{"id":26627,"date":"2021-04-16T18:18:35","date_gmt":"2021-04-16T12:48:35","guid":{"rendered":"https:\/\/www.subex.com\/?p=26627"},"modified":"2021-04-16T18:18:35","modified_gmt":"2021-04-16T12:48:35","slug":"redefining-rating-assurance-with-analytics","status":"publish","type":"post","link":"https:\/\/dev.enki.studio\/test\/?p=26627","title":{"rendered":"Redefining Rating Assurance with Analytics"},"content":{"rendered":"

Significance of Accuracy in Rating Assurance\u00a0<\/strong><\/p>\n

Revenue Assurance has been accepted by the telco world as\u00a0a critical\u00a0solution to improve data quality and processes. The solution\u00a0can help\u00a0CSPs increase profits, revenues, and cash flows and gain a strong Revenue market<\/a><\/strong> share\u00a0without influencing demand. Globally today, most of the Revenue Assurance<\/a> controls managed by the RA teams focus on the completeness of data used by business processes\u00a0focussed towards revenue generation, thereby\u00a0confirming the undisputable value the function brings to the business.<\/p>\n

However, let’s check some other dimensions relevant to RA.\u00a0TMForum\u00a0RA guides show the completeness, validity, accuracy, and timeliness of data quality.\u00a0To\u00a0delve deeper into\u00a0accuracy, a very critical aspect for any RA function;\u00a0most of the time, accuracy\u00a0is associated with\u00a0quantitative measures attached to the charging and rating processes\u00a0such as:<\/span>\u00a0duration accuracy, data volume accuracy, or rating accuracy.<\/p>\n

In this article,\u00a0we aim to look closer into rating accuracy specifically. According to RAG (Risk Assurance Group),\u00a0“Data records accurately describe the event or object in the real world that they correspond to.” As a result, the rating accuracy of a tariff applied for a usage event or to a recurring or one-time fee is consistent with the Marketing teams’ intent.<\/p>\n

Limitations of the current approach in Rating\u00a0Assurance\u00a0<\/strong><\/p>\n

To\u00a0date,\u00a0rating accuracy is achieved\u00a0by using a complex approach that consists\u00a0of re-performance of rating processes and then comparing the output of the parallel rating with the actual rate. This approach\u00a0comes with a\u00a0set of limitations:<\/p>\n

Sample-based method:<\/strong>\u00a0There is a sample of usage events utilized for a\u00a0selection\u00a0of price plans. Though the\u00a0tried and tested\u00a080\/20 rule is a powerful way of cutting through\u00a0the clutter around a\u00a0complex decision the outcome remains\u00a0only\u00a0as\u00a0a sub-set of the rating universe tested.<\/p>\n

High efforts required<\/strong>: Configuration of a rate plan, an add-on to a rate plan, maintaining it, etc.,\u00a0are\u00a0activities\u00a0which\u00a0require massive efforts.<\/p>\n

Sampling criteria:<\/strong>\u00a0It is difficult to identify and arrive at valid sampling criteria, i.e., widely agreed and approved by the company. At the same time, these criteria should be regularly updated\u00a0to avoid\u00a0sample bias (providing assurance over the subscribers in the sample).<\/p>\n

Shift from traditional to\u00a0<\/strong>analytics-based accuracy for rating assurance\u00a0<\/strong><\/p>\n

Due to the\u00a0above-mentioned limitations, it becomes necessary to\u00a0identify\u00a0and execute\u00a0alternative ways to deal with\u00a0uncertainties coming from rating accuracy. However,\u00a0the alternative way has to satisfy several objectives,\u00a0to be considered effective in validating rating process accuracy. A few of the goals are listed as follows:<\/p>\n