Driving Innovation
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Telecom Industry
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Bridging Gaps:Power of Geospatial Matching
Match locations precisely, optimize operations, and gain a deeper understanding of your world with powerful geospatial matching.
Goal
- Implement a geospatial matching algorithm with third-party retailers.
- Leverage the strategic advantages of location intelligence for business model transformation and optimization.
- Create a significant and lasting impact on operations.
- Enhance the value delivered to customers and partners through the initiative.
- Implement a geospatial matching algorithm with third-party retailers.
- Leverage the strategic advantages of location intelligence for business model transformation and optimization.
- Create a significant and lasting impact on operations.
- Enhance the value delivered to customers and partners through the initiative.
Technique
- Haversine distance function, Matrix Creation, Comparison, Matching Algorithm.
- Haversine distance function, Matrix Creation, Comparison, Matching Algorithm.
Impact
- Expanding Market Reach.
- Optimizing Resource Allocation.
- Efficient inventory management.
- Reduced transportation expenses.
- Expanding Market Reach.
- Optimizing Resource Allocation.
- Efficient inventory management.
- Reduced transportation expenses.
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.
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.
Unveiling Loyalty: Predict Customer Lifespan
AI models pinpoint high-value customers, driving targeted engagement and long-term growth.
Goal
- To identify high, medium, and low-value customer segments.
- To provide personalized offers and experiences to customers.
- To allocate resources efficiently to businesses for targeting customers with the highest CLV potential and predict customer churn.
- To identify high, medium, and low-value customer segments.
- To provide personalized offers and experiences to customers.
- To allocate resources efficiently to businesses for targeting customers with the highest CLV potential and predict customer churn.
Technique
- Feature Engineering, Segmentation Techniques, RFM Analysis, Clustering and classification modeling, Visualization.
- Feature Engineering, Segmentation Techniques, RFM Analysis, Clustering and classification modeling, Visualization.
Impact
- Guided resource allocation, marketing strategies, and customer service efforts.
- Offering cross-selling and upselling opportunities to customers with CLV potential.
- CLV helps businesses identify risks associated with over-reliance that encourages diversification and risk management strategies.
- Guided resource allocation, marketing strategies, and customer service efforts.
- Offering cross-selling and upselling opportunities to customers with CLV potential.
- CLV helps businesses identify risks associated with over-reliance that encourages diversification and risk management strategies.
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