Slide The module enables real-time data streaming from multiple data sources into one centralized place and uses a wide range of machine learning models to help companies create and foster a Data-Driven Culture. Analytical Module
and Big Data
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Analytical Module
and Big Data

The module enables real-time data streaming from multiple data sources into one centralized place and uses a wide range of machine learning models to help companies create and foster a Data-Driven Culture.
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    Did you know that 84% business think that AI will enable them to obtain competitive advantage?

    A company that manages to react to customer needs in real time and offer the right products/services on the preferred channel will become the customer’s first choice, and thus emerge as the market leader.

    Those that fail to embrace Artificial Intelligence (AI) and the benefits that come from unlocking data, on the other hand, risk being left behind by those that do.

    By observing and listening to customer needs, companies can maximize their retention rate, attract new customers, and accelerate into the digital future.

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    Did you know that 84% business think that AI will enable them to obtain competitive advantage?

    A company that manages to react to customer needs in real time and offer the right products/services on the preferred channel will become the customer’s first choice, and thus emerge as the market leader.

    Those that fail to embrace Artificial Intelligence (AI) and the benefits that come from unlocking data, on the other hand, risk being left behind by those that do.

    By observing and listening to customer needs, companies can maximize their retention rate, attract new customers, and accelerate into the digital future.

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    Process flow

    DATA COLLECTION
    AND PREOCESSING

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    Leveraging immense amount of
    collected data for gaining essential
    customers insights

    ML MODELS
    DEVELOPMENT

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    Using AI prediction and ML
    algorithms with high-performance
    measures for finding correlations
    between key factors and patterns in
    customer behavior

    ENHANCING
    CUSTOMER

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    Providing companies with multi-beneficial
    insights about their customers, which
    results in better customer understanding,
    enhanced personalization and customer
    experience, risk minimization, and
    reduced expenses

    Next best offer

    Model for product and service recommendation, Next Best Offer, calculates customer tendency to make a purchase, or inclination to use a certain product or service that the company provides. This predictive model makes decisions based on the customer’s previous actions and behavior toward the company.

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    Next best offer

    Model for product and service recommendation, Next Best Offer, calculates customer tendency to make a purchase, or inclination to use a certain product or service that the company provides. This predictive model makes decisions based on the customer’s previous actions and behavior toward the company.

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    Event signaling module

    Signaling-based Analytics has started by the identification of all events related to a custom-er. Life events, work events, lifestyle events and events related to a company’s products and services. These events represent triggers for signaling in real-time that enables the company to create a highly personalized approach, conduct lightweight and precise targeting and to react on time. Every action made by the customer is tracked. Actions could be transactions, logs on the company’s application, visits to the company’s physical location, website visits, conversations with the Chatbot, product-related or service-related actions.

    BANKING INDUSTRY
    If the bank defines a specific event with the following rules.

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    Whenever these conditions are fulfilled appropriate offer (for instance: Cash Loan) could be assigned to the customer which accomplished them.

     

    Complex Event Processing Module stands behind real-time monitoring of an immense number of events. CPE Module allows a company to create aggregated events, which are signaled when occur. The company’s employees could set rules according to the company’s needs. Obtained results will help them to decide how to approach their customers. Furthermore, the additional value of the solution is machine learning models. Event Tendency models calculate customer tendency to execute defined events. These models provide flexibility to the companies, many customers who have almost fulfilled all rules would not be missed. On the other hand, Event Prediction models predict how certain is a customer to complete defined events. Marking specific events related to customers and giving them convenient offers increases customer engagement and satisfaction.

    GAMBLING INDUSTRY
    If the betting platform defines a specific event with the following rules

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    Whenever this condition is fulfilled appropriate discount or bonus could be assigned to the customer which accomplished them.

     

    Event signaling module

    Signaling-based Analytics has started by the identification of all events related to a custom-er. Life events, work events, lifestyle events and events related to a company’s products and services. These events represent triggers for signaling in real-time that enables the company to create a highly personalized approach, conduct lightweight and precise targeting and to react on time. Every action made by the customer is tracked. Actions could be transactions, logs on the company’s application, visits to the company’s physical location, website visits, conversations with the Chatbot, product-related or service-related actions.

    BANKING INDUSTRY
    If the bank defines a specific event with the following rules.

    Image module

    Whenever these conditions are fulfilled appropriate offer (for instance: Cash Loan) could be assigned to the customer which accomplished them.

    Complex Event Processing Module stands behind real-time monitoring of an immense number of events. CPE Module allows a company to create aggregated events, which are signaled when occur. The company’s employees could set rules according to the company’s needs. Obtained results will help them to decide how to approach their customers. Furthermore, the additional value of the solution is machine learning models. Event Tendency models calculate customer tendency to execute defined events. These models provide flexibility to the companies, many customers who have almost fulfilled all rules would not be missed. On the other hand, Event Prediction models predict how certain is a customer to complete defined events. Marking specific events related to customers and giving them convenient offers increases customer engagement and satisfaction.

    GAMBLING INDUSTRY
    If the betting platform defines a specific event with the following rules

    Image module

    Whenever this condition is fulfilled appropriate discount or bonus could be assigned to the customer which accomplished them.

    Selecta’s success

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    NBO model empowers
    companies to recognize the
    clients who will purchase
    their products or services
    with 85% reliability.

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    Our ML algorithm enables
    banks to categorize correctly
    over 95% of customers’
    transactions and embrace
    new, prosperous
    opportunities.

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    Using machine learning
    powers companies with an
    immense capability to
    identify precisely potential
    premium clients.

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    NBO model empowers
    companies to recognize the
    clients who will purchase
    their products or services
    with 85% reliability.

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    Our AI algorithm enables
    banks to categorize correctly
    over 95% of customers’
    transactions and embrace
    new, prosperous
    opportunities.

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    Using machine learning
    powers companies with an
    immense capability to
    identify precisely potential
    premium clients.

    From unstructured data
    to a satisfied customer

    Selecta is collecting data from all possible sources, whether it is a website visit, a transaction in eBanking, or logs into a personal account in the app or website, a conversation with operators from Call center, a chat with a company chatbot, or a simple visit to a shop or branch office. This data is processed and represents inputs for machine learning models. Each of these models has outputs which represent benefit to the company – how to increase sales, retain existing customers, and increase customer satisfaction. The Next Best Offer model provides a personalized offer for your customers, Segmentation model provides better customer profiling, while Customer Lifetime Value predicts how much a company will have a value from each customer.

    From unstructured data to a satisfied customer

    Selecta is collecting data from all possible sources, whether it is a website visit, a transaction in eBanking, or logs into a personal account in the app or website, a conversation with operators from Call center, a chat with a company chatbot, or a simple visit to a shop or branch office. This data is processed and represents inputs for machine learning models. Each of these models has outputs which represent benefit to the company – how to increase sales, retain existing customers, and increase customer satisfaction. The Next Best Offer model provides a personalized offer for your customers, Segmentation model provides better customer profiling, while Customer Lifetime Value predicts how much a company will have a value from each customer.

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    Layer
    Layer
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    Layer
    Layer

    Models

    CUSTOMER SEGMENTATION

    The main aim of Customer segmentation is to provide a better understanding of customer behavior, as well as tracking movement of the customers between segments. Segments’ characteristics are used for suggesting the company how to approach which customer profile, and migrations between the segments enable tracking customer satisfaction.

    CHURN PREDICTION

    Churn model will recognize clients who intend to stop using a certain product or service. The goal of this model is early identification of customers who are likely to leave the company, so the company can react and retain them. This information could be available through Selecta’s 360 customer profile, or could be used for sending campaigns to the specific group of clients. The aim of this prediction model is to improve the retention rate. When it comes to using the churn model in banking, the model recognizes customers who have up to 9 times greater possibility to close the current account or leave the Primary segment.

    EARLY WARNING SYSTEM

    We are working on developing an early warning system (EWS) that helps credit risk analysts make quicker and more informed decisions. When banking is concerned, the Early Warning System model predicts the status of the bank’s corporate or small business customer for the next 6 months, based on transactions and other types of customer data. This implies that a potential or existing problem in their business can be identified, so it has minimum influence on the bank.

    INTELLIGENT TRANSACTION CATEGORIZATION

    Machine learning is used for transaction categorization in the banking sector. The model enables categorizes customer payments (card and domestic) by using a machine learning model. It enables the bank to track and monitor changes in spending categories and provide statistics to the customer through different channels. Furthermore, categories can be used for customer segmentation, understanding of customers’ habits, and personalized approach in terms of products and services bank offers to them.

    CUSTOMER LIFETIME VALUE

    Customer Lifetime Value predicts the value of a customer for a one-year period. It models customers’ purchasing behavior in order to predict what their future value will be.

    PREMIUM CLIENT DETECTION MODEL

    Premium Client Detection model enables the company to approach the customers who have the highest probability to become premium with the appropriate offers and shift them to the premium/VIP segment. This model calculates customer tendency to become premium based on his or her previous behavior towards the company and compares identified behavior with the behavior of the premium customer segment.

    SMART FINANCIAL PLANNING

    Smart Financial Planning consists of:
    » Spending Tendency Detection model
    The model tracks changes in customers’ spending behavior. It enables the identification of customer hobbies and habits.
    » Spending Tendency Prediction
    The model predicts customers’ spending on different
    spending categories, or subcategories.
    » Peer comparison
    Peer comparison represents predicting customer’s
    needs through comparison with his or her peers (for
    example: how to save, product recommendations).
    » Overspending prediction
    Overspending prediction recognizes time periods in
    which customers spend the most.
    » Saving Recommendation
    Identification of customer’s eligibility for savings.

    EVENT HUB SIGNALING

    EventHub signaling starts by identification of all events related to a customer. They include life events, work events, lifestyle events and events related to company products. All events represent triggers for signaling that enables the company to create a highly personalized approach, conduct lightweight and precise targeting and to react on time.

    Models

    The main aim of Customer segmentation is to provide a better understanding of customer behavior, as well as tracking movement of the customers between segments. Segments’ characteristics are used for suggesting the company how to approach which customer profile, and migrations between the segments enable tracking customer satisfaction.

    Churn model will recognize clients who intend to stop using a certain product or service. The goal of this model is early identification of customers who are likely to leave the company, so the company can react and retain them. This information could be available through Selecta’s 360 customer profile, or could be used for sending campaigns to the specific group of clients. The aim of this prediction model is to improve the retention rate. When it comes to using the churn model in the banking, the model recognizes customers who have up to 9 times greater possibility to close the current account or leave the Primary segment.

    We are working on developing an early warning system (EWS) that helps credit risk analysts make quicker and more informed decisions. When banking is concerned, the Early Warning System model predicts the status of the bank’s corporate or small business customer for the next 6 months, based on transactions and other types of customer data. This implies that a potential or existing problem in their business can be identified, so it has minimum influence on the bank.

    Machine learning is used for transaction categorization in the banking sector. The model enables categorizes customer payments (card and domestic) by using a machine learning model. It enables the bank to track and monitor changes in spending categories and provide statistics to the customer through different channels. Furthermore, categories can be used for customer segmentation, understanding of customers’ habits, and personalized approach in terms of products and services bank offers to them.

    Customer Lifetime Value predicts the value of a customer for a one-year period. It models customers’ purchasing behavior in order to predict what their future value will be.

    Premium Client Detection model enables the company to approach the customers who have the highest probability to become premium with the appropriate offers and shift them to the premium/VIP segment. This model calculates customer tendency to become premium based on his or her previous behavior towards the company and compares identified behavior with the behavior of the premium customer segment.

    Smart Financial Planning consists of:
    » Spending Tendency Detection model
    Model tracks changes in customers’ spending behavior. It
    enables identification of customer hobbies and habits.
    » Spending Tendency Prediction
    Model predicts customers’ spending on different
    spending categories, or subcategories.
    » Peer comparison
    Peer comparison represents predicting customer’s
    needs through comparison with his or her peers (for
    example: how to save, product recommendations).
    » Overspending prediction
    Overspending prediction recognizes time periods in
    which customers spend the most.
    » Saving Recommendation
    Identification of customer’s eligibility for savings.

    EventHub signaling starts by identification of all events related to a customer. They include life events, work events, lifestyle events and events related to company products. All events represent triggers for signaling that enables the company to create a highly personalized approach, conduct lightweight and precise targeting and to react on time.

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    Get to know
    your customers
    better.

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      Saga d.o.o. Beograd
      Member of New Frontier Group
      64a Zorana Djindjica Blvd.
      11070 Belgrade | SERBIA
      saga.rs
      selecta@saga.rs

      Get to know
      your customers
      better.

      GET LIGHTPAPER

      Request a lightpaper

        Saga d.o.o. Belgrade
        Member of New Frontier Group

        64a Zorana Đinđića Blvd.
        11070 Beograd | SERBIA

        saga.rs
        selecta@saga.rs