This section provides a comprehensive walkthrough of how Genesis Data Agents can be utilized to automate deep data analysis.
Introduction
In this example we’ll be demonstrating an autonomous process that utilizes data from the database, to generate a deep, actionable analysis of marketing campaign performance. This specific example is using television metrics data focused on TV show campaigns to uncover insights about campaign engagement, audience reach, and cost efficiency.
Tool Information
The tools used in this example are not limited to only marketing campaign analysis.
Below you can find the structure of an autonomous process and where it says “User Prompt you can copy and paste the structure for reference.
Genesis Tip
It’s recommended to familiarize yourself with creating an autonomous process on the “How To Use Process Runner Tools” section.
Note
The below is meant to be a reference example for structuring a step-by-step process. For an autonomous example that you can try yourself and follow along refer to “Autonomous Data Pipeline Building”.
You can copy and paste the structure below for reference.
The process showcases the potential of leveraging Genesis as a central hub to execute, analyze, and summarize complex marketing campaigns—saving time and improving outcomes.
Streamlining Performance Analysis:
By automating data retrieval and enrichment, the workflow showcases how Genesis data agents can make campaign performance metrics instantly accessible for decision-making.
Enhancing Insights Through Metric Calculation:
Calculating key performance indicators such as CTR (Click-Through Rate) and Conversion Rate transforms raw data into clear measures of success or underperformance.
Data Enrichment for Contextual Understanding:
Integrating TV metrics (e.g., TOTAL_VIEWERSHIP
, DEMOGRAPHIC_REACH
, TIME_SLOT_RATINGS
) with campaign performance data provides a holistic view of how campaigns performed in the competitive TV environment.
Highlighting Success and Gaps:
Identifying top-performing campaigns to emulate and underperforming campaigns to improve drives better resource allocation and creative strategies.
Guiding Action with Proactive Reporting:
The workflow culminates in an automated email that summarizes insights, offers actionable recommendations, and engages key stakeholders for further dialogue. Such communication bridges the gap between data analysis and marketing strategy.
The first step is accessing campaign metrics stored in a Data Clean Room (DCR) environment. These metrics served as the foundation for subsequent analysis.
Genesis Tip For Snowflake Users
While Data Clean Room (DCR) is not necessary. For users who use Snowflake as the data source,
Genesis Data Agents can also utilize data within Data Clean Rooms.
Action:
marty-4x9g3v_dcr_report_results_d9pq3i
was executed using the _run_query tool
.Tool Information
To learn more about notes refer to “How To Use Notebook Tools”.
This query fetched impressions
, clicks
, and conversions
for campaigns tagged by their respective quarters (e.g., 2024Q2, 2024Q1).
To evaluate campaign performance, two critical metrics were calculated Click-Through Rate (CTR) and Conversion Rate (CR) they were processed through Python to include quarter-wise CTR and Conversion Rate for easy comparison.
Reasoning: This ensures that key marketing performance indicators are aggregated across multiple quarters for trend analysis.
Objective: To contextualize marketing performance, campaign data from the Data Clean Room was enriched by joining it with TV viewership metrics. This provided a richer dataset exploring audience reach and engagement alongside campaign metrics.
Action:
Integrated campaign data with GENESIS_BOTS_ALPHA.MARTY_4X9G3V_WORKSPACE.TELEVISON_METRICS
, which contained:
- TOTAL_VIEWERSHIP
: Number of viewers per quarter.
- TARGET_DEMOGRAPHIC_REACH
: Percentage of target audience reached.
- TIME_SLOT_RATINGS
: Performance of TV time slots.
- CPM
: Cost-per-thousand impressions (cost efficiency).
- TV_SHOW_NAMES
: Associated TV campaigns.
Used SQL to execute a JOIN, linking campaign performance with TV metrics via the QUARTER
attribute.
Reasoning: Using the campaign data from the Data Clean Room + the television metrics data from the database allowed for a comprehensive analysis. Breaking down which campaigns reached the desired demographics, Cost-effectiveness via CPM and Popular TV shows, and their time slots.
Objective: The enriched data was sorted by Quarter
, and the top-performing and underperforming campaigns were analyzed based on CTR and Conversion Rate.
Finally, the results were summarized in an actionable email tailored for stakeholders. This email provided:
Action: Insights were synthesized into a visually engaging email using the following elements: Subject Line: Highlight of successes and room for improvement. Body Content: Data-driven breakdown, high-performance analysis, underperformance insights, and actionable recommendations. Call-to-Action: Invited stakeholders to dive deeper into Slack/Streamlit discussions.
_run_process
was the backbone, dictating the sequence of actions like querying, enriching, analyzing, and reporting. Each step triggered auxiliary tools for specific tasks._run_query
fetched raw performance data from a Snowflake database where campaigns were logged.
search_metadata
and get_full_table_details
ensured that the right companion datasets (like TV metrics) were accurately identified and structured for enrichment._send_email
to auto-compose an informative and engaging report. _run_query
function, ensuring a consistent and reusable approach for database extraction.Genesis Tip
To learn how to create notes refer to “How To Use Notebook Tools”.
This section provides a comprehensive walkthrough of how Genesis Data Agents can be utilized to automate deep data analysis.
Introduction
In this example we’ll be demonstrating an autonomous process that utilizes data from the database, to generate a deep, actionable analysis of marketing campaign performance. This specific example is using television metrics data focused on TV show campaigns to uncover insights about campaign engagement, audience reach, and cost efficiency.
Tool Information
The tools used in this example are not limited to only marketing campaign analysis.
Below you can find the structure of an autonomous process and where it says “User Prompt you can copy and paste the structure for reference.
Genesis Tip
It’s recommended to familiarize yourself with creating an autonomous process on the “How To Use Process Runner Tools” section.
Note
The below is meant to be a reference example for structuring a step-by-step process. For an autonomous example that you can try yourself and follow along refer to “Autonomous Data Pipeline Building”.
You can copy and paste the structure below for reference.
The process showcases the potential of leveraging Genesis as a central hub to execute, analyze, and summarize complex marketing campaigns—saving time and improving outcomes.
Streamlining Performance Analysis:
By automating data retrieval and enrichment, the workflow showcases how Genesis data agents can make campaign performance metrics instantly accessible for decision-making.
Enhancing Insights Through Metric Calculation:
Calculating key performance indicators such as CTR (Click-Through Rate) and Conversion Rate transforms raw data into clear measures of success or underperformance.
Data Enrichment for Contextual Understanding:
Integrating TV metrics (e.g., TOTAL_VIEWERSHIP
, DEMOGRAPHIC_REACH
, TIME_SLOT_RATINGS
) with campaign performance data provides a holistic view of how campaigns performed in the competitive TV environment.
Highlighting Success and Gaps:
Identifying top-performing campaigns to emulate and underperforming campaigns to improve drives better resource allocation and creative strategies.
Guiding Action with Proactive Reporting:
The workflow culminates in an automated email that summarizes insights, offers actionable recommendations, and engages key stakeholders for further dialogue. Such communication bridges the gap between data analysis and marketing strategy.
The first step is accessing campaign metrics stored in a Data Clean Room (DCR) environment. These metrics served as the foundation for subsequent analysis.
Genesis Tip For Snowflake Users
While Data Clean Room (DCR) is not necessary. For users who use Snowflake as the data source,
Genesis Data Agents can also utilize data within Data Clean Rooms.
Action:
marty-4x9g3v_dcr_report_results_d9pq3i
was executed using the _run_query tool
.Tool Information
To learn more about notes refer to “How To Use Notebook Tools”.
This query fetched impressions
, clicks
, and conversions
for campaigns tagged by their respective quarters (e.g., 2024Q2, 2024Q1).
To evaluate campaign performance, two critical metrics were calculated Click-Through Rate (CTR) and Conversion Rate (CR) they were processed through Python to include quarter-wise CTR and Conversion Rate for easy comparison.
Reasoning: This ensures that key marketing performance indicators are aggregated across multiple quarters for trend analysis.
Objective: To contextualize marketing performance, campaign data from the Data Clean Room was enriched by joining it with TV viewership metrics. This provided a richer dataset exploring audience reach and engagement alongside campaign metrics.
Action:
Integrated campaign data with GENESIS_BOTS_ALPHA.MARTY_4X9G3V_WORKSPACE.TELEVISON_METRICS
, which contained:
- TOTAL_VIEWERSHIP
: Number of viewers per quarter.
- TARGET_DEMOGRAPHIC_REACH
: Percentage of target audience reached.
- TIME_SLOT_RATINGS
: Performance of TV time slots.
- CPM
: Cost-per-thousand impressions (cost efficiency).
- TV_SHOW_NAMES
: Associated TV campaigns.
Used SQL to execute a JOIN, linking campaign performance with TV metrics via the QUARTER
attribute.
Reasoning: Using the campaign data from the Data Clean Room + the television metrics data from the database allowed for a comprehensive analysis. Breaking down which campaigns reached the desired demographics, Cost-effectiveness via CPM and Popular TV shows, and their time slots.
Objective: The enriched data was sorted by Quarter
, and the top-performing and underperforming campaigns were analyzed based on CTR and Conversion Rate.
Finally, the results were summarized in an actionable email tailored for stakeholders. This email provided:
Action: Insights were synthesized into a visually engaging email using the following elements: Subject Line: Highlight of successes and room for improvement. Body Content: Data-driven breakdown, high-performance analysis, underperformance insights, and actionable recommendations. Call-to-Action: Invited stakeholders to dive deeper into Slack/Streamlit discussions.
_run_process
was the backbone, dictating the sequence of actions like querying, enriching, analyzing, and reporting. Each step triggered auxiliary tools for specific tasks._run_query
fetched raw performance data from a Snowflake database where campaigns were logged.
search_metadata
and get_full_table_details
ensured that the right companion datasets (like TV metrics) were accurately identified and structured for enrichment._send_email
to auto-compose an informative and engaging report. _run_query
function, ensuring a consistent and reusable approach for database extraction.Genesis Tip
To learn how to create notes refer to “How To Use Notebook Tools”.