“Leads increased, but quality declined” can sound like a useful conclusion while leaving the next action unclear.
The detail matters.
In a fictional example, 100 leads at a 40% qualification rate produce 40 qualified leads. A later period produces 150 leads at 30%, or 45 qualified leads.
The rate fell, but the number of qualified leads rose. Whether that is a better result depends on cost, capacity, downstream value and whether the periods are comparable.
A report that highlights only the falling rate may push the team toward the wrong response. A report that celebrates only lead volume can be equally misleading.
ChatGPT can help connect the measures before drafting the story.
Try this prompt:
Turn these marketing results into a decision-focused summary: [attach].
Business objective: [objective]
Decision facing the team: [decision]
Definitions, periods and relevant changes: [details]
Check the relationship between counts, rates, costs and downstream outcomes. Flag comparisons that are not like for like.
Write four short sections:
1. What changed.
2. What the evidence supports.
3. What remains uncertain.
4. The next decision or investigation.
Show the figures behind the conclusion. Separate observed results from possible causes. If the data cannot justify a recommendation, specify what we need to learn.
The conclusion may be to keep the campaign running while investigating a particular segment. It may be to change the offer, correct the measurement or pause spend.
The useful report makes the reasoning visible enough that another person can challenge the recommendation.
Happy prompting!
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