How new technologies improve time-proven customer service approaches

“Customer service is undergoing changes due to disruptive technologies and digitalization.” CX managers, IT technicians and software architects from the support sector are often confronted with this statement – often followed by the instruction to make the company’s service fit for today’s technological and digital opportunities. But how do technologies improve existing customer service approaches? Where are the opportunities and challenges of the technologies and communication channels in service? And where are the limits of technological progress?

Customer support in the 1990s and 2000s: Everything used to be better then....?

In the 1990s and early 2000s, customer service took place almost exclusively via non-online platforms: telephone, fax, letter, shop, rarely email. Customer calls were distributed and routed through ACD systems. CRM systems and email management for call center and customer service were almost non-existent. The service staff had support tools, for example for diagnosing technical faults, but only the agents could access the results – not the customers. Furthermore, the service staff only had limited access to technical data, and the tools contained only a limited scope of step-by-step instructions which the agents could pass on to the customers. In addition, there was one tool for each service area: for technical troubleshooting, for invoice handling, for customer data management. The service agents had to juggle them simultaneously. The multitude of tools fragmented the internal ID so that the agents had to provide support largely without any automation. For the IT and software architects and technicians, this resulted in a huge workload.

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With the expansion of the Internet, an increasingly large group of people could resort to online services and communication platforms. This technological development and the customers’ changing communication habits greatly affected customer service. Companies called for solutions counteracting the fragmentation of their internal IT, simplifying the service staff’s work and providing troubleshooting options for customers.

Technologies in customer service: Progress, opportunities & challenges

Interactive Voice Response (IVR)

Development:

IVR, initially a simple static system which put callers on hold and told them the approximate waiting time, became a dynamic routing system. Today, routing via the push of a button or speech input (part-of-speech) is commonly found. IVR systems often use simple speech recognition via keyword recognition based on stored word categories. Today, IVR systems are most efficient if linked with the internal CRM system, NLU or AI-based technical diagnosis. This gives the system access to existing customer data, ensures it understands speech better and can ask for customer number, order number and product name or start an initial technical diagnosis. Clear-cut issues can thus be solved without any human contact, while more complex problems are identified via targeted routing and forwarded to a competent call agent – to the customer’s benefit.

Challenge:

Despite major progress, speech recognition via NLU is not yet technically mature and still leads to comprehension problems – for example with dialects. Compared to NLU, part-of-speech often appears clumsy but is used frequently with IVR. It is doubtful whether customers will continue to accept part-of-speech in future. Another challenge for IVR is the effort involved in integrating it with the internal CRM system or the technical diagnosis. When linked, the systems and technologies achieve good routing results and may help to improve customer experience, but starting and administrating the integration is a lot of work. Additional manpower and time are required primarily in the initial phase.

Opportunities and benefits in customer service:

Supplements support via phone
Available 24/7
Eases the workload of the call center agents
Improves routing
Context is considered (AI and CRM): Simple issues can be handled automatically without personal contact, more complex ones can be pre-screened accurately

Natural Language Processing (NLU)

Development:

In the early days, the Fourier decomposition restricted speech recognition to a small number of words and variations. Today, the method is a precursor of NLU and goes beyond automatic speech recognition. For customer service, the technology can be used in texts (email, social media posts, chatbots) as well as in spoken language (telephone, smart speaker). Owing to greater computing power, more memory space and machine learning technology, NLU is able to learn speech variations and semantic variants. Thus, NLU can understand the context and intent of a customer and collect relevant information through targeted questions.

Challenge:

Despite huge progress in speech recognition, the NLU still has comprehension problems if customer’s written inquiries contain typos or very long and complex sentences. With oral input, dialects, accents, speech defects and background noise are problematic. Whether in written or oral communication, NLU is bad at interpreting irony and sarcasm. Accordingly, despite their enormous progress, NLU systems are limited to certain topics and recurrent questions. Conversations therefore do not proceed freely but in fixed flows. Analog, there is the customers’ acceptance threshold: Customers proceed on the assumption of human communication and thus human comprehension in a conversation. If the system does not understand them, they tend to give up after a few attempts. It is another challenge for customer service to find good use cases for the NLU and to test them with users and customers. This requires a lot of initial work because speech models must be taught by speech and/or input examples. To ensure highly accurate speech recognition, the system must also be taught (new) information manually.

Opportunities and benefits in customer service:

Can be used for written and spoken language
Easy to use with text and voice – customer service does not have to think about user interface and usability
Human-like contact – the NLU comprehends the meaning, context, and correlation and draws its conclusion
Recognizes the customer’s intent
Automation of linear tasks with clearly defined scope
Issues can be solved without human contact and handled faster
More complex tasks are accurately pre-screened and forwarded to competent call agents
Information is easy to procure
Available 24/7

Cloud

Development:

From the desktop computer over internal servers to cross-location computing centers – in recent years, the possibilities for saving and sharing data have progressed significantly. Via cloud tools, customer service can outsource data and services. Individual service components such as voice mail, email or phone calls can for example be decomposed into microservices and made available in the cloud. These microservices are independently scalable: If a lot of emails need to be sent, this microservice is scaled up, without affecting the other microservices and customer channels. After some initial dislike, companies increasingly rely on cloud services. This in confirmed by an IDC survey: 90 per cent of the participating IT decision-makers pursue a cloud strategy.

Challenge:

Before introducing a cloud, a company has to decide: company cloud or external cloud provider? A company cloud means labor-intensive in-house administration, but the data remains in the company. If a company opts for a cloud provider, the internal costs will be lower, terminating the service will be easy, but the data will be stored externally. For this reason, the provider and its data handling policy should be check for GDPR conformity. Other risks involved in using a cloud are malfunctions which make access to the outsourced data, tools and services temporarily impossible. To ensure availability, it is recommended to maintain basic customer service applications such as automatic call distribution (ACD), IVR, or a PBX telephone exchange system as local components within the corporate network.

Opportunities and benefits in customer service:

Few hardware resources required to store the data
Less administration
Lower costs
Customer service has stationary or mobile access to the data, tools and services in the cloud
Data and services can be used and processed in parallel with others
Customer service is independent of location
Service components can be decomposed into microservices and are individually scalable

Unified Agent Desktop

Development:

Collect data, provide pricing and product information, handle complaints, send documents, receive telephone calls, perform technical measurements, initiate downstream processes, and process emails: In the past, service employees had to operate many applications and tools in parallel. Today, a Unified Agent Desktop makes the agents’ work easier. It integrates the individual applications and tools in a single interface.

Challenge:

The key question is the usability of the Unified Agent Desktop. Despite the numerous functions, its interface must be clearly structured so that the service agents can benefit from this tool. Furthermore, the multitude of functions may cause bugs and errors. The challenge therefore is to ensure perfectly functioning IT technology and high quality of the functions and information. And there is the effort for integration: Conventional communication channels (PBX telephone exchange system, ACD, CRM, ERP, telephone systems (CTI and SIP) must be integrated with state-of-the-art channels (chat management functions, mobile and tablet apps) in a common interface. The more channels and existing interfaces, the greater the effort involved. Integrating older, non-standardized interfaces can be very time-consuming.

Opportunities and benefits in customer service:

Primarily the service agents benefit, because all information and functions are provided in one interface:

No more tool changes
Reduced error rates
Desktop automation: Repetitive steps run automatically

Customers benefit from faster processing of issues

Omni-Channel

Development:

If customers change from one channel, e.g. telephone service, to another channel, e.g. the shop, the context of their issue will be lost. Today, all service channels can be integrated into the newly developed single-source platforms, allowing for an omni-channel approach: The context of a customer’s issue will not be lost, not even with channel hopping. The data in the single-source platform makes it possible to use technologies such as machine learning and to improve customer service by automation.

Challenge:

A single-source platform needs good quality data. The challenge for the customer service is to find and collect the relevant data. Another factor is the integration of various channels in the single-source platform to make customer support omni-channel capable. Depending on the number of channels to be integrated, this takes a lot of time and manpower. It is advisable to proceed successively and integrate one channel after the other.

Opportunities and benefits in customer service:

Life cycle management: tracking all customer actions
Faster issue handling
Prevents data silos: All data is stored in one place
Data can be evaluated with ML to pass on recommendations for action to customers via self-service and service agents by means of internal tools.

Where are the limits of the new technologies in customer service?

Various factors can set limits for the technologies, for example users, budget, and technical aspects. But in most cases, use of new technologies is not limited by one factor alone but by a combination of several aspects.

Limits and opportunities presented by users

Users can limit the development of technologies by refusing to accept them. There are various reasons for this behavior, and there are ways to positively affect acceptance.

Discrimination: Technologies will not be accepted as long as the discriminate against certain groups of people due to their sex, age, ethnicity and religion because of the technical immaturity of their basic learning models. Once this deficit is remedied, the acceptance of these technologies will increase and they will be welcomed as objectively evaluating partners in professional and private life.
Capability: (Potential) users ask themselves: Can the technology really do what it promises? Does the system tell the truth? Technologies still must submit proof of their capability to do things right. If they do so by delivering good results which effectively help the users in their work, it will positively affect this acceptance criterion.
Trust: Users – and more often than not also the developers – cannot understand how the machine arrives at its results. Without transparency, users find it difficult to have trust in a technology. To remedy this distrust, AI and ML processes could be made to present their solutions together with some explanation: “Based on variable X and variable Z, the solution is Y.” A full display of the complexity behind these decisions will not be possible, but giving some explanation will increase the understandability. The technology will lose its reputation of being a ‘black box’.
Loss of data and control: This primarily affects cloud solutions. Companies fear that their data might get into the wrong hands, that they no longer control their outsourced data, or that they cannot access their data. Strict compliance with the GDPR and guaranteed data availability help to relieve these concerns.
Safety and security: How safe and secure are the new technologies? Users ask this question from the perspective of data protection, and also of whether the technologies can guarantee the safety of humans. Here, technologies must submit proof of their high quality and ability to guarantee safety and security.
Competition: Partner or destroyer of jobs? Companies can alleviate their employees’ distrust against new technologies by communicating openly with them and listening to their concerns before, during and after the introduction of the technology. It is also helpful to evaluate the improvements after the introduction, compared to the previous work without the new technology.

Technical limits – hardly any. Budget limits – certainly!

Technically, almost everything is possible. Some limits could be presented by the available memory and computing power. The crucial point is the financial viability of the technological innovations. Technology at any price is not profitable or economical – the output must be right in relation to the input. The calculation must therefore include the initial expenditure before the introduction of the technology, the introduction itself, the long-term support and possible retrofits. These aspects can limit the feasibility and set boundaries.

In summary:

New technologies have enhanced time-proven service approaches; today, they make it possible to better adapt to individual customers’ wishes and requirements.
Thus, they offer the chance to raise service to a new level – but first, various challenges must be met. Some of them are of a technical nature, but also the available budget and the attitudes of customers and employees may limit the use of new technologies.
If the support is aware of and tackles these challenges, there is nothing to prevent customer service with new technologies from becoming a success.