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    Lead Scoring

    Lead scoring evaluates contacts or companies against defined criteria to prioritise further handling. A score may represent fit with an offer and observed activity. It supports a decision but proves neither purchase intent nor a specific probability of closing a sale.

    Lead Scoring explained

    Which contact merits a personal follow-up now? That task should come before assigning points. A useful model accounts for the offer, market and available handling capacity. A large company is not automatically a suitable customer, nor is a small one automatically unsuitable. What matters is whether relevant requirements match the offer.

    Separate fit from activity: a suitable use case and a repeated visit provide different information. Points, time windows and exclusions can be configured depending on the tool. Also record missing or outdated information. A low value caused by missing data differs from a confirmed mismatch.

    Limit signals that can accumulate easily: repeated requests, test contacts or automated clicks. Frequent email opens may have a technical cause. An explicit request for a conversation should not be blocked by an arbitrary point threshold. Sales needs the underlying information and a way to correct the assessment.

    Review completed intake cohorts: which prioritised contacts were suitable, and which good enquiries were overlooked? Account for the different opportunities given to contacts receiving intensive attention versus those left unhandled. A high score is not evidence that the method works. Record rule changes and check their impact on existing handovers.

    Examples

    Hypothetical application

    A maintenance-software provider separates use-case suitability from with its specialist . A direct demo request receives personal assessment even when little click data exists. Repeated automated link checks do not increase priority. The team later compares its assessments with conversation outcomes.

    Key Points

    • Define the next handling decision before assigning points.
    • Distinguish fit, activity and missing information.
    • Review overlooked suitable contacts and human corrections too.

    Practical application

    Start with a manageable decision and a few understandable criteria. Test typical enquiries, missing data and automated repeat requests. Agree who makes corrections and when rules are reviewed against actual outcomes.

    Useful measures

    Suitable prioritised contacts

    Check the share of relevant contacts against agreed criteria.

    Missed suitable enquiries

    Examine low scores too and record reasons for incorrect assessments.

    Effort per useful handover

    Include maintenance, assessment and handling effort.

    Common mistakes

    • Presenting points as certain purchase intent.
    • Treating unknown details as confirmed reasons for rejection.
    • Evaluating the model only on successful contacts that received intensive attention.

    Sources and context

    Frequently Asked Questions about Lead Scoring

    No. A point score initially ranks records under a set of rules. A probability estimate requires a prediction explicitly designed and evaluated for that purpose.

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