Motive SMP is an omni-channel service management platform that helps telecom customer service teams resolve issues faster and more efficiently. Powered by machine learning–driven workflows and proven across home, mobile, and field care channels, SMP enables service providers worldwide to boost customer satisfaction, strengthen brand loyalty, and significantly reduce operational costs.
Discover how machine learning (ML) can transform service delivery and how SMP ML solution meets the customer’s needs — by accelerating call resolution, automating routine tasks, and enabling proactive support through actionable, data-driven insights.
SMP has implemented ML using Dynamic Intelligent workflows (DIWF). Dynamic Intelligent workflows introduce some novel concepts – Activities, Context variables, Pocket of Flexibility (PoF) and Next Best Action (NBA) Engine.
An Activity is a type of workflow with a defined contract for data inputs and outputs. The explicit control of data scoping allows the Activities to be reordered without side effects. A Remediation Activity is a special type of activity used for problem resolution.
A Pocket of Flexibility (PoF) is a workflow which aims to solve a customer issue in the shortest possible time using multiple Remediation Activities. Upon execution, a PoF tries to solve a customer issue by executing these Remediation Activities in the most efficient order.
Next Best Action (NBA) Engine is a core component of the SMP ML solution, providing a runtime environment for executing ML algorithms. NBA Engine leverages ML to optimally choose the next best action to be executed based on what worked best before and how long it took, as learned from training data. The NBA training process uses historical data from PoF workflow executions to build a trained model for each PoF. These PoF models are based on prediction of the effectiveness and execution time of each remediation activity. The predictions are based on the context variables that have been selected for each PoF.
A powerful capability is the ability to define Context variables (ML features) for a Pocket of Flexibility. These variables enable the ML engine to select the most effective and time-saving Remediation Activity for a certain customer context. For example, certain home devices may respond differently to different remediations, so using the device type, firmware version, etc. will allow the workflow to automatically start with the best remediation for that context.
By learning from every execution, these DIWF-driven PoFs become a form of predictive maintenance, continuously optimizing how issues are resolved for each customer context.
Machine learning is the capability of a machine to learn from data and improve its performance over time without being explicitly programmed. It enables machines to analyze the data, recognize patterns, make predictions, and adapt to new information automatically. Data is the fuel for machine learning to work effectively:
SMP ML plays a pivotal role in improving the customer experience by providing fast, seamless and personalized interactions. It helps meet customer expectations by turning data into actionable insights.
Dropped calls, network outages, or billing issues are a few examples of common issues faced by telecom customers. By embedding machine learning into customer care portals, SMP can guide users to faster resolutions, and provide a seamless, user-friendly experience.
From interactive chatbots that offer instant support to intelligent workflows that suggest the best possible solution in best possible time, SMP covers it all and delivers experiences that feel effortless and intuitive. The result? Happier customers, increased loyalty, and stronger brand reputation. For telecom customers, this level of ML-driven personalization is becoming a key telecom customer experience differentiator that directly impacting churn and lifetime value.
Traditionally, system performance has been a significant concern for telecom customers. However, with the integration of ML, identifying performance bottlenecks and resolving them has become faster and more efficient. This enhanced efficiency is achieved through several SMP key capabilities:
Telecom customers aren’t just buying connectivity — they’re buying experiences. They expect telecom service providers to understand their needs, anticipate their problems, and offer seamless, personalized experiences across every touchpoint. Whether it’s a self-service portal, a chatbot conversation, or a troubleshooting workflow, personalization has moved from a “nice-to-have” to a strategic differentiator.
For telecom customers, personalization means seeing relevant information right when they need it—whether it’s a dashboard that shows exactly how much data they’ve used, a chatbot that remembers their previous issues, or timely alerts about potential service disruptions in their area. It also means receiving helpful recommendations tailored to their usage, like suggesting a better plan or an add-on service that fits their needs.
SMP meets the growing demand for personalized customer experiences through its React framework–based customer care service portal, which seamlessly integrates with multiple backend systems to deliver tailored support. Businesses can design workflows aligned with specific operational needs and customer scenarios, ensuring flexibility and efficiency. At the heart of this personalization is SMP’s Dynamic Intelligent Workflow (DIWF) engine, which leverages machine learning to automatically analyze customer and device data in real time. By understanding the unique context—such as device type, issue history, and user behavior—DIWF intelligently determines the most effective remedy. For instance, it may recommend a simple reboot for one device while initiating a reset for another, all without requiring manual intervention. This intelligent automation not only accelerates issue resolution but also enhances the overall customer experience, making interactions faster, more accurate, and highly personalized.
Personalizing customer experiences can play a big role in transforming how customers engage with your brand — ultimately leading to higher satisfaction, loyalty, and lifetime value.
Proven success stories build customer confidence and strengthen brand loyalty. Leading Tier-1 telecom giants worldwide are already partnering with the Motive SMP brand—and are seeing measurable improvements in customer care experience and operational efficiency.
These DIWF use cases show how Machine Learning in a service management platform can simultaneously improve telecom customer experience, reduce OPEX, and increase service reliability across high-speed internet and VoIP services.
Case Study 1: High-Speed Internet (HSI) – Reducing Outages & Truck Rolls Through Predictive Troubleshooting
Challenge:
A Tier-1 broadband provider in APAC was struggling with high call volumes related to HSI issues such as:
These issues often resulted in repeat calls, long handling times, and unnecessary technician dispatches.
Solution:
SMP’s ML-driven dynamic intelligent workflows were deployed to:
Impact:
Significant uplift in NPS, with customers experiencing faster resolution and fewer service interruptions
Case Study 2: VOIP – Rapid Resolution of Call Failures Using ML-Based Diagnostics
Challenge:
VOIP subscribers for a Tier-1 operator in APAC often reported issues such as:
Such cases typically required deep dives into provisioning, SIP registration status, profile mismatches — leading to long support interactions.
Solution:
ML-driven DIWF leveraged SMP’s unified integration to:
Impact:
Conclusion:
Across HSI, IPTV, and VOIP services, SMP’s ML-based dynamic workflows have proven to:
By connecting to multiple backend systems, analyzing live data, and executing corrective actions, SMP empowers service providers to deliver resilient, efficient, and customer-centric technical support at scale.
To stay ahead in a competitive market, CSPs are accelerating investments in telecom AI, moving from reactive support to fully autonomous, agentic AI-enabled customer care. With rapidly emerging AI technologies, telecom service providers are leaning towards predictive use cases. They’re expecting a system with much more intelligence to predict the issues before they occur by recognizing the pattern and performing corrective action to avoid outages and service disruption. SMP product roadmap is already aligned with future needs of our customer and adopting also moving from reactive to predictive and more of Agentic AI where system handles everything from identifying the issue and performing actions without human intervention. Agentic AI will be a new game changer and will take customer experience to the next level.
As we move forward, the combination of AI-driven automation, hyper-personalization, and always-on support will define the next level of customer experience—seamless, predictive, and built around real customer needs. ML-based DIWF can co-exist with newly developed AI models for predictive use cases.
Maximizing business value always starts with keeping customers happy — and that’s where machine learning (ML) can really make a difference. In the telecom world, SMP ML helps service providers not only improve customer satisfaction but also drive better efficiency and higher returns. Here’s how:
Enhanced Customer Satisfaction and Loyalty
Workflow Optimization through Intelligent Automation
Reduction in OPEX and CAPEX
Easy Maintenance and Continuous Learning
The best part about ML is that it keeps getting better over time. SMP ML learns from past data and interactions, improving accuracy and performance automatically — which makes ongoing maintenance simpler and less resource-intensive.
Unified Omnichannel Experience
Whether a customer connects through the app, website, chatbot, or call center, SMP’s ML-driven workflows ensure a consistent experience across all channels through the use of reusable activities. This approach minimizes the effort required to maintain separate workflows for different platforms while providing customers with a smooth, seamless journey.
Faster Time to Market with Built-in Flexibility
SMP helps telecom providers bring new services and features to market much faster. With its reusable workflows and activities, teams can easily adapt existing processes instead of building everything from scratch. The platform’s built-in design tools and intuitive, rich UI framework make it simple to create and customize solutions that fit different business needs. Plus, its easy integration with existing systems means less time spent on complex setups and more time focusing on innovation. Together, these capabilities help telcos respond quickly to changing market demands while maintaining high quality and consistency across offerings.