If you have been following our blog, you probably have noticed that this year we have been publishing a lot about chatbots. Indeed, most of the companies in the AI sector have done the same and it is not a coincidence. As messaging apps usage grows, chatbot adoption increases. Every company wants to offer their clients more value and new channels of personalized communication.
Rates and stats also show this trend. In the survey conducted by Oracle among marketers and strategy officers, 80% of respondents said they have already used chatbots or are planning to within the next 2 years.
Chatbots will be an essential tool for marketing: once most companies have a bot available for customers, the next logical step is to use the information extracted from bot-customer interactions to see what customers are asking, what they want and what they need. To extract and analyze customers feedback received by your chatbot you will need text analytics tools such as sentiment analysis or entity extractions. Shortly, we will expand on this topic.
Automated technologies: while companies are adopting chatbots, the majority of businesses is still using a live chat with a person instead of using automation technology. However, this will change in the close future. In the same survey conducted by Oracle, 48% of participants said that they are already using automation technology, but this technology is expected to grow till 88% before 2020. Automated chatbots allow companies to reduce costs, answer faster, and manage more clients in a shorter time. If you want to know more about how to automate your chatbot just message us!
Text over voice for customer service channels: since 2014, there are several studies that affirm customers prefer text over voice for customer services. 81% of customers find frustrating to be tied to a phone while waiting for customers service help. What does this mean? That call centers will be replaced by chatbots. They have multiple benefits such as availability 24/7 or instant responses − however, most bots only support buttons, giving the feel that users should deal with some sort of call center again.
In order to avoid pain to users your bot should be conversational, allowing users to type their queries and use sentence rewriting and NLU techniques. Get to know more about them here.
Chatbots for retail: retailing is one of the sectors in which users demand more information. In the beginning people used to shop online using websites, later we adopted apps, but it is expected the majority of consumers will adopt chatbots as their preferred channel to shop. Why? It allows for personalization, if a user asks for a dress the bot can suggest also matching heels. Okay, apps and webs can do this as well, which is the difference then? Via chatbot businesses can send users personalized discounts based on their preferences, also users can ask quickly for a particular product without struggling searching on a website or track their order without looking on their email.
Adoption of NLP tools: If you want to build a conversational bot able to understand user’s utterances you can benefit from several NLP services. The most basic one is a good lemmatization service to avoid training the bot with infinite variants. On top of this, a competent parsing tool can be vital to help the bot find the relevant information in a sentence. Sentiment and Entity extraction can be very useful in the postprocessing and analysis of the data you have collected. And finally, a good NLU service that finds the relevant information in a sentence and allows you to discard the “fillers” is invaluable for your bot’s understanding.