How do municipalities draw up credible budgets when the revenue collection value chain is not in complete congruence? This uncertainty brings about the misalignment between budgets and meeting service delivery goals. Consequently, it comes as no surprise as to why there is a decline in the delivery of reliable services by the municipalities.
Municipalities generate revenue enhancement strategies to improve their cash positions as revenue enhancement strategies are the beacon of hope for driving much-needed revenue. However, a good revenue enhancement strategy without a clear and implementable plan is futile. Hence the question must be asked, how do we improve municipal revenue collection in a sustainable way?
The greatest asset of any municipality is the land within its jurisdiction. Each land parcel is zoned according to the land use guidelines. The options include residential, agricultural, business, etc. This informs which tariffs are to be used for levying. The land use and market value determine the property rates and/or taxes collected from each property. Incorrect zoning and/or misalignment in zoning pose risks to the revenue collection value chain through under-billing.
Erroneous billings are a serious detriment to the revenue collection value chain. There is a high correlation between non-payment and incorrect or inaccurate bills. The negative impact of the non-payment of debtor’s accounts places significant burden on municipal cash flow and impedes service delivery. We have seen that municipalities that have fixed their data issues have seen improvements in billing and a decline in disputes and non-payment. The buzzword is “data cleansing”. Data cleansing is not a magic wand that improves revenue collection, but it is the fundamental step to ensuring that the data landscape is correct, complete, and, most importantly, reliable.
In our experience, we have learnt that the bulk of the problems lie in data management. The data management is mostly in shambles and customer data is in silos across the different departments. Additionally, where data is stored, the records are not always complete and correct. Incomplete and disparate customer data impedes the municipality’s capacity to generate accurate, reliable, and complete billings for its residents, resulting in non-payment of services due to continued disputes.
Municipalities will be capacitated through data cleansing, to store data that is consistent, of good quality, and readily integrates with other departments. By the time the billing cycle runs, customers are levied for the correct services on the correct tariffs, which subsequently eliminates errors and reduces disputes and non-payments.
The lowest granular level at which a municipality should understand its consumers is at the land parcel. “Know your customer” is quite a phenomenon in the banking sector. Likewise, municipalities must define what knowing their customers means in their world. It cannot be business as usual when properties are not billed for “consumed” services and meters are faulty for months on end. Perhaps, as a start, knowing your customers for municipalities should entail keeping accurate and correct details on property ownership, size, extent, and market value, among other things. Understanding the various services provided at that property becomes a crucial next step as it impacts billing. In cases where there is consumption, accurate readings must be recorded and billed accordingly.
What then becomes evident through the implementation of an effective data cleansing project is the elimination and/or reduction of issues of incorrect billing, illegal connections, inaccurate consumer data, a lack of effective systems to verify indigent status, and unregistered properties, subsequently improving the revenue collection process. Fixing the data landscape fundamentally solves billing woes, and improves the revenue collection value chain.
