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

    DDATA COLLECTION AND PREPROCESSING

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    Leveraging immense amount
    of collected data for gaining
    essential customers insights

    ML MODELS
    DEVELOPMENT

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    Predicting customer behavior
    by using AI prediction
    and high-performance ML algorithms

    ENHANCING CUSTOMER RELATIONS

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    Creating personalized experienced
    and strong relations based
    on deep customer understanding

    Advanced analytics

    Machine learning models enable companies to personalize approach towards their customers, understand their needs and to create and successfully optimize customer journeys. Analytical module consists of the wide range of ML models, which is devided in three groups. These are: Core models, Spending models and Event-related models.

    Core models

    Core models are Next Best Offer models, RFM Customer Segmentation models, Churn prediction models and Customer Lifetime Value model.

    Enhanced campaigning process, increased conversion rate and customer satisfaction due to personalization as well as ensuring understanding of customer behavior and their preferences are key benefits of Next Best Offer and Customer segmentation models. With Churn prediction models the goal is early identification of customers who are likely to leave. Making decisions about the customers in which the company should invest the most and tracking effects of conducted campaigns are supported by Customer Lifetime Value model.

    Spending models

    Spending models are Transaction Categorization, Advanced Segmentation, Smart Finance Planning models (Overspending model, Saving plan recommendation) and Premium Detection model.

    Pointing out customers’ habits as well as creating personalized offers and bonuses based on customer behavior is supported by Spending models. Additionally, Transaction Categorization allows banks to track and monitor changes in customer spending. Advanced Segmentation model enables tracking migration between the segments, as well as fostering customer loyalty and satisfaction. Overspending detection and Saving Plan model help customers to be aware of their spending behavior and manage their finances better.

    Event-related models

    Event-related models are Event Tendency and Prediction models.

    Event models enable identifying customers’ tendencies for important events that a company has created. In this way, numerous customers who almost fulfill the rules would not be missed. Besides tendencies, event prediction models predict how certainly the customer will complete defined events. In a nutshell, the company is aware of customers who satisfy the events completely and also the ones who are very inclined to make them.

    Event signaling module

    Each customer’s action represents trigger for signaling in real-time that enables companies to create a highly personalized approach, conduct lightweight and precise targeting and react on time. Behind real-time monitoring of an immense number of events stands the advanced Complex Event Processing Module. This Module allows the company to create aggregated events, which are signaled when occur. The company’s employees could set rules according to their needs. Furthermore, event-related models provide significant flexibility in identification of the right customers.

    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.

     

    Event signaling module

    Each customer’s action represents trigger for signaling in real-time that enables companies to create a highly personalized approach, conduct lightweight and precise targeting and react on time. Behind real-time monitoring of an immense number of events stands the advanced Complex Event Processing Module. This Module allows the company to create aggregated events, which are signaled when occur. The company’s employees could set rules according to their needs. Furthermore, event-related models provide significant flexibility in identification of the right customers.

    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.

    Early Warning System module

    EWS module is powered with machine learning model which predicts the status of the corporation or small business client for the next 6 months, based on wide range of customer data. This implies that a potential or existing problem in their business can be identified, so it would have minimal influence on the company. In this way, the company will know that the problem exists and where to look for in further communication with the client. All statistics and visuals will be available over a user-friendly interface.

    Early Warning System module

    EWS module is powered with machine learning model which predicts the status of the corporation or small business client for the next 6 months, based on wide range of customer data. This implies that a potential or existing problem in their business can be identified, so it would have minimal influence on the company. In this way, the company will know that the problem exists and where to look for in further communication with the client. All statistics and visuals will be available over a user-friendly interface.

    Analytical 360° customer view

    Analytical 360° is user-friendly application that enables the companies to:

    • Track technical metrics and performances of the ML models
    • Have an overview of the general and individual customer data
    • Make data-driven business decisions

    Analytical 360° customer view

    Analytical 360° is user-friendly application that enables the companies to:

    • Track technical metrics and performances of the ML models
    • Have an overview of the general and individual customer data
    • Make data-driven business decisions

    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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    Reliability of our EWS model
    is about 80%
    in predicting
    default status of
    the bank’s client

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

    Selecta’s success

    Image module

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

    Image module

    Reliability of our EWS model
    is about 80%
    in predicting
    default status of
    the bank’s client

    Image module

    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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    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