Sriya.AI's World's 1st Large Numerical Model (LNM) Predicts Wireless Subscriber Churn with 99% Accuracy

Large Numerical Model (LNM) is to numbers what LLM is to text. Sriya.ai's LNM powered by its proprietary AI Square algorithm created history last week.

ATLANTA, GA, USA, May 6, 2024 /EINPresswire.com/ -- Sriya.AI, an emerging deep-tech leader in numerically inclined artificial intelligence solutions, is empowering telecom and wireless companies to drastically improve their internal operations while mitigating key risks associated with the industry. Telecom and wireless companies have suffered significantly with high customer churn rate and a low new subscriber rates, a double whammy, which has resulted in much lower profits, valuations and stock price.

Predicting which customers are likely to cancel their subscription and take proactive measures to retain them is a problem for every consumer facing industry. This problem is critical for telecom industries, as it directly impacts their revenue and customer base. To solve this problem, telecom companies collect data on customer behavior, such as call duration, internet usage, customer demographics, and other variables. This data is used to train machine learning models to identify patterns that are indicative of customers who are likely to cancel their subscription. The models can then be used to predict which customers are at risk of cancellation of subscription, allowing companies to develop targeted retention strategies such as offering discounts or upgrading services to retain these customers.

Telecom companies have deployed many AI-ML models based on XG Boost, Random Forrest or Deep Neural Networks to identify the root causes for high subscriber churn with 85-91% prediction accuracy. However, no model till now anywhere in the world has published a 99% or greater prediction accuracy. lat week, Sriya.AI's introduced the World's 1st Large Numerical Model (LNM) which was able to predict wireless subscriber churn with greater than 99% accuracy and precision and without any hallucination effect (gross errors) associated with LLMs.

Large Numerical Model (LNM) is to numbers what Large Language Model (LLM) is to text. Sriya.AI's LNM is powered by its proprietary AI Square algorithm. AI Square involves AI teaching or learning from AI to improve business outcomes which in this case were improving subscription rates and decreasing customer churn rate.

The data set used consisted of over 100 variables and 1 million customer records. This data set contains different variables explaining the attributes of telecom industry and various factors considered important while dealing with customers of telecom industry. The target business outcome variable here is “subscription_status” which explains whether the customer will subscribe or not. We used this data set to predict the customers who would subscribe or who wouldn't subscribe depending on various variables available. The results revealed a great accuracy rate of 99% and an area under ROC curve (AUC) score of 0.99 for the Sriya model, showing its effectiveness in accurately predicting subscription rate. In addition, Sriya.AI was able to identify the root causes for low subscription and high churn rates and also provide actionable insights which when implemented could result in improved subscription and reduced churn rates.

The Sriya (SXI) technology framework employs a robust combination of mathematical techniques, including statistical analyses, algebraic operations, and machine learning algorithms, to process data effectively and generate comprehensive scores. Initially, the Base SXI score is calculated through statistical measures and algebraic computations, forming the foundation for subsequent steps. 5-10 Machine learning algorithms further refine this score to create the Final SXI scores, which serve as benchmarks for business outcomes. The Reinforcement Agent optimizes the system dynamically by adjusting weights based on performance, employing deep learning architectures and iterative processes to enhance correlation between SXI scores and outcomes. In the optimization process, the Reinforcement Agent adjusts weights iteratively, rewarding positive improvements in both SXI scores and class delineation accuracy. It penalizes lack of improvement by providing adjusted weights along with additional weightage for important features. Through this iterative approach, the SXI scores are continuously refined until maximal accuracy is achieved, enabling better decision-making regarding business outcomes.

“Sriya.AI's LNM improves outcomes for these critical processes, offers an enhanced customer experience while increasing revenue and decreasing losses,” says Srinivas Kilambi, CEO of Sriya.AI and inventor of AI Square. “Each of these areas can be broken down into numbers, and our algorithms can turn data from any telecom/wireless company into a clear source of innovation.” Telecom services companies collect data with every customer interaction and employee action. Sriya.AI’s system, which includes 5 US provisional patents and has already revolutionized operations at more than 25 companies, leverages its own creation–large numerical models–to empower businesses to detect churn and accurately identify high-reward subscribers with up to 99% accuracy.

Behind these high-value insights is Sriya.AI’s agnostic Machine Learning tool, which has nearly perfect accuracy and precision while requiring 30 times less data than today’s traditional models. AI Square leverages a company’s unique data, allowing it to check, fix, and use data to increase its value over time. Rather than just providing analysis and insights, Sriya.AI products allow AI to learn from AI to provide tailored insights that continue to improve over time. These developments are what separate large numerical models (LNMs) from large language models (LLMs), placing a distinct focus on improvements with accuracy to learn, grow, and optimize business outcomes.

Insights based on patterns in data can lead telecom companies to gain or lose money depending on their accuracy. At the end of the day, reducing risk in these select areas is the key to remaining competitive in the rapidly changing telecom industry. From improving customer retention to increasing informed decision-making, artificial intelligence can support telecom/wireless companies starting on day one. Sriya.AI shows that these changes are possible and achievable with the right approach to AI solutions. All that is needed is the will to accept that change is the only constant in business and companies need to innovate or they could perish.

Srinivas Kilambi
Sriya.AI LLC
+1 770-714-0963
sk@sriyaai.com
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