Enhance the speed, precision and effectiveness of human efforts with AI in telecom and media

Automate traffic forecasting and ROI analysis, making your rollout investments more accurate and optimized. With SAS, you can predict and refine capacity planning at a detailed level using key traffic KPIs. By combining your engineers' expertise with machine learning algorithms, we make optimizing network investments not only quicker and easier, but also more reliable, giving you trustworthy insights for fact-based decision making.

The value of this solution:

• Cost savings: Customers have seen payback in as little as seven months, with net benefits estimated at up to $5.2 million.
• Operational efficiency: Customers have reduced network rollout time by 40% compared to traditional design methods and cut the forecasting process by up to 90% compared to using Excel.
• Highly accurate forecasting: Customers have increased forecast accuracy by an average of 25% compared to their current radio-planning tools.

AI techniques used in this solution: 

• Machine learning automatically identifies cells in the network in need of network enhancements.
• Forecasting enables you to quickly and reliably predict outcomes and plan better for the future by automating large-scale time series analyses and hierarchical forecasts.

How AI helps:

• Optimize CapEx by only rolling out network quality enhancements when they're actually needed.
• Avoid unnecessary network capacity investments by understanding when traffic spikes are anomalies and when they're not.
• Predict necessary upgrades so you can plan for multiple site visits on the same day, saving time and reducing OpEx.

The AI models provide:

• Automated, highly accurate mobile traffic forecasting, access-network traffic forecasting, and ROI analysis of valuable customer usage and movements.
• Automatic identification of areas that need network enhancements, including optimizing latency and bandwidth for critical workloads.

Predict fraud and identify fraudster network rings based on changes and inconsistencies in customer behavior patterns using adaptive machine learning, reinforcement learning and social link analytics. Identify fraudulent transactions in real time and reduce false positives to protect revenue and the customer experience.

The value of this solution:

• Increased revenue: Customers have reduced bad debt by more than 25%.
• Fraud detection and prevention: Customers have significantly reduced fraud by more than 75%.
• Risk mitigation.
• Customer satisfaction.

AI techniques used in this solution:

Adaptive machine learning predicts fraud based on changes and inconsistencies in customer behavior patterns and provides automated model building and real-time risk assessments.

How AI helps:

• Identify fraud in real time to reduce revenue loss.
• Stop first-party, third-party and synthetic ID fraud before fraudsters can open new accounts.
• Mitigate payment fraud risk without impacting the customer experience.

The AI models provide:

• Identification of emerging threats and automatic suggestions of new rules and scenarios in real time for the most accurate transaction risk assessment.
• Detection of changing population behaviors via automated model building so that algorithms get smarter and deliver more accurate results.
• Better fraud detection by confirming the most effective machine learning algorithms through champion-challenger evaluations, simulations and benefit estimations.

Manage your call center schedule, forecast incoming calls, streamline complaints processes and automate conversation transcript analysis. Use insights from transcripts and even social media to improve communication and prevent customer churn. Decrease complaint handling time and costs while increasing the volume handled. Improve resource planning to boost productivity. AI and GenAI help you capitalize on large language models (LLMs) to cut costs and create niche use case integrations.

The value of this solution:

• Cost savings: Customers have saved up to 15%.
• Operational efficiency: Customers have reduced complaint handling time by up to 40% and increased the volume of complaints handled by up to 20%.
• Better customer experience and lower churn.
• Greater productivity.

AI techniques used in this solution:

• Machine learning, optimization and natural language processing enable you to create highly accurate forecasts, analyze communication, make operational improvements and improve customer service.
• LLMs help you understand and update model metadata. Analytical capabilities like forecasting, machine learning and deep learning can be easily integrated with LLMs and GenAI in business processes, delivering real value through automation and ease of use.

How AI helps:

• Create a more consistent customer experience with increased personalization.
• Upskill employees faster.
• Reduce complaint handling time and associated costs.
• Increase call center productivity and efficiency.
• Lower overhead costs by needing fewer call center staff.
• Prepare schedules faster, accommodate ad-hoc changes and generate a new schedule quickly and accurately.
• Enable better cooperation with employees and adapt quickly to employees' flexible scheduling needs.
• Ensure the best possible KPIs.
• Improve sales by including Next-Best Offer/Next Best Action (NBO/NBA) along with anti-churn tasks.

The AI models provide:

• Improved resources and skill sets based on a number of call-center queues, opening hours, available shifts and SLAs to fulfill.
• The ability to meet customer SLAs.
• Automation and efficiencies.
• Accelerated schedule preparation.
• Quick and accurate rescheduling.
• Improved KPIs (SLAs are kept).

Unlock valuable insights from physical documents for better decision making and analytics, even from blurry documents, forms with checkboxes or handwritten papers. Intelligent document processing quickly structures and extracts information from paper files – like NPS feedback forms and site documentation – delivering greater accuracy.

The value of this solution:

• Greater productivity.
• Maximized operational efficiency.
• Reduced complexity.

AI techniques used in this solution:

Computer vision models, machine learning models, natural language processing and text analytics decipher and extract information from paper documents. Combining text analytics, computer vision and optical character recognition methods provides greater value than any individual technique.

How AI helps:

• Significantly reduce the hours required for staff to research and find information.
• Expand the amount of useful information available for insights.
• Score RFPs on the predicted likelihood to close based on the attributes extracted from the documents.
• Modernize and improve customer feedback collection so you can use all feedback instead of just a sampling.
• Modernize and accelerate the agreement- and contract-handling process.
• Modernize claims filing and information systems.

The AI models provide:

• Automate the extraction of key information from images or documents into a structured format for better analytics and decision making.
• Supplementation of any current ocular character recognition/robotic process automation (OCR/RPA) processes to significantly improve the accuracy and quality of the information extraction.
• The ability to search extracted data and use that data for investigations, measurements or feeding existing processes.
• Analysis of vast quantities of unlabeled agency formats to identify common instructions templates.

Gain a 360-degree view of your customers by integrating data from all sources in real time. This enables quick decisions that enhance engagement. Break down data silos by using a unified analytics platform that incorporates transactional systems, social media and IoT devices. Interactive dashboards visualize customer journeys and highlight touch points across all channels. Activate your unified customer data with AI-powered journey orchestration to deliver the right message to the right customer at the right time and through the right channel.

The value of this solution:

• Greater customer engagement, including higher conversion rates.
• Increased revenue and productivity: Spend less time and effort to create and execute more effective marketing campaigns.
• Faster decision making: Intelligent decisioning has helped customers grow their share by 5% during a market contraction.
• Maximized agility.

AI techniques used in this solution:

• Machine learning provides deep learning algorithms that analyze complex customer data patterns and reinforcement learning that continuously improves decision strategies.
• ​Natural language processing interprets unstructured customer feedback.

How AI helps:

• Extract actionable insights from massive volumes of business and customer data.
• Uncover associations contained in the data ​so you can compile distinct journeys.
• Enable real-time decisions by analyzing data in motion and take immediate action on customer behavior as it happens.
• Optimize engagement with analytics that determine the next-best action (NBA) for each customer interaction.
• Improve decision quality and increase your competitive advantage by incorporating AI models from a common repository created in your preferred language.

The AI models provide: 

• Sophisticated A/B/n testing that allows for multi-armed bandit experiments to continuously optimize customer interactions.
• Predictive, real-time capabilities that let you forecast customer behavior and trigger immediate responses.
• A visual interface that enables self-service data preparation so users across the business can analyze data.
• Interpretation of unstructured customer feedback.

Empower marketers with generated synthetic data to complete data that reflects the diversity of their customer base, enabling them to understand customer behaviors without infringing on privacy or introducing bias.

The value of this solution:

• Increased revenue and market share.
• Accelerated innovation.
• Greater productivity.
• Maximized operational efficiency.

AI techniques used in this solution:

Synthetic data provides a cost-effective solution for filling data gaps, including specific customer subsegments or irregular network issues. It also protects sensitive private and proprietary information.

How AI helps:

• Save money on the high costs of data collection.
• Improve precision in predictions, reducing errors in risk assessment while retaining explainability for decisions.
• Streamline analytics, accelerating data processing and decision making.
• Create more effective models with improved data quality.
• Optimize production processes for greater efficiency and productivity.
• Increase data monetization safely, avoiding legal risk.

The AI models provide:

• Trustworthy, simplified data augmentation and generation.
• More complete data that can make the data set more useful.
• Better protection of sensitive data.

Put the power of personalized advertising in your brand’s hands. This ad delivery solution works in your ecosystem and enables effective ad personalization based on your marketing and advertising data.

The value of this solution: 

• Better customer experience: Customers have increased program viewership by 80% in just four years.
• Increased revenue: Customers have increased ad revenue by 10%.
• Scalability: Customers have managed 1,500 personalized campaigns monthly and scaled from serving 36 billion to 60 billion ads per year.
• Regulatory compliance maintained.

AI techniques used in this solution:

Machine learning enables you to manage customers, business rules and analytics in a converged solution.

How AI helps:

• Make ad content decisions based on real-time testing and live results.
• Create a positive feedback loop that encourages iteration and improvement through personalized advertising.
• Improve advertising efficiencies by streamlining the ad-serving process, reducing the need for manual data movement.
• Utilize the zero-, first- and second-party data and audience information you need without the cost and risk associated with data movement and duplication.
• Increase ad personalization and targeted ad precision with information on customer behaviors, interests and demographics that reside outside of traditional advertising solutions.
• Create more robust audiences using data from multiple platforms, resulting in better performance and ROI.
• Maintain control over your advertising delivery engine so you can understand which data and analytical models you're using to deliver personalized advertising on your owned and operated channels.

The AI models provide:

• Automation of delivery, user data flows and campaign optimization.
• A deep understanding of viewer preferences for highly personalized campaigns.
• Highly-targeted, real-time ad decisioning on an immense scale.

Grow your business by improving forecasting and optimizing ad inventory and pricing. Gain automated rate card creation and campaign-specific dynamic pricing recommendations. Analyze cross-platform audience data to identify the target audience for accurate forecasts, seasonality, past performance, channel and more. Provide ad buyers with clear, tangible ROI.

The value of this solution:

• Increased revenue.
• Highly-accurate forecasting.
• Greater productivity.
• Competitive advantages.

AI techniques used in this solution:

Machine learning enables reliable forecasting faster and explores more alternative scenarios to provide optimization strategies for inventory and pricing.

How AI helps:

• Identify your target audience so you can achieve key attention metrics and provide ad buyers with clear ROI.
• Quickly and automatically produce large numbers of reliable forecasts, including large-scale time series analyses and hierarchical forecasts.
• Optimization enables you to consider more alternative pricing scenarios and determine the right ad price to accomplish revenue and inventory sales goals.

The AI models provide:

• Fast, automated and reliable forecasting for accurate information and increased productivity.
• The ability to optimize pricing and inventory.