• Client Challenge:

• With digital interactions now a large part of consumer behavior, a great opportunity exists for companies to capture, analyze and leverage those exchanges for decision-making. Our client , A large U.K.-based bank recognized this opportunity.
• The bank performed couple of marketing and email campaigns for the sales of mutual funds. After the campaigns, the data related to customers response to campaign was collected.
• The client requirement was to analyze the collected data and provide the result so that the client management could make the future plans for the better service and satisfaction to customers and growth of business and increase of revenue.

• How We Helped:

Design approach:

• To provide the result so that the client management could make the future plans for the better service and satisfaction to customers, growth of business and increase of revenue we decided to perform customer segmentation so that client can make separate and better plans for different segments.
• We decided to use the unsupervised learning technique of machine learning for customer segmentation.
• There are many types of clustering techniques like k-means, hierarchical and density based. We decided to use k-means as it is fast, simple and very flexible.
• We preprocessed the dataset and applied the k-means clustering technique. We provided the different visualizations of clusters and the clustered datasets to the client.

• Client Challenge:

• Nowadays many companies have been using Social Media Marketing to advertise their products or brands, so it becomes essential for them that they can be able to calculate the success and usefulness of each product.
• With digital interactions now a large part of consumer behavior, a great opportunity exists for companies to capture, analyze and leverage those exchanges for decision-making.
• The client requirement was to the analyze the collected data from twitter and other data sources and categorized the users sentiments about the bank investment funds and other products and services.
• The requirement was to provide the effective result set and visualization so that the client management could make the future plans for the better service and satisfaction to customers and growth of business and increase of revenue.

• How We Helped:

Design approach:

• To provide the result so that the client management could make the future plans for the better service and satisfaction to customers, growth of business and increase of revenue we decided to perform sentiment analysis using natural language processing so that client can make separate and better plans for different segments.
• Sentiment analysis is a type of natural language processing for tracking the mood of the public about a particular product.
• A basic task in sentiment analysis is categorizing the polarity of a given text at the document, sentence whether the expressed opinion in a document, a sentence or an entity feature is positive, negative, or neutral. sentiment classification looks, at emotional states such as "angry," "sad," and "happy.

We decided to use the natural language processing technique for customer sentiment analysis using NLTK and other libraries of Python

• Client Challenge:

• An accurate travel time prediction is of high value for freight transports, as it allows supply chain participants to increase their logistics quality
• Accurate travel time estimates are important for efficient management of transport operations and logistics in supply chains.
• At warehouses, ports, or other hubs, capacities concerning staff, ramps, forklifts, etc. can be scheduled accordingly.
• Consequently, manufacturers and logistic service providers can enhance their efficiency, optimize their processes, and increase planning accuracy
• The client requirement was to the develop a system that can predict freight travel time so that management could make the future plans for the better service and satisfaction to customers and growth of business and increase of revenue.

• How We Helped:

Design approach:

• To develop a system that could predict the freight travel time so that the client management could make the future plans for the better service and satisfaction to customers, growth of business and increase of revenue we decided to use machine learning technique and develop a freight travel time prediction model.
• Only ML algorithms can adequately deal with complex and dynamic behavior during transports. ML has the ability to deal better with complex and non-linear relationships between predictors and can process complex and noisy data.
• We decided to use tree based algorithms, RF, boosting Algorithms and SVM and select the best model for freight travel time prediction.
• We used the AWS services S3, Glue and SageMaker for this model training and deployment.

• Client Challenge:

• With digital interactions nowadays the digital image classification has become very important concept to solve real life problems in different domains.
• The client requirement was to the develop a software that can perform image classification prediction in predefined categories whenever required.

• How We Helped:

Design approach:

• To perform image classification prediction, we decided to use deep learning algorithm and develop Convolution Neural Network model. We decided to train the CNN model on training dataset and perform evaluation on test dataset.
• CNN image classifications takes an input image, process it and classify it under certain categories.

In deep learning CNN models to train and test, each input image passes through a series of convolution layers with filters, Pooling, fully connected layers and Softmax function is applied to classify an object.