Over the past couple of years, we have implemented more than 60 successful chatbot projects for medium and large-sized businesses from various industries ranging from energy, tourism, financial service providers, education to IT service help desks and more. Our team quickly noticed that there are certain questions that potential and existing customers ask over and over again. Thus, we have summarized the most common chatbot FAQs for you in this article.
Chatbots are essentially a form of automated service that customers can communicate with via text or voice on different channels, e.g. website, Facebook Messenger, phone, other applications or via voice assistants such as Amazon Alexa, Google Assistant.
What’s important to understand is that there are two types of chatbots — the ones structured by hardcoded questions/answers and the ones that are able to learn through Machine Learning.
The first type has a smaller knowledge base and limited skills. They can only give correct output based on specific instructions, meaning the questions asked must correspond to their set programming.
Chatbots that are able to semi-automatically learn can understand natural language and therefore do not need as specific commands. That means bots based on Machine Learning get smarter with every interaction. The effort behind these automated systems is of course much greater.
As already mentioned, we differentiate between two types of chatbots: Rule-based and AI-based chatbots.
With a rule-based chatbot, possible user queries and potential answers are defined in advance. If a question is asked that has not been previously defined, the chatbot will not be able to assist in answering the question.
In contrast to rule-based bots, chatbots based on Artificial Intelligence are able to process natural language. This is done with the help of Natural Language Understanding (NLU). AI-based chatbots are able to learn semi-automatically.
Chatbots are suitable for many industries and use cases. They are well-known to be able to provide excellent customer service, but there are also some great examples of chatbots in marketing & sales.
Ask yourself these questions before implementing a chatbot:
- How often does your company have direct customer contact?
- Does your customer service receive recurring requests frequently?
- How intensive are your customer requests?
- Which communication channels do your customers prefer?
- What added value can a chatbot create for your business?
Two very important factors for the overall performance of a chatbot are the structure and quality of the data that are available for answering questions. This is where Knowledge Graphs come in.
Knowledge Graph is a synonym for a special kind of knowledge representation. It stores facts in the form of edges between nodes in a graph. In addition, most knowledge graphs also store the schema of the data. Knowledge Graphs develop their full potential, especially with large and complex data structures.
When used for chatbots, a Knowledge Graph offers two direct advantages — improved data integration and simultaneous improvement of conversations.
New data sources can be integrated more easily since they only have to be brought to a single specific format and scheme. Knowledge Graphs also offer increased flexibility for expanding existing knowledge. That way, new facts are saved directly as new nodes and edges in the graph.
It is also possible to link several Knowledge Graphs without any problems, either using the same nodes or adding new edges. This allows to build up a modernly managed corporate knowledge base, which can be retrieved via API and voice interfaces using natural language.
For more information on chatbots, voice assistants and Conversational AI download our Ultimate Chatbot Guide For Businesses.
Unfortunately, this question is not as easy to answer as “How much is a can of soda?”.
The investment that you put into a chatbot depends on various factors. Things such as the complexity of the bot, its AI capabilities, how it is built, technical integrations, infrastructure, launch & post-launch support and more have to be considered when you calculate or compare the cost of a chatbot.
While you can sometimes get started for free with very basic bot builders, for bots built with more advanced bot builders you often pay setup fees that can range anywhere from € 1000 to € 15000+ and monthly retainers between € 100 to € 5000+.
Here’s an overview of our chatbot pricing.
Again this depends on the type of chatbot and bot builder or service provider that you chose for your technical implementation.
At Onlim, we define and implement individual content together with customers as well as any necessary interfaces to data sources. Moreover, preconfigured modules s are available through our Conversational AI platform. After the technical setup is done, thorough tests and optimizations are done before the chatbot is set live. Customers can then manage any chatbot content, analytics and more through our SaaS platform.
Our current record is 3 weeks until going live. On average, we expect a lead time of 6–10 weeks after the formal order.