Differentiating customer experience starts from your website. If web-shop is the superstore for your brand – allowing prospective customers to walk-in, check out and purchase your products, wouldn’t you need your best salesperson to greet your customers, answer their product questions and assist in making a purchase, and in turn, delighting them? Precisely what Prajwal discussed in his last post “Why your brand should move beyond Contact Us?”
Just the concept of automating and scaling up one-to-one conversations using technology appealed to lots of brands and they tried to leverage Chatbots. This was in 2015. Naturally, most brands were thrilled when they implemented a Chatbot expecting that the experience will drive prospective users to make a purchase and save cost by handling customer service issues and FAQs.
But, despite the best of their intentions most of these traditional chatbots (I call them chatbot 1.0) failed to deliver the user experience that was as seamless, delightful and efficient as they envisioned them to be.
1. The traditional chatbots are not intelligent enough
Most chatbots work based on rule-based or decision-tree logic. Means, the experience is limited to the thoughtfulness of the customer journey designed by the brand, and to the list of possible FAQs that the brand can come up with. Machine intelligence is driven by the data on which the chatbot is trained and often brands struggle to furnish enough relevant data needed for effective training. (refer what our CTO has to say regarding “relevant data” in our Curious Much Conversation talk series here)
The other aspect is the self-learning capability of the chatbot and most often traditional chatbots miss this critical part. Wouldn’t it be naïve to expect your sales associate to increase his skills and win deals, handle customer queries without learning on what you got to offer?
“Intelligence is the ability to adapt to change.” Stephen Hawking
An employee becomes an asset when he or she masters your products or services through continuous learning. Likewise, Domain-specific conversational AI matures into an indispensable asset for businesses with its continuous learning capability. Learning for a Vertical AI Bot is not limited from its conversations alone with customers, but by enriching the product details from various other sources; managing future conversations efficiently. Capturing vertical/product specific Intents and customer contexts truly help the AI to mature into a Vertical AI.
2. Failure to understand the context and intent.
To make the conversation more appealing for customers, management of context is of paramount importance. Pure-play chatbots can’t handle contextual information for an extended period. Losing track of conversations context means ending in frustrating conversation and disgruntled customers.
A Vertical focused conversational AI can have far richer conversations because of its ability to predict the intent of the users, apply the context, and recognize relevant entities that are meaningful to the brand’s domain and product category.
A common marketing narrative from traditional chatbot players is that they are Vertical AI and that they can identify “intents” accurately. Peel through the layers and you will see the Intents library with 5-10 unique intents that are generic across industries. The true measure of a vertical conversational AI is the depth of unique intents relevant to the specific domain that it can identify and predict.
3. Switching over to human expertise when the technology fails.
To achieve seamless conversational experience, being transparent is key. The shopper needs to know upfront that they are talking to a machine and an option to switch to an agent when in need exists. Additionally, a true domain specific conversational AI should also be able to accurately predict when is the right time to switch to a human agent and this is where the self-learning capability of the AI makes a difference.
Domain-specific conversational AI can turn conversations into a pleasant one by handing over the conversation at the right moment to a human agent. Not just stop there, but also allow the individual agent to replay the conversation, make him aware of what the shopper did right before connecting to him and resume the conversation from exactly the place where AI switched over – all this in a seamless experience for the shopper.
In summary, domain-specific conversational AI platforms that understands your specific product segment or industry and comes pre-loaded with its own intelligence (intents library, entities library, etc) can help you jumpstart your journey towards creating a delightful customer experience.
Curious Much? Let me know your thoughts at firstname.lastname@example.org and let’s start a conversation.
Author: Joseph Mathew (Joseph.Mathew@asksid.ai)