How to Optimize Your Chatbot With Next-Gen Digital Workers

Digital Workers, Live Chat Bots, Omnichannel AI

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Optimize Your Chatbot With Next Generation Digital Workers

 

Learn How to Use Next Generation Digital Workers to Optimize Your Chatbots For an Incredible Customer Experience

Chatbots have become an expected channel when interacting with a business, they’re nearly as ubiquitous as phone lines and websites. Nex-gen Digital Workers take this key customer channel and add optimization at a highly advanced level.

Research firm Gartner predicted that by 2020, nearly 90% of all customer service conversations would be powered by chatbots – and that people converse more with chatbots than their spouse !

That’s before anyone knew the COVID-19 pandemic was coming to massively upend life as we know it.

Chatbots solve a number of critical problems for businesses.

By leveraging next-gen digital AI technology chatbots can;

  • decrease customer wait times
  • reduce customer churn
  • help businesses scale
  • increase sales and loyalty

With nearly three-quarters of retail businesses losing customers due to wait times, chatbots stand to have a significant impact on any business that uses them.

As consumers have become accustomed to chatbots, expectations in regards to the features chatbots offer have increased. Almost half of consumers expect 24/7 service from retailers, which is likely influenced by our experience with always-on chatbots.

But just because chatbot conversations have come into mass usage doesn’t mean they are all working like well-oiled machines.

What’s Wrong With Chatbots Out of the Box?

Low Complexity Level + High Development Effort

Chatbots frequently run into challenges because of how they are built. In the evolution of online customer service, chatbots were introduced to support live chat with human agents. Customers were coming to live chat to get questions answered, so chatbots were born to answer questions.

Their main goal was to fill a gap and make the live chat experience more efficient, which, in many cases, they did.

However, being built to understand a question and provide an answer means that chatbots offer very scripted, rigid experiences.

These experiences have to be developed by internal teams and require a lot of scenario mapping, which is not fool-proof. They only allow chatbots to answer questions.

How Does This Affect Chatbot Interactions?

Lets take a look at some of the common interactions we see between customers and chatbots;

 

 

The Non-Conversation: A customer might go to a website for support and engage with a chatbot that says, “Hey, I’m a robot, I can handle simple tasks. Speak to me in short sentences.” At this point, the customer may start guessing what types of keywords will get them to the answer they need. The customer might go through a number of different types of sentences or keywords before landing on one that works.

The Non-Answer: Another customer might be planning to place an order and ask a chatbot for the type of shipping options available for their local area. The chatbot could answer with an offer to help calculate the shipping cost of the order. This is some sort of answer, but it isn’t what the customer was looking for.

The Facepalm: A routine request from customers is “Where is My Order?”, or WISMO. A customer might initiate a chat to ask when the order will arrive, and the chatbot could explain how to navigate to the website to track the order. Most customers are savvy enough to know how to do that – they could have gone to the account page to check the status, or dug through their email messages.

What’s the Next Phase of Chatbot Evolution? Digital Workers.

To take customer chat interactions to the next level, businesses will need to employ tools with much higher levels of complexity. Enter digital workers: robust, highly-programmable software programs that can perform the work of humans by leveraging automation and artificial intelligence (AI).

 

Before we get into exactly how digital workers do what they do, let’s walk through an example.

Levi’s leverages a chat experience called Indigo on their website. Let’s say a customer comes to their site, opens up the chat, and says,

“I’m looking for a pair of 501’s for my sister. Can you help?”

Without anticipating this specific question, standard chatbot experiences would simply break.

But with a digital worker to manage the workflow, Indigo understands that 501 is a style of jeans and that sister is a female entity.

 

using digital worker chatbots can anticipate customer questions

 

The 501 style comes in shorts and jeans, so Indigo asks the customer, “Would you like shorts or jeans?”.

Once the customer answers shorts, Indigo delivers a series of product recommendations where the customer can shop directly.

Next question: the customer asks “What about the white color?”.

 

digital worker chatbots do more than answer questions

 

Without anticipating this question, Indigo would not be able to help. Using a digital worker, Indigo understands that white is color and filters the recommended shorts based on white.

 

digital worker chatbots deliver results accurately

 

The customer could just as easily ask “Show me everything that is under $50” and Indigo will return the appropriate results.

The way dialog flows were built for chatbots is that the business has to anticipate the utterances from customers – like “can you tell me shipping methods” or “I want to find my order.”

Once the chatbot understands the utterance, it goes through a nest of rules that must be defined by the business.

If a customer says “shipping method” or asks “can you tell me shipping methods”, then provide this answer.

Maintenance for these types of automation tools is very extreme.

 

Digital Workers Solve For Top Customer Care Use Cases.

Digital workers do so much more than just provide answers. They solve customer problems – without the massive amounts of data training required for simplistic chatbots. Using simple data integration, long-term memory, and a pre-built context for the industry, digital workers are able to combine datasets with artificial intelligence to more closely mimic the human interaction.

 

By removing the heavy lifting of chatbot entity training, businesses can focus on serving the entire customer journey – from awareness to retention.

The key is to automate customer interactions so customers don’t have to go find an answer.

When they engage in a chat, customers should be able to solve for any number of use cases: store locations and hours, returns from item identification to printing a label, policy questions, product recommendations, WIZMO, and the like. And as long as users are logged in, all the interactions will be completely contextual.

Digital Workers Will Revolutionize Chat.

Chatbots by themselves do provide benefits, but businesses will need to stay ahead of the curve and adopt digital workers to further enhance the shopping experience and retain brand loyal customers.

Want to know how digital workers might fit into your company’s chatbot conversations, and where they would have the biggest impact?

Contact Linc for a customized demo.

We’ll walk through your data sets and show you the types of conversations your customers could be having.

 

 

 

 

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