Telecom, Media and Tech

With increasing online content viewership, consumers expect personalization from the brands they engage with and believe they are recognized as an individual when sent special offers and customized recommendations. In this respect, implementing AI has the potential to spearhead personalization and ensure the delivery of an effective brand experience. AI can do this using predictive personalization.
Be it online music videos, web series or film content on OTT platforms, pumping content over internet offers the biggest advantage of being able to closely understand the preferences of viewers which is not possible in case of cable television or cinemas. For online content consumption, it is possible to observe choices of content genre, music directors, actors, directors and even interest at frame-level at individual viewer level. While it is impossible to humanly conduct such a content affinity analysis for millions of customers manually, Machine learning models can assist in collating and summarizing such aspects. Further, ML models can offer near accurate recommendations to the viewers based on their past viewership history. That is how OTT platforms construct the “recommended for you” section. The ML models consider the types and genre of the movies that you watched previously and by analyzing your viewing trends, it recommends similar movie titles. As customer expectations evolve, personalized content curation can help brands getting a lead from competitors by customizing the content for target audience.

Setting the right price for a good or service is a very old problem in economic theory. There is vast amount of pricing strategies that depend on the objective sought. One company may seek to maximize profitability on each unit sold or on the overall market share, while other company may need to access a new market or to protect an existing one. This defines the different scenarios for problem of Pricing the products accurately. Pricing Team can leverage tremendous power of Machine Learning and build effective pricing solution based on real time inputs. Now retailers can consider factors such as Competition, Weather, Season, Special Event / Holiday Season, Macroeconomic variables, Operating costs and warehouse information to determine the initial price, the best price, the discount price and the promotional price. A Machine learning model designed for product pricing can take historical data and different characteristics of the product as well as unstructured data such as Images and texts and would learn the pricing rules with no explicit fixed rule and can adapt to changes in a much more dynamic way. Such models can learn about buying behavior and price elasticity of customers from past transaction patterns and recommend right pricing for products.

A telco typically has numerous enterprise clients with varying duration of contracts for different connectivity services. While part of these contracts data are entered in enterprise systems like SAP, most contracts are stored as MS Word documents which are signed as agreements. Typically, dozens of such agreements are up for renewal every month and updating the new terms at multiple places is a tedious task. Fields like newly agreed rates, duration, discounts, service levels, payment terms, etc. have to be changed every time, which takes up large amount of human resource time. You can use intelligent automation which can peruse through emails, PDFs, MS Word and MS Excel documents to identify and pick new data fields (e.g. rates, SLAs) which have to be inserted in the new contract. Such an intelligent automation system would include RPA for operating ERP and other systems while OCR for reading unstructured data like PDF. Once the new clause related data are picked up and organized, RPA can draft new versions of agreements and make data entries into ERP systems which could be approved by the Commercial and Sales teams.
Such an intelligent automation can save precious time of resources in doing mundane tasks and improve customer satisfaction for turning around the renewal agreements quickly.

Optimizing network operations and maintenance is not only optimizing communication networks but also the people, processes and systems that maintain and manage telecom networks. Continuous increase in demand for higher bandwidth reliability, low latency in data transfer, and optical fiber network rollout has made operators critical to plan, engineer and operate infrastructure with very low error margin. GIS technology can help telecom organizations in optimizing their resources using location intelligence. It can enhance awareness through a common mapping of location and maps. Integrating web-based dashboards allows you to monitor the real time network operation, analyze network capacity using location analytics and provide restoration time to your customer in case of outage. GIS also helps you in reducing cost by optimizing network, assets, people and processes. It maximizes network resource usage through network suitability analysis for new wireless or fixed line service planning. GIS has potential to enhance a service provider workflow in operating the infrastructure seamlessly resulting in increased customer satisfaction.