HOW TO GET CHANNEL OPTIMIZATION INSIGHTS WITH HATCH AI

**Task:** Analyze channel engagement performance and provide optimization recommendations based on current filters

**Steps:**

1. **Organize data**: Build a table per channel showing engagement metrics 
2. **Track changes**: Show quarter-to-quarter shifts (absolute + %) for each metric.
3. **Identify patterns**: Highlight where engagement improved, declined, or stayed flat.
4. **Impact assessment**: Classify engagement trend as Positive / Negative / Inconclusive.
5. **Channel recommendation**: Suggest Optimize / Maintain / Reduce focus / Inconclusive. Add a one-sentence reason grounded in engagement data (e.g. “+18% Avg. Time, stable CTR — deeper attention building”).
6. **Strategy note**: Provide short guidance per channel (e.g. “Boost personalization,” “Test interactive elements,” “Reconsider underperforming placements”).
7. **Summaries**:
    - Per-channel report
    - Recommendations table (current engagement trend vs. action)
    - Overall strategy (key priorities, risks, cross-channel learnings).

**Output format:**

- **Intro** (timeframe, method, goal)
- **Channel report(s)** (data table, engagement changes, impact, recommendation, reasoning, notes)
- **Summary table** (engagement recommendations)
- **Overall strategy**

This prompt delivers a data-driven view of how each marketing channel is performing in terms of engagement quality and audience interaction. By analyzing signals such as views, average time spent, completion rates, and interaction depth, it surfaces which channels are effectively capturing and sustaining attention, and which may be underperforming against expectations or benchmarks.

The resulting insights allow marketers to make informed optimization decisions, reallocating spend and effort toward the channels that yield the strongest engagement-to-outcome correlation. Rather than simply reporting on activity levels, this prompt helps connect engagement behavior to business impact. It empowers teams to continuously refine their channel mix, testing creative approaches, targeting strategies, and personalization tactics, to drive stronger resonance, higher content efficiency, and improved return on marketing investment.

Demand generation | Content production | ABM

Engagement

Channel Engagement Performance Analysis

Introduction

This analysis examines channel engagement performance based on the current filter settings. The goal is to identify which distribution channels are performing well, which need optimization, and to provide strategic recommendations for improving overall engagement and conversion.

Channel Reports

Channel Engagement Metrics

Direct Channel

• Engagement Impact: Positive
• Recommendation: Optimize
• Reasoning: Highest engagement score with strong lead generation (11 leads) and longest average read time (13:01)

Strategy Notes:

• Direct traffic shows the highest engagement but lacks source attribution
• Implement UTM parameters to better understand the true sources of this traffic
• Consider adding personalization to further improve the already strong engagement metrics

Google Channel

• Engagement Impact: Positive
• Recommendation: Maintain
• Reasoning: Strong lead generation (12 leads, highest among all channels) with excellent read rate (60%)

Strategy Notes:

• Google is delivering quality traffic with high engagement and the best lead conversion
• Continue SEO optimization and consider expanding keyword targeting
• Test different content formats to maintain the high read rate

LinkedIn Channel

• Engagement Impact: Neutral
• Recommendation: Optimize
• Reasoning: Good reader count but lower engagement metrics and moderate lead generation

Strategy Notes:

• LinkedIn traffic shows potential but engagement metrics need improvement
• Test more interactive content formats specifically for LinkedIn audience
• Consider more targeted posting strategies to improve quality of traffic
• Focus on lead generation: Google is currently the most effective channel for lead generation. Consider applying similar content strategies to other channels, particularly LinkedIn which has good reach but lower conversion.

Cross-Channel Learnings

Content that performs well on Google (with 60% read rate) should be analyzed for insights that can be applied to other channels.
The long read time on Direct traffic (13:01) suggests this content is highly engaging - identify what's working here and replicate across channels.

Recommendations for Improvement

• Implement proper tracking: Use UTM tags to understand channel effectiveness to better attribute traffic sources.
• Enhance personalization: Consider implementing personalized experiences for higher engagement and conversion, particularly for LinkedIn traffic.
• Optimize lead conversion: Review and implement strategies for engaging, qualifying and converting more leads across all channels.
• Benchmark performance: Use analytics benchmarks to compare your content performance with industry standards and set realistic improvement goals.