Reference no: EM133967939

Artificial Intelligence Programming in Business Analytics

Assessment Title: Generative AI for Predictive Analysis

Task

Follow the steps within the provided Google Colab notebook to complete the Python code and analyse the dataset.

Work in a group of 4-5 to use the built-in code and additional research to extract insights, answer the embedded quizzes, and identify a business problem.

At the end of the second hour of class, upload your completed notebook with responses and code changes to the portal.

a slide deck of your team at the end of the presentation session.

Assessment Description

Learning outcome 1: Develop exploratory software to ethically source, store, prepare, and analyse data for AIapplications.

Learning outcome 2: Create an AI application within a business context by applying fundamental software programming principles.

Learning outcome 3: Utilise AI to analyse and evaluate business decisions and processes.

Background:
Imagine your team is part of a marketing or influencer agency, and your team has been tasked with analysing social media performance to optimise digital marketing strategies. Your goal is to identify trends, understand audience engagement, and formulate data-driven business recommendations.

This assessment is designed to simulate real-world business analysis workflows, consolidating your practical knowledge of Python, data exploration, external research, and generative AI for marketing strategy development.

The Google Colab notebook for this assessment will be provided before the class. Your work should reflect a combination of data-driven insights and industry research, leading to a well-supported marketing strategy in Part B of the assessment.

Assessment Instructions

Part A: Exploration and Analysis

Objective:
This section enables students to explore a social media dataset, placing themselves in the role of a marketing or influencer agency. Students will analyse the dataset provided to extract key statistics and insights. Get dependable, budget-friendly assignment help-starting today!
The datasets and Notebook will be provided to you at the beginning of class.

Detailed Tasks:
Dataset Selection and Exploration:
Perform descriptive analysis to explore the dataset and identify key performance metrics, such as impressions, clicks, or conversions.
Use built-in notebook prompts and visualisations to guide analysis.
Examples of required analysis include:
Summary statistics (mean, median, standard deviation).
Distribution visualisations (e.g., histograms, box plots).

Insight Generation from Dataset:
Interpret the dataset findings to identify patterns or trends, such as:
Which type of content generates the most engagement?
What demographics contribute most to conversions?
Use these insights to outline potential business problems related to marketing campaigns or audience engagement.

Quiz Responses and Critical Thinking:
Complete embedded quizzes in the notebook to reflect on insights from both the dataset and external research.

Business Problem Formulation:
Synthesise insights from the dataset and external research to define a clear and actionable business problem that can be solved using AI.
Example problems include:
"How can the agency increase engagement among younger demographics?"
"What content strategies can optimise ad spending for higher conversions?"
Justify how the identified problem aligns with stakeholder needs and goals.

Ensure you have:

Completed Python notebook with quizzes and analysis.
Identified a clear business problem tied to your stakeholder position as the markdown text.
Created a markdown and written down the names of your team members

Part B: Generative AI Marketing Strategy

Objective:
Collaborate as an agency to utilise generative AI tools creatively and strategically to design a marketing campaign. The campaign should align with insights derived in Part A and effectively target the stakeholders.

Detailed Tasks:
Create an engaging slide deck that incorporates the below.
Strategy Planning and Research
Define the target audience based on insights from Part A.
Specify campaign objectives (e.g., increasing brand awareness, driving engagement).
Use insights and additional data to identify the most effective marketing approaches for the selected audience.
Outline the types of generative AI outputs needed (e.g., visuals, slogans, jingles).

Story Engineering and Prompt Design
Create a coherent campaign narrative. Examples include:
Slogans or ad copy (e.g., crafted with ChatGPT).
Scripts for video or audio content (e.g., using Writesonic, Sona AI).
Record all generative AI prompts and outputs, iteratively refining them to align with campaign objectives.

Creative Asset Development
Use generative AI tools to produce assets such as:
Visuals: Logos, banners, or promotional posters (using DALL·E, MidJourney, or Leonardo AI).
Videos: Short advertisements or clips (using Synthesia or similar tools).
Audio/Voice: Jingles or voiceovers (using Suno AI or Murf).
Ensure all assets are coherent with the campaign narrative and resonate with the target audience.

Appendix and Documentation
Provide a detailed log of all generative AI interactions:
Prompts used.
Output received.
Refinements made.

Team Presentation - Present the submitted slides engagingly.
Presentation Content
Overview of Part A insights and how they informed the campaign.
Demonstration of generative AI outputs (e.g., banners, jingles).
Explanation of the campaign's alignment with the business problem and
stakeholder objectives.
Presentation Style:
Use creative tools (e.g., PowerPoint, Canva, Prezi).
Ensure clarity, conciseness, and engagement within the 5-minute time limit.
Include a team brand identity (e.g., logo or tagline created by the team).