How organisations can optimise bots for best performance

Bots have been steadily increasing in popularity with organisations and the reasons are clear.

They offer numerous benefits for contact centres and divert calls from overworked operators by providing an automated response that can resolve a range of difficulties. They execute triage while gathering data to assist agents in decreasing average handle times and enhancing First Call Resolution.

Furthermore, bots can serve as a concierge for businesses that use multiple service pathways by listening for intent and automatically locating the next-best resource, which can significantly reduce transfers.

As a result, businesses have high expectations for their bots. When a company launches a bot for the first time, it is interesting to observe how the technology will impact KPIs.

Unfortunately, if the bot is unfamiliar with the preferences of a company’s customer base, the interactions can quickly turn sour, and this is becoming more common.

Globally, the use of bots has been rapidly expanding, with the market size in terms of revenue predicted to reach $6.7bn by 2027.

Similarly, consumers are also increasingly using bots for service interactions, however, their satisfaction in those interactions has declined.

According to “The State of Customer Experience” report, only 25 per cent of consumers polled in 2021 say they were satisfied with their bot interactions, compared to 35 per cent in 2017.

Customers will avoid using a bot if it causes them frustration rather than satisfaction, and over time, performance deteriorates, eventually leading to organisations parting ways with their bot.

AI and the self-help myth

With AI, there still exists a “set it and forget it” myth. With contact centre bots, most managers believe that once the bot is developed, the work is done. They believe the bot will automatically know what to say, even when customers have issues that the bot was not trained to handle.

Meanwhile, customers are unaware of the bot’s capabilities and expect it to do more than it can. Managers continue to monitor results and KPIs, but when they notice a decline in performance, they blame the bot and become convinced that the organisation’s relationship with the bot is doomed to fail.

Like any relationship, it takes a bit of work to ensure everyone is happy. Building better bots necessitates more than simply training a language model and then deploying it in a channel. Bots must be evaluated on a regular basis to ensure that they meet KPIs, and models must be refreshed as their usage expands.

As bots communicate with a larger audience, such as an organisation’s customers, they encounter more and varied ways in which customers request assistance, and this information is constantly evolving.

Changes in policies, product pricing strategies, and locations are just a few of the things the bot needs to be aware of. Without training, the bot won’t know how to respond to new requests. Ensuring that the bots can answer with constant precision increases their adoption and usage. When bots are optimised, they can respond to 100 per cent of the initial interaction as a baseline.

However, providing an extraordinary end-to-end experience involves more than a precise response. Bots must be able to recognise when the conversation has progressed beyond their abilities to assist and act accordingly.

Orchestrating customer journeys

Bots that are developed and operated in isolation are doomed. Too frequently, though, businesses assume a bot is ready for anything. High-level orchestration is required to ensure that a bot is tightly integrated into the client journey. In an ideal case, the bot can listen to the journey in real-time, personalise the conversation depending on the context of the journey, and then seamlessly transfer the call to an agent who can handle the issue.

Train, measure, analyse, optimise, repeat

Effective bots are built through training; they’re measured and analysed, and they are optimised based on performance. They are also regularly re-trained to ensure that they are constantly learning and improving their understanding of customers and meeting their evolving needs. Without the right tools and best practices, it’s difficult to give bots the attention they need. Below are some ideas to get started:

• Business-centric build tools:

Having actual business users design bots, either directly or in tandem with conversational AI engineers, ensures that the bots have a clear business purpose.

• Integrated interaction workflow:

Rather than building a bot and then attempting to wire it into the interaction flow, it is quicker to create the bot as part of the interaction flow during the development phase.

• Out-of-the-box reports:

In certain instances, bot frameworks only allow the export of data or data logs. Data mining and manipulation require considerable time. Frequently, by the time the data is ready for analysis, it is too late to make any meaningful decisions. It is more effective to have real-time, actionable, out-of-the-box views of performance and model accuracy.

• Bots actionable analytics:

Actionable analytics go beyond mere reports; these analytics allow businesses to use what has occurred previously as a guide for what should happen next. This provides data-driven suggestions as to where the bot might be improved.

Ultimately, bots must assist genuine clients with genuine problems. Everyone desires bots that can provide assistance, answer questions, and demonstrate empathy. Businesses want highly effective bots that are ready to interact with customers effectively, and that begins with optimising bots from the very beginning.

Amr AlMasri is the regional director at Genesys – Middle East

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