Top Customer Service Challenges of 2023 and How to Solve Them
December 20, 2022
More than 60% of customers polled by Zendesk in 20221 claim that their expectations for the standard of customer service they receive are higher than they were in 2021.
Support teams that don’t meet these customer expectations have quite a bit to lose. According to a recent Hiver survey2, after just one negative experience with a company:
72% of customers would switch to another company.
52% would dissuade friends and family from buying from the company.
36% would share their negative experiences via social media or review websites.
Don’t let these stats scare you — let them motivate you and your customer support team. Get ready for the year ahead by learning about customers’ support expectations in 2023 and how you can meet them.
Customers Want Increasingly Speedy Resolutions
Customers don’t want to wait for long periods when they need support — they want prompt, real-time responses.
According to Zendesk’s most recent Customer Experience Trends Report, 76% of customers want brands to engage them immediately after reaching out to a company for support. Hiver’s 2022 State of Customer Support Survey found that 33.7% of customers expect a resolution within two hours, and 52.17% expect a resolution within 24 hours of contacting a company.1
Businesses are aware that they could stand to improve in this regard. Less than 20% of companies polled by Zendesk rated their ability to quickly resolve customers’ problems as “extremely strong.”1
Tip: Embrace AI-powered chatbots and predictive support analytics
To speed up your response times, use technology to automate repetitive customer service (CS) tasks and free your agents up for higher-value work. Add chatbots powered by artificial intelligence (AI) to all your live communication channels (messaging apps, websites, social media, in-app).
AI chatbots can handle routine service needs like starting returns, changing/tracking shipping, and more.
You can also program your chatbot to automatically answer basic and frequent customer questions so it can offer round-the-clock, immediate support. Even if the bot needs to transfer the issue to a live agent, customers will likely appreciate the prompt chat response.
Organizations can also use predictive analytics like sentiment analysis to recognize potential customer issues and proactively work to avoid them. With the help of natural language processing (NLP), AI mines customer conversations to identify positive or negative customer sentiments.
When your CS team can identify early onsets of negative sentiment in customer communication, they can work proactively toward improving the customers’ experience, addressing issues quickly — sometimes, even before customers realize an issue exists.
How to Watch for Possible Chatbot Frustrations
Most customers are happy to interact with chatbots. Zendesk found that 66% believe chatbots make their lives easier, saving them time and effort, and 69% have no problem letting chatbots assist them in solving simple customer questions.1
But chatbots can also be a source of frustration if they are not working well. More than half (54%) of customers told Zendesk they get frustrated with the number of questions they have to answer before a chatbot decides to transfer them to an actual agent.1 Hiver found that close to 40% of customers get frustrated when chatbots can’t relate to and understand their issues properly.2
And while more and more companies are investing in chatbots, many admit that they haven’t figured out how to optimize their use. Zendesk found that half of businesses have been disappointed by the performance of chatbots and 57% blame frustration on “their ad hoc approach to AI integration.”1
Tip: Track key chatbot metrics to optimize interactions
Monitor and analyze interactions between chatbots and your customers so you can figure out ways to improve these conversations. Here are a few metrics you can track to monitor and refine your chatbot’s performance.
Fall-back rate (FBR)
A fall-back answer is given to customers by a chatbot when it doesn’t understand the question. Gauge your chatbot’s helpfulness by calculating how often it provides this fall-back response.
Example: 5,000 messages were provided, 100 of which were fall-back answers.
5,000 – 100 = 4,900
4,900/5,000 = 0.98
0.98 × 100 = 98%
Your chatbot’s fall-back rate is just 2% (100% – 98%).
Investigate the messages that receive fall-back answers and decide whether it makes more sense to teach the chatbot about this topic or program the chatbot to transfer the customer to a live agent.
Goal completion rate (GCR)
Measure the success customers have in finding a resolution when engaging with chatbots by tracking goal completion rate (GCR). This metric shows what percentage of users that engaged with chatbots were able to reach their objective — like answering an onboarding question or scheduling a meeting with a live agent.
Example: 5,000 interactions occurred, and 2,400 customers completed their goals.
2,400/5,000 = 0.48
0.48 × 100 = 48%
Your GCR is 48%
Is your chatbot’s conversational flow too lengthy or confusing? Is a button in your messaging not working correctly? When you keep track of low goal-completion rates, you can see which chatbot use cases need to be looked into and potentially revised.
Human takeover rate (HTR)
This metric shows how often your chatbots can solve a customer query with no help from your live agents. Human takeover rate (HTR) is a more nuanced metric because there are times when human interaction is the preferred interaction.
Example: 5,000 interactions occurred, and 900 customers requested a transfer to a human agent.
900/5,000 = 0.18
0.18 × 100 = 18%
Your HTR is 18%
Follow HTR to identify potential hiccups in your chatbot flows and gaps in their knowledge. HTR can also show you instances where it might be better to abandon chatbots entirely and immediately provide human interaction.
With enough HTR data, you can even create segmented chat experiences where bots engage customers based on characteristics — such as age, profession, education level, and more.
Sharing Customer Details Across Agents
Customers are becoming increasingly annoyed when they have to repeat themselves. It’s a widespread issue in customer service today, with almost 66% of customers surveyed by Hiver stating that they need to repeat their issues “frequently.”2
This situation often happens when agents don’t have access to earlier conversations, so they ask questions the customer has already answered. Companies are constantly adding new communication channels to their customer service systems but aren’t doing enough to connect omnichannel conversations among their channels and agents.
Companies that can connect and organize omnichannel conversations effectively stand to benefit. According to Zendesk, 92% of customers are likely to spend more with companies when they don’t have to repeat themselves in conversation.1
Tip: Centralize customer data to make it readily accessible to agents
Zendesk found that 71% of customers in 2022 expect companies to properly share their information among agents so that they don’t have to repeat themselves. Make customer information — both individual details and conversation history — accessible to all agents with a customer relationship management (CRM) platform.1
Adopt software that enables you to organize all customer conversations in one place and give customer service reps full access to the context behind each inquiry they field.
WhatsApp Business Platform can integrate with CRMs to help realize these benefits. Companies that utilize WhatsApp, or Facebook Messenger, as their default communication channel get saved conversation history as a basic feature.
Best of all, when data is centralized and shared, customers can use any channel they prefer at any time, knowing that they can seamlessly start each new conversation right where the last one left off.
Enabling Customers to Solve Their Issues Independently
Customer support goes beyond simply being available to help customers when they have an issue. Companies that want to excel in customer service must enable customers to obtain answers on their own.
Self-service decreases agent workloads. According to McKinsey’s State of Customer Care 2022 report3, 65% of businesses that were able to decrease call volume significantly cited providing knowledge bases and other self-service options as a key driver.
But best of all, customers love being able to find answers independently. Zendesk found that 70% of customers expect companies to offer self-service portals, and 89% will spend more with companies that enable them to resolve issues independently.1
Tip: Let customer feedback guide resource creation priorities
When building self-service resources, let your customers guide you toward the type of content they want and need most.
Ask customers directly via surveys what type of self-help content they’d like to see.
Program chatbots to ask customers what they need help with and offer them multiple-choice options to collect data quickly.
Add a “Was this helpful?” button at the end of help desk articles to encourage quick yes/no responses and gauge the effectiveness of your self-help content.
You can also use a digital adoption platform to observe customer interactions with your resources. Look for trends that could uncover ineffective content. Do customers regularly seek help from live agents after reading particular articles? This could be a clear sign that a particular article or type of content needs to be updated or completely revamped.
When you prioritize the resources that you are creating based on customer feedback and behavior, you’re helping the agents who are tasked with creating and maintaining self-help content portals. You’re also giving customers the type of content they want and need to solve problems independently without wasting time creating resources they don’t find helpful.
Keeping Your Customer Service Agents Happy
Nearly half of customer service managers surveyed by McKinsey said that agent attrition has increased over the past 12 months.
According to Gartner, voluntary employee turnover in the US is predicted to jump nearly 20% in 2022 compared to 20214. Before the COVID-19 pandemic, the annual average of voluntary employee turnover was 31.9 million people, compared to 37.4 million people in 2022.
Zendesk polled agents1, and their responses coincided with these claims. Very few agents are satisfied with:
Overall workload (15%)
Career path options (14%)
Quality of training (20%)
How CS is perceived by the rest of the company (17%)
To continue offering great customer service, companies must begin prioritizing support agent retention.
Tip: Provide CS staff with the right tools, training, and career advancement opportunities
Respondents in McKinsey’s State of Customer Care survey said that their top priority over the next 12-24 months would be retaining and developing the best people. Of the customer service leaders surveyed, 34% said a focus on motivating and building trust with employees is an effective strategy, and 11% said providing their CS agents with new career paths and opportunities is key.3
If your company is serious about retention, you must invest in paving a path for your top employees to learn, achieve, and grow within your company.
The Right Tools: Provide them with the tools they need to do their jobs better. When Forrester surveyed active Meta Business Messaging customers, they found that 78% of respondents saw an average reduction of agent turnover of 19% year-over-year due to messaging apps reducing total call load and making it easier to successfully help customers.5
The Right Training: Provide CS staff with constant learning opportunities. Make them feel valued by investing in their continued education and training.
The Right Opportunities: Give your top performers opportunities to advance their careers. Promote for managerial positions from within your CS team instead of hiring outside candidates.
Listen to Your Frontline Staff to Stay Ahead of the Curve
No one has more insights into the state of your customer service operation than your own support agents. Keep them in the loop and include them in your planning and strategizing.
Daily interactions between agents and customers are a great source of unfiltered and actionable insights into the current state of the customer service experience you provide and where it can stand to improve.