Empower Agents with Modern Tools to Deliver Great Customer Service

Customers expect service interactions with companies to be tailored to who they are, what they have previously done with the company, and their current context. Customer service leaders struggle to effectively support their agents as customer expectations rise and self-service technologies pick off the simple, reproducible inquiries.

Typical challenges that customer service agents face include the following:

  • Customer context is lacking. Many customers are authenticated on a website when they call customer service. Not only do agents not have visibility into these customer interactions, but they also frustrate customers by asking them for data that they have already provided.
  • Knowledge and data are disconnected from the agent desktop. In a study of how agents spend their time, [24]7.ai found that agents spend up to 35 percent of their work days searching for information from a knowledge base that is disconnected from the agent toolset.
  • The agent desktop has too many applications. Customer service agents use dozens of disconnected applications in the course of resolving a single customer issue, often duplicating data from application to application or performing repetitive, manual tasks. Customer service leaders can't enforce standardized processes. Agents take a long time to get trained on processes and applications, and their frustration with their tools causes them to turn over quickly.
  • Training gaps are common. [24]7.ai found that agents spend more than 10 percent of their work days reaching out to subject-matter experts and leads for help. Customer service leaders struggle to train and coach their employees to handle the technically and emotionally more complex interactions.

How to Assemble the Building Blocks of a Modern Agent Desktop

Great customer service rests on deep understanding of the customer, the context of their inquiries and their journeys, access to the right knowledge and data, automation to free agents from repetitive tasks, artificial intelligence to focus agents on work that matters, and agent empowerment to do the right thing for each customer.

No single-vendor workspace solution offers all of these capabilities today. This means that technology-powered customer service organizations must assemble these workspaces themselves. To do this, customer service leaders should do the folowing:

  • Build a solid customer service agent desktop foundation. Start with a modern agent desktop solution from a customer service vendor. These solutions allow for basic customer identification, such as phone numbers; email addresses; social handles; transaction-system-based customer IDs; and customer understanding, such as tier, products owned, and customer sentiment. These solutions also allow inquiry capture, workflow, and resolution.
  • Maximize agent productivity with efficiency. Use process guidance to standardize agent actions through disconnected applications. Options include scripts that guide agents through conversations and actions; tip balloons that break down processes into step-by-step instructions for agents to follow; unified agent desktops that present agents with a single pane of glass for all of their applications; and robotic process automation, where software robots mimic human actions.
  • Improve agent effectiveness with better content and coaching. Curated content from within a knowledge base can only take you so far. Organizations should add peer content as well as cognitive search solutions to extract information from file systems, bug databases, streams, APIs, and other applications. Explore chatbots to help assist the agent. Look for ways to make training more bite-sized and available within the agent desktop, with content directly tied to quality results.
  • Leverage data insights to improve customer intimacy and predict next-best actions. Use customer success solutions to surface a health score to help agents better understand their customers. Use customer analytics to extend profile data with demographic and relationship data for better matching. Use predictive analytics to provide next-best actions for agents to take. Use real-time speech and text analytics to surface customer emotions.

Together, these tools improve outcomes by helping agents understand the value of customers and their journeys and, ultimately, allow great service delivery.

Kate Leggett is a vice president and principal Analyst at Forrester Research.

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