The Use Cases page shows how Sparkflows solves various Big Data and ML use cases.

The various use case solutions are combined together to form larger Big Data Applications.

Churn Data Analysis

Churn Analytics is used to identify customers who are likely to discontinue using the services/product. This is applicable across various industries like Telecom, Banking, Financial, Retail etc.

Combine all customer data to predict their service experience and propensity to churn, and take preventitive action.

Spam Detection

Spam pervades any information system, be it e-mail or web, social, blog or reviews platform. Spam deteriorates the quality of search results and deprives legitimate websites of a revenue that they might earn in the absence of spam. Various anti-spam techniques are used to prevent spam.

 Detects spam based on the content of the email, either by detecting keywords or by statistical means.

Sentiment Analysis

The applications for sentiment analysis are endless. It's being used in social media monitoring and VOC to track customer reviews, survey responses, competitors, buisness analytics and situations in which text needs to be analyzed. Sentiment analysis is in demand because of its efficiency.

Determines the attitude of a speaker, writer, or other subject  and classifies whether the expressed opinion is positive, negative, or neutral.


The construction of systems that support users in their (online) decision making is the main goal of the field of recommender system. Although they aim at the individual decisions of users, these systems have a significant impact in a larger sense because of their mass application - as, for instance, Amazon. com's Recommendation Engines.

Apply Content & Collaborative filtering for building Recommender Systems.

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Clickstream Analysis

Clickstream Analytics is the process of collecting, analyzing and reporting aggregate data about which pages a website visitor visits and in what order. The path a visitor takes through a website is called clickstream.

Collects, analyzes and reports aggregagte data about the pages of a website that a visitor visits and in what order.

Customer Segmentation

Customer segmentation is the process of dividing a broad consumer or business market, normally consisting of existing and potential customers, into sub-groups of consumers (known as segments) based on some type of shared characteristics. In dividing or segmenting markets, researchers typically look for shared characteristics such as common needs, common interests, similar lifestyles or even similar demographic profiles.

Divides customers into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending. 

Demand Prediction

The need for a network demand model arises as the importance of access to the internet increases for the delivery of essential services. The ability to predict bandwidth demand is critical for efficient service provisioning and intelligent decision making in the face of rapidly growing traffic and changing traffic patterns.

 Predicts the future demand for the product and allocates resources to meet anticipated demand.

Fraud Detection

Internet transactions have recently raised big concerns, with some research showing that internet transaction fraud is 12 times higher than in - store fraud. Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence.

 Searches to spot patterns and detect fraudulent transactions.

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