A weekly lead quality dashboard should show accepted enquiries, qualified enquiries, disqualifications and pending reviews by campaign. Pair the qualification rate with its denominator and the amount of work still pending. This makes campaign comparisons easier to interpret.
A manager sees that one campaign has a 75% qualification rate and another has 45%. The first had four reviewed enquiries. The second had twenty. Both figures describe real observations, but they carry different amounts of evidence and may be affected by how quickly sales reviewed each cohort.
Pick one cohort and define each count
Group enquiries by acceptance date in an explicit timezone. Include records accepted during that week and evaluate their current status at a recorded cutoff time. Each enquiry should be counted once, using its stable ID.
For the example below, reviewed enquiries are those with a qualified or disqualified decision. A pending enquiry is still awaiting a decision. Confirmed spam can be tracked as a disqualification reason or a separate bucket, depending on where review happens. State your policy and apply it consistently.
| Metric | Calculation |
|---|---|
| Review coverage | Reviewed enquiries ÷ accepted enquiries |
| Qualification rate among reviewed | Qualified enquiries ÷ reviewed enquiries |
| Qualified share of accepted | Qualified enquiries ÷ accepted enquiries |
| Pending review | Accepted enquiries − reviewed enquiries |
Show a dash when a denominator is zero. A campaign with no reviewed records has no observed qualification rate yet.
Worked example with three campaigns
| Campaign | Accepted | Qualified | Disqualified | Pending | Qualification among reviewed | Review coverage |
|---|---|---|---|---|---|---|
| Paid search | 30 | 9 | 11 | 10 | 45% (9/20) | 67% (20/30) |
| Partner email | 5 | 3 | 1 | 1 | 75% (3/4) | 80% (4/5) |
| Unknown | 8 | 2 | 2 | 4 | 50% (2/4) | 50% (4/8) |
| Total | 43 | 14 | 14 | 15 | 50% (14/28) | 65% (28/43) |
The partner campaign has a higher observed rate across a small set. Paid search already has nine qualified enquiries and ten still pending. A practical next step is to finish reviewing the oldest pending enquiries and inspect the most frequent reasons among the eleven paid-search disqualifications.
Compute the total rate using total qualified divided by total reviewed. Averaging the three campaign percentages would give equal weight to campaigns with very different review volumes.
Add fields that explain the weekly numbers
Show the most common disqualification reasons, oldest pending age and the share of enquiries with unknown attribution. Use those fields to choose the next investigation. For instance, a rise in unsupported requests can prompt a landing page review, while a rise in unknown attribution can prompt a check of form payloads and CRM mapping.
Keep recent weeks visible as provisional until they reach your agreed review cutoff. If a week's figures can change when sales updates a record, either preserve snapshots or label the report with its calculation time.
What should the weekly review produce?
Record one action, an owner and a date for each recurring issue. An example is “Review the five unsupported service requests from paid search, compare them with ad copy, and update the landing page by Friday.” Then check whether the next comparable cohort shows a change in the same reason.
Before sharing the dashboard, verify that the status counts add up, duplicate submissions are excluded according to policy, and campaign totals match the underlying enquiry records. A dashboard earns its place when people can trace the numbers and agree on what to do next.
