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  • Writer's pictureDeborah Cromwell

9 Ways Interaction Analytics Drives Agent Performance Improvement in the Contact Center

Updated: Apr 6, 2023

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Interaction Analytics for the Contact Center

Interaction Analytics Drives Agent Performance Improvement through Automated Real-Time Guidance and Next-Best-Actions

Solutions that can help contact centers maximize resources to service more customers using the same or fewer agents while minimizing time to resolution are necessary for achieving profitability and a competitive edge.

Interaction Analytics (IA) powered by Artificial Intelligence (AI) and Natural Language Processing (NLP) is one of the most effective solutions for automating analysis of all the data captured across channels throughout the customer journey and utilizing that data to propel interactions for agents along the way.

Utilizing Insights from the Customer Journey and Interactions

Having the ability to capture data from various steps of the customer journey, organize and manage big data, and analyze and provide insights related to both the customer experience and the performance of entities engaged in the journey (agents, back-office employees, enterprise systems and telephony systems) is invaluable especially when insights can be available per journey and interaction or viewed as trends over time.

Data resulting from interaction analytics can be applied in real time across agent workflows to help them capitalize on opportunities and boost efficiency while minimizing escalations, thus increasing their overall success and job satisfaction. In this way, IA is a proven win-win-win solution for the contact center, agent and customer.

According to a report published by DMG Consulting, “Recent innovations in the area of natural language processing (NLP) and the cloud have made real-time features viable for front-line contact center employees. Real-time solutions position agents to optimize their performance by giving them alerts, tips, best practices, scripts, knowledge articles, and other guidance they need to achieve the best outcome for each transaction.”

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Real-time Guidance and Next-Nest-Actions Help Deliver Agent Performance Improvement

Capabilities Necessary to Deliver Real-time Guidance and Next Best Actions that Support Agent Excellence

Removing Known Barriers and Automating Time-Consuming Processes

Desktop productivity analytics can be instrumental in delivering real-time guidance and next best actions. With the ability to add pop-ups to an agent’s workflow profile that examines the steps in progress and the content of the transaction workflow during interactions with customers, contact centers can remove known barriers and automate time-consuming processes to drive interactions forward proactively.

Real-time guidance and next-best-action recommendations presented to agents in the form of automated call outs, pop-up boxes, scripts, knowledge records and more make it easy to assist agents during their interactions without major disruption and the need for intervention by supervisors and coaches.

Factoring in Customer Profiles, Interaction Histories and Agent Skills

Prompts and recommendations are most effective and have the greatest impact on agent performance, and ultimately customer satisfaction, when they draw on analysis of data derived from customer profiles, interaction histories and factor in agent skills at the same time.

If agent tenure can be determined to have a relation while conducting certain transactions, then the content of the guidance and next best action could be aligned with the level of the agent’s experience. For example, the next best action or guidance for a new agent can be detailed in many steps, while guidance for more experienced agents may only require a reference to a particular topic.

Discovering Customer Intent, Propensity and Preferences

Advanced IA that includes intent analytics can discover customer intent and propensity to behave in a certain way. For example, certain intents or the tendency of the customer can be analyzed and then trigger the launch of pop-ups as defined in the transaction workflow to encourage the agent to act or to make a recommendation that would influence a customer’s next decision in a way that could benefit them.

Understanding customer preferences can also be discovered directly using interaction analytics that look at known subjects and discovered indirectly by using intent and tendency analytics. In either case, managers can add a list of next-best-actions driven by the type of preference to launch pops-up as next-best-actions or guidance.

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Interaction Analytics Powered by AI is Easily Justified as Its Impact on Agent Performance Contributes to the Bottom Line.

Benefits of Real-time Guidance and Next Best Actions that Positively Impact the Bottom Line

Contact centers can reap the benefits of interaction analytics to achieve their objectives in developing top performers and employee excellence thus improving customer satisfaction and loyalty and increasing business productivity.

There are many circumstances in which guidance and next-best-actions delivered by IA can impact each of the objectives above. Here we will identify the types of situations that guidance and next-best-actions can specifically address.

1. Alerting Supervisors and Agents about Escalations, Emotionally Charged Situations and Non-Compliance

For situations that escalate or become emotionally charged, it is imperative they be detected immediately and managed with care and expediency. Critical events such as a cancellation of service or an irate customer can easily be discovered by IA during transactions through customer and agent sentiment analysis.

Such discoveries can be used to launch instructions that walk agents through a process of de-escalation to mitigate potentially negative experiences. In addition, interaction analytics solutions with messaging capabilities (SMS, email or outbound calls) enable agents to communicate to supervisors and/or alert supervisors if a certain threshold of escalation (often defined in the workflow by the contact center) is reached.

2. Identifying Sales and Retention Opportunities

One way interaction analytics can help contact centers identify opportunities for increased sales and improve retention of customers and programs is by monitoring the paths customer transactions follow based on customer requests. Real-time guidance and next-best-actions can be used to remind agents to upsell or offer incentives for retention. For example, if a customer is purchasing a certain product while opportunities for upselling are available, IA can launch instructions and recommendations related to the sale when the interaction workflow reaches this specific step.

3. Improving Sales Closure or Conversion Rates

Real-time guidance and next-best-actions can be added to the agent workflow to arm them with proven closure techniques and conversion tactics (sales rebuttals, sales value propositions, description of ROIs, competitive analysis and more) to inform and persuade buyers and close sales.

4. Kicking Off Real-Time Workflows

In some cases, interaction analytics can launch a workflow during a transaction by allowing managers to add the metadata of a particular step that causes the kick-off. Interaction analytics will monitor the workflow. If that step is reached, it will automatically follow the metadata and launch the workflow.

5. Reducing Agent Onboarding Ramp-up Time

One way to reduce the time it takes an agent to onboard is by leveraging IA regarding the workflow. Interaction analytics can tap into an agent profile using proper sequences, duration for each step, and pop-ups at entry/exit or based on certain events. During the transaction, IA can monitor agent performance and compliance and provide real-time guidance throughout. A process like this can significantly reduce agent onboarding and training efforts.

6. Improving Accuracy of Agent Responses to Customers

Information that needs to be communicated by agents to customers can trigger real-time guidance and next-best-actions for the related steps in a transaction workflow. They can be launched automatically when the transaction reaches those steps and agents can accurately deliver the information provided.

7. Reducing Agent Average Handle Time

Reducing the amount of time agents spend during interactions can be achieved by removing the bottlenecks in their workflow and by delivering the right knowledge exactly when they need it. IA can do both these things via automatic launch of workflows, automated access to Knowledge Management Systems and the retrieval of accurate data, automated next-best-action and guidance to move the process forward. All these capabilities significantly contribute to reducing the average handle time and even first call resolution.

8. Decreasing Agent Attrition

Through real-time guidance and next-best-actions, contact centers can achieve a higher rate of successful outcomes and realize improved agent performance while at the same time freeing-up supervisors. The more success and incentives earned by agents, the higher their rate of satisfaction. Their achievements directly correlate with their loyalty to the contact center and a reduced rate of attrition.

9. Enhancing Employee Engagement

Real-time guidance and next-best-actions inherently offer a collaborative environment in which agents are continuously engaged during transactions and, depending on the IA solution, can be in 2-way communication with supervisors for additional support as needed.

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Happy and High-Performing Employees Positively Impact Customer Loyalty

AI-Driven Interaction Analytics Supports Employee Success and Customer Satisfaction

Reliance on live agents is still very much necessary. With the cost of labor accounting for most of a contact center’s expense, an investment in interaction analytics powered by AI is easily justified as its impact on every aspect of agent performance contributes significantly to a workforce that performs smarter and faster with a higher rate of success and employee satisfaction.

Happy employees tend to remain in their positions for longer periods of time and deliver experiences that keep customers satisfied and willing to promote brands they like via personal contact, online reviews and across social channels. Interaction analytics undoubtedly helps to accomplish this objective.

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