Three ways to make your chatbots more likeable (and more likely to achieve KPIs)

By Jeff Goodhue

Have you ever met a chatbot you liked but wished it was smarter?

Wherever your favorite chatbot lives – website or mobile app – it's basically a digital program that uses AI to interact with you. Chatbots do their best to provide missing information or make suggestions based on your inputs. Flashback. Anyone remember one of the first chatbots commonly known as Clippy? (10:59)

No surprise, not all chatbots are helpful. They might not have the information you need when you need it or the conversation flow might be awkward and slow. These issues can negatively affect customer perception, Net Promoter Score (NPS) and achievement of key performance indicators (KPIs), such as sales conversion or case completion time.

Good news: You can increase chatbot likeability and improve time to customer value with support from business automation. The following three patterns combine chatbots and specific business automation tools to make your chatbots smarter and faster:

1. Combine chatbots with AI-driven document capture so they can answer more questions

To design a personalized chatbot that anticipates your user's needs, it needs data from your user as they type into the chatbot interface and data from unstructured sources.  For example, what if your banking chatbot knew you submitted an application form and could automatically extract the form’s values, looking up the application’s status from another system? That would be useful.

Figure 1. General pattern combining a chatbot with intelligent content analysis

Figure showing general pattern combining a chatbot with intelligent content analysis

If you're a business analyst or developer, check out Content Analyzer’s Get Started page to see how easy it is to configure document classification and extraction of values and paragraphs.

2. Combine chatbots with business rules and Natural Language Understanding (NLU) to improve conversation quality

Building a primary chatbot conversation flow normally goes quickly because many of the branches and leaves can be enumerated, including the rule conditions that control flow. 

But as chatbots take on more responsibilities, onboard new business areas and increase personalization, two functional areas become more important: NLU and business rule management.

  • NLU: If your chatbot needs to understand more complex user intents, such as longer sentences and paragraphs, or unstructured documents, AI-based NLU should be used. A continuous training loop that governs and tracks accuracy of chatbot responses should also be considered.
  • Business rule management: If your chatbot needs to respond more personally with specific recommendations and next-best actions, business rules should be used to set up and control those types of decisions. For example, rules can recommend a new product based on the current conversation and the user’s likelihood to buy, or next-best action rules can determine the best promotion based on the customer’s retention score.

If you’re an architect or developer, check out this reference architecture to see an example of this pattern in action.

Figure 2. General pattern combining a chatbot with declarative and predictive decisioning

Figure showing general pattern combining a chatbot with declarative and predictive decisioning

3. Combine chatbots with RPA bots and processes so they act faster

Once your chatbot can answer more questions, more personally, your users will probably want it to act faster.

Imagine your user wants to change an address, purchase a subscription plan or open a new account, all of which require task and process automation. The chatbot could tell her where to go, leaving her to complete the task.  A smarter chatbot could launch the process for her and provide a status, which requires integration into robotic process automation (RPA) bots and business processes. If your chatbot can only repeat FAQs and provide personalized responses but not take action, the experience is nothing more than interactive help documentation.

Figure 3. General pattern combining a chatbot with task and process automation

Figure showing general pattern combining a chatbot with task and process automation

To wrap up, a chatbot is only as good as its last conversation with you, like a restaurant is only good as its last meal served. To keep customers coming back to your site or app, chatbots can be a key differentiator. The more likeable, the more helpful, the better.

Note: The capabilities highlighted within these patterns are part of the IBM automation software platform that enables you to automate any type of work at scale, but you can find them as single tools, too.

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