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Improve operational processes, maximize profits and form a data driven marketing or business strategy.

More than 50% of the telecom companies will have integrated AI in their business in 2020. AI, or more generally Data Science, can be used for multiple purposes within the telecom industry. Three main categories can be identified to add real value to the bottom line of any organisation within the Telecom industry. These categories pertain to the operational processes of a company, maximizing profits and substantiating marketing or business strategy from (real-time) data.

Improve current operational processes

Fraud detection

By applying unsupervised clustering algorithms or supervised classification algorithms, fraudulent behaviour can be detected and trigger a response in near real-time. What type of model is appropriate is dependent on the data and the type of fraud you want to detect. Common types of fraud in the telecom industry are: bypass fraud, toll number fraud or credit card fraud.

Smart traffic prediction, network management and path optimization

Historical data, structured and unstructured, can be utilized in order to identify the root causes of network connectivity problems and outages. Similarly, they can be used to predict future problems. Network performance data can be used to identify malfunctioning- and likely to malfunction hardware to trigger an automatic restart.

Chatbot implementation or optimization

Working with chatbots can be a frustrating (or hilarious) experience. However, the benefits of implementing a system correctly can be tremendous. Either by lowering the cost of customer service or by enhancing the user experience. Specialized chatbot experts can implement or optimize a current system.

Infrastructure repair identification

Several telecom companies are experimenting with drone video footage in order to identify signal towers that need manual repairing. Supervised learning algorithms can be used to identify potential repairs from the drone video footage.

Maximize profit

Customer segmentation

In order to maximize profit, the business proposition has to adapt to different types of customers you have. To do so segmentation can be used through unsupervised learning algorithms. Four important segmentation schemes are: Customer value segmentation, customer behavior segmentation, customer life-cycle segmentation, and customer migration segmentation.

Customer churn prevention

By creating an accurate model with supervised learning algorithms, customer behavior that might result in defecting from your service can be identified. Smart data platforms can bring together customer transaction data and data from real-time communication streams to disclose insights and enable preventive actions to keep the customer from churning.

Lifetime value prediction

Telecommunication companies can be enabled to measure, manage and predict the customer lifetime value (CLV) of individual customers. Customer lifetime value is a discounted value of all the future profits and revenues generated by the customer. Failing to predict this value may result in profit loss.

Price optimization

In highly competitive markets it can be critical to your profit to adopt dynamic pricing. Pricing emerged as a tool to limit congestion and increase revenue at the same time. Based on dynamic pricing insights the interdependencies between pricing, promotion, and future revenues can be defined to optimize your profit.

Data-driven marketing and business strategy

Real-time analytics

The telecommunication industry is famous for its long-term experience in dealing with significant data streams for years. Due to the rapid development of the internet and the evolving of 3G, 4G, and even 5G connections, telecommunication companies face the challenge of constantly changing customer requirements. Data science (and cloud technology) experts in dealing with high-velocity data can deliver real-time insights from this tremendous data stream.

Sentiment analysis with social media

Customer sentiment analysis is a set of methods applied for information processing. This analysis allows an assessment of the customer's positive or negative reaction to the service or product. Analysis of the aggregated data also allows for revealing recent trends and reacting to the customers’ problematic issues in real-time. Customer sentiments analysis largely relies on text analysis techniques.

Data Visualization to support marketing and business strategy

Real-time analytics and data visualization combine the data related to customer profiles, network, location, traffic, and usage to create a 360-degree user-centric view of the product or service. The different categories of this user-centric view can be visualized in order to support marketing and business strategy

Service Recommendation and Business Personalization

The recommendation engine is a set of smart algorithms depicting customers' behavior and making a prediction about possible future needs of the product or service. The most popular approaches here are collaborative filtering and content-based filtering.

50% of telecom companies will have integrated AI in their business in 2020.

Key components

Sub area's; telecom equipment, telecom services and wireless communication. Wireless communications, processing systems and products, long-distance carriers, domestic & foreign telecom services.

Global footprint

3% of all employees in the world are active in the telecom sector. The telecom industry contributes with 4% to the global GDP.

Threats

BlockChain, IoT, Cyber attacks.

What value can Data Science & AI bring to your company?

Data capturing

Using smart cameras, nodes and IoT sensors, you can gather more information about the developments of your goods.

Machine Learning

Using deep learning techniques and image recognition, you automatically convert your data to actionable insights.

The data ecosystem

Connecting data sources from different stakeholders in the value chain, you can create an ecosystem and profit from large scale data analysis.

ING

An optimal approach to Productizing Artificial Intelligence

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