Malaysia Telecom Data | Network Switching Sentiment Map | Price, Speed, Service, Roaming, Bundles | Switching Likelihood Index, Demographic Breakdowns

This data shows how consumers decide whether to remain loyal to a telecom provider or switch to a competitor, and it does so by measuring the factors that most influence their decisions. It captures the relative weight that consumers place on price, network speed, customer service, roaming quality, and bundled content, then produces an overall switching likelihood index that reflects how close a consumer is to making a change. Because this is consumer-reported data, it goes beyond what usage logs or network statistics can tell you. It does not just track what consumers do after they switch; it captures what they are thinking about before they switch, which is the critical lead indicator for churn. The key strength of this data is that it is not limited to one market or one provider. Rwazi is able to produce this type of consumer sentiment data in any country, for any operator, and at any frequency. It is as relevant for a prepaid-heavy market in Africa as it is for a postpaid market in Europe, as valuable for emerging economies as for mature telecom systems. This global reach means that the switching sentiment map is not just a dataset; it is a scalable framework for understanding how consumers in any market weigh their options. It is representative of the type of consumer-level insight that is possible when zero-party data is collected directly from the source. For telecom operators themselves, this data represents a competitive edge in churn management. Traditional retention strategies rely on waiting for consumers to show signs of disengagement—fewer top-ups, declining usage, missed payments—before intervention. By that point, churn is often inevitable. With switching likelihood data, operators can see where churn risk is building long before usage drops. If consumers in a given city report that price is the dominant driver of switching, and their switching likelihood index is rising, an operator can preemptively deploy promotions, adjust packages, or create targeted campaigns that blunt the risk. If, on the other hand, network speed emerges as the primary trigger, the operator can accelerate investment in coverage or emphasize speed improvements in advertising. This data gives operators the ability to align intervention with the factor that matters most to consumers, not with generic churn-prevention tactics that often miss the mark. For marketers, the value extends beyond telecom. Although the dataset is framed around provider switching, the underlying mechanics—capturing consumer sentiment on drivers of loyalty and risk of defection—are industry-agnostic. In financial services, consumers weigh fees, digital experience, customer service, and product bundles in similar ways before deciding whether to switch banks. In insurance, factors such as premium cost, claims service, and add-on benefits play the same role. Even in retail, consumers weigh price, product availability, service quality, and loyalty rewards when deciding whether to keep shopping at one store or move to another. The switching sentiment framework that Rwazi delivers is transferable, allowing any industry that cares about retention to borrow from the same approach. From an investor perspective, the ability to quantify switching likelihood at the consumer level offers unique foresight. Investors often evaluate telecom companies based on subscriber growth, ARPU (average revenue per user), and churn rates, all of which are lagging indicators. This data provides a leading indicator of churn by showing how consumers are leaning before they actually make a change. If investors see that a provider’s consumers score high on price sensitivity and high on switching likelihood, they can anticipate downward pressure on ARPU and rising churn in the next quarters. Similarly, if bundled content importance is rising across markets, investors can see that providers who build strong partnerships in entertainment, gaming, or streaming may be better positioned to defend market share. For regulators and policymakers, this type of data creates transparency into consumer welfare and market competitiveness. If consumers consistently report that price is the overwhelming driver of switching, it may indicate insufficient competition on service quality. If, conversely, service quality and roaming are rising in importance, it could reflect progress in network buildouts or the success of roaming agreements. By tracking switching sentiment across time, regulators can measure whether policy interventions are changing consumer perceptions and whether markets are becoming more balanced. This data also has applications for technology providers and equipment vendors. If network speed consistently emerges as the top switching driver in multiple markets, it signals a readiness for investment in 5G or other high-performance infrastructure. Vendors can use these insights to guide go-to-market strategies, ensuring that their pitches to operators are backed by evidence of consumer demand. Content providers can also find value here. If bundled content importance is high and rising, it shows that partnerships with telecom providers are a key gateway to consumer reach. For streaming services, gaming platforms, or music apps, this data quantifies how central telecom bundles are to their growth strategy. The broader significance of this dataset is that it demonstrates the global scalability of consumer-driven insights. The switching sentiment map shown here is representative of what can be produced anywhere in the world. The variables can be adapted, the frequency adjusted, and the granularity customized. Some clients may want weekly insights to monitor churn risk in real time. Others may want quarterly reports to align with strategy cycles. Some may focus on city-level segmentation, while others may prefer national benchmarks. Rwazi’s ability to deploy this model across geographies and industries means that what is shown here is not an isolated case study but a repeatable framework. From a business use case perspective, the possibilities are diverse. A global telecom company can benchmark switching sentiment across all its markets, identifying where churn risk is highest and which levers—price, speed, service, roaming, or bundles—will be most effective in reducing it. A marketing agency can use the same data to design campaigns that emphasize the factors that consumers care about most in each segment, creating hyper-relevant messaging that improves conversion and retention. An investment firm can use the switching likelihood index to anticipate financial results for telecom operators months before they are published, gaining an informational edge. Even a retailer outside telecom could use the framework to build their own switching sentiment model, replacing network speed with product availability or roaming with store location, and then apply the same method to their industry. Ultimately, this data provides clarity on consumer decision-making at the moment where it matters most: the decision to stay or to leave. By quantifying the relative weight of price, speed, service, roaming, and bundled content, it provides a multidimensional view of loyalty. By aggregating those dimensions into a switching likelihood index, it provides a benchmark that can be tracked over time and compared across markets. And by embedding this in a global, scalable platform, it ensures that no market is out of reach and no consumer segment is invisible. This is not only telecom data. It is a model for how to understand consumer loyalty and defection in any industry, in any geography, and at any cadence. It is representative of the global coverage that Rwazi provides, showing the power of zero-party data to unlock insights that traditional metrics cannot. For businesses of all kinds, the ability to see churn before it happens is transformative. This data makes that possible.