Value-creating customer dialog in times of digital automation
by Sophie Hundertmark and Nils Hafner
A book from Springer Fachmedien
My new reference book Customer Dialogue Management – Value-creating customer dialogues in times of digital automation is online. I wrote the book together with Nils Hafner from the Lucerne University of Applied Sciences and Arts. It is an editor’s bench with many exciting contributions from other guest authors.
Abstract
This book provides an overview of the current state of customer dialog management in times of digital transformation. The rapid development in customer management due to digitalization requires innovative approaches in order to meet the increasing demands of customers and to enable faster and more relevant customer communication. Renowned authors from science and practice present approaches for optimizing customer dialogue strategies. They provide an overview of the possibilities of modern marketing automation, address the degree of utilization of artificial intelligence and its potential in marketing as well as the role of data collection and use. In addition, examples of automated dialogs that sell are presented. The authors of the article also address the often neglected customer dialog in service. The organizational form of the customer contact center is more relevant than ever. The most important key figures and benchmarks in customer service are shown and it is emphasized that even automated contacts are essentially based on the basic prerequisite of trust. In addition, the experiment from Lucerne University of Applied Sciences and Arts on the design of optimal chatbots “Be the bot” will be presented. Practical examples from HUK24, Swisscard AECS and CSS Krankenversicherung round off this work.
This reference book is a valuable tool for anyone dealing with the challenges of modern customer dialog management and looking for sustainable strategies.
Strategic customer management
Strategic customer dialog management – making data-based automation decisions
Nils Hafner
The aim of this article is to present a procedure for evaluating customer dialogs according to their relevance for customers and companies and to derive measures from this evaluation. However, the basis for this must be a customer-oriented catalog of concerns that explains why customers are contacting the company and why the company is contacting the customer. The result of the evaluation of dialogue types in the value-irritant matrix is a clustering according to the basic strategies “Avoid”, “Simplify”, “Exploit” and “Automate”. The automation decision, which many companies make opportunistically today, is thus systematized. However, these strategies have a wide range of effects on the organization, and the change must be measured and managed using specific KPIs. Ultimately, the approach presented ensures an improved customer experience.
The four building blocks of a people-centered culture: the ingredients for a healthy breakfast
Gregorio Uglioni
A positive, healthy and inspiring people-centered culture is crucial to the company’s success. It consists of employee-centricity, customer-centricity, leadership and observation of the environment. It is important to hire employees based on their cultural fit and treat them as valuable assets. Customer feedback and data should be used and utilized to retain customers. Managers should serve as role models and promote employee development. The environment is also relevant. The return on investment of such a culture is manifold.
Effective customer dialogue management through a customer-centric organization
Dominik Georgi, Jan-Erik Baars
A key success factor for effective customer dialogue management is a customer-centric organization. The more customer-centric the company organization is, i.e. the more customer-centric the management/culture, processes and implementation in the company are, the more the customer can be placed at the center of dialogue management. Customer dialog cannot be effective without customer centricity. Only if it is focused on customer needs (and all too often this is not the case in reality) will customers be enthusiastic and loyal and thus contribute to high customer value. This article introduces two measurement tools that can be used to help companies on their journey to becoming more customer centric. The Customer Centricity Score measures how company members perceive customer centricity, while the Customer Impact Score measures this from the customer’s perspective. The resulting values and their comparison provide important information on where companies can start to increase their customer centricity.
Focus on the digital customer – HUK24’s personal insurance machine
Sebastian Pyka, Uwe Stuhldreier
Advancing digitalization and the use of internet-based technologies are permanently changing consumer behaviour. Consumers are increasingly collecting digital experiences and turning them into their digital expectations. As a result, traditional industries and providers, including insurers, are faced with the challenge of developing strategies to meet these customer expectations – e.g. in terms of interactions and dialogs. This is the only way to ensure long-term competitiveness. As a purely digital insurer, HUK24 has successfully mastered this challenge and has been growing dynamically and profitably for years to become Germany’s fifth-largest motor insurer. Essential to this success is the focus on customers and their digital expectations, which are directly manifested in the vision of the “personal insurance machine”, in the marketing guiding model of the “HUK24 growth diamond” and thus in digital dialog management.
Customer dialogs in marketing
AI and customer dialogs – why the human factor is still important
Claudia Bünte
Customer dialog is important and faces a dilemma: it is expensive if it is done well and alienates customers if it cuts corners in the wrong place. Artificial intelligence is one of the tools for reducing these costs and optimizing the customer experience. And artificial intelligence has now become a tool to be taken seriously. It can already be used successfully in many places for customer dialog. But building dialog systems with AI is also costly – especially for small and medium-sized companies. The solution for the future therefore lies technically in two areas, in platform ecosystems and with full SaaS providers. However, the half-life of the individual applications is short. The human factor is therefore crucial. The aim is to develop the creators in customer dialog into managers of the various tools and channels. This article looks at the key developments over the next few years and provides practical tips on how you can successfully use the human factor to take your customer dialog to the next (AI) level.
Customer dialog management in the age of marketing automation
Ralf T. Kreutzer
The complexity of a one-to-one customer dialog can no longer succeed without marketing automation. All companies are therefore called upon to work on ways to automate their dialog measures. This article shows how automation can be successfully implemented.
Data strategy with data expertise
Sarah Seyr
Business becomes science. In marketing in particular, a data-driven approach has become established and data literacy is considered a key skill. Data literacy involves conceptual and critical skills to develop a successful data strategy. A basic understanding of artificial intelligence (AI) is also essential. Depending on the maturity of the AI, analysis methods in marketing can be categorized along the scale of analytics, automation and augmentation through to adaptation for real-time decisions. Transactional, non-transactional and online data form the basis. Data mining is used to calculate results in models and derive findings. The data situation and data quality pose major challenges in practice. However, approximate values, according to the Good Enough Data mindset, can also be sufficient for decisions.
Dialogs that sell: Building customer loyalty with conversational automation
Automated customer dialog plays an important role in the current marketing landscape and connects companies and customers via digital channels. This chapter shows how these dialogs can be successfully automated, especially in the context of direct-to-consumer strategies. The new world of Marketing 5.0 will be presented and the success factors for customer dialog management will be explained. Two practical examples from Rivella/Focuswater and Birkenstock illustrate the successful implementation of conversational marketing and virtual product advisors. The examples show how brands can use automated customer dialogs to collect valuable information and increase customer loyalty, engagement and conversion rates. Companies should ensure that all relevant contact points are covered and that transparency and data protection are guaranteed. The use of dialogue systems offers the opportunity to create a personalized customer experience, boost sales, reduce costs and communicate the brand message effectively. Companies should therefore familiarize themselves with this technology and integrate it into their e-commerce strategies.
Customer dialogs in service
Contact center as a central communication hub
Harald Henn
Contact centers emerged in the 1990s as companies’ response to poor telephone availability. The large “telephone exchanges” have now become indispensable, central switching and interface points for communication with customers: Communication hubs. Increased customer requirements and the strategic realignment of contact centers require new organizational models, adapted control processes and an IT landscape that includes digitalization concepts and is geared towards the strategic goals of the customer experience strategy.
Key figures and controlling in customer dialog
Rémon Elsten
Customer service is still perceived by top management as a pure cost center in most organizations. At the same time, many companies are allocating large sums of money to set up new customer experience departments and create WOW moments, and are paying expensive strategy consultants to develop a CX strategy – without any significant effects, as savings are being made in customer service at the same time. This contradiction can be resolved by a cleverly designed management cockpit with the right key figures. This should serve to make the cost and benefit effects of a combined CX and service excellence strategy transparent and point the company in the right direction. This excursus can therefore be seen as a basis for discussion for jointly agreed targets between the areas of Operations and Finance as well as Marketing and Sales.
Trust in automated customer dialogs
Anna V. Rozumowski, Marc K. Peter
Trust is an important aspect of customer relationships. If customers do not trust a company, a brand or a product, they will opt for another – more trustworthy – one. The digital transformation is progressing and companies need to make processes more efficient, with the use of automated customer dialogs being an important aspect. This chapter explains how trust can be created in automated customer dialogs. The chapter begins with a description of how trust can be defined and which aspects are relevant to the topic of trust. The perception of the salesperson plays an important role in sales conversations, and this also influences the decision for or against a purchase. The concept can be transferred to digital customer dialogs. Because trust also plays an important role in conversations with a chatbot (automated customer dialogs). The chapter explains various success factors and ends with tips for practical implementation.
Be the Bot – insurance customers in the role of a chatbot
Sophie Hundertmark
Chatbots are computer systems that simulate a human conversation. Numerous research studies have already shown that the design of dialogs and the way in which a bot communicates has a major impact on user satisfaction and ultimately on the success of chat technology. With the help of a new method in which chatbot users are asked to put themselves in the role of a chatbot and write their own chatbot texts, important insights into the design of chatbots were gained. The results strongly recommend that companies take a differentiated approach when designing chat dialogs. Not all customer groups have the same expectations of a chatbot, and the expectations of the design of the chatbot response (length, empathy, reference) also vary with regard to the area of application, i.e. the use case.
Although long chat responses are generally expected, the younger groups tend to prefer short responses for the “change address” and “report damage” use cases. The same applies to extroverted users: they generally expect abbreviated answers and place the least value on references from the bot. When it came to changing the address, it was mainly men who wrote long chatbot responses; women tended to keep it short here.
Finally, different expectations can be observed among users who have chatted with chatbots before and those who have never chatted with bots.
Understanding, solving and learning from customer concerns
Kai Duttle
The first Swiss financial company introduced automated speech recognition in customer service back in 2018. Together with the further development of skill-based routing, this has since significantly increased both the efficiency of customer service and customer satisfaction.
Digital Product-Experience @ CSS
Garry Bachmann
Introduction of a Conversational User Interface (CUI) at CSS: In 2018, CSS planned to overhaul its website with a focus on user-centric experiences. One idea was to develop a chatbot called “Smart Insurance Assistant” (SIA), which would act as a possible universal entry point for users. The somewhat different and more neutral approach of the Conversational User Interface (CUI) consisted of various interaction triggers and intent classes for all defined processes. These were also supplemented by micro-applications for specific tasks.
Experience has shown that creating dialogs requires new skills and that training natural language processing (NLU) is demanding. SIA has developed into a kind of permanent prototype that is constantly being adapted. Activating users and increasing relevance are key challenges. Integration with customer service and data protection will remain future priorities.
Outlook: the customer dialog management of the future
Interactions in conversation theory and on the basis of a calculation with words
Edy Portmann
This essay combines conversation theory with computing with words to bring about a more human-centered internet resp. to evoke a holistic approach to our chatbot development. In the first part of the chapter, the cybernetic theory of conversation à la Gordon Pask is examined on the basis of the online text by Pangaro (2002). In the second part, conversation theory is combined with computing with words within the framework of a soft approach (i.e. soft computing à la Lotfi Zadeh). These as yet unrealized visions of cybernetics and systems theory will be highlighted here as a possible alternative to artificial intelligence and its chatbot technology. In conclusion, the essay summarizes the above visions once again and reaffirms their pending implementation as a more sustainable, because more human-centered, future technology.
Large language models in customer dialog – opportunities, risks, prospects
Nils Hafner, Sophie Hundertmark
The aim of this article is to present a procedure for integrating large language models (LLMs) into the automation of individual customer dialogs. It should be noted that the market for LLMs has been very dynamic since OpenAI introduced ChatGPT in November 2022. It is important to understand how LLMs are structured and that there are different LLMs that are suitable for different use cases in marketing, sales and customer service. The authors present a checklist for selection, possible use cases, a list of providers and an implementation plan. They provide an insight into one of the first application projects at Helvetia Insurance Switzerland and give advice on implementation.
