Advanced Analytics and AI in Telecommunications – Notes from Tiger Analytics
There is rich and abundant data available in the telecommunications industry, and this data has been particularly relevant over the past two years. Bandwidth consumption hit an all-time high amid the global health crisis, as all businesses and educational institutions moved towards a digital workspace model.
However, despite this digital switch-over first, some key challenges have caused the growth of the sector to decline. These challenges include:
- Intense price competition across the sector from historical players and newcomers offering unique business models.
- Growing adoption of services from OTT providers (Ex: WhatsApp for voice calls, Messenger for messaging, etc.) and,
- Increase capital spending to put in place new infrastructure to deliver improved connectivity and 5G services.
This article will discuss the main growth opportunities for the telecoms sector in acquiring new customers while simultaneously increasing the value of existing customers.
- 360 customer view to enable targeted growth from the existing customer base.
- Loyalty of the clientele.
- Customer service experience.
- Capitalize on the growth of the B2B segment.
360 Â° Customer View – Improved Customer Understanding
The 360-degree view of the customer, as the name suggests, is all about the overall picture. It provides a complete understanding of customers by aggregating data from all touchpoints. This data traces the customer’s journey through different services, all on a central platform.
We can further augment internal data sources with structured and unstructured external data sources, such as company profiles, demographic reports, social media data, etc. Unfortunately, this rich external data is usually stored in silos or never used.
Companies tend to be reluctant to embrace the 360 ââcustomer view because of its challenges. One of the most common is the difference between entity names used in various internal systems and third-party data sources. It was here that the implementation of AI-based string matching algorithms allowed several disparate sources to be merged.
Similar to the example above, solutions can be found for businesses that find it difficult to implement Customer 360 View because the benefits outweigh the challenges. Let’s see some of the advantages:
- Unified view of customers across all departments, from business development to customer support.
- Scalable data that can be processed faster and at lower cost.
- Enabling AI and analytics use cases (not exhaustive) such as:
- The precise increase in external data has led to better functionality and therefore to better accuracy of predictive models and better understanding of customer behavior.
Customer retention through attrition prediction – The cost of customer retention is much lower than the cost of acquiring new customers
The offering of voice, messaging, mobile music and video services by OTT producers such as WhatsApp, Messenger, Netflix, Spotify, etc., has made data the main offering for telecom companies.
Due to the ongoing price war and data-hungry plans with competitive prices, customers are spoiled for choice. While the basic product is the same or has very few differences, the competition is strong and offers many options. This has led to an increase in the customer churn rate.
Therefore, it is crucial for telecommunications companies to understand the reasons for this loss of business and to anticipate the avenues that lead to increased loss of business.
One way to do this is to use machine learning models that can predict customer attrition. This can be done using past customer transactions, network quality, product pricing, product usage, customer feedback history, complaint log, demographics, and social media data. (if applicable).
Targeting the right customers to run loyalty campaigns is essential. The ones chosen will be directly linked to the campaign budget, the cost of retaining each client and the additional revenue generated by each client.
This process is especially important, as even retaining a small percentage of customers who are on the verge of disappearing can lead to an increased impact on long-term revenue.
Customer service transformation
If the products offered are similar and the competition is strong, how can the telecom operators be differentiated? The answer is customer service. This is one of the main differentiators between companies.
In this digital-first world, there is a growing demand for the transformation of the customer experience and the adoption of new technologies, such as chatbots and AI-enabled dialogue systems.
A common challenge is to provide the customer with all the correct information regarding their purchasing product. Often, customer service agents manage a product line and may not be equipped to answer all customer questions. This increases the time that customers spend waiting or in queues, which leads to dissatisfaction.
This is where intelligent, AI-enabled customer service systems can reduce wait times and help deliver the most relevant solutions or recommendations to customers. This can be done in one or more ways:
- Forecasting inbound call volumes to optimize short and long term staffing and training.
- Virtual assistants to provide quick resolutions to all simple customer queries and redirect the rest to the appropriate customer service agents
- Allowing the representative to have a 360-degree view of the customer helps them understand the customer’s case and history without getting a lot of input from the customer.
- Allow the team to have a real-time analytics engine to recommend the right offer / product to an existing customer based on their profile, demographics, and agent interaction.
Growth in the B2B telecoms segment driven by digitization and 5G
The B2B business model enjoys high margins (compared to B2C), with customers willing to pay more for different services. It is characterized by a diverse list of products, customized solutions, prices and multiple partners. On the other hand, it increases the length of the sales cycle.
A common growth use case (aside from the common telecommunications use cases discussed above) specific to the B2B segment is to reduce the length of the sales cycle by using AI and analytics in product solutions. and pricing. This leads to a better customer experience, thus increasing customer acquisition.
Here are the main differences in the characteristics of the two segments:
Historically, most telecom providers have prioritized analytics use cases to capture the growth of the B2C segment. However, with the advent of digitalization, all businesses rely on the telecommunications industry for reliable 5G broadband data and enterprise mobile plans. He is valued that by 2035, sales of $ 13,200 billion will be made possible by the 5GÂ² ecosystem.
As a result, the next decade is likely to see the B2B segment grow much faster than the B2C segment. As a result, focusing on B2B use cases will help telecom companies capture a greater share of the growing market.
Benefits of implementing AI and advanced analytics (examples)
To really understand how AI and analytics are helping to transform this growing industry, let’s take a look at some real-life examples.
Customer 360 – Data Governance System for Asian OTT Video Service Provider
Problem: The client was eager to develop a holistic understanding of the user’s program viewing behavior for smarter programming and advertising decisions.
Solution: The solution was to create a data lake to process internal and third-party data from structured and unstructured data sources. Some key challenges included creating a process for data governance, handling inconsistencies between multiple sources, and creating a flexible system that allows for new sources of data.
Delivered value: The result of the exercise was a data lake that could process 100 GB of data volume per day with varying speeds, ensuring data availability for data analysis projects across multiple themes.
Here are selected case studies executed using Customer 360 view datasets:
Unsubscribe Prediction – User Behavior Prediction Model Generating $ 4.5 Million Annual Revenue
Problem: The client, a telecommunications giant, wanted to identify the customers most likely to turn to their video on demand (VOD) business.
Solution: The main challenges were huge data volume, limited metadata on VOD content, an ever-changing user base, and limited subscriber demographic information. The solution involved creating a random forest-based churn classification model based on characteristics extracted from past customers’ RFM behavior, month-to-month rate of change in purchases, demographics and content metadata.
Delivered value: A total of 73.4% of potential churns were captured by the richest 50% of the population reported by the model, resulting in revenue retention of up to $ 4.5 million per year.
Customer Service Transformation Case Studies
Telecom B2B – Pricing system for a leading Asian telecommunications company.
Problem: The client was looking to shorten their B2B product sales cycle, which currently took up to 4+ weeks to produce the initial quote.
Solution: The bottleneck in the process was identified as the involvement of third party costs and the delay in their receipt. The solution involved building ML models to forecast third-party spending to reduce wait time and provide customers with an upfront quote.
Delivered value: The business impact has been reduced the turnaround time for an initial quote from four weeks to a maximum of one day.
The future is brighter, smarter, faster
The applications of AI and predictive analytics in the telecommunications industry are endless. With digital transformation being the key goal of any business today, combining AI and analytics can not only help deliver superior performance, but also give a business the uniqueness needed to survive in a market. fierce.
For more information on services and use cases, please contact us at https://www.tigeranalytics.com/.