Product Title: Multi-Agent Marketing Campaign Planning System

Date: 8th February 2025

Author: Shikhar Agrawal, Yash Upadhyay (Product Team)

1. Overview

1.1 Problem Statement:

Performance marketing processes are time-consuming and inefficient, limiting strategic data-driven decision-making. Additionally, organizations face high costs managing campaign planners, analysts, and creative production without optimal data utilization.

1.2 Solution:

A pre-defined multi-agent marketing campaign planning system streamlines campaign design, planning, and deployment based on business goals. It organizes specialized agents into crews to generate recommendations for objectives, brand mapping, audience targeting, channel selection, scheduling, budget allocation, and campaign naming. The system integrates LLMs and custom Python models/API callouts for data-driven, context-aware decision-making.

2. Purpose and Scope

2.1 Purpose

• Primary Objective:

  To provide users with end-to-end recommendations for campaign planning and deployment. This includes:

  • Determining the best campaign objectives from a set of nine predefined industry campaign types.
  • Mapping the brand to its correct industry category.
  • Identifying the optimal audience segments.
  • Recommending the best channels for the campaign.
  • Suggesting an optimal campaign schedule.
  • Allocating budget appropriately across selected channels.
  • Generating a memorable campaign name.

• Secondary Objectives:

  • To support creative generation and campaign deployment (further details to be added in subsequent versions).

2.2 Scope

• Crew: Objective Suggestion: Single agent to suggest campaign objectives based on the user’s marketing goal.
• Crew: Campaign Planning: A series of agents working sequentially to map the brand, identify the target audience, recommend channels, schedule the campaign, allocate budget, and finally name the campaign.
• Integration with external tools/APIs:
  • LLMs (for objective suggestion, audience identification in the absence of historical data, channel recommendation, schedule recommendation, and campaign name generation)
  • Custom Python functions that may include calls to existing models (for brand mapping, account integration checks, historical data analysis, budget allocation, etc.)

2.3 User Stories

• As a marketing manager, I want to receive AI-driven recommendations for campaign objectives, so that I can align my campaigns with business goals efficiently.
• As a brand strategist, I want the system to map my brand to the correct industry category, so that my campaigns target the right audience and channels.