By Joni Martikainen, Data and AI Lead, Enreach
In all its forms, the phone remains one of the primary forms of customer engagement, so it is a valuable source of insights to help understand their behaviour and support business growth. For instance, these calls can help understand if there are any service issues, determine satisfaction, and whether a sales campaign is working.
Of course, listening and manually analysing voice calls is time-consuming, laborious, and often just not viable. Yet, not having voice-based contact examined means this data's ultimate value is lost.
The answer is conversational analytics (sometimes called speech analytics), which removes the manual effort of listening through all those calls, using AI technologies to observe keywords, phrases, behaviour, and sentiment. Calls can also be categorised, and compliance requirements supported. Plus, the benefits of conversation analytics can extend beyond the contact centre to every department as a tool to drive growth and profitability.
For service providers, speech analytics is a route towards providing additional services to new or existing business clients and making the most of the current upswing in interest around the CX. From a technological point of view, speech analytics does not have to be a demanding or complicated process.
Without drilling too deep into the technicalities, speech analytics can be divided into three main categories. The first is speech-to-text, which, as its name suggests, converts voice to machine-readable content that can be analysed further, such as by searching for keywords and phrases.
The second category is sentiment analytics, which examines the tone of the caller’s voice, supported by identifying keywords to help understand how that person feels about a situation. The results are typically scored as positive, negative, or neutral.
Thirdly, intent analysis looks at what the customer wanted when they called: was it to purchase something, make a complaint, or was there a query? Better insight into customer intent means improvements to their future needs can be more accurately met.
Here are some example scenarios of conversational analytics in action.
An insurance company discovered customers could not find the correct place to schedule an appointment on its website. This issue was discovered through speech analytics. Once the website was amended, online bookings increased, and inbound calls to request appointments decreased.
The same company could also monitor customer interactions and identify areas where service agents could improve. The most common pain points can be discovered through conversational analytics, and agents are then trained to address those. Likewise, churn signals can be flagged, and those customers proactively contacted with an offer to remain.
AI-powered insights can help sales teams determine which methods of customer engagement have the highest success and then train teams accordingly. For example, speech analytics can provide insight into the buyer’s intent, common questions or hesitations to help sales reps close a deal.
Speech analytics can help to reduce the effort of monitoring the quality of agents’ interaction with customers, to spot patterns and identify areas where processes could be improved. Additionally, this data can be used within customised training manuals to help speed up the onboarding process.
Last but by no means least, conversational analytics can help ensure that regulatory standards are being met by spotting any deviations from pre-determined, compliance-ready scripts. In addition, Payment Card Industry (PCI and Personally Identifiable Information (PII) can be automatically redacted from call recordings, for instance, triggered when the agent says words such as ‘credit card number’.
These example scenarios demonstrate that speech analytics deliver multiple advantages, removing much of the guesswork around customer service, marketing and sales, improving operations and supporting compliance. Furthermore, since this rich mine of information is already being recorded and stored by many organisations, making the most of this valuable data makes sound business sense.