REVENUE DATA ANALYTICS & CONSUMER PROFILING

REVENUE DATA ANALYTICS & CONSUMER PROFILING

The Facts National Treasury Publication of the Local Government Section 71 information for the Fourth Quarter ended 30 June 2019. Aggregate municipal consumer debts amounted to R165.5 billion (compared to R143.2 billion reported in the fourth quarter of 2017/18) as at 30 June 2019. A total amount of R4.2 billion has been written off as bad debt. Government accounts for 6.2 per cent or R10.3 billion. Similar as in the previous financial years the households still represent the largest component of debt owed to municipalities at 71.7 per cent or R118.6 billion. It needs to be acknowledged that not all the outstanding debt of R165.5 billion is realistically collectable as these amounts are inclusive of debt older than 90 days (historic debt that has accumulated over an extended period), interest on arrears and other recoveries

A credible, fair and transparent mechanism is necessary to increase or improve collection from debtors. Debt collection or recovery stands at the end of the revenue value chain and it is therefore important to start at the beginning. The South African Local Government Association (2011:22) argues that “a hundred percent debt collection rate is not possible, because services are also provided to the poor, but lack of data integrity and incorrect billing remains a problem in South African municipalities. South African Local Government Association (2011) also argues that interest on debt is growing and adding interest to irrecoverable balances merely increases the amount due to the municipality. Local municipalities are consequently unsustainable due to low revenue collection rates and the constant increasing of outstanding debtors. In order for a municipality to collect all outstanding municipal debts and enhance or maximise their revenue, the municipality need to focus of revenue enhancement strategies; Today I will focus on two of many strategies that have been tried and tested at my Municipality with relatively positive results – DATA ANALYSTICS AND CONSUMER PROFILING

Data Analitics The success of any Revenue Enhancement Strategy in a municipality, lies around utilising the insights that is unlocked from good representative and complete datasets. These insights, can only be unlocked by doing proper profiling of the debtors book. One of the major challenges that faces Municipalities today, is the lack of proper data to do this profiling. There are various factors that contributes to this lack of data completeness and limited insight. Some of these challenges are: Incomplete data capturing at point of application of customers Lack of customer management systems capabilities Incomplete basic information such as ID number, Company registration number, Trust number etc Incorrect categorisation of customers such as residential, business, municipal own etc Lack of proper data completeness for profiling Ability to analyse large datasets Lack of access to external datasets to complete the profiling A mechanism that allows the enhanced datasets to be accessed for specific data insight extraction in the form of Exception and Business Intelligence reports

Internal Data As a starting point, the following datasets are recommended for this intervention: Municipal own databases; Supply chain database HR Database Customer information - Header /Master file Age Analysis Current Indigent file Past years Auditor General reports

External datasets Department of Home affairs ID Verification Name/ Surname verification Living status Government Gazette – Estates and Executors Date of death Estate info Executors info Private Sector consumer data – access as per NCA Permissible purposes Credit activeness Credit exposure Months in arrears Monthly instalments

External datasets - cont Attribute data Property ownership - Deeds Company data – CIPRO Other assets/ ownerships such as eNatis Employment information Contactability Telephone details Addresses Household/ shared addresses Possible relatives and other relational information Other Municipal datasets

Consumer profiling Although the phrase “know your customer” may seem insignificant to most people, it has a very important meaning in the business world. The process of knowing your customer, otherwise referred to as KYC, is what businesses do in order to verify the identity of their clients either before or during the time that they start doing business with them. After going through the entire process of analsying your debtors using the various internal and external data sets what do you get; Information of judgments Highlights potential bad payers Highlights municipal / government employees List properties that may be linked to debtor through marriage Identify customers under debt review, administration orders and insolvent. Identify consumers that meet Councils Indigent criteria Identify potential write offs / legal collections

Collections prioritisation The balance of the book can now be prioritised into various collection campaigns. A typical Revenue / Collection strategy would be based on the following 3 areas: Contactability of the debtor – this should be a combination of internal datasets and external datasets to ensure proper Customer Due Diligence can be applied and contactability are maximised. Ability to pay – this can be determined by a combination of internal and external datasets and would be important to do payment arrangements Willingness to pay – this mostly relates to the customer’s perception of the service but could also include certain items like political factors i.e. specific wards with special circumstances. These can only be done with the right datasets to properly profile and identify the areas.

Conclusion Municipal consumer debt is a complex challenge that requires multi-faceted solutions. Addressing the challenge requires not only interventions aimed at promoting greater levels of payment by consumers but also, perhaps more importantly, improvements to municipal service delivery and administrative processes. GARBAGE IN - GARBAGE OUT PARADIGM GARBAGE DATA PERFECT DATA GOOD RMS EXPERIENCED AND COMMITTED STAFF GARBAGE RESULTS POOR RMS LACK OF COMMITMENT GARBAGE RESTULTS