What Are Contextual Chatbots? How They Can Make A World Of Difference In User Experience?
Today, it doesn’t matter how powerful your chatbot is, but what matters is the underlying technology which can solve the right set of problems. There are different usage patterns of a chatbot that makes it unique from the others. When you have completed your research about the chatbots, makes sure what functionalities you want to incorporate in it.
This will have a solid impact on both the developmental plan and end-user experience. Contextual chatbots in terms of placement works differently. Contextual chatbots are kind of advanced chatbots. Their basic aim is to try to figure out what user intends i.e. in which sense or proportion the user is asking a question or doing some random stuff on the website. It then revert those sentiments and behaviour according to the intentions of the user. These chatbots remember the previous things a user has already asked or performed and based on that, it presents a more thoughtful answer.
For instance the chatbot can ask if a user want a specific type of food. Then the user enters a food type or asks for suggestion. In both the cases, the data in the first input i.e. the type of restaurant is saved as a context.
Another example of contextual chatbot is when a user orders a pizza. If the user has already given his location and preferences, the bot will not ask him the same basic question again. It will simply ask for confirmation and voila! The order is placed and out for delivery.
For more versatility, contextual chatbots need a lot of data and vast knowledge-base for training. During the conversation, these bots learn as much as possible from the user utterances & past user journey on the website or mobile app. By going through all these, it becomes capable of predicting possible actions to perform in real-time.
Is Conversational Smartness Enough?
Right timing is the most vital aspect of a smart chatbot. Retaining a user by sending a perfect message is where the contextual chatbots comes into play.
For instance, if a first time user or even a returning user comes to your website, you will not want your chatbot to introduce the user about your company. These monotonous formalities might annoy the user and create difficulties in retaining them.
Who won’t appreciate that whenever a user browses through pages on a website, he should be treated according to the context of that page. For example, if a person comes to browse through clothing section of an e-commerce website, your chatbot should assist the person in choosing the right clothing without creating complexities like a normal bot do. So the contextual chatbot should start the conversation coming directly to the point without unnecessary talks.
How Suddenly Appearing Chatbots Better Than The Others?
Another salient feature of a contextual chatbot placement is its ability to pop up at the right place and at the right time. If a person is going through the pricing section of a product, the bot should be able to pop up instantly and assist him on pricing. If he is navigating the discount section, the chatbot can instantly pop-up and offer him a promo code. Just imagine when a user clicks on a loan button or link, the chatbot remembers it and start talking it directly on finance and loan options.
When a visitor lands on a website, he quickly wants to search for a desired product that fits his taste. If he is unable to find that, he would get bored and leave and might never return. A contextual chatbot should be able to guide the user to their preferred products.
These chatbots first target the user, especially the most engaged one by analyzing their time spent for a few seconds on a particular category of products. Then it pops us automatically to assist in purchasing by offering discounts. This can really take the user experience to the next level. The timing and preferences of chatbot visibility can also be customized by analyzing browsing behaviour, traffic source, pages visited, buying habits, gender, age, etc.
Custom chatbots can be incredibly useful for a business allowing them to keep an eye on who sees different messages at different point of time. These chatbots let the business see what page or link the user has come from. This would eventually accelerate sales for the business.
What Makes Contextual Chatbots So Special?
Virality is what every product maker strives for; it’s the perfect customer acquisition channel. Automating workflows and finding relevant leads is the motto of every business. They want to acquire customer and grow faster. The next generation of communication will be instant and no one would want to spend much time on just buying and searching through the web. Contextual chatbots analyze the intent of the user and grabs the gist to offer what they are looking for.
No matter the visitor is in early stage of browsing or the buying stage, the custom made bots ensure that the outcome is fruitful and no potential lead is left behind. And this comes from user interface and how you take a user from point A to point B quickly and efficiently. If the user has wonderful experience, if they are loving it from start to finish, if they are getting huge value by talking to the chatbot, they will definitely share.
So if your chatbot goes viral on being amazing as compared to just based on a specific feature, just cheer as you have done it.
The Bits & Pieces That Makes Chatbot A Contextual One
When it comes to chatbots, we know they have no automatic knowledge of their own, so they can’t use context like we humans do. However, it’s possible for us to provide or feed them with the right information and tools so that they can utilize the context on their own. These kinds of chatbots are called as contextual chatbots. Let’s explore more about contextual chatbots and how the context can be the most powerful tool of a chatbot.
Situational Context
Humans are always conscious of the situational context. For instance, we know we are standing outside our home, public or sitting with a friend or lying on our bed. Are we standing in the shiny afternoon or middle of the night? These situations also changes how and what we talk.
In terms of chatbots, they live in different locations including apps, websites or even smart devices. So knowing which channel the user is chatting through should be simple, even the physical location can also be determined. We can use this information to tailor different conversations on different channels.
Lingual Context
When we talk, we actually draw a lot of information stored in our memory about the world we live in. We tend to gather words into sentences without reconsideration. We are able to comprehend different meanings of the words they are being used and who is using and where it is being used.
The vocabulary of the chatbot is often limited and it is basically derived from what we train it on. A fine set of training data and the ability to add synonyms can really help your chatbot understand more variations in a phrase. This could be well-planned before the execution of chatbot. Once the chatbot is live, we should constantly review what the bot understood in order to add more valuable training data and make the bot smarter.
Persistence Context
The exchange of dialogues between two people is what we call as conversation. In other words, you can say it’s a series of questions and answers. During a conversation, the context of conversation holds a special place. It reflects user intent and adjusts the conversation for better understanding.
In real world, if you speak to someone who forgot everything you said from sentence to sentence, it can create an annoying experience. Moreover, understanding individual sentences can create more complexities. Contextual chatbots should be able to keep some sort of context when figuring out individual sentences during a conversation. Even if the task is just to keep track of the data being collected.
The more it remembers the things said before, the more it gets personalized. The best method to begin using the context is to gather analytics. This will allow you to understand common patterns a chatbot can understand then predict and tailor the conversation.
Emotional Context
Humans are more sensitive and thoughtful irrespective of the type of emotions used during a conversation. They also know how the emotions are conveyed through change of words or vocal tone. When it comes to computers, they are less good at analyzing these sentiments however; they are improving with the time.
If someone is integrating this into the chatbot then the best bet is to know the tasks chatbot will be handling. If analyzing sentiments is not the key part of the tasks, then it should not be integrated immediately as the technology isn’t perfect yet.
How Can We Make Chatbots More Contextual?
Contextual talk in terms of chatbots is incomplete without the proper context of the user too. Here are some of the tips which can make contextual chatbots more powerful:
Guiding the user
Conversation between two individuals is a two-way act. Just as the chatbot is getting context from the user and learning something, the user is also getting context from the chatbot. Therefore, we must ensure that the chatbot is guiding the user during conversation to make him understand its limitations. We must be clear when any issue arises, the user should have options.
Reflective language
Secondly, the language should be reflective enough. Users are more likely to use the same wordings as your chatbot does. These words or phrases can be used to teach the user the type of phrases chatbot can understand. But don’t make the chatbot say anything that it isn’t able to understand.
Active listening
Thirdly, active listening should be an important part of your chatbot. Try repeating back key portions of the conversation your chatbot has collected from the user. Key portions of the conversation here mean the portions on which your chatbot will take some action. This will help the user decide whether the chatbot has understood and also decide if they want to proceed with some actions.
Integrating Chatbots With Third-party APIs
Last but not the least or say a bonus point is to integrate these chatbots with third-party APIs. This means sending relevant data to the third-party API which executes some kind of action in the system. For instance, if a user is interacting with a chatbot for the first time but has interacted with the business earlier through other means, the chatbot can start the conversation by collecting just basic information such as name or phone number. This information then could be send to the enterprise CRM or business database to find if there is any match in the system. If there is a match, the chatbot skips the initial set of questions that are no longer needed.
Conclusion
Self-learning is the key to build contextual chatbots. The timing of chatbots should be perfect enough to drag user’s attention in no time. By quickly eliminating any buying obstacles, the potential customers can easily move down the funnel towards the right product and eventually it leads to lead conversion. We must ensure the conversational text in mind and always be cautious about the integrating third-party systems to offer the user a really game-changing experience.
Developing contextual chatbots need complex architecture and not all enterprises can afford time to build such systems from the scratch. Our research lab is being heavily involved in developing & deploying conversational interfaces for businesses across the globe. Makerobos make the companies aware about the rapidly changing bot technologies with specific experience in dialogue management, contextuality and conversational AI. If you think we can better assist you in creating a powerful conversational bot, say hello to makerobos …
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