Subex Blog

Get access to a curated list of the latest blog posts covering a wide range of topics from the telecom industry.

Archives: Social Articles

What literacy was for the past century, data literacy is for the twenty-first century. Most employers now prefer people with demonstrated data abilities over those with higher education, even data science degrees. According to the report, only 21% of businesses in the United States consider a degree when hiring for any position, compared to 64% who look for applicants who can demonstrate their data skills. When data is viewed as a company’s backbone, it’s critical that corporations assist their staff in properly utilizing data.

What is Data Literacy?

The capacity to understand, work with, analyze, and communicate with data is known as data literacy. It’s a talent that requires workers at all levels to ask the right questions of data and machines, creates knowledge, make decisions, and communicate meaning to others. It isn’t only about comprehending data. To be educated, you must also have the confidence to challenge evidence that isn’t performing as it should. Literacy aids the analysis process by allowing for the human element of critique to be considered.

Not only for data and analytics professions but all occupations, organizations are looking for data literacy. Companies that rigorously invest in data literacy programs will outdo those that don’t.

Why is it Important?

There are various components to achieving data literacy. Tools and technology are important, but employees must also learn how to think about data to understand when it is valuable and when it is not. When employees interact with data, they should be able to view it, manipulate it, and share the results with their colleagues. Many people go to Excel because it is a familiar tool but, confining data to a desktop application is restrictive and leads to inconsistencies. Employees receive conflicting results even though they are looking at the same statistics because information becomes outdated. It’s beneficial to have a single platform for viewing, analyzing, and sharing data. It provides a single source of truth, ensuring that everyone has access to the most up-to-date information. When data is kept and managed centrally, it is also much easier to implement security and governance regulations. Another vital aspect of data culture is having excellent analytical, statistical, and data visualization capabilities. Complex data may be made easy using data visualization, and simple humans can drill through data to find answers to queries.

Should Everyone be Data Literate?

A prevalent misconception regarding data literacy is that only data scientists should devote time to it; instead, these skills should be developed by all employees. According to a Gartner Annual Chief Data Officer (CDO) Survey, poor data literacy is one of the main roadblocks to the CDO’s success and a company’s ability to grow. To combat this, 80% of organizations will have specific initiatives to overcome their employees’ data deficiencies by 2020, Gartner predicts. Companies with teams that are literate in data and its methodologies can keep up with new trends and technologies, stay relevant, and leverage this skill as a competitive advantage, in addition to financial benefits.

How to Build Data Literacy?

1. Determine your company’s existing data literacy level.

Determine your organization’s current data literacy. Is it possible for your managers to propose new projects based on data? How many individuals nowadays genuinely make decisions based on data?

2. Identify data speakers who are fluent in the language and data gaps.

You’ll need “translators” who can bridge the gap and mediate between data analysts and business groups, in addition to data analysts who can speak naturally about data. Identify any communication barriers that are preventing data from being used to its full potential in the business.

3. Explain why data literacy is so important.

Those who grasp the “why” behind efforts are more willing to support the necessary data literacy training. Make careful to explain why data literacy is so important to your company’s success.

4. Ensure data accessibility

It’s critical to have a system in place that allows everyone to access, manipulate, analyze, and exchange data. This stage may entail locating technology, such as a data visualization or management dashboard, that will make this process easier.

5. Begin small when developing a data literacy program.

Don’t go overboard by conducting a data literacy program for everyone at the same time. Begin with one business unit at a time, using data to identify “lost opportunities.” What you learn from your pilot program can be used to improve the program in the future. Make your data literacy workshop enjoyable and engaging. Also, don’t forget that data training doesn’t have to be tedious!

6. Set a good example

Leaders in your organization should make data insights a priority in their own work to demonstrate to the rest of the organization how important it is for your team to use data to make decisions and support everyday operations. Insist that any new product or service proposals be accompanied by relevant data and analytics to back up their claims. This reliance on data will eventually result in a data-first culture.

So, how is your organization approaching Data Literacy? Is it one of the strategic priorities? Is there a plan to get a Chief Data Officer? Feel free to share your thoughts in the comments section below.

Download Our Pov on Getting Started with Augmented Analytics

Download Now!

Big Data has been making significant strides in the last decade. Databases have grown to be so large, complicated, and fast-moving that the traditional systems are no longer capable of handling them. Consequently, the duties of data analysts are becoming increasingly challenging that eventually leads to discrepancies and unreliable conclusions. This is where augmented analytics comes in to save the day. It ensures democratization of AI across the entire data value chain. It also automates most of the processes involved in analytics and gives bite-sized inputs for human analysts. However, with a new augmented analytics tool emerging in the market every day, it might be difficult for analysts to choose the best one from the available options. So, in this blog, we have put together a set of ten things to look for in an augmented analytics platform.

1. Conversational Analytics

This feature enables you to converse with your data using conversational AI and natural language processing (NLP). With Conversational AI, analytics becomes more like a Google search where you get your query answered in natural language. This analytics model uses a natural language query (NLQ) to ask questions to the analytics platform and get answers to the questions in a natural language using Natural Language Generation (NLG). This simplicity eliminates the requirement for technical expertise in query languages such as SQL, R, or Python, thus enabling anyone in the organization to become a citizen data scientist.

2. Automated ML

Machine Learning deciphers patterns from historic data and understands things that are invisible to the human eye. For example, ML models reveal linkages and hidden patterns in the datasets in a much more efficient way. Also, the processing is done much faster.

Automated ML also simplifies the otherwise time-consuming processes such as the creation of dynamic what-if simulations, speculation of information such as trends, significant influencers, and outliers, and much more.

3. Intelligent Predictive Analytics

Businesses can create more accurate and data-driven decisions faster and more efficiently with predictive analytics. It enables you to speculate about possible events and be prepared for the future. To automate advanced predictive analysis and enrich your business experience with more data and insight, use three types of machine learning algorithms: classification, regression, and time-series analysis.

4. Smart Insights

Smart insights offer you a consumable form of reports. This feature allows you to delve further into your data visualizations without going through the process manually. You may reveal the key contributors behind values or variances, understand the factors influencing your KPIs, and more with a single click. On the fly, sophisticated algorithms offer additional visualizations for your data story, as well as text explanations and context provided in natural language.

5. Reduced Analytical Bias

Allowing the machine to analyze data can help reduce analytical bias. Assumptions are made when you don’t know what you’re looking for. Those assumptions can often lead to the usage of specific evidence to back up those assumptions. Augmented analytics can reduce bias by analyzing a larger range of data and focusing just on statistically significant elements.

6. Faster Data Preparation

Data from numerous sources is integrated considerably more quickly using augmented data preparation. Data quality and enrichment recommendations are generated automatically by the system, and even automates the profiling, tagging, and annotation of your data and cleans it for accurate analysis in a fraction of the time.

7. Immediate Automated Analytics

Because analysis is automated and can be configured to run at any time, the heavy lifting of manually sifting through large volumes of complex data (due to a lack of skills or time restrictions) is greatly minimized. If your data management tools detect a spike, dip, or shift, they can also automate the dissemination of that information, allowing users to act quickly.

8. Improved Data Transformations

Automating boring but important data transformation activities, such as physically connecting schemas together or doing comparison calculations, is possible with augmented data preparation. Without the need for manual involvement, algorithms discover schemas and connect data from many sources.

9. Machine assisted insights

These insights can be in machine-generated visualization, calculation creation, and variance analysis and are often triggered by the user asking a question. Augmented analytics platform auto-generates the analysis of a problem, calculations, and building of any charts. It ensures deeper analysis of a query to answer vital questions quickly.

10. Auto-Visualization

Augmented Analytics platform makes the creation of dashboards and visualization simple for business users. It draws on data relevant to the user’s natural language query and auto-visualizes in seconds instead of manually visualizing the data to create the graph, scatter plots, and pivot tables. These capabilities will enable users to adjust the visualizations tailored to their needs and audience.

Is your organization is planning to adopt Augmented Analytics platform? If yes, feel free to share your thoughts in the comments section below.

Learn how augmented analytics can help transform your approach to enterprise AI

Schedule Demo

Contract Lifecycle Management (CLM) essentially means that contracts or agreements and relationships between organisations are efficiently handled by carefully planning all phases of contract management that lead to the reduction, removal or mitigation of financial, legal and procurement risks. Although it may be enough for businesses with just a small number of contracts to deal with a simple contract database or contract archive, more fully featured CLM systems incorporate substantial features and functions to better handle what is an increasingly complicated and vital environment for many businesses.

Stage 1: Contract Request

Contract LifeCycle Management begins with the Contracts Requesting process where the contractual process is requested or initiated by one party and then uses that data to draught or author the contract document. This is typically the first step of Contract Lifecycle Management.

Stage 2: Contract Authoring, Review and Red lining, Contract Negotiation

A contract or agreement document is created or produced in the Contract Authoring or Drafting stage. This contains all clauses, terms and conditions. Contract approvers and signing parties are usually decided at this stage and approving and signing details are recorded in the contract document.

Stage 3: Approval and Lawyer or Legal Review

The Agreement Document prepared in the previous step will be sent to internal or external approvers and the Contract Document shall be passed to the next step in the life cycle of the CLM until they have approved the document to progress further.

Stage 4: Execution or Agreement Signing

Contract document which is approved is sent to respective stakeholders for authorization either online or using offline processes.

Stage 5: Contract Database or Repository Storage

When the legal document is signed, it is permanently stored in an agreement archive that is easy to retrieve. For future reference, all contractual meta data and documents are indexed and saved.

Stage 6: Records Management

Via secure storage of records with backups and preservation policy enforcement policies, this stage allows full control of all sensitive business documents, giving assurance that the critical records are safe for highly compliant global information management.

Stage 7: Easy Search and Retrieval

This stage allows company users to search, apply filters and retrieve relevant documents from the contract system quickly.

Stage 8: User Activity and Reporting

For easy retrieval, each stage collects user activity logs and the contract meta data and documents are properly indexed. This stage guarantees that business users can retrieve and mine contract data easily and reliably and generate in-depth reports.

Stage 9: Contract Renewal , Amendments and Disposition

It must be renewed to remain in active status until the contract hits its expiration date. If the contract is not renewed on time, it will trigger financial damage to the parties involved in the contract.

Enabling end-to-end partner lifecycle management for profitable partnerships

Request Demo

No matter what the specialty of your organisation is, every day, you possibly enter into a variety of contracts. Such agreements may bind partners and transactions, or may build legal bonds. The higher the amount of active contracts you have, the more profits you make. But, do you treat these contracts in such a way as to achieve this? On average, due to inadequate contract management, telecom companies lose 9.2 percent of sales. There are many reasons why it is hard to handle contracts. They can take a substantial amount of time to copy, register and store, and there is an exorbitant price and payment monitoring that can create revenue holes. The solution is to leverage automated contract lifecycle management.

1. Shortens Approval Duration

Telecom Contract Management software is designed to make the contract process fully automated. Customizable automated workflows speed up the process of analysis and improve performance. The system can analyze the present Telecom Service Provider Service Agreements and other contracts such as Minimum Annual Revenue Commitment (MARC), and Service Level Agreements (SLA). With telecom contract management contracts are thoroughly analyzed for contract compliance. It compares and verifies any revisions, or modifications to the original signed agreements. Invoices are also cross-checked. Commercial contract terms and conditions regarding telecommunication agreements are researched.

2. Increases Contract Visibility

Unlike paper-based contract management systems, it allows for maximum enforcement and extensive documentation to store all files in one unified, digital archive. You will make sure the workers are operating from the most up-to-date contract models and using the new clauses by centralising the contract repository. Each service provider’s name, specific contract information such as telecom contract start date and expiration date can be retrieved at a glance. Most importantly, the contract termination date must be noted so that if needed it can be cancelled before renewal. Additionally, by giving them access to the platform via a password-protected website, you can motivate your mobile workers. That way, all the new contract templates and clauses are available from anywhere at any time to all approved employee members.

3. Improves Audit Preparation

A centralised platform with features such as audit trails that provide access to the entire contract history with the click of a mouse should be provided by contract management software. Your company should maintain an accurate auditing contract history and maintain a complete audit trail for compliance with internal policy.

4. Never Miss a Renewal Date

You can improve renewal awareness by archiving contracts automatically upon expiration and using warning notifications based on the rules you define. For a company, contract extension is the smallest hanging fruit, but also the most missed opportunity. You may customise alerts with contract management software using default settings or custom settings such as recurrence. These alarms will not reset until the appointed team member has hit the next deadline, so you can be confident that action is being taken against the date of renewal.

Enabling end-to-end partner lifecycle management for profitable partnerships

Request Demo

The telecom industry is evolving rapidly as laws change, and companies join new markets. Telecom risk management experts must become aware of potential risks on the horizon in preparation for these alterations. Laws on privacy and net neutrality, new forms of cyber threats, reputational risks, COVID-19, and other variables are all likely to have a profound effect on telecom firms.

Data Breaches in Digital Supply Chain

Telecom firms have long been at the forefront in cybersecurity and data breach prevention as the controllers of data networks. Although efforts have traditionally concentrated on protecting owned networks and facilities, however, new cyber threats have emerged further beyond the control realm of telecom providers. If the interface between telecom companies and third-party technology vendors increases, so does the possibility of data breaches along the digital supply chain from attacks or errors. Online support companies, providers of cloud services, hardware partners, and others may expose telecommunications companies to threats that may impact sensitive systems and data.

Risk of Autonomous Vehicles

Telecom companies, mainly mobile network providers, have a positive outlook on the autonomous vehicle industry as a potential vertical for growth. The number of people interested in renting or purchasing an autonomous vehicle has increased by nearly 15%. Telecom providers and their new 5G networks will be crucial to the emergence of autonomous vehicles, and several manufacturers are looking at collaborations that go beyond basic relationships with contractors. However, a new collection of regulatory risks for telecoms firms will be launched by joining the autonomous car industry. Current automotive regulations are still complex, and autonomous vehicle regulations are in flux around the world and make matters worse. Until technology advances and the laws become more defined, it will be especially risky to work in this sector.

New Risk Landscapes

With emerging technology such as 5G network planning, autonomous vehicles, and IoT expected to transform the way consumers live and work, it is an exciting time for the telecom industry. Nevertheless, with these developments come new sources of risk. To ensure their companies survive the risks posed by these emerging risk vectors, telecom risk management professionals will need to be on their toes through 2021 and beyond.

One stop solution to address all types of frauds across Voice, Data and Digital Services

Request Demo

Frauds and malware cost companies a whopping $42B worldwide. Enterprise fraud management (EFM) software facilitates the detection, analytics, and management of fraud across clients, accounts, products, and processes that are part of an organization. Using any channel applicable to a user, the solution tracks and analyses user activity and actions at the application level (rather than at the device, database, or network level). Since rule-based detection is not enough, these solutions monitor within accounts. It also analyzes actions, looking for organized crime, scam rings, corruption, or misuse among related users, accounts, or other entities.

Rules-Based Detection Maybe Inadequate

Conventional fraud management is based on rules; it only has specific knowledge of past fraudulent attacks. It relies on manual intervention by experts to create, apply, and continuously evolve these rules. When such a system is deployed, companies might stay one or two steps behind fraudsters. The traditional approach to Fraud detection has been through Rule Engines, which could be:

If-Else Conditions
Thresholds
Expressions
Analyzes Data Patterns

These are widely known as deterministic solutions where an action is triggered by an event.

Real-Time and Predictive Fraud Identification Solutions

Rule-based fraud identification can be enhanced by combining sophisticated predictive fraud models and real-time analysis of enormous data. Instead of manual input of domain information or thresholds, Machine Learning Algorithms extract data from past fraudulent behaviors and build models. These models are then used to analyze real production datasets to determine whether certain activity is fraudulent or not. An advantage of using models is the capability for simultaneous multi-perspective analysis to conclude whether an event is a fraud or not. To make models more accurate and enhance fraud detection. Analytic techniques such as pattern analysis to single out anomalous behavior and link analysis to analyze hidden frauds can put companies one step ahead of fraudsters. Fraud solutions like Subex Fraud management are powered by machine learning (ML), artificial intelligence (AI), and real-time transactional data analysis can also help identify and prevent real-time fraud.

Eliminate Silos in Risk Monitoring

A crucial aspect of effective enterprise fraud management innovation is the comprehensive capture and integration of enterprise data across data silos. This is important because anomalies cannot be easily identified using data from one department alone. A surge in purchase activity can only be anomalous if there’s no relevant surge in order placing. A common data silo that can be accessed by all could be the solution to this.

Next-Generation Authentication Mechanisms

Leveraging AI and ML technologies, organizations can detect data anomalies in real-time and make timely decisions. This empowers them to take proactive action and avert significant losses. For example, AI/ML techniques can use facial recognition technology to identify high-risk blacklisted individuals. Companies need to verify customer identities while complying with high customer expectations. Technologies like voice and speech recognition and desktop analytics can help prevent fraud. Such next-generation enterprise fraud management solutions will offer diverse benefits that include the lower total cost of ownership, enhanced staff productivity, and protecting brand reputation. Since malicious attackers will always find better ways to commit fraud, companies need future-proof enterprise fraud prevention solutions to avoid loss in revenue and reputation.

Find out how customer protection is more critical than ever.

Learn More

The technology evolution and changing consumer behaviour has accelerated the digital transformation journey for any industry. And most of all, it has impacted the Telecom sector the most, as they are at the center of this change. Be it healthcare, fintech, e-commerce, media, or entertainment; telecom has a role everywhere. One factor that has made this possible is telecom operators leaving the legacy business methods and creating an ecosystem of partners to collaborate and co-create the new age innovative services. As the partner ecosystem is at the heart of this transformation, making the best partner experience for telco partners is of utmost importance. There is a need for a shift in putting the partner experience in parallel to customer experience.

Strategy

Many telecom operators have set their priority on creating a digital ecosystem strategy. As the revenues from traditional services are declining, there is an urgency to diversify the service offerings, and the partner will play an essential role in this diversification. Telcos, however, has a strong understanding of the telecom ecosystem, but they lack the business acumen for the new lines of business. For example, what kind of services can they launch for the healthcare industry, the requirements, and how customers will consume the service. All this knowledge will come from the incumbent partner who carries the relevant experience. Telecom operators should look to create and endorse an interconnected and robust partner group that generates and captures value in the market to foster this ecosystem evolution. And to do this, with their current knowledge of engagement, they need to consider what the partners expect and the most significant pain points.

Why it’s Important

The importance of creating a partner ecosystem is no longer a hidden knowledge. TMForum has mentioned in one of its reports that to monetize 5G services by delivering vertical-specific services, CSPs need to create experiences that can attract the right partners and quickly onboard them into the ecosystem and collaborate to develop new offerings for its customers. This requires that business support systems (BSS) ensure that all parties participating in the digital ecosystem can generate value.

How to create the best Partner Experience.

1. Create the best onboarding experience- this will be the first instance of the partner looking at your ecosystem. So, the onboarding process has to be easy and user-friendly. It should be robust enough to gather all the relevant information that the Telco will require to make the right decision about the partner.

2. Self Care Portal- The CSPs need to provide self-care capabilities and access to critical business reports and dashboards to their partners to be aware of what is happening to their business. They should be able to see what value they are adding to the ecosystem and, if not, how they can be more productive.

3. Easy communication- to help them quickly exchange the messages, ask questions or enquire, or raise disputes. This will reduce the to and fro of information, and both CSP and the partner can focus more time on business-critical processes.

4. Dispute resolution process- It should be easy for the partner to raise any dispute, and the resolution process should be smooth and fast. This will help create high trust in the partnership and a healthy and profitable partnership for a longer time.

A platform to manage all aspects of your digital and traditional partnerships.

Request demo now!

Changing customer expectations push telecom companies to respond through more attractive deals, packages, and price cuts. Given these difficult dynamics in the market, the management of the customer base to minimise churn should be among the highest priorities of any senior telecom executive. Worldwide research shows that companies adopting a systematic, analytical-based approach to base management can minimise their turnover by as much as 15%. One of the top-level goals of telecommunications companies is the study of customer data in an effort to recognise and reduce customer churn. Having the right method to conduct this analysis and get ideas about how to reduce churn coefficients in such a challenging market.

Intelligence Gathering Using Big Data

The holistic view of your client that marketers could only dream of in the past can now be developed. And this should be their objective: to have a way to aggregate and use all the various datasets you have for individual customers to their benefit. This includes your data warehouse’s transaction data, which informs you how much each client spends on your services and when. It also provides service call information that helps you to understand how good or bad things are going for individual customers; and information that relates to network efficiency or web logs that can tell you about delays and downtime. All this information can help you create a rounded image of each customer.

Micro-Segmentation

All this information can be used by Telcos to build micro-segments of customers, which can help you personalise your products and services to small groups of customers with a high risk of leaving. A library of 50+ deals was generated by one leading telco, targeting such a micro-segment with marketing offers, and slashing their churn rate over 18 months by 10-15 percent. The secret to improving customer churn behaviour is to be able to recognise and rapidly evaluate fresh deals on individual micro-segments, understand, and adapt different aspects such as value, messaging, and delivery mode.

Implement a Data Deep-Dive

In order to discover hidden patterns and better understand consumer behaviour, telecom companies should look to implement cutting-edge analytical techniques that apply sophisticated algorithms to their aggregated data sets. This is especially helpful in forecasting why consumers would want to quit. To define over 50 variables that contributed to customer turnover, as well as their relative value, one leading operator used an analytical technique called ‘function discovery’. Relevant variables, such as combinations of phone forms, data use, and call-center contact history, were among these variables. If any of these combinations were hit by a customer, the programme could accurately predict that the client was on their way to leaving.

Rise of Robots

Advanced analytics such as campaign analytics also helps you to conduct AI-driven predictive analytics through advanced approaches such as predictive behaviour modelling, a mathematically intensive approach that can reliably predict churn in a particular micro-segment of the consumer. Another is the study of customer retention (aka ‘survival analysis’), which can show how many new consumers over time will remain customers. Thirdly, the suggestion of the next best deal (NBO) will foresee what the customers want before they do. NBO will help you put together a highly tailored deal and direct your client to it at the right time, using the most convenient platform for them, and they can find the most cost-effective and appealing solution. Lastly, you can use sentiment analysis to text in social media comments, reviews, emails, and web chats using Natural Language Processing (NLP). This can identify the positive, negative, or neutral emotions customers feel regarding products and services.

Benefits of Predictive Analytics

Collective Data: Telecoms can connect to all of their marketing data sources, compile all of their information in one place, and prepare it for review.

Reports and Dashboard: Telecoms can create reports and dashboards to get a full picture of their marketing channels’ results.

Proactive Solutions: Predictive Analytics platform analyzes data and provides proactive ideas on how to improve your campaigns and increase ROI.

Safety: All data is completely safe. The analytics platform offers the best data security and adheres to stringent data protection standards.

From Default to Debt: Stemming the Flow with Bad Debt Analytics

Read Blog

The method of monitoring network components such as routers, switches, firewalls, servers, etc. is Network Monitoring. The Network Monitoring Tool is an application which collects useful data from various parts of the network. It will assist in network management and control. Performance monitoring, fault monitoring, and account monitoring will be the subject of network monitoring.

5 Key Features to Look out for

With increasing competition and higher than ever capital expenditure, customer expectations and network usage are increasing. This adds further pressure to telecoms who also have to stay updated with technology evolution (5G and IoT). This calls for an enhanced network capacity management solution that features intelligent network investment plans, comprehensive predictive capacity analytics, customer experience analytics, and capital cost optimization. These features can help bring about the best benefits for telecoms.

1. Network Investment Planning

Companies want accurate investment plans for the network as it will help them make wise decisions to leverage potential growth of the network, optimise their spending, enhance customer service, and maximise the return on investment in the network. In one planning framework, Capacity Management combines technological, customer experience and financial factors. To fit different situations, the user should change the relative impact of these variables. The output is relevant advice on when, where and what to invest in. It leverages proprietary models of machines and deep learning that help telecoms reliably predict the growth of the network.

2. Predictive Analysis

Telecoms need to efficiently utilize their current resources to their full potential. A good network planning tool offers detailed insights into the present state of their network to understand how it can be optimized to improve performance without any capital investments.

3. Smart Network Capacity Audits

Managing numerous suppliers for the process of performance acceptance associated with new site planning is a daunting activity on which network teams lose countless hours. The capability of Smart Capacity Audits and Acceptance brings automation to compare the current performance of sites with baseline goals and offers insights in the form of automated reports that can be used for processes of vendor acceptance. It automates various kinds of audits, such as results, installation, defects, and more.

4. What-If Simulations

Planning teams need to be prepared with scenario-based simulations to gain insights into their network’s impact of change. A good network planning tool offers insightful methods to run simulations to facilitate the unknown and unforeseen in any business strategy which can influence the network. These techniques integrate advanced analytics to provide multivariate simulations that allow planners to respond properly to any scenario.

5. Consumer Experience Analytics

With sky-rocketing customer demands, telecoms need to guarantee a superior

Quality-of-Experience (QoE) to meet and exceed customer expectations. A good network planning tool equips telecoms to identify network bottlenecks and issues affecting QoE. It helps them address these problems from an end-to-end perspective and provides in-depth insights into High Value Customers (HVCs) to help network managers find important hotspots for increasing capacity.

Learn more on how our capacity management can help your organization

Learn More