How Augmented Analytics can help telecom companies win in the digital world?
Augmented analytics is the future of data and analytics. Traditional Business Intelligence systems can no longer handle data sets that have grown in size, complexity, and speed. As a result, a new generation of business intelligence tools, known as Augmented Analytics technology, has emerged. Augmented Analytics in telecom can process enormous amounts of data, including call detail records (CDR) in the telecommunications industry, to uncover patterns, detect, and anticipate network problems using complex machine learning algorithms. It identifies the most valuable accounts based on available data and keeps the company’s database up to date. It addresses the challenge of siloed operations and helps:
- Improve Accuracy and Speed
Data scientists, according to Forbes, spend over 80% of their time preparing and cleansing data. However, no matter how many staff are working on data processing and analysis, the time delivery is still less than adequate. The speed of delivery is increased significantly with Augmented Analytics. Requests are processed and analyzed in real-time by an AI-powered system, allowing consumers to receive results in seconds. Traditional BI tools required a lot of IT help and entailed a lot of manual operations. People’s involvement in most BI software operational procedures, such as cleaning and preparing a large amount of data, analyzing and processing it, and presenting the results in a suitable format, clearly increased the danger of a mistake owing to the human factor. Robust IT systems with advanced Augmented Analytics in telecom at its core can conduct jobs with high precision and zero errors.
- Reduce Bias
According to Gartner, integrating machine learning and automated service-level management will cut manual data management activities by 45 percent by the end of 2022. Companies can use augmented analytics to liberate their employees from regular data entry. With AI in telecom, they can concentrate on more vital duties as a result. Unsupervised machine learning systems may learn from data without any further technical assistance. It also has fantastic visualization features, allowing users to compare millions of patterns in seconds.
Let us see how augmented analytics in Telecom is a game-changer in marketing.
The beauty of augmented analytics is that it can take data from various sources, such as Google Ads, Facebook, or any other platform, and apply robust machine learning algorithms to reveal crucial insights that can save money and boost the bottom line. A budget allocation feature, for example, can figure out what combination of spending across multiple marketing channels will generate the most income for a given budget. Data science in Telecom does so by learning about historical outcomes and recent expenditure patterns, then building a regression model for each channel to see how spending on each of them has affected revenues over time (going up to several years).
Through trend recognition, augmented analytics, and data science in telecom can improve the effectiveness of marketing. This feature enables businesses to spot developing patterns that may influence their operations, such as market shifts or competitor activity. It frees up time that would otherwise spend manually investigating trends, allowing marketers to respond more quickly to rapidly changing market conditions.
High-Value Use Cases for Augmented Analytics
1. Providing and Maintaining Good Service
Telecom companies can instantly analyze through tens of millions of CDRs. It recognizes patterns that may point to problems, create scalable data visualizations, and use predictive maintenance technologies to minimize dropped calls, poor sound quality, and other issues. It may cause customers to seek a new provider using machine learning and eventually AI systems.
2. Combating Fraud
While scams involving automated calls or premium rate charges may not pose a direct danger to telecom firms, they do have the potential to reduce customer satisfaction in the long run. The faster telecom companies can spot suspect call or client trends, the more effectively they can prevent fraud (including first-party or true-party fraud). True-party fraud is easier to detect using AI tactics and techniques like improved anomaly detection. Companies that can tell the difference between legitimate credit failures and frauds might concentrate their collection efforts on the instances that are most likely to yield a profit.
3. Churn Prediction
One of the major challenges for telcos is the high churn rate in telecommunications, which is believed to be between 20 and 40 percent per year. Providers may construct better profiles of their consumers and sketch out a strategy to keep their loyalty using churn analysis and churn prediction methodologies, determining who is likely to churn and who might still respond positively to marketing initiatives.
4. Improving Customer Satisfaction
Reduced service calls, expensive for operators, and pulling technicians away from their duties are a vast value gain for telecoms. With Augmented Analytics in telecom, teams can identify unjustified service calls and review technician performance statistics to improve customer service by analyzing vast volumes of data using machine learning techniques.
Is your organization planning to adopt Augmented Analytics? If yes, then what are the challenges your organization is facing? Feel free to share your thoughts in the comments section.
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