Driving Innovation
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Text Analytics
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Feeling Pulse: Text Reveals Emotion
Decoding opinions, understanding trends, and driving better decisions with sentiment analysis.
Goal
- To identify high-level topics in the dataset, providing an understanding of subscriber feedback and their performance over time.
- To employ sentiment analysis on the verbatim responses associated with each identified topic to categorise sentiments as positive, neutral, or negative.
- To determine sentiment variations across different areas.
- To identify high-level topics in the dataset, providing an understanding of subscriber feedback and their performance over time.
- To employ sentiment analysis on the verbatim responses associated with each identified topic to categorise sentiments as positive, neutral, or negative.
- To determine sentiment variations across different areas.
Technique
- Text Preprocessing, Topic Modelling, Sentiment Analysis, Time Series Visualization, Visualization.
- Text Preprocessing, Topic Modelling, Sentiment Analysis, Time Series Visualization, Visualization.
Impact
- Enable strategic decision-making by providing a clear understanding of the key topics in subscriber feedback.
- Pinpoint specific areas to guide targeted efforts and enhance customer satisfaction.
- Provide insights into subscriber opinions through verbatim sentiment analysis.
- Enable strategic decision-making by providing a clear understanding of the key topics in subscriber feedback.
- Pinpoint specific areas to guide targeted efforts and enhance customer satisfaction.
- Provide insights into subscriber opinions through verbatim sentiment analysis.
 Goal
Technique
Impact
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Conquered: Tailoring Success with Segmentation
Unlock distinct customer groups, target effectively, and maximize marketing impact.
Goal
- To segment the customers based on their data to identify different clusters.
- To target specific customers falling under a particular cluster based on their requirements.
- To understand the needs of different customer groups for improving their customer experience.
- To segment the customers based on their data to identify different clusters.
- To target specific customers falling under a particular cluster based on their requirements.
- To understand the needs of different customer groups for improving their customer experience.
Technique
- Statistical Analysis, K-means clustering, Hierarchical clustering, Distance-based clustering, Visualization.
- Statistical Analysis, K-means clustering, Hierarchical clustering, Distance-based clustering, Visualization.
Impact
- Increase sales and revenue through customised promotion and product recommendation.
- Customer satisfaction.
- Increase sales and revenue through customised promotion and product recommendation.
- Customer satisfaction.
Cash Insights: ATMs Reveal Cash Trends
Analyze spending patterns, optimize services, and boost engagement with ATM transaction data.
Goal
- To forecast accurate transaction volume for the ATM network, enabling effective cash flow.
- To identify suspicious patterns in ATM transactions.
- To enhance security measures and safeguarding against potential fraudulent activities.
- To forecast accurate transaction volume for the ATM network, enabling effective cash flow.
- To identify suspicious patterns in ATM transactions.
- To enhance security measures and safeguarding against potential fraudulent activities.
Technique
- Statistical Analysis, Data Modelling, Time Series Forecasting, Visualization.
- Statistical Analysis, Data Modelling, Time Series Forecasting, Visualization.
Impact
- Proper planning of cash flow and better liquidity optimization.
- Improved risk management.
- Customer satisfaction.
- Proper planning of cash flow and better liquidity optimization.
- Improved risk management.
- Customer satisfaction.
Personalized Shopping: The E-commerce Edge
Unlock hidden customer groups, personalize offers, and boost sales with smarter segmentation.
Goal
- To predict the customer’s lifetime value using RFM and k-means clustering.
- To predict the review score for the next order or purchase.
- To provide more accurate and relevant product recommendations to customers.
- To find best valued customers segment.
- To predict the customer’s lifetime value using RFM and k-means clustering.
- To predict the review score for the next order or purchase.
- To provide more accurate and relevant product recommendations to customers.
- To find best valued customers segment.
Technique
- Statistical Analysis, K-means Clustering Algorithm, Sentiment Analysis, Visualization.
- Statistical Analysis, K-means Clustering Algorithm, Sentiment Analysis, Visualization.
Impact
- Improved targeted marketing.
- Personalised service, sales and marketing as per the needs of specific groups.
- Informed decision-making and optimize offerings.
- Enhanced customer experience.
- Improved targeted marketing.
- Personalised service, sales and marketing as per the needs of specific groups.
- Informed decision-making and optimize offerings.
- Enhanced customer experience.
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