Sales & Wholesale

Inbound lead qualification and scoring

Score inbound trade enquiries for fit and likely value using firmographic data, stated requirements, and the pattern of accounts that converted historically, and route the strong ones immediately.

Decision supportRepetitive judgmentRevenueTime saving

15–30%

increase in qualified lead conversion rate

Opportunity assessment

Business Impact
3

Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.

Feasibility
4

Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.

Data Readiness
3

Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.

Risk Exposure
4

Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.

Change Complexity
3

Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.

Tooling required

Specialist AI toolStandard LLM

Things to consider

  • Speed of first response predicts conversion more strongly than score quality does. If the scoring adds latency it will destroy more value than it creates — score in seconds or not at all.

  • Training on historically converted accounts encodes historical bias about who you sold to. Review periodically whether the model is systematically deprioritising a segment you now want to grow.

  • Never auto-reject. Route low scores to a slower queue rather than to a bin, and audit that queue quarterly for missed opportunities.

  • Experiment starter: Score six months of past leads without letting the score affect routing. Compare the score distribution against what actually converted. If the top quintile converted at three times the bottom, deploy it as a routing input.

AI Transformation Playbook

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