09. Recommendations

Subgroup A

Recommendation:

After evaluating reasons behind customer behaviour of customers in different segment, we can try to develop personalized marketing strategies for customer in different segments.

Implementation roadmap

1. Define Personalization Objectives

   • Set Clear Goals: Establish objectives for each segment, such as increasing engagement, boosting conversion rates, or enhancing customer retention.
   • Align with Business KPIs: Ensure personalization efforts support overall business goals and key performance indicators.

2. Content and Offer Development

   • Tailor Content to Segments: Create customized content, offers, and messaging that resonate with each customer segment.
   • Maintain a cohesive brand voice across all channels while tailoring messages to segment-specific preferences.

3. Testing and Optimization

   1. Conduct A/B Testing: Test different personalized messages and offers to identify what resonates best with each segment and also to determine the most effective communication channels for each segment, such as email, SMS, social media, or in-app messaging.
   2. Analyze Performance Metrics: Monitor key metrics such as open rates, click-through rates, conversion rates, customer satisfaction scores and churn.
   3. Iterate Based on Insights: Continuously refine personalization strategies based on data-driven insights and customer feedback.

4. Training and Alignment

   • Educate Internal Teams: Provide training for marketing, sales, and customer service teams on personalization strategies and tools.
   • Align Cross-Functional Efforts: Ensure all departments are aligned in delivering a cohesive personalized customer experience.

5. Compliance and Privacy Management

   • Adhere to Data Privacy Regulations: Ensure all personalization efforts comply with relevant data protection laws and regulations.
   • Maintain Transparency with Customers: Clearly communicate data usage policies and provide options for customers to manage their preferences.

6. Continuous Monitoring and Improvement

   • Establish Feedback Loops: Regularly collect and analyze customer feedback to assess the effectiveness of personalization efforts.
   • Stay Updated with Industry Trends: Keep abreast of emerging personalization technologies and best practices to continually enhance strategies.

Expected impact of each recommendation

1. Increased Customer Retention: Tailored experiences foster loyalty thus reducing churn rates (Twilio, 2022). We are expecting to improve customer retention ie reduce churn rate by 50% (Ascend2, 2023).

2. Optimized Marketing Efficiency: Targeted strategies ensure resources are focused on high-potential segments, helping to increase revenue by 50% (Ascend2, 2023).

Subgroup B

Implementation roadmap

Expected impact of each recommendation

Bonus Questions

Prioritized list of recommendations and their expected impacts

Customer Review Sentiment Analysis: As the features are fresh, we can continuously make changes to our system, from data collection to model improvements. Note that our recommendations below are part of an unexhaustive list of recommendations.

A proposed integration into our system with adjustments includes:

1. Model Improvements: Search for better pre-trained models than our current ones or build our own model using this dataset as our training data, to increase accuracy. Another way would be to change the way we collect reviews from customers; getting them to give their review in a specific format suitable for our models.

2. Create data pipelines: For large e-commerce businesses in the world today, many hire data engineers to handle data flow across pipelines, turning raw data into usable form for our analysis. To keep up to date with a significant inflow of real-time reviews, we need to use data engineering tools such as Apache, Snowflake and Amazon Web Services. While our current project does not use these, we hope to integrate them into the data science workflow in our e-commerce platform.

3. Further Model Improvements: Consider improving our model to handle reviews of different languages, so we can expand our business to other countries. We can also conduct deeper-scale analysis on reviews, such as demographic and income groups, getting insights into each group’s interests and tuning our marketing/product handling methods to fit their needs.

4. Pre-launch: Implementation of systems to handle large numbers of incoming reviews and firewall to prevent hackers from getting unauthorized access to our systems and customers’ sensitive data. This phase ensures that system failures are avoided, and if there are, a prompt response is needed.

5. Post-launch: If possible, consider improving the model. Future plans include the implementation of new systems to cater to customer groups’ activities, based on insights gained from different customer groups’ reviews (such as category of products bought or customer characteristics like income, age or race).

Implementation roadmap

Below is an image of our preferred roadmap to integrate the Large Language Model system for aspect-based sentiment analysis to guide you through each recommendation. We can follow a similar integration path for our general sentiment analysis and extraction of issues feature.