Now a days telecom spends on technology and hence data science is no exception. Today is proof enough that the telecommunication industry uses a lot of science and technology.
With the increasing connectivity the data is also increasing. With our daily calls, messages, etc., we are generating a huge amount of data. So it is no surprise to know that Data Science in Telecom Industry is helping to handle such a large amount of data.
take a crisp look about Data Science
What is Data Science
It is one which uses various tools, algorithms, and machine learning principles to discover hidden patterns from raw data. It is used to take decisions or make predictions. It is a multidisciplinary field which uses the knowledge and insights from structured and unstructured data using scientific systems and algorithms.
The crisp definition of data science could be “Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.”
Data Science can be useful to Building effective business strategies and marketing campaigns, Streamlining operations, Maximizing profits, Visualizing data etc.
Data Science use cases on Telecommunications
- Competitor Analysis using NLP
Using Natural Language Processing web scraping tools can extract the competitors offers on Data, Voice, SMS etc.
2. Contextual promotions
Customer locations can be detected in real-time by telecoms. This information is used to send contextual promotions by partnering with different merchants. These promotions come with a high conversion rate. This way, the telecom company gets a cut or a commission for each transaction and also helps generate more revenue.
3. Fraud Detection
The most common fraudulent activities in the Telecom world are unauthorized access, fake profiles, misuse of credit/debit card information, etc. Thus the Telecom industries are using various unsupervised machine learning algorithms for detecting unusual user activities and preventing frauds.
4. Price Optimization
Every Telecom company is aiming to have the largest number of subscribers. Pricing of products plays a very important role whenever it comes to increasing subscribers or users. The Telecom industry is using advanced Big data and Data Science solutions for the of various aspects. This will help them in “setting the optimal price of products” according to customers of different segments.
5. Improve customer experience
A detailed analysis of telecom data can help businesses to identify factors that impact customer experience. Telecom analytics helps companies to collate and analyze data obtained from call centers, CRM systems, and other sources to understand the biggest pain points of their customers.
6. Reduce fraud Losses
- Credit card fraud- This is the usual charge back fraud which impacts the corresponding verticals and this can be minimized by correlating real-time transactions with historical activity.
- Bypass fraud-This refers to unauthorized traffic within a telecom network. Companies prevent this by using big data to review the source of transactions, the cost of the call, and the destination number, in real-world situations.
- Toll number fraud-Real-time call analysis helps reduce this type of fraud as it is possible for telecom companies to lose thousands of dollars very quickly if someone calls toll numbers that cost $5 per minute or higher than that.
7. Reduce Customer Churn rate
Churn with respect to the Telecom industry, is defined as the percentage of subscribers moving from a specific a service provider to another in a given period of time.
It comes under predictive analytics.
Telecom service providers who are just about to start their analytics journey can start with the basic steps that involve the use of descriptive, diagnostic, and advanced analytics. Starting with such an approach will enable telecom companies to gain a 360-degree view of the industry, thereby contributing to the key objective of improving business decisions.
8. Analyze the potential of new offerings
Telecom analytics solutions that leverage predictive modeling act as a key enabler of business success by helping Telco’s develop innovative offerings to generate new revenue streams based on customer preferences.
9. Chat bots & What’s up integrations
Customer care support chat bot, transaction chat bots, what up, Facebook and other integration of chat bots can improve quality of customer care and transition speed.
Chat bots also understand the subtlety and nuance of language far better than IVR systems do. A chat bot can understand the various phrases you might use to communicate your intent and immediately identify what you mean.
Now a days a chat bot can tell validity, data usage, SMS usage, present balance, store user complaints, share promotions, change user details in database and send conformation mails/SMS regarding customer demographic data updates etc.
Chat bot What’s up integration
Whats App's created seamless integration options, making it just as simple to create a business Whats App presence as an individual would.
What’s more, you can automate your communication with clients. And that’s the beauty of using chat bots.
10. Improving Customer Satisfaction
A major value add for telcos is to reduce service calls, which represent heavy costs for operators and divert technicians away from their work. By analyzing massive amounts of data using machine learning techniques, teams can identify unwarranted service calls and assess the performance data of technicians to further improve customer service.
11. Contextual Recommendation system
Using unsupervised method like clustering methods can group the specific people like
Core — Your Best Customers(champions)/High valued customers, Loyal — Potential Loyalists, Whales — Your Highest Paying Customers, Promising — Faithful customers, Rookies — Your Newest Customers, Slipping — Once Loyal, Now almost Gone(Good and About to sleep), Can’t Lose Them (AT RISK) etc. Divide the customers and treat separately using segmentation, Unsupervised techniques like clustering etc.
12. Distribution Network Analytics
To find the sales pattern of Recharge Top up’s .Recharge pattern analysis is the main component in revenue analysis. Here objectives can be fulfilled like find distributer performance and also predict the future performance as well, To suggest plans to improve the performance of the distributor, To leverage recharge counts and recharge revenue.
13. Customer Segmentation & RFM Analysis
In RFM analysis, Recency, Frequency and Monetary indicators are employed for discovering the nature of the customers. Results indicated that RFM effectively clusters the customers, which may lead telecom top managers to generate new strategies for increasing their abilities in CRM.
The central idea is to segment customers based on when their last recharge was, how often they’ve recharged in the past, and how much they’ve spent, overall. All three of these measures have proven to be effective predictors of a customer’s willingness to engage in marketing messages and offers.
14. Price optimization
Big data solutions help in reviewing several metrics in real-time to set a price for each product offering. Prices are tested among different segments of customers from various regions to decide on the most optimal price. This is a win-win situation for both the customers and the telecom as customers get the price they want and steady revenue is generated for the telecom.
15. Real-time network analytics
Telecoms need to continuously monitor their network to avoid any issues well in advance. Big data has made this possible. Implementation of data science allows getting alerts if a part of a network is experiencing unusual traffic as this can impact customer experience. It also suggests the right time to upgrade the network to add more capacity while bringing on more customers. This helps them focus on areas which will deliver positive results.
Conclusion:
Data science, machine learning, and artificial intelligence are inevitable when it comes to the future of the telecommunications industry.
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