treatment information, pricing, objection, consultation request, post-consultation follow-up) and treatment category to route to the relevant response framework
Message generation engine
trained on the group's top-performing coordinator conversations, generating 2–3 response options per enquiry with clearly differentiated tone and approach for the coordinator to choose from
Objection handling library
structured response frameworks for the 12 most common conversion objections, embedded into the generation engine and updated quarterly based on what was working
Personalisation signals
integration with CRM to surface patient history context — previous enquiries, treatments received, consultation notes — enabling coordinators to reference prior interactions accurately
Performance analytics
per-coordinator conversion tracking, response time monitoring, and A/B visibility on which message approaches performed best, feeding into quarterly content updates
How we worked
Scope
Enquiry classification design, message generation engine training, objection handling library, CRM integration, and coordinator analytics dashboard.
Timeline
8-week build. Coordinator pilot with 3 team members in weeks 6–7. Full team deployment in week 9. Performance review at 30 and 90 days.
Operating model
Clinical and commercial lead reviewed all objection handling frameworks before deployment. Coordinator team leads held content approval rights for message library updates. Weekly team review of conversion metrics in the first month.
Outcomes
What changed
Coordinator conversion rate improved by 38%; time-to-first-response reduced by 64%; new coordinator training time halved.
Average coordinator conversion rate improved from 24% to 33%
a 38% relative improvement — with the lowest-performing quartile showing the largest gains (from 16% to 27%)
Time-to-first-response reduced from an average of 4
1 hours to 1.5 hours as coordinators spent less time drafting responses from scratch
Training time for new coordinators to reach competent conversion performance reduced from approximately 6 weeks to 3, as the agent scaffolded performance from day one
Coordinator team reported higher job satisfaction scores at 3-month review, attributing reduction in response anxiety and clearer guidance on objection handling as primary factors
Governance
Trust, collaboration & governance
All generated messages reviewed by coordinator before sending — the agent offered options, humans made the call
No clinical claims in generated messages without explicit sign-off from clinical lead — all treatment-efficacy language held to the same standard as the clinic's own marketing
Coordinator performance data shared at team level, not used punitively at individual level during the calibration period
Quarterly content refresh based on conversion data to prevent message fatigue and ensure the library stayed current with treatment menu changes
Reframe
The performance gap was knowledge distribution, not selection. The agent gave everyone top-performer craft, immediately.
Across every engagement, the goal is the same: engineer a system that makes better decisions — faster, more consistently, and at scale — than the process it replaces.
Next steps
Related services
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context — we will identify the highest-leverage intervention areas before any scope is agreed.