Will Chatbots replace Apps? Are Chatbots the new Apps?
The year 2008 when Apple launched the App Store, that brought revolution in digital experience. Everyone wanted a mobile app for their business and soon we had thousands of mobile app and hundreds of mobile app development providers. I have seen many mobile apps built by businesses but quickly taken out because they were not really serving the purpose and customers were not finding any value in having them. Why? Because mobile apps needed the enterprise data, embedded in multiple systems, to be made available in the form of lightweight APIs (something that did not exist), and that too in a certain structure. This in turn, meant that enterprises adopting mobile apps had to then invest significant time and money to prepare the data, build the APIs and then expose it. A costly mistake to jump in without the holistic understanding and more importantly without answering the question “Why my business needs a mobile app?”!
Fast forward a decade and we are seeing the same pattern emerge for new age digital experience – Conversational AI or Chatbots, targeted towards always-on digital native generation who loves more conversational experience than the old-world menu driven websites & mobile apps. Most businesses see this as an opportunity (and rightfully so) and want to capitalize. Wouldn’t it be great to serve your customers in an easy flexible conversational medium that can understand the intention of the customer and respond with contextually relevant answers in natural language? Sure it will be. But before you dive in and start building chatbots for your business, if we have learnt from the past and want to avoid our past mistakes, it is important to assess chatbot advantages and disadvantages and ask a few fundamental questions around your organization’s data readiness.
Here are the top 5 questions that you must strive to answer for your business before attempting to build and launch your chatbot.
Top1: What is the use case I want to solve using chatbots?
Don’t build chatbots because chatbot technology is the next exciting thing technology world has to offer and everyone is building one. Identify the problem or AI use case where conversational AI agents are the best option and then build one that solves for that specific problem. Or else it will be like putting the cart before the horse and you will end up being disappointed.
Top 2: What data will your chatbot app need and Do you have this data?
Driven by the use-case and customer journey you implement, this might vary from customer service data, orders data, product data, inventory data etc. Make sure you draw out the data map and assess whether this data exists within the organisation or not? It is quite common that you might not have all the data that will be needed and in these scenarios you must figure out alternate sources – either gather data from external public sources or generate your own data. (We will soon share in a different article some of the hacks we have figured out to generate your own data when it does not exist)
Top3: In what format should you provide it to your conversational AI?
Do you need to build APIs in a certain structure that will expose this data or Can you expose your existing data as raw extracts and the conversational app can intelligently make sense out of it? Look at building a full stack chatbot solution where the chatbot is wired to the backend data ingestion and data enrichment algorithms extremely tightly.
Top4: How to make sure that you are able to extract knowledge and insights from your chatbot app and take these learnings to the other touchpoints?
I will again recommend you to build a full-stack chatbot solution that comes with its own Analytics capability and getting insights from the context rich conversation data should be as simple as a click of a button. Remember, one of the primary business case chatbots deliver is that if designed properly, these AI applications can deliver unique precision marketing insights that even GA cannot deliver. The reason being that these insights are coming out of conversation data that typically contains a lot more customer context information than the web-click data on which your GA runs.
Top 5: How to break the I-C-E and how do you keep it up-to-date?
ICE– Intent, Context and Entities are the defining building blocks of NLP (Natural language processing) and here is what each of them means for your business.
- Intents – Intent in simple terms mean ‘the question behind the question’. It is about predicting the “intention” of your customer from the message she sent to your bot and the bot then responds accordingly. Deeper the intents understanding, richer will be the conversational experience for the end user. Shallow conversational apps will decipher 3-5 top-level intents while Vertical AI chatbots will be able to interpret and predict 50-100 intents that are specific to your business and your product category. Remember, in the world of conversational apps and virtual assistants, the most important aspect differentiating one solution from the other is the capability to predict the user’s Intent. Go for vertical AI apps that understands your domain ontology deeply and there-by the accuracy and depth of their intents prediction model will be far higher than the commonly available horizontal chatbot apps.
- Context – This refers to the context in which your customer has sent the message. Is she trying to discover products when looking at a category or is she asking a question on the material when looking at a specific product? Finer the context, more engaging the conversational experience.
- Entities – these are domain specific significant terms such as denier, pregnancy suitable, veins and scars, weight, plus size, etc which has a meaning in relation to your business and products. Wider the entities more relevant will be the conversational app response capabilities.
Success or Failure of your Conversational AI solution is fundamentally driven by the data preparedness of your organisation and answering the above questions upfront will help you to avoid the same mistakes we witnessed during the Mobile App era.
I look forward to your feedback, thoughts, brickbats – just reach me at email@example.com and let’s start a conversation.