The call centre world, unsurprisingly, ranks as one of the highest adopters of data analytics platforms year on year. This is largely due to the invaluable insights we gain through the analysis of thousands of calls received each day by the typical call centre. With speed being of the essence in making the right decision at the right time for each caller many call centres are turning to machine learning to automate their data analysis and make crucial customer experience decisions within seconds.
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Reducing call duration and increasing first call resolutions
Whether you’re running an inbound or outbound contact centre, the interaction between your company representatives and your customers is a crucial area for customer success. Thanks to machine learning algorithms, businesses are able to manage those customer-facing moments more efficiently. According to techtarget.com, “Emotion analysis through text and speech analytics can paint a more complete picture when combined with the overall first call resolution (FCR) metric, indicating the level of confidence customers feel about whether the answer they received has resolved the issue at hand.”
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Machine learning algorithms are also helping customers reach the right representative in a shorter space of time (and alleviating much of their frustration in the process) by smartly routing calls based on their nature to the right person with the appropriate knowledge and skill level. This, in turn, reduces call duration periods, repeat calls or call abandonment rates by unhappy callers. On-screen prompts based on the machine learning analyses of callers’ moods, or other indicators, are also allowing contact centre agents to more effectively deal with customer queries or problems and reduces the need for customers to make often frustrating repeat calls to the business.
Machine Learning to determine the right time and number to call can be used in call centres for both sales to reach prospects and debt collection to reach debtors.
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Reducing call volumes and increasing customer satisfaction
Machine learning can be used to reduce call volumes by eliminating the need for customers to call when there is, for example, a network fault for a telecommunications company / ISP or reception issues with a satellite company. By analysing voice/speech patterns, emotions and words from incoming calls, machine learning can identify 1) that there is an issue (anger or irritation based on the tone of voice), 2) what the issue might be (“slow line speed” or “no reception”) and 3) where it might be (based on the caller’s location). By analysing an influx of calls coming in and identifying these types of patterns, machine learning could kick off a notification to technical support to notify them of the issue and enable the contact centre to send out a pre-emptive SMS / email / WhatsApp to all subscribers in the affected area or post a message on Twitter / Facebook to let them know that they are aware of the issue and are working on having it resolved.
Increasing revenue by seizing sales opportunities
To serve the customer better – and boost profits in the process – businesses need to make smart decisions on the fly. Direct interchanges between consumers and call centres are golden opportunities to make this happen. According to John Magliocca, chief consultant for contact centre services outsourcing company, ISG, “There have been efforts underway to put contact data to work to best understand the current mood of the customer and other information that can immediately mould client strategy and direction [for some time].” With volumes of customer and transaction data available, machine learning platforms can inform contact centre staff on ideal product suggestions based on past purchases or upgrade a subscription service to premium if a customer’s financial situation has changed. Data-driven solutions will continue to inform customer insights while simultaneously helping businesses raise bottom lines.
Machine learning will require user buy-in
Although machine learning can enrich the customer experience considerably, it would have little impact on productivity, profitability or customer satisfaction levels without buy-in from the people intended to use it. Forrester advises business leaders who want to adopt new technology platforms as a means to improve the customer experience, “Senior executives set the tone for the need for a customer-centric culture and new processes and tools that support customers more effectively. Leaders must plan for changes in the work practices needed to meet customer management goals”. This means involving users in new technology implementations that will ultimately add value to the workforce and importantly, the customer.
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