Conversational Business/ Conversational AI Communication and interaction are increasingly controlled and determined by algorithms. Bots and messaging systems are hotly debated and are considered mega trends for the next few years. The postulate “markets are conversations” is being reinterpreted against the background of conversational business. At the forefront are new communication interfaces which, as a logical next evolutionary step, bring efficiency and convenience advantages. But it is by far more than “Alexa, please order me a pizza” or “Dear Service-Bot, how can I rebook my flight?”.
On the one hand, the digital transformation is being driven by technological developments and innovations, and on the other hand, the increasingly smart and empowered consumer is increasingly becoming a driver. In terms of e-commerce, it is technologies such as messaging systems, marketing automation, AI, Big Data and bots that enable the transformation of existing e-commerce systems towards a higher level of maturity in terms of algorithmic business. On the other hand, the networked and informed consumer is forcing a real-time company to (re-)act quickly and competently. Thus, e-commerce is not faced with the question of whether it has to change, but rather how it has to change. These two lines of development are currently discussed under the term “Conversational Commerce”.
Conversational Commerce enables the optimization of customer interaction through intelligent automation. The overriding goal of conversational commerce is to lead the consumer directly from entertainment to the purchase of a product or service. This includes, for example, the processing of payment transactions, the use of services or the purchase of any product. Messaging and bot systems are increasingly being used to simplify interaction between consumers and companies via voice and text-based interfaces. In this way, the entire customer journey from product evaluation to purchase and service can be optimized through greater efficiency and convenience.
In addition to algorithms that control communication via keywords and communication patterns, artificial intelligence is increasingly being used here to learn from preferences and interactions. This enables the systems to better adapt communication to the situation and also control it proactively. However, this does not mean automation and real-time messaging at any price; rather, it must be systematically examined which touch points of the customer journey should be automated under cost-benefit aspects, how and when, and supported by conversational commerce technologies. The following sections illustrate the use and application scenarios of conversational commerce. In addition, the DM3 model is presented as a systematic process model with which the complex task of conversational commerce, which includes strategic, organizational and technological tasks, can be successfully implemented.
Until now, customers who want to contact a company have either had to fill out forms or call hotlines, often with long waiting loops. However, this type of communication can often be one-way, annoying and slow for the customer. On the other hand, communication with friends, acquaintances and colleagues increasingly takes place via messaging platforms such as WhatsApp or Facebook Messenger. We can now observe the emergence of a new communication paradigm in which companies use messaging platforms, chat bots and algorithms for both customer interaction and internal communication. This is driven primarily by advances in Artificial Intelligence, which make it possible to create adaptive algorithms and chat bots that can automate communication while still feeling human.
The new communication paradigm brings with it many trends, such as Conversational Commerce (customer service and purchase via conversation), Personal Butlers (digital personal assistants who handle purchases, bookings and planning for the user), Algorithmic Marketing (embedding algorithms and bots in all steps of the marketing process) and Conversational Office (integration of messaging platforms combined with bots in internal business processes). A quick connection to the new communication paradigm can result in more efficient work processes, higher customer loyalty, increased turnover and a competitive advantage for companies.
For customers, the increase in convenience is particularly important, as tedious tasks can be completed within minutes. If companies oversleep the trend, it can happen that in the future they will not be taken into account when choosing services or products. On the other hand, there are many dangers lurking for companies, as it is easier to disappoint customers and damage brands. Furthermore, the increased use of bots and algorithms can lead to job cuts. It is therefore essential for companies to understand the new trends and know their risks.
The popularity of communication platforms is constantly increasing. Since 2015, more people use applications (apps) for communication than social networks. Current figures can be found in the study Conversational Business https://www.conversationalbusiness.de.
BUT WHY IS MESSAGING BOOMING COMPARED TO OTHER APPS?
We are increasingly observing app fatigue among users. In fact, only a very limited number of apps are used by each user every day. This may be due to the app jungle that consumers are confronted with. The frequently heard phrase “There’s an app for” seems not only true, but understated. For every conceivable application area, the consumer is confronted with at least a dozen apps. This makes it difficult to find the right app. Often the extended use of an app – in addition to a company’s website – is also unclear. Every newly installed app also means that one has to get used to a new user interface. Messaging apps, on the other hand, are all similar in structure and layout, and are easy to use, even for new users.
SUBJECT MATTER AND AREAS
Conversational Commerce describes a young trend in the consumer sector. The term was coined by Chris Messina, who is currently a developer at Uber, and gained popularity and acceptance through the hashtag #ConvComm. Basically, the concept is not a new one, since every form of commerce traditionally started in a conversation. In times of online shopping, however, the conversation has receded into the background, as the large number of customers cannot be served in one-on-one and in real time. When buying on the Internet, therefore, one-way communication is increasingly used, with the customer filling out contact forms or sending e-mails. Direct communication with companies by telephone is often possible, but often involves charges and long waiting loops. All in all, these currently dominant forms of contact are associated with waiting times for the customer and are therefore at a disadvantage compared to the classic sales talk.
Conversational Commerce, on the other hand, offers individual, bi-directional real-time communication with the customer without requiring unrealistic amounts of personnel. Conversation can take place using chat bots that are either integrated into platforms such as WhatsApp or Facebook Messenger or can be found alone on the company’s website. Chat conversations can be used to provide product advice, the sales process, purchasing and customer care, making it easier for the customer to consume. Since the customer interacts with the company or the brand in the same way as with a friend, one also speaks of the “brand as a friend” concept. Therefore, companies whose chat bots can conduct conversations that feel natural and human-like to the user benefit.
CONVERSATIONAL COMMERCE AND AI IN THE GAFA PLATFORM ECONOMY
The aim of the so-called GAFA economy (Google, Amazon, Facebook, Apple) is to have the best possible knowledge of consumers’ ecosystems and to be able to serve them accordingly. Whoever can best accomplish this task can also best place his or her own products with consumers. It is not for nothing that the GAFAWorld develops systems to monopolise access to the consumer. This new form of market exploitation brings with it the risk of abuse of market power and can result in heavy penalties, as Google recently found out. Those who have the direct interface to the customer in the form of a bots or messaging system, who know consumer preferences and behaviour across all areas of life, determine information, advertising and purchases. If the consumer selects his or her own favourites from the hit lists for a Google search or an Amazon product search, the bot recommendation is usually reduced to a product or information.
Bot sovereignty thus replaces active evaluation by the consumer. The fact that this approach is highly relevant and lucrative is demonstrated, for example, by Amazon’s efforts to gain control over the customer through the Dash button and the DRS (Dash Replenishment Service) system under the convenience cover. This shows how Amazon is trying to penetrate the consumer’s ecosystem. The still manual automation of ordering a new detergent at the touch of a button is just the beginning. The next step is a voice-controlled dash button. But the system can do even more: an automatically operating DRS system enables connected devices to automatically order products from Amazon. This means that the system tracks the consumption of the product and thus knows the stock of, for example, detergent, toothpaste or printer cartridges. If the product is nearing its end, the order process is triggered.
One of the biggest strengths, but also the biggest criticism, of the Alexa Ecosystem is the integrated and automatic AI-based analysis of customer interaction. This way, the customer’s digital data track can be used so that his Alexa gets to know him properly. The cloud not only stores the settings of the DASH buttons, but also derives and stores the customer’s preferences and needs based on purchasing behavior and search queries. With the help of AI, this information can be used to create high-quality forecasts for further customer communication so that this knowledge can be incorporated into cross-selling strategies.
Fig. AI, Big Data and Bot based platform of Amazon
Location-based data and services can also be collected and offered through location services. The possible number of data points to be recorded, which can be correlated with customer behaviour, appears to be almost infinite due to the manifold uses and widely spread thematic events in the Amazon ecosystem. But not only the text- or data-based analysis of customer behaviour is relevant. Due to the massive progress in Natural Language Processing (NLP), i.e. digital language processing, both the factual level of the customer statement can be analyzed and the current mood of the customer can be determined. This enables an emotionalization of the bot-customer relationship through trained empathic behavior of the bot, which is close to interpersonal communication.
For companies, the deep integration into the everyday world of the customer provides unique opportunities for data collection and analysis. By centralizing and monopolizing the customer interface, companies can bind consumers in their “commerce bubble” based on comprehensive preference and behavioral profiles. One consequence of this development could be that emotional brand loyalty loses relevance and marketing becomes more objective. This is because purchase decision processes are now made more rationally than before. The development of smart homes and smart products is leading to rationally prepared purchase decisions – bots now increasingly represent people. The refrigerator “decides” when milk is bought later. A digital representative of the customer is logically immune to emotional and empathic advertising, which thereby loses its meaning. The ideal value of the brand is irrelevant for the customer bot, which in the ideal case, through the customer’s digital signature, acts objectively as his representative in e-commerce.
This makes the access of companies and customers to the platform more important than the brand itself. Whether the bot is also immune to the interests of the provider (its master and ruler) may and should of course be critically questioned by customers. Data-based marketing (intent-based marketing) is constantly increasing. Marketing departments are already collecting massive amounts of behavior-based data. When Alexa, Siri and Google Assistant enter the living rooms, the comparison with a Trojan horse is not far-fetched. For example, if providers find out “up close and personal” that a person has married, there may soon be a new generation. This information can be worth its weight in gold. It remains to be seen how the benefits of greater convenience can be reconciled with the danger of market abuse of monopoly-like commerce ecosystems. The trend towards voice-based interaction shows that consumers are open to new convenience technologies. This year, one in five Google queries were made by voice. A 50 percent rate is predicted for the year 2020. In ten years, it is estimated that around 75% of Google queries will be made by voice. While the current communication is still between the consumer and the corporate bot, there will be increased communication between the consumer bot and the corporate bot in the coming years.
Marketing activities must therefore be adapted to the bot channels. A rethink will also have to take place in SEO and SEM. The so-called “Bot Engine Optimization”, BEO for short, transforms the guiding principle “Rule the first page on google” into “Rule the first bot answer”. The focus is on personalized one-to-one campaigns from bot to customer. Of course, companies have always used database marketing and analytical CRM to analyze data about consumers in order to target products and communication to target groups to be as profitable as possible. However, companies and consumers are increasingly no longer meeting in traditional markets, but the provider is internalizing the market in a certain way. Amazon has long since ceased to be a trader of products, but rather a smart ecosystem that intelligently collects, analyses and uses data to keep consumers in its own commerce bubble.
APPLICATION AREAS IN E-COMMERCE
Chat offers can be used at various points in e-commerce, for example to qualify requests in advance, to provide leads with information (nurturing) or to provide automated information in the service. Chat bots are currently primarily used as inbound touch points to answer consumers’ questions about products, companies and campaigns. Increasingly, outbound scenarios are emerging in which chat bots actively communicate with customers according to defined rules and events (Drip Communication by Nurture Bots). Engagement bots go one step further by actively interacting with users as market and brand ambassadors. The best-known example here is the Tay chat bot from Microsoft. Unfortunately, the community has trained him negatively, so that he posted right-wing extremist and sexist contributions. Within one day Microsoft apologetically removed Tay from the net. In order to get customer insights about surveys as automatically as possible, so-called Poll Bots can also be used.
TRENDS THAT FAVOUR CONVERSATIONAL COMMERCE
Conversational commerce is mainly driven by the large Internet companies that operate a messenger and/or chatbots, such as Facebook, WhatsApp, Telegram, Slack, Apple and Microsoft. The progress in conversational commerce is primarily driven by two developments: A communication trend and the rise of artificial intelligence. The former can be seen in the popularity of messaging services, the use of which is growing explosively. Apps and services that are used to communicate with friends and acquaintances have established themselves – unlike most other apps. As the proportion of mobile natives (users who have grown up with mobile digital services) continues to grow, the use of messaging services is likely to continue to increase. Given the large number of people who use messaging apps, the next logical step for companies is to offer their services there. Instead of convincing customers to install a new app, companies pick up their customers where they can already be found, as chatting is already integrated into everyday life. Developments in the field of AI also make the existence and further development of conversational commerce possible, for example in terms of the performance of voice recording, which is growing by 20% a year.
Nowadays, it is already possible to capture 90 + percent of spoken and typed language, thanks to natural language processing, also called Natural Language Processing. In addition to the two essential criteria for the growth of conversational commerce, there are other trends that are conducive to its progress. One example is the so-called Quantified-Self-Movement, also known as Lifelogging. It refers to people who record and analyze personal data about their day, such as food consumed, air quality, mood, blood oxygen levels, and mental and physical performance. In some cases, wearables, i.e. devices that can be worn on the body, make it possible to record these values, for example by means of electronics and sensors processed in the fabric of the clothing. Together with advances in data science, this trend has the potential to personalize customer interactions in conversational commerce and to anticipate consumer needs. The integration of seamless payment technologies is essential for the execution of entire purchasing processes in the context of conversational commerce. These are increasingly available to third-party providers on relevant messaging platforms through APIs.
EXAMPLES OF CONVERSATIONAL COMMERCE
Probably the oldest implementation of Conversational Commerce has taken place through WeChat, a mobile cross-platform messaging service from China, which was launched in 2011 by the holding company Tencent. Via WeChat, you can communicate with friends and acquaintances and use the services of countless companies. Among other things, you can call taxis, order food, buy movie tickets, book doctor’s appointments, pay bills and record your daily fitness program. WeChat is a chat-based interface with many additional features such as mobile payment, chat-based transactions, media and interactive widgets. A powerful API makes it possible for a wide variety of companies to “make friends” with their customers. More than ten million companies are connected to the chat platform and the popularity among small businesses is growing. In contrast to the USA and Europe, where services have so far mostly been offered in specific apps, in China the focus has been on combining messaging and consumption much earlier. WeChat is now one of the largest stand-alone messaging apps in terms of the number of active users:
Instead of changing existing infrastructures as in the USA and Europe, many markets in China can be opened up for the first time by mobile applications and payment systems. In 2016, Facebook opened the doors to other companies’ in-house messengers by integrating a complete chatbot API into the platform. Mark Zuckerberg explained the decision as follows: “I don’t know anyone who likes to call a company. And nobody wants to install a new app for every business or service. We think that everyone should be able to write to a business the same way you would write to a friend”. An overview of the different chatbots already in circulation is available on the botlist.co website. An often mentioned example from the USA is the integration of the taxi service Uber into the Messenger. By clicking on the address sent in the messenger, a menu opens which suggests, among other things, the option “Request a ride”. If there are available drivers, the taxi can be ordered in the next step by a click. The ride is automatically charged to a credit card that has been configured in advance for all services. The Facebook Messenger API user interface also allows the integration of maps, product photos and other interactive elements into the chat conversation.
CHALLENGES FOR CONVERSATIONAL COMMERCE
All chat bots work in a similar way, they are based on matching patterns in the text and react to certain keywords. But what are the challenges facing currently active chat bots, and why is conversational commerce not yet more common? One reason seems to be that the integration of AI has not yet been largely realized. For example, the author of an article in the magazine c’t criticizes that there is currently no bot that can learn the interests and preferences of users and act proactively without being triggered by the user. In an article in the journal Absatzwirtschaft the author describes that the integration of AI in bots is still lagging behind.
By observing the decisions and activities, the bots could get to know the user better. The author sees another challenge in the bots’ adaptability; the program should be able to adapt its own settings to external influences. Another demand on bots is that they act with foresight and start processes on their own initiative, such as reminding the user to buy coffee. Bots should also become social, so that they can develop a kind of “social life” among themselves and communicate with each other. However, it is questionable whether these are the reasons why conversational commerce is not yet more widespread, not least in Germany. Technically, the learning ability, adaptability and predictability of chat bots is certainly feasible. Thus, there are a large number of libraries for developers to integrate the learning ability and predictability of chatbots.
ADVANTAGES AND DISADVANTAGES OF CONVERSATIONAL COMMERCE
Of course, the use of chatbots in conversational commerce brings many benefits not only for consumers but also for businesses. The human-like conversations, the better and faster service and the presence of the brand can lead to closer customer loyalty. Many consumers appreciate the services specially tailored to their needs. In the end, improved services increase customer satisfaction. The reputation and awareness of the brand or company can also be increased. In addition, companies gain more insight into the wishes and needs of their customers as well as into the purchasing process and context. However, it should not be forgotten that conversational commerce can also bring disadvantages or potential problems.
One example is consumer concerns about data protection and privacy. Transferring chat histories to companies is not compatible with German law. It could also increase the likelihood of data misuse, as criminals could gain access to payment data and other information. It is also unclear how transparent the activity of robots in conversational commerce is to be dealt with. Should consumers be told that they are currently chatting with a bot? Since telephone customer service will become less important with the use of chatbots, job cuts are also to be expected. It is therefore important for companies to develop strategies to avoid frustration among employees, for example by finding new jobs within the company.
ROADMAP TO CONVERSATIONAL COMMERCE: E-COMMERCE MATURITY MODEL – PLATFORM CHECKLISTS
In recent years, e-commerce has evolved through various stages of maturity due to technological developments and changes in customer behavior. The challenge for companies is to identify the relevant technology and market trends and to evaluate them accordingly. Currently, companies are faced with the challenge of climbing the next maturity level – so-called Conversational Commerce. This level of maturity currently seems desirable, as current developments could revolutionize the sales industry. This means that those who are slow in implementing conversational commerce may lose customers to the competition. On the other hand, companies could also benefit from public attention by, for example, involving bots early on.
The leap to conversational commerce is not a gradual but a fundamental development of e-commerce. It is not just another touchpoint that can be used voice-controlled. Rather, it is about a new eco-system that automatically triggers and coordinates ordering processes based on customer and situation. Intelligent assistants either carry out instructions from the consumers or independently recognize the need for action, such as reordering detergents or making travel bookings according to the appointment calendar. However, it is also crucial that the transition to conversational commerce is well thought out and planned. One way to do this systematically is the following DM3 model.
THE DM3 MODEL AS A SYSTEMATIC PROCEDURE MODEL FOR CONVERSATIONAL COMMERCE
In order to determine the optimal conversational commerce strategy and roadmap, the digital media maturity model (DM3) is used as a basis for a digital assessment of the current situation and the next steps of the transformation. In the process, the current customer touchpoints are recorded and evaluated with regard to their automation and technology support. For this purpose, customer journey tracking and analytics tools are used to measure and analyze the consumer via different touchpoints such as websites, display, email and social media. This also makes it possible to analyze which touchpoints have a direct conversion function and which have more of an assistant function. Conclusions can also be drawn about the temporal cause-effect chains. The large number of digital touchpoints and end devices and their extremely variable use by the customer can no longer be optimized by experience and gut feeling alone.
Digital transformation in e-commerce: Maturity Road to Conversational Commerce
Each touchpoint must be analyzed both on its own and in interaction with other touchpoints in terms of costs, benefits and risks. Only in this way can the current and future optimal conversational commerce strategy be derived. This usually involves evaluating the trade-off between cost, benefit and risk. For example, a high degree of automation of a touchpoint can bring efficiency benefits, but on the other hand it can also result in high costs and possibly a suboptimal customer experience. A systematic comparison of costs, benefits and risks is therefore essential. This is not about 0/1 decisions. Rather, it must be decided which degree of automation makes sense at which touchpoint and when.
PLATFORMS AND CHECKLIST
The platform question for conversational commerce is one of the more operational steps. Companies should first decide on the platform on which their customers are already located. Facebook Messenger can be a good choice in many European countries and the USA, where the number of users is very high. If the customer base consists primarily of millennials (the generation born around 1980-1999), Snapchat might be a better choice. In many countries WhatsApp, Viber or Line also dominate. If the target group is mainly located in China, WeChat is the most suitable platform. The next step should be to consider whether there are enough resources to not only create a bot, but also to maintain it. This applies to both expertise and personnel.
If the expertise is not available in the company, it is advisable to call in a partner for the technical implementation. However, the time and cost of maintaining the bot in the long term should not be underestimated. Because although the bot is automated, time is needed to a) promote the bot, b) check the cases where the bot could not help, c) measure customer satisfaction and d) constantly work on improving the bot. Another important point to consider is how to maintain and promote the company’s brand personality through conversational commerce. It is particularly important that the brand values are conveyed in online chat, as these conversations have a very human touch. This requires that a consistent brand personality exists; if in doubt, it should be created as soon as possible before using conversational commerce.
Fig.: Derivation of individual recommendations for action based on conversational commerce
It is also central that there is a clear, meaningful and well studied use case for the use of chatbots. Which goal should be achieved with the bot, and is this – even in the early stages – feasible? Does the use of bots improve the service for the customer? A negative example are the countless apps which have no advantage for the user compared to the website. Each interface to the brand will be used by the customer in a different way, so it is necessary to research how the interaction with the customer changes in detail when a new interface is inaugurated. By analysing the current communication with the customer, it is possible to identify areas where the use of a bot is appropriate. In general, it is worthwhile for companies if the bots are implemented gradually and in clearly defined areas.
In other words, the use of chat bots should be limited to those areas where it works best. The rest should be left to people until the technology is mature. This also increases acceptance by customers. If, for example, an airline’s entire booking system is changed over from the very beginning, this can be very risky, because the probability that it will not work smoothly right away is very high. Chris Messina stresses that a bot should never be used for spam. In conversational commerce, frustrated customers can have a huge impact on a company’s success because they interact with the brand in the same way as they interact with a human being. If, on the other hand, a company is able to provide a convenient, personalized and meaningful service to the customer, it can benefit significantly from conversational commerce.
CHECKLIST FOR COMPANIES
On which messaging platform are my customers located?
Are there sufficient resources in terms of expertise and personnel available to maintain the bot in the long term?
Does my company have a brand personality, and is there a strategy to convey this in online conversations?
Is the area in which bots are to be used clearly defined, and can the bots achieve the planned goal without disappointing customers?
In a study conducted by the Institute for Retail Research (IFH) in Cologne in early 2016, it was found that 57% of consumers surveyed in an internet representative survey had already used conversational commerce. Since every second person in this group was older than 50 years, the implementation of conversational commerce seems to appeal not only to younger people. The IFH recommends the use of Conversational Commerce especially for industries with an increased need for customer consultation. According to the study, the consumer electronics, tourism, banking and insurance industries seem to be particularly well suited for the use of conversational commerce. The majority of respondents also indicated that they could imagine purchasing sports and leisure goods as well as clothing and accessories through conversational commerce.
Conclusion and outlook
E-COMMERCE – THE CARDS ARE BEING RESHUFFLED: THE BATTLE FOR THE NEW E-COMMERCE ECO-SYSTEM
Those who have the direct interface to the customer in the form of their own bots, who know consumer preferences and behaviour across all areas of life, determine information, advertising and purchases. If the consumer selects his or her own favourites from the hit lists for a Google search or an Amazon product search, the bot recommendation is usually reduced to a product or information. Bot sovereignty thus replaces active evaluation by the consumer. The fact that this battle is highly relevant and lucrative is demonstrated, for example, by Amazon’s efforts to gain control over the customer through the Dash button and the DRS system under the convenience cover, as well as the numerous investments made by Facebook and Microsoft in smart bot and messaging systems. The promising platform-independent messaging and bot system of the former Siri inventors, Viv, was purchased by Samsung in October 2016, who will certainly interpret the platform independence differently now. Similar to the app economy, which was boosted by strong players such as Google and Amazon, the bot economy will also need an industry leader. However, a pure analogy to the App Store will not be enough.
A Bot Store would again be stuck in the application silos and would not do justice to the bot logic as a lubricant for holistic transactions. For the companies, the deep integration into the customer’s eco-system offers unique possibilities for data collection and analysis. By centralizing and monopolizing the customer interface, companies can lull consumers into their commerce bubble based on comprehensive preference and behavioral profiles. Of course, companies have always analyzed data about consumers in order to target products and communication to target groups and thus be as profitable as possible. It is also perfectly legitimate for companies to act according to their profit maximization approach. However, companies and consumers increasingly do not meet in traditional markets, but the supplier internalizes the market to a certain extent. Amazon has long since ceased to be a trader of products, but rather a smart ecosystem that intelligently collects, analyses and uses data to keep consumers in its own commerce bubble.
MARKETS ARE FINALLY BEING TALKED ABOUT
Markets are conversations reloaded: The “markets are conversations” postulate formulated in the 1999 Clutetrain Manifesto is reinterpreted against the background of conversational commerce. Communication and interaction are increasingly controlled and determined by algorithms. The advantage of this is that the conversations with companies demanded by the responsible consumer are now possible “at scale”. Bots can work in parallel in 24/7/365 mode. Personalized conversations were often hindered by economic and efficiency obstacles on the part of companies. On the other hand, the pseudo-human dialogue means a loss of empathy and emotions. However, it is less about the classic man-versus-machine battle, but rather about the intelligent orchestration and balancing of both approaches. Of course, computerisation and algorithmisation in e-commerce is not new. Google has long been determining which products we see, Facebook’s news algorithm decides on our news feed and real-time bidding controls which ads we see.
What is new, however, is the extent of algorithmic coverage across the entire transactional value chain. In addition, the increasingly widespread “value-added versus data” mechanism reduces consumer sovereignty. As a result, the consumer sovereignty in the form of transparency, which is largely achieved through the internet, and the ability to visibly evaluate companies and products for everything, are jeopardised. A kind of bot sovereignty replaces consumer sovereignty. Since the current and future bots are offered in particular by the GAFA (Google/Amazon/Facebook/ Apple) corporate world or are developed by companies on their platforms, the consumer no longer has any real sovereignty. The GAFABots offer him convenience without having to pay for it directly. However, the consumer then no longer makes really sovereign decisions. It is to be expected that in the course of the next few years, a change in conversational commerce in Germany will be observed – following the examples of China and the USA. Many online businesses will presumably use bots to offer their customers better and faster service.
It is not yet clear how far conversational commerce will expand across the various industries. It is clear that bots will continue to improve and that response and recommendation algorithms will be further refined. In the long term, an optimally individual and automated interaction between customers and companies can be expected, which will have advantages for both customers and companies. Overall, an increasingly data-driven and analytical business will have to answer the question of the right balance between automation and personal interaction. It remains to be seen who will win the billion-dollar race in conversational commerce. Equally exciting are the corresponding implications for the consumer. Will they emerge strengthened by the appropriate bot power in the form of digital assistants who know their actual preferences and represent them accordingly, or will they become even more the plaything of a perfectly designed data and analytics ecosystem of digital giants? After Internet, mobile and IoT, we are now in what is certainly the most exciting phase of our digital transformation.