Contact centres five years after COVID-19
Although the COVID-19 coronavirus is here to stay, the WHO has confirmed the virus is no longer a global emergency, and the extensive lockdowns of 2020 no longer plague contact centres across Australia.
The contact centre landscape looks different in 2025. However, businesses learnt a lot about where their processes were most — and least — resilient to sudden operational changes.
What hasn’t changed is that customer experience remains a strong contributor to success, and that means contact centres remain vital to delivering on business priorities.
One of the key changes during the pandemic lockdowns was a sudden rush to empower as many people as possible to work from home. However, we know that contact centre representatives perform better when they can effectively collaborate, and accordingly, the 2024 Australian Contact Centre Industry Best Practice Report indicated that remote work declined from a peak of 76% in 2022 to 55% in 2024. Despite that significant decline, most contact centres also expected no further change to the status quo.
The elephant in the room — one that’s new to the post-pandemic world — is generative AI. While some predictions for customer experience in 2025 consider it highly probable that much customer service work will be offloaded onto generative AI systems and agents, machine learning systems, although increasingly powerful, still lack the capacity for critical judgement that comes standard with human beings. If organisations want them to be executed well, the tasks that require good judgement and empathy must remain the domain of human staff.
But whether businesses are supporting an AI agent or human staff, the need for underlying data sufficiency to support excellent customer experience remains at least as strong in 2025 as it was back in 2020.
So, what steps can you take to ensure that reliable data is what’s coming out of your CRM?
1. Streamline.
Simplicity, standardisation and clarity are vital to data management and can help contact data stay relevant for longer.
Data quality management needs clear and consistent data input rules. Define required fields, specify date formats and naming conventions, and standardise how and where the log of each record’s edits is maintained.
Businesses can also reduce the amount of manual data entry and double- or even triple-handling required of staff by making sure systems are appropriately integrated. Aside from the obvious benefit — that is, to encourage efficiency by limiting the time spent on repetitive administrative tasks — it will also reduce opportunities to lose data quality in the transfer between systems.
2. Communicate clearly and train staff
The Harvard Business Review once reported that two thirds of managers are uncomfortable communicating with employees. However, organisations need to communicate with and train staff effectively.
Even with consistent data input rules in place, communication between a business and its employees does not always operate at its most effective. At a minimum, staff should understand their data management obligations for compliance and effective daily operations: what constitutes sensitive data, how to keep your customer data updated, and how it’s best used. Good training can reduce the creation of duplicates and loss of data, and in the longer term averts the productivity loss of both making and correcting mistakes.
Quality training affects your bottom line in other ways, too. It improves employee performance and productivity. It reduces double-handling and lessens the need for direct supervision. It also improves staff retention, which reduces the number of new hires for the organisation to onboard.
3. Keep it clean
Even with clear policies and well-trained staff, data degrades. How fast that degradation happens depends on factors like the kind of data kept, the sector in which a business operates, and what data quality framework is used for assessment.
This is why regular data cleansing and data validation are recommended. Historically, we might have recommended a clean-up every 6 to 12 months. In 2025, it’s possible to automate real-time data cleansing to ensure that your data is always up to date and ready to be put to work.
