Savings in the long run
We're talking about the long term here, but increasingly, platforms specializing in chatbot marketing are emerging, which offer ready-made systems and infrastructure that can be customized to suit your needs, making the return on investment much faster than in the case of building a solution from scratch.
The implementation cost is basically a one-time thing. Depending on how it was designed, a chatbot may require updating its knowledge base or rules over time. Expanding a chatbot with new skills is also largely a one-time action, which means that chatbots do not have to generate monthly costs. The ease of scalability and the ability to handle a large number of users at the same time is impossible to overcome by a single employee or a small team.
Minimizing the response time to customers' questions
The e-commerce market is very competitive, which is why it is so important to solve the problems of potential customers in a short time. A virtual advisor can help provide information about products, can also recommend other products or help in the complaint process - if the process is successful and user-friendly, then the user can be motivated to use the services again in the future. In a more complex case, the system will also be able to direct the case to the appropriate advisor, which is really its next advantage.
What types of tasks can machines perform?
It is impossible to list all the tasks that this type of system is suitable for. Among the most common applications, it is worth noting:
Improving the quality of service by minimizing the waiting time for a response . Implemented systems can be expanded over time with an additional set of knowledge, or certain algorithms can be implemented in them, which, in combination with artificial intelligence models, will perform even complex tasks within the application (of course, everything in accordance with the intention of the person who designs such a system - I mean a situation in which the system will never perform an action that has not been previously designed by the person implementing the functionality of the entire system).
Increased availability of support for the target group , as the system can conduct multiple conversations simultaneously and independently.
The chatbot marketing system has rich data about users, which can be used to personalise recommendations for services and products .
Areas where chatbots can be used to achieve business goals
customer service — complaints, additional product gcash database information, scheduling a visit, shipment statuses, FAQs, help with navigating the website;
marketing – product recommendations, personalization of experiences;
internal company needs – e.g. onboarding new employees.
A practical example of using a chatbot
One of the most popular chatbots used by Poles is Mat. InPost uses many channels simultaneously (Messenger, WhatsApp, Signal, chatbot embedded on the website and in a dedicated application). Available functions include:
receiving reports of returns and problems;
handling matters related to shipment delivery;
ability to answer questions from an extensive knowledge base.
Another interesting solution is voice chatbots, which do not require us to use a keyboard and cope well with human speech. In the case of InPost, this works better for dictating a shipment number.
Why are chatbots currently experiencing their second youth?
Today, thanks to ChatGPT, there is a lot of talk about chatbots, but they existed decades ago and did not attract that much attention.
Before so-called generative chatbots based on language models became widespread, solutions based on predefined rules were used for a long time. So what is the practical difference? Will AI-based chatbots make rule-based chatbots obsolete?
Let's start with the fact that generative chatbots are able to generate responses that by definition are supposed to imitate human-written text. Without going into details, this is possible thanks to, among other things, the use of deep learning algorithms applied to huge data sets used to train very large artificial neural networks. Thanks to modern architecture, so-called chat models are able to very often generate "meaningful" and helpful answers to questions asked by the user.
And it is precisely because language models can create a personalized response that their great popularity stems from. A few years ago, the most popular approach was to use chatbots based on previously prepared rules.
These types of solutions are based, among other things, on the presence of specific keywords in messages sent by the user. Rules that must be prepared in advance are used by the engine to provide answers based on detected keywords. Very often, these types of chatbots are equipped with UI elements that help to provide the appropriate course of interaction. Communication with the user is predictable, because the scenarios result directly from a previously designed decision tree.
Despite their limitations, chats based on NLP algorithms combined with rule systems have their advantages over the newer generation:
the creator of the chatbot has full control over the responses provided;
certain actions can only occur at specific points in a conversation;
these types of huts are cheaper to implement and use.
