Conversational commerce is mainly driven by the major Internet companies that operate a messenger and/or chatbots, such as Facebook, WhatsApp, Telegram, Slack, Apple and Microsoft. Progress in conversational commerce is being driven primarily by two developments: A communications trend and the rise of artificial intelligence. The former can be seen in the popularity of messaging services, whose use is exploding. Apps and services that serve 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) steadily increases, the use of messaging services is likely to continue to grow. Due to the large number of people using messaging apps, the next logical step for companies is to offer their services there. Instead of convincing customers to install a new app, companies are picking up customers where they already are because chatting is already integrated into everyday life. The development in the field of AI also makes the existence and further development of conversational commerce possible, for example, in terms of the performance of speech recognition, which is increasing by 20% per year. Today, it is already possible to capture 90+ percent of spoken and typed language, thanks to natural language processing, also called Natural Language Processing.
Apart from the explained two essential criteria for the growth of Conversational Commerce, there are other trends that favor its progress. One example is the so-called Quantified Self movement, which is also known as lifelogging. It refers to people who record and analyze personal data throughout the day, such as food consumed, air quality, state of mind, 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 through electronics and sensors incorporated into the fabric of clothing.
Together with advances in the field of data science, this trend has the potential to personalize customer interactions in conversational commerce as well as predict consumer needs. The integration of seamless payment technologies is essential for the completion of entire purchase 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
Arguably the earliest implementation of conversational commerce has been through WeChat, a cross-platform mobile messaging service from China launched in 2011 by holding company Tencent. WeChat can be used to communicate with friends and acquaintances as well as to use services from countless companies. Among other things, you can call cabs, order food, buy movie tickets, book doctor’s appointments, pay bills, and record your daily fitness routine. WeChat is a chat-based interface with many additional features, such as mobile payments, chat-based transactions, media, and interactive widgets.
A powerful API makes it possible for a wide variety of companies to “befriend” their customers. More than ten million businesses are connected to the chat platform, and its popularity among small businesses is growing.
In contrast to the U.S. and Europe, where services have so far mostly been offered in specific apps, China has been focusing on combining messaging and consumption much earlier. Meanwhile, WeChat is one of the largest standalone messaging apps in terms of active users: instead of changing existing infrastructures as in the U.S. and Europe, many markets in China can be tapped for the first time through mobile apps and payment systems.
Facebook opened the doors to its in-house Messenger to other companies in 2016 by integrating a full chatbot API into the platform. Mark Zuckerberg explained the decision as follows: “I don’t know anyone who likes calling a business. And no one wants to install a new app for every business or service. We think everyone should be able to write to a business in 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 cited example from the USA is the integration of the cab service Uber into Messenger. Clicking on the address sent in Messenger opens a menu that suggests, among other things, the option “Request a ride”. If there are available drivers, the cab can be ordered in the next step via one click. The trip is automatically charged to a credit card that has been configured in advance for all services. The Facebook Messenger API user interface also allows you to embed maps, product photos, and other interactive elements into the chat conversation.
Challenges for Conversational Commerce
All chatbots work in a similar way, relying on matching patterns in text and responding to specific keywords. But what challenges do currently active chatbots face, and why isn’t conversational commerce more common yet?
One reason seems to be that AI integration has not yet been widely realized. The author of an article in the magazine c’t, for example, criticizes the fact that there is currently no bot that can learn the interests and preferences of users and take proactive action without being triggered by the user.
In an article in the magazine Absatzwirtschaft, the author describes how 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 adaptability of the bot; the program should be able to adjust its own settings to external influences. Another requirement for bots is that they act proactively and start processes on their own initiative, such as reminding the user to buy coffee. The bots should also become social, so that they can develop a kind of “social life” among themselves and communicate with each other. It is questionable, however, 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 chatbots is quite feasible.
For example, there are a large number of libraries for developers to integrate the learning and predictive capabilities 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. Die menschenähnlichen Konversationen, der bessere und schnellere Service sowie die Präsenz der Marke können zu einer engeren Kundenbindung führen. Many consumers appreciate the services tailored specifically to them. Improved services ultimately increase customer satisfaction. The reputation and awareness of the brand or company can also be increased. It also gives companies more insight into their customers’ wants and needs, as well as the buying process and context.
However, it is important to remember 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 transparently the activity of robots in conversational commerce should be handled. Should consumers be told that they are currently chatting with a bot? As telephone-based customer service will become less important due to the use of chatbots, job cuts can also be expected. It is therefore important for companies to develop strategies to avoid employee frustration, for example by finding new jobs within the company.
