The energy industry is one of the biggest in Australia. The abundance of resources available in the country is such that Australia is one of the biggest exporters of Coal, Uranium, and LNG. Australia is slowly beginning to pick up a sizeable market share in the country. Nearly 40 percent of the country’s energy consumption is coal with oil and gas at 34 percent and 22 percent respectively. Therefore, there is a clear case for the use of an RPA tool.
Coal accounts for 75 percent of the electricity produced in the country with Gas at 16 percent. With coal exports in the billions, it is clear that the utilities and energy industry in Australia has a market size in the billions. Several players such as Origin, AGL, Simply Energy, and Energy Australia have heavily profited from the energy industry in the country.
While the ever-increase competition and lucrative market share encourage the formation of several companies in the space, the ability to manage data becomes a challenge. Most utility players in the space accept payments from customers almost on a day-to-day basis. The sheer volume of payments received presents a challenge, especially to track complete vs partial payments against invoices, discounts to be applied based on prompt payments or other campaigns, aging of debt and collections.
The client, one of Australia’s fastest-growing utilities suppliers with over 700,000 customers, had a unique problem to surmount.
the problem faced
Due to the pandemic and it’s grappling effects, the utilities supplier sought to find a new way to incentivize customers to clear invoices in a timely manner. Therefore, the discount offers for payments made by the ‘due date’ went a long way towards retaining customers.
However, customers who cleared the invoice on time with the discount percentage availed, were still seeing the discounted amount as balance due.
- At times, discounts were not being applied in the system due to enhanced processing times due to holidays and unresponsive payment gateways, which was taking the receipt of the payment of the invoice “past the due date”.
- This discrepancy led to the back-office team in the organization manually verifying the accounts to ascertain whether or not the customer was eligible for the discount offer.
- This verification process resulted in burning several man-hours that could have otherwise been put to better use.
- The discrepancies that could not be verified led to customer dissatisfaction and eventual customer churn and loss in revenue.
Applying discount offers goes a long way towards customer satisfaction and subsequent retention.
solutions offered by infoveave
- The RPA bot scanned through customers who had made the payment with the discount on the invoice. The bot also filtered out the customers whose payments were received post the ‘due date’.
- The bot also went on to calculate the number of days (deviation) from the due date to the date the amount was received. This verification is done by tracking the Invoice ID and matching it with the date the payment was received, a task that would have otherwise been executed by a simulating agent.
- The bot then creates a list of the customers with deviations away from the due date (for payments after the due date). Should the deviation value be higher than three days, the customer is alerted of the balance payment that is due.
The RPA bot was trained to accomplish all of the above tasks end-to-end in a timely manner, something that helped the company
- Save time by training and effectively deploying an RPA bot
- Effectively retain customers by error-free reporting
- Save costs by automation and remove the use of a simulation agent
One of Australia’s fastest-growing utility service providers with over 700,000 customers across the country.
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Why use an RPA?
- A trained RPA bot is capable of executing boring and rule-based tasks.
- Save capital by deploying a bot that can complete a string of tasks.
- Performing tasks that are prone to human error.
- Completing these tasks in a time-bound, effective, and efficient manner.
Infoveave features used
- A trained RPA bot
- Pricing simulation based on competitor pricing, vehicle configurations, location, channel incentives and other factors.
- Competitor Analysis
- Peer-to-peer benchmarking
- Address Book
- Dashboard and Reporting
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