Artificial intelligence in the form of chatbots or voice assistants is more and more finding its way into customer communication. They provide enormous support in customer service and can be implemented in different industries helping with numerous use cases. So what should be considered when implementing a customer service chatbot? Are there any basic rules that you should pay attention to?
Yes, absolutely. There are several factors that ensure a high level of acceptance and satisfaction by the user and the company itself.
How to implement a successful customer service chatbot
1) The use case
The most important factor is a concrete use case. Many companies or organizations ignore the benefits for the user and consider the use case purely from the company’s point of view. First and foremost, it’s about finding out where the chatbot is going to be used and how it can truly enhance the experience for the user while also providing the best possible support for employees.
In addition, the chatbot should not only provide value to a few, but to as many users as possible. This might sound obvious, but experience shows that it’s not. Chatbots are often used for processes that cause a lot of effort internally, but would only require one email to be sent from the user’s perspective. The added value from this use case is not sufficient, neither for the company nor for the customer, which of course does not promote the acceptance of the new technology.
High usage is essential for a successful chatbot, so if there is no specific added value, you should definitely re-assemble your team and revise the concept.
2) The implementation
Once a great use case has been defined, it comes to the implementation. Unfortunately, we see lots of projects fail at exactly this step. Three of the key factors for a successful chatbot implementation are the project team, the scope of the use case and the integration and usage of different data sources.
First, the right people have to be included in the project. When it comes to a customer service bot, someone from the customer service department must be on the project team. They are the ones who know exactly what users want to know, how they ask for it, and how the respective interactions usually take place and look like.
Second, the scope of the use case plays an important role. At the beginning, it’s crucial to focus on a clearly defined topic that you want to go into detail about. What exactly does that mean you might ask? Well, our bots are “trained” with sample questions to then be able to lead individual conversations. The more relevant and extensive the collection of sample questions, the more questions the chatbot can answer on the topic and the better it’s performance will be.
In addition to the right project team and an in-depth topic, the connection to internal or external data sources (including the corresponding modeling of this data) for the flexible extension of the content of the chatbot is essential. As mentioned above, the more added value the chatbot offers to employees and users, the more successful the project will be. Data sources can provide enormous value to the overall “knowledge” of the chatbot.
3) Multi-channel capability
One reason for the success of chatbots is the factor that customer communication habits are taken into account. For example, 60% of consumers value companies that can be reached through messaging apps and respond to requests. Therefore, chatbots are perfect for providing 24/7 support through your customers’ usual communication channels.
However, considering the rapid growth of intelligent personal assistants such as Amazon Alexa, which can already be found on more than 100 million devices, or Google Assistant (already on 1 billion devices), it is obvious that companies should also think about voice assistants, to be prepared for the communication requirements of the future.
4) Testing & Optimizing
Learning systems “live” from a variety of interactions. Only then can the chatbots be optimized quickly. Despite all the preparation in the set-up, going live doesn’t finish the chatbot project. On the contrary – this is where the real optimization begins. Therefore, resources for the continuous improvement of the chatbot have to be taken into account even after the launch.
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