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Month: September 2019

Blog Use Cases

Use Cases of Machine Learning in the Telecommunications Sector 

 

Machine Learning (ML) and Artificial Intelligence (AI) are becoming a key implementation in every industry with high value and efficiency being provided across the board. The telecommunications sector is no exception and is at the forefront of adopting Machine Learning to optimise operations, increase revenue, transfer and visualise data, enhance customer relations, and improve marketing and sales strategies.  

 

Data transfer, exchange, and analysis is key in the telecommunications sector with the amount of data increasing every day. This is why all systems and methods relating to data need to be relevant and accurate.  

Below are a few of the use cases where Machine Learning and Artificial Intelligence have provided benefit in the telecoms industry: 

 

Product & Service Recommendation Engines 

 Recommendation engines in many industries come up in our digital life. It would not be effective to market product recommendations without taking customer preferences into consideration. By analysing historical customer data (their behaviour and preferences), Machine Learning algorithms are able to predict which products a particular customer will most likely purchase.  

This is done through filtering. Collaborative filtering makes assumptions about a customer’s preferences based on behavioural similarities to other customers (assuming that people with similar profiles have similar interests). Content-based filtering makes assumptions based on previous customer product history – recommending items similar to those they have already purchased. Customers are more likely to upgrade or repurchase products that align with their needs or preferences. This is why telecom companies have implemented this ML method to increase revenue.  

 

Anomaly Detection & Fraud 

 Due to the fact that the industry attracts a significant number of users every day, this brings high chances of fraudulent opportunities. Fraud, such as fake profiles, theft, illegal authorisation, and account cloning, have a negative impact on the customer-company relationship.  

This is why telecommunications companies are implementing unsupervised Machine Learning algorithms to spot abnormal characteristics or activity in customer behaviour. 

By analysing past data of customer behaviour, ML algorithms are able to visualise and present these anomalies to analysts in real time. This is especially efficient as it allows telecommunications companies to alert their customer of suspicious activity almost immediately thus protecting the customer-company trust relationship. 

 

Customer Segmentation for Effective Marketing 

Successful marketing in any industry relies on accurately segmenting and targeting groups according to their preferences and characteristics.  

 

The key to success for the telecommunication companies is to segment their target markets and target marketing content accordingly to each group. This golden rule is relevant to the various areas of business. Machine Learning algorithms can create highly accurate customer segments based on shared customer characteristics. This allows telecommunications companies to better strategise and reach customers with more relevant marketing material (and increase their chance of a sales conversion)  

 

(Did you know that ML customer segmentation can be done in real-time. As customers change preferences and patterns, they are re-segmented to different groups based on whichever characteristics are most prominent. This means targeting the customer with irrelevant marketing material is less likely to happen – and saves time for those who once needed to re-segment groups manually).  

 

 

Optimised Product Development 

Companies in the telecommunications industry need to provide their customers with products that satisfy their needs and wants in order to continue to keep customers loyal and to generate revenue. Machine Learning algorithms can analyse data across different departments to ensure customer feedback is taken into account to provide the most customer fulfilling products are created. By analysing data such as customer feedback, marketing intelligence, and product stock, ML algorithms can present the most data-driven product suggestions for development. 

 

Conclusion 

Data science and its benefits in the telecommunications industry is not a new occurrence. However, Machine Learning has taken efficiency within the industry to the next level. Those companies that have implemented ML in their processes are experiencing fewer issues to resolve, control or occur from happening at all. 

With the right partner and expertise, telecommunications companies are experiencing these benefits for themselves. Intellify is AWS’ Partner of the Year in Data, Analytics, & Machine Learning and has worked with telecommunications companies in the past on bringing Machine Learning capabilities and culture to their organisation. Contact us to learn more about how we could help you do the same based on your own requirements.