July 25, 2022
Organizations can make self-service a more desirable option by developing an easy-to-use system that resolves issues efficiently. As opposed to forcing people into channels they don’t want to use.
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Even with phone and email decreasing in popularity, the preference for self-service may not be growing as quickly as the customer care industry would hope. We can partially attribute the delay in growth to poor design, where customers experience more challenges than solutions. A negative experience with self-service can reinforce a customer’s preference for human-assisted channels.
While self-service is more cost-effective, organizations often overestimate its popularity among customers. It is worth considering that the preference for self-service varies on age and region. Still, nine out of ten customers say they would prefer to deal with a person even if they knew self-service would resolve their issue.
Customers mainly engage through a combination of channels or human-assisted only. At the same time, more than half (53%) of organizations are actively trying to shift customers between channels.1
As more and more companies push customer care towards self-service, few organizations deploy these technologies well. Getting passed from one representative to another is frustrating. The same is true in a self-service environment where customers have to repeat information, are asked too many questions, and the solutions are not easy to find or unavailable.
1 in 10 transactions begin on self-service*
Only 4% of transactions (1 in 20) finished on self-service*
For example, customers feel they waste their time if they cannot reach a solution via a chatbot and have to transfer to an agent. These experiences discourage customers from choosing to use a chatbot in the future given a choice.
Less than half of transactions start and end in self-service, which means there is a significant opportunity for improvement in the customer’s journey. Often, organizations design self-service from a cost savings perspective and do not fully consider whether they are improving resolution and decreasing customer effort.
Rather than forcing people into self-service channels, organizations can make self-service a more desirable option with easy-to-use systems that resolve issues efficiently. Artificial intelligence (AI) that can make self-service less difficult, reduce customer effort, or increase resolution is progress toward making self-service more appealing.
Building customer knowledge about the use and benefits of self-service is essential. Customers select the channel they think will lead to the quickest solution and more readily adopt new technology when they are confident they can complete the task. Once customers accept the technology, they motivate others by telling them how easy it is to use.
We typically see that customers need a set of pre-defined options without being given too many. Providing too many choices makes their journey more difficult instead of reducing their effort. Customers appreciate pre-screened alternatives geared toward their needs.
Three main factors that drive satisfaction with self-service technology:
- Access and delivery of core services
- Availability of various support services and systems that contribute to service delivery
- Error-free and accurate delivery
When it comes to AI, the fundamental motivations for using it are providing a better customer experience and reducing costs. Organizations primarily focus on customer-facing AI, with some using it to support frontline staff and back-office processing.
In terms of implementation, AI is not necessarily different from other advanced technology solutions. But some organizations get excited about the promises of innovative technology. Then, they try to look for a problem for which they can implement the technology.
In reality, organizations should identify problems first and determine the right solution.
- Define the problem or goal you want to achieve and identify opportunities.
- Research other potential solutions such as knowledge management systems, transaction recording or AI.
- Next is the learning process.
When we talk about AI, we often refer to machine learning. After organizations identify the opportunity and deploy AI solutions, there’s usually a pretty long but necessary learning process.
For instance, we worked with a center in Japan, and the resolution rate for the bot was around 30% to start, not very good. But within six months, the AI reached an 85% resolution rate for simple transactions.
Natural language processing (NLP) is currently the least used solution. However, organizations realize the potential value and are moving toward NLP relatively quickly. Most respondents stated their organizations plan to implement it within 18 months.
Altogether, research shows that organizations have a clear focus on further developing self-service technologies and redirecting traffic from human-assisted to self-service. The general principle is to entice customers into using self-service with a design that reduces effort and resolves issues.
- COPC Inc. 2022, Global Benchmarking Series, Contact Center Technologies
COPC Inc. conducts industry-leading research that informs planning and development strategies. Additional resources around timely issues affecting contact centers and customer experience are available in our Global Benchmarking Series 2022.
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