Activity: Project Step 4-Planing Machine Learning solutions for your startup company

Tips to develop your ideas for project step 4

Tips to develop your ideas for project step 4

by Jasmine Hu -
Number of replies: 4

Suppose that my role is the marketing manager of Mian Beauty, I want to use machine learning (ML) to make marketing more effective. I have constructed the following prompt to ask ChatGPT to give me some ideas. 

Prompt: 

I am the marketing lead at a makeup startup company, can you give me some suggestions on how to use machine learning to make my marketing more effective?

ChatGPT Response:

Personalized Product Recommendations

Train a recommendation engine (collaborative or content-based filtering) using customer browsing, purchase, and review data.

➡️ Example:

  • Recommend eyeshadow palettes based on previous purchases or shades often browsed together.

  • Suggest tutorials that match the customer’s makeup skill level.

➡️ Toolkits: TensorFlow Recommenders, Surprise (Python)


Based on ChatGPT's response, identify the Problem and develop the Solution

Example:

Problem: How to find potential customers of Mian Beauty

Solution: Contact popular e-commerce platforms and try to collaborate with them to get customer data (e.g. consumer preferences, transaction history, browsing history, etc.) After you receive customer data, use machine learning to train these data and develop a recommendation model. Then, use this model to recommend our products to potential customers based on their consumer preferences. 






In reply to Jasmine Hu

Re: Tips to develop your ideas for project step 4

by Jiacheng (Jason) Shan -
We are currently adjusting the color, and our issue is that the same color may have different effects on faces with different skin tones. We will combine photos of different skin tones and races with standardized light source color test photos, gradually adjusting the colors suitable for each skin tone. We also need to understand the color differences of the same skin tone under different light sources.

Do you think there are any areas that need to be modified
In reply to Jiacheng (Jason) Shan

Re: Tips to develop your ideas for project step 4

by Jasmine Hu -
Hi Jason, you're off to a great start. Your plan to collect photos representing different skin tones under various lighting conditions is thoughtful and inclusive. Here are a few suggestions to further strengthen your data collection proposal:

Clarify your data sources. Will the images come from public datasets, simulated data, or mock photo shoots? This will help define data quality and ethical considerations.

Address privacy and ethics. If your proposal involves real people, even hypothetically, briefly mention how you would handle consent and data protection.
In reply to Jasmine Hu

Re: Tips to develop your ideas for project step 4

by Chenzhe Ma -
Answers:

1. Problem: Low Customer Retention

Solution:

ML Approach: Build a churn prediction model using historical purchase frequency, engagement (email opens, clicks), and customer demographics.
Action: Identify at-risk customers and target them with personalized re-engagement campaigns (e.g., "We miss you!" discounts or free samples of their favorite product category).
Tools: Scikit-learn (Logistic Regression/Random Forest), Google Analytics + CRM data.
2. Problem: Ineffective Ad Spend

Solution:

ML Approach: Use clustering algorithms (e.g., K-means) to segment audiences based on behavior (e.g., "luxury buyers," "discount seekers," "tutorial watchers").
Action: Allocate ad budgets to high-intent segments (e.g., retarget tutorial watchers with product-linked ads).
Tools: Facebook’s Lookalike Audiences, Python’s Scikit-learn.
3. Problem: Low Conversion from Social Media

Solution:

ML Approach: Deploy image recognition on user-generated content (e.g., Instagram posts) to identify trending makeup styles or unsolicited product mentions.
Action: Partner with micro-influencers whose followers align with detected trends (e.g., "glossy lips" trend → collaborate with influencers who post lip tutorials).
Tools: OpenCV, Google Vision API.
4. Problem: Poor Email Marketing Performance

Solution:

ML Approach: Train a NLP model to analyze email open/click rates and A/B test subject lines/content.
Action: Send emails at optimal times/personalized content (e.g., "Your shade of foundation is back in stock!").
Tools: GPT-3 for generative subject lines, Mailchimp’s predictive analytics.
5. Problem: Inventory Mismatch with Demand

Solution:

ML Approach: Use time-series forecasting (e.g., ARIMA, Prophet) to predict regional demand for products (e.g., "matte lipsticks sell more in winter").
Action: Adjust local ad campaigns and stock levels ahead of demand spikes.
Tools: Facebook Prophet, TensorFlow.
In reply to Chenzhe Ma

Re: Tips to develop your ideas for project step 4

by Jasmine Hu -
Hey Chenzhe, great job outlining several strong options. I recommend focusing on Problem 1: Low Customer Retention with the churn prediction model. It's a classic and highly applicable use of machine learning, and your proposed data sources and actions are clear and realistic. This problem also ties well to long-term business impact, which will strengthen your proposal overall.