Predictive Analytics

The complexity of business environments today means that identifying rules and connections between different features is often beyond human processing capabilities. Predictive analytics brings together advanced analytical capabilities, including statistical analysis, predictive modeling, data mining, text analytics, optimization, real-time scoring and machine learning for the prediction of data or events. Bell.One™ Predictive Analytics solution helps businesses and organizations to discover patterns and trends in structured and unstructured data so they can go beyond knowing what has happened to anticipating what is likely to happen next. Based on a single platform, Bell.One™ Predictive Analytics helps both business and technical users quickly explore data and develop actionable insights.

Key Features

  • Focus on a wide range of business needs and skill sets. Affords an easy-to-use predictive analytics solution that meets specific needs of different users and industries and accommodates skill levels from beginners to advanced.
  • Comprehensive predictive analytics capabilities. Provides a deep library of machine learning algorithms, data preparation and exploration functions, and model validation tools in one single platform.
  • Support of multiple data sources and environments. Easily connects all data sources and streamlines the data mining process to extract the most value from data and create highly accurate predictive models.
  • Real-time decision optimization capability. Delivers timely recommendations during customer interactions to support an intelligent decision-making process.
  • Easier insights with advanced visualization techniques. Provides a single platform for analytics visualization, enabling the user to identify data patterns and relationships that are not initially evident.
  • Ability to create, collaborate and share insights with the entire team. Shares insights with a few clicks on the web and mobile devices.

Use Cases

Banking & Financial Services

The banking and financial services industry with its enormous repositories of transactional and customer profile data, has rich potential for the application of predictive analytics.
  • Supporting consumer analytical models focused on customer lifetime value analysis, customer service call center analytics and deposit growth analytics
  • Influencing customer purchase behavior through real-time targeted and personalized offers
  • Reducing financial losses through real-time fraud detection and prevention
  • Optimizing delinquency models that can predict the probability of loan default
  • Augmenting card and customer data with new-age parameters to derive competitive product pricing models, innovative rewards, assess creditworthiness for underwriting and recommending optimal lines of credit
  • Deriving deeper insights into portfolio performance, liquidity positions and working capital requirements


Predictive analytics is applied to many areas of the retail industry. It can be a huge benefit to retailers, enabling them to plan their business in every aspect and respond quickly to market changes. In addition to customer behaviors, predictive analytics also tracks economic indicators, promotions, discounts and allocation between stores to optimize stock management and supply chain. This information allows retailers to allocate the right products to the right store at the right time and, thereby, avoid product waste.
  • Predictive analytics helps retailers obtain insights about customers’ purchase history and foresee their next actions and make recommendations about relevant products.
  • Targeted campaigns, based on collecting and analyzing data from various data sources such as demographics and market insights, lead retailers to accomplish higher conversion rates.
  • Predictive analytics collates product demand, pricing history, competitor activity and inventory levels and automatically sets optimal prices in order to respond to market changes in real-time.
  • Predictive analytics helps a company to invest wisely by forecasting the probable revenue from a potential store location based on demographics, property market, competitive activity, market conditions, customer purchasing power, purchase behaviors, etc.

Oil, Gas & Utilities

The energy industry is facing an urgent need to operate at the highest levels of efficiency while increasing productivity and controlling costs. For the oil, gas & utilities industries, production forecasting is a highly complex task, requiring advanced predictive analytics tools to achieve robust forecasts. A predictive analytics solution uses historical operational characteristics for each energy asset and compares it to real-time operating data to detect subtle changes in equipment behavior. The predictive analytics helps to identify changes in system behavior well before traditional operational alarms, creating more time for analysis and corrective action. Specifically, it has the capability to
  • Present an organized approach for the maintenance and process-control of production operations across various assets
  • Identify and mitigate potential operational and financial risk factors and avoid situations endangering workforce or operations
  • Leverage available data to optimize production processes and secure constant operations quality

Government & the Public Sector

Government agencies have been key players in the advancement of computer technologies. To better understand and respond to citizens’ needs and allocate public resources more efficiently, these agencies use predictive analytics to leverage data and develop innovative solutions to contemporary urban challenges, improve service and performance, and better understand citizens’ behavior. They also use predictive analytics to detect and prevent fraud and enhance cybersecurity.
  • Use demographic data to gauge public sentiment, create better policies and deliver on needed services
  • Provide a fast and powerful instrument for detection, analysis and prevention of fraud
  • Provide predictive support for traffic forecasting and flow analysis, weather and climate study, and flight and shipping tracking

Health Insurance

Predictive analytics has the power to change the way providers support health and wellness by equipping individuals with knowledge to manage their health and drive preventative care. In addition to detecting claims fraud, the health insurance industry is taking steps to identify patients most at risk for chronic disease and to find what interventions are best.
  • Adding value with personalization helps insurance providers create meaningful relationships with members, fostering an environment where customer loyalty can thrive.
  • Equipped with knowledge from predictive analytics, insurers can better engage with members on wellness and prevention early in their lives. They can build a stronger relationship from the get-go, fostering loyalty and improving member retention.


Manufacturers are not only interested in quality control but also in ensuring that the whole plant is functioning at an optimal level – uptime, staff efficiency, timely measurements, and the best product possible. With predictive analytics, it is possible to improve manufacturing quality, increase equipment return on investment and overall equipment effectiveness and anticipate needs across the plant and enterprise. In addition, it is able to enhance a brand’s reputation, outpace the competition, and ensure consumer safety. For manufacturers it is very important to identify factors responsible for reduced quality and production failures as well as to optimize parts, service resources, and distribution.
  • Predictive analytics looks at the history of machine failures and compares those instances to sensor data from the machine to spot patterns before a breakdown.
  • Machine utilization combines demand forecast with resources on the manufacturing floor to achieve an optimal schedule.


Telecom providers have experienced unprecedented data growth in the last few years. The advent of smartphones, mobile broadband, peer-to-peer traffic, growth in mobile data volume, social media chatter and the increase in video-based services have all contributed to a greatly increased amount of data. Predictive analytics delivers insights to help telecom service providers proactively improve customer experience, reduce costs and increase margins. Predictive analytics helps telecom providers to make special customer offers to reduce customer churn. Based on data about big public events, daytime migration and information about competitors, telecom providers can plan their resources and form better rate plans.
  • Predictive analytics is applied to track customer-related data in real time and predict problems with accessing basic telecom services (issues with voice, data, and SMS, such as unsuccessful connection setups, dropped calls, low bandwidth, etc.).
  • Analysis of consumption of services and bandwidth in specific regions helps telecom providers with planning locations for infrastructure investment.
  • Telecom providers use predictive analytics to protect their customers and their bottom line by proactively detecting fraudulent activities.
  • Telecom providers can generate more revenue and create better customer experience by tracking and analyzing customer clickstreams to understand their preferences and propensity to buy.

How it works

Predictive analytics is based on applying known results to develop (or train) a model that can be used to predict values for new data. Simulation provides results in the form of a forecast that represents the probability of the target variable (for example, revenue or income) based on estimated value derived from a set of input variables.

There are two types of predictive models:
  • Classification models
  • Regression models
Three of the most widely used predictive modeling techniques are decision trees, regression and neural networks:
  • Regression (linear and logistic) is one of the most popular methods in statistics. Regression analysis estimates relationships among variables.
  • Decision trees are classification models that partition data into subsets based on categories of input variables.
  • Neural networks are powerful and flexible techniques capable of modeling extremely complex relationships. The power lies in their ability to handle nonlinear relationships in data, which are increasingly common as more data is collected.


  • Increased revenue and profitability with deeper customer-related insights
  • Improved decision-making process
  • Increased competitive edge
  • Easy access to the latest innovations
  • Streamlined operations
  • Risk reduction and fraud detection
Get in touch with us today!