Artificial intelligence (AI) is a powerful tool whether you use it to improve your customer satisfaction (CSAT) score, to smoothen communication with consumers, or to update outdated processes. By implementing AI in your call center, you can opt for better business intelligence that ultimately helps you understand your agents and consumers better. Add to this a predictive analytics toolset, and you’ve armed your team with insights that will help to spot risks early and set you up for long-term success.
Any consumer-facing business can profit from adopting AI and machine learning technologies, especially those that rely heavily on call center operations. Incorporating AI into your contact center opens the door for automated quality assurance (QA) and sentiment analysis through highly customizable speech analytics (SA)Throughout the remainder of this article, we’ll learn how.
100% QA of callsÂ
Traditionally, contact centers analyze a random sample of 1% or less of their calls. This method is rather self-selecting and not always statistically representative. AI-poweredSA can provide 100% analysis across all calls and help you visualize what is happening on the floor. The data exported for QA purposes can always be used for business intelligence, process optimization, consumer experience improvements, and fine-tuning your calling strategy.
With AI-powered SA, you also get to automatically classify types of calls based on the words and phrases that typically get used within calls, without having your QA team sit for days listening to hundreds of recordings. By monitoring live agent calls randomly, and automatically flagging phrases of concern, your QA team can seamlessly translate these learnings into agent trainings to help them comply with regulations, deal with difficult conversations, and develop the advanced conversational skills.
Agent dashboards Â
New-age speech platforms that include AI can enable call center managers to immediately see agent performance trends in the forms of agent scorecards and have them inform your training programs. With effective scorecards aligned to the KPIs of your choice, you can identify areas where agents can be coached to improve their performance. The automation provided with AI can help your managers create scorecards based on 100% of calls rather than a small sample.
AI can analyze audio from customer calls to determine how different scripts impact customer response and satisfaction. This information further reveals the need for any additional training or process optimization.
Predictive AnalysisÂ
Many businesses are unaware that they are sitting on a goldmine of data in their contact center. These analytics, which synthesize data from a variety of data sources, can help you understand not only what has happened, but what is likely to happen next. Using AI for predictive analytics and risk assessment ultimately helps you develop better outreach strategies, translating to better CSAT rates. It’s now possible to design algorithms with predictive models based on demographic, social, and economic data. This can help your find the best channel to reach each consumer on, then automate the outreach.
Real-time compliance coverage Â
With real-time prompts, machine learning can alert agents when they deviate from the pre-approved script. For example, in the accounts receivable management (ARM) industry, this helps agents stick to reading the mini-Miranda and any other required scripts based on FDCPA and Reg F guidelines. This way, agents get to correct themselves to avoid a compliance violation on the spot and not make the same mistake on the next call. The transcription-based analytics (TBA), which helps you convert conversational speech to text, automatically creates a text version of an unsuccessful phone call, allowing unlimited text searches, helping you determine the root cause of a non-compliant call. This gets you ahead of any compliance issues before they blow out of proportion.
Sentiment AnalysisÂ
Agents always need to identify an approach that’s empathetic to the consumer, yet effective from a business standpoint. By getting it right, they can increase their performance score and improve brand loyalty. Call centers that embrace AI and machine learning in their practice can achieve the same.
Imagine having an AI that can read the emotions in a consumer’s voice, so your agents know exactly what they think when they talk to them. This means no more guessing if the customer is distressed or not. The sentiment analysis module of a SA platform can read emotions through customers’ tone of voice by evaluating the language used, as well as voice inflections, the rate of speech, and the amount of stress in the voice to decode the emotional outcome of ongoing interactions. This helps your team leads to automatically mine abusive calls and direct agents to use a more upbeat, positive tone the next time.
Virtual agentsÂ
Most delinquent consumers prefer to communicate and pay through channels they were comfortable with. Having virtual agents integrated into your contact strategy can remove the stress a consumer has in speaking with a human. AI-powered chatbots aka virtual assistants, don’t just help agents make contact with a consumer, but actually, improve the quality of communication. They work across platforms and hence help you scale your outreach without losing the human touch. Better still, AI can tailor each massage to the individual and even mimic the user’s style based on their chat history.
Enhanced A/B testing
The final benefit of using AI is its advanced ability to A/B test. With its ability to learn from past calls, data metrics, and predictive algorithms, you can analyze the effectiveness of any calling strategy. This can help you conduct rapid A/B testing to give you a clear idea of how to adjust the tone of your agents, use the right call to action on the call, and the optimum time to call.
In ClosingÂ
Traditional calling approaches, largely characterized by a one-size-fits-all strategy, are inefficient in today’s current economy. With the rapidly changing technological and regulatory landscape, the customer contact industry is evolving like never before. AI’s capacity to use data and behavioral science is a powerful means of understanding customers on a more personal level, automating redundant processes, and helping businesses do more with less.
