AI Agent for Campaign Optimization via WhatsApp
Budget: $750 – $1,500 USD
AI Agent for Campaign Optimization via WhatsApp
Objective: Develop an AI assistant that acts as a campaign consultant for media agencies, answering questions, suggesting optimizations and generating reports via WhatsApp, integrated with platforms such as Google Ads, Meta Ads and Google Analytics.
Main Functionalities
Interaction via WhatsApp:
Receive questions in natural language (e.g.: "What is the ROAS of Campaign X?").
Respond with text, graphics (e.g.: PNG/PDF) or links to dashboards.
Allow commands via buttons or quick menus (e.g.: "Generate CPA report").
Natural Language Processing (NLP):
Recognize intentions such as:
Analysis of KPIs (ROAS, CPA, CTR).
Optimization suggestions (bid adjustment, budget reallocation).
Generation of reports.
Handle contextual questions (e.g.: "Why did the CPC go up yesterday?").
Integrations with External APIs:
Google Ads API: Fetch campaign data, adjust bids.
Meta Business Suite: Collect engagement metrics.
Google Analytics: Extract conversion data.
Data Analysis and Machine Learning:
Cross metrics (e.g.: CPA + LTV) to identify opportunities.
ML model to predict trends (e.g.: seasonality).
Generate proactive alerts (e.g.: "CPA above expected in Campaign Y").
Security and Compliance:
End-to-end encryption.
OAuth 2.0 authentication for APIs.
LGPD compliance.
Proposed Architecture:
Frontend: WhatsApp Business API (receives/processes messages).
NLP Layer: Processes the query and extracts intent/parameters.
Backend:
Query external APIs (Google Ads, Meta Ads).
Runs ML models (e.g.: ROI prediction).
Generates structured responses.
Data Lake: Stores historical data for continuous training.
(Example: User → WhatsApp → NLP → Backend → External APIs → Response)
Data Flow
User Input:
Message received via WhatsApp (e.g.: "What is the average CPC of Google Ads?").
NLP Processing:
Identifies intent ("fetch CPC") and parameters ("Google Ads").
API Query:
Backend searches for real-time data from integrated platforms.
Analysis and Response:
Applies ML models (if necessary) and formats the response.
Output:
Message sent via WhatsApp (text + attachments, if applicable).
Objective: Develop an AI assistant that acts as a campaign consultant for media agencies, answering questions, suggesting optimizations and generating reports via WhatsApp, integrated with platforms such as Google Ads, Meta Ads and Google Analytics.
Main Functionalities
Interaction via WhatsApp:
Receive questions in natural language (e.g.: "What is the ROAS of Campaign X?").
Respond with text, graphics (e.g.: PNG/PDF) or links to dashboards.
Allow commands via buttons or quick menus (e.g.: "Generate CPA report").
Natural Language Processing (NLP):
Recognize intentions such as:
Analysis of KPIs (ROAS, CPA, CTR).
Optimization suggestions (bid adjustment, budget reallocation).
Generation of reports.
Handle contextual questions (e.g.: "Why did the CPC go up yesterday?").
Integrations with External APIs:
Google Ads API: Fetch campaign data, adjust bids.
Meta Business Suite: Collect engagement metrics.
Google Analytics: Extract conversion data.
Data Analysis and Machine Learning:
Cross metrics (e.g.: CPA + LTV) to identify opportunities.
ML model to predict trends (e.g.: seasonality).
Generate proactive alerts (e.g.: "CPA above expected in Campaign Y").
Security and Compliance:
End-to-end encryption.
OAuth 2.0 authentication for APIs.
LGPD compliance.
Proposed Architecture:
Frontend: WhatsApp Business API (receives/processes messages).
NLP Layer: Processes the query and extracts intent/parameters.
Backend:
Query external APIs (Google Ads, Meta Ads).
Runs ML models (e.g.: ROI prediction).
Generates structured responses.
Data Lake: Stores historical data for continuous training.
(Example: User → WhatsApp → NLP → Backend → External APIs → Response)
Data Flow
User Input:
Message received via WhatsApp (e.g.: "What is the average CPC of Google Ads?").
NLP Processing:
Identifies intent ("fetch CPC") and parameters ("Google Ads").
API Query:
Backend searches for real-time data from integrated platforms.
Analysis and Response:
Applies ML models (if necessary) and formats the response.
Output:
Message sent via WhatsApp (text + attachments, if applicable).