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Oct 15, 2018

Why Natural Language Technology Might Let You Down

[24]7.ai

Why Natural Language Technology Might Let You Down

We live in an era of smart devices and what some call pervasive computing, where consumers interact with smartphones and other devices all day long. Increasingly, this interaction takes place via virtual assistants like Apple’s Siri, Microsoft’s Cortana, and Google Home. These "natural language” applications allow computers to derive meaning from what people say or type, and offer accurate, meaningful responses. The idea of having a productive conversation with computers was once a fantasy, but is now a common, everyday experience.

Customer care leaders know that the popularity of natural language technologies in our everyday lives has brought about a whole new level of expectation when it comes to interacting with companies.

Consumers today expect to be able to use your digital channels to speak, or type, their issues in
conversational language and get their problems solved quickly.

It means that natural language technology must be a central part of your customer experience strategy, however, we’re already seeing natural language technology on its own simply isn’t enough.

Why Natural Language Alone Is No Longer Enough
To understand why natural language is not a complete enough solution for today’s consumer, you need only look at how customers behave when interacting with companies today:

  • 9 out of 10 customers are crossing devices in a single journey
  • 4 out of 5 customers use three channels to get things done with customer service
  • 2 out of 3 consumers are using smartphones and tablets simultaneously
  • 1 out of 2 customers are on a website when calling the contact center
  • 1 out of 4 customers are on a mobile app when calling the contact center

As customers switch devices and channels at various points along their journey, they expect the steps along the way to be connected, seamless, and progressive. No customer wants to repeat steps or restart a journey when they enter a new channel. They want the kind of sophisticated, intelligent, seamless experience that natural language technology alone simply cannot deliver.

Let’s look at how natural language lets consumers down, by taking a look at a very common type of customer journey.

A Credit Card Customer’s Card is Declined
Customer Scenario: Susan Jones is attempting to pay for a purchase at an online retailer when her credit card is declined. Thinking she may be over her credit card limit, she logs into her online banking account and sees that she is in fact not over the limit. Wanting to get to the bottom of this, she calls her credit card provider and reaches a natural language IVR. The IVR asks, "How may I help you today?" and Susan responds, "My card is not working."

What Happens Next with Natural Language: A natural language solution would interpret “My card is not working” and based on this statement may narrow down the range of probable reasons to a) the card has been blocked due to suspected fraud, b) the customer reached her credit limit, or c) the customer’s payment is past due. The IVR may then offer Susan the option of proceeding to speak with a fraud agent, or a billing agent as the next step.

These might be the correct options to provide Susan with next, but, wouldn’t it be better if the credit card provider was equipped to know about any preceding activity that Susan took? For example, knowing about the visit she made to her online account would be helpful in determining the most optimal path to resolution for this customer.

This is where the combination of natural language with predictive technology delivers faster resolutions and far superior experiences for customers.

What Happens Next with Natural Language + Predictive Technology:

Assume now that Susan’s credit card provider had a predictive customer experience platform that tracked and shared all customer activity across channels in real time.

Once authenticated, the platform would ‘know’ who Susan was and what Susan was doing online or in a store before Susan made her call. In this case:

  • from her account profile, it would be known that Susan has an overdue bill, and
  • from her web activity, it would be known that Susan logged into her account and viewed her
  •  credit card balance just 2 minutes ago.

By combining this data with her statement, "My card's not working," in the IVR channel, the credit card provider has much better insight, and a much greater ability to discern the root of the problem and the correct solution path for Susan.

In this case, it can be determined that the most probable cause of the card suspension was not because of fraud or over-limit spending, but because of a missed payment.

The IVR can then, in a very politely scripted way, inform Susan as to the reason why her card isn’t working and offer the options to pay the balance owing now.

Compare this outcome with the earlier options offered to Susan, where Susan might have chosen to be transferred to speak to a fraud agent, or a billing agent.

Either of those paths would have opened the door to longer journeys involving more steps, costly live agent interactions, and a possible transfer from one agent to another before finally arriving at the resolution.

However, natural language combined with prediction technology delivers faster, better journeys with higher resolution and fewer mistakes along the way, all at a lower cost to the company.

Natural Language + Prediction is the New Formula for Customer Care
As we saw in the example above, natural language technology deployed in a single channel simply doesn’t ‘know’ what to do next in situations where a user’s intent is unclear or under-specified. And these situations are very common.
Natural language technology on its own has a 40 percent failure rate in identifying actionable intent. That is, identifying the real intent of a customer on a journey.

Why put millions of customers in the hands of a technology that you know will fail 40 percent of the time? This is a recipe for letting your customers down, driving your costs up, and steadily eroding your loyalty.

The remedy for avoiding all this is to add predictive technology to natural language, and enable the sharing of data across channels with an omnichannel customer experience platform.

It is the only way to prepare for the new reality of serving a sea of customers who now expect you to:

  • receive and understand questions posed in natural language,
  • know who they are and what they want to accomplish, and
  • deliver smooth, seamless experiences that are in no way interrupted or slowed as they cross devices and channels.

With fewer and fewer interactions requiring human interaction, equipping your enterprise with connected, predictive omnichannel technology has never been more important.

Natural Language and Prediction in Every Channel
The customer journey example above illustrated the use of natural language and prediction in the voice channel, however, the benefits are compounded when deployed across all channels. For example:

  • Highly relevant marketing offers, perfectly timed with a customer’s propensity to make a
  • purchase decision can be offered in real time.
  • Customers who are online using your virtual agent can be routed to live chat, based on specific activity, language triggers, or customer profile data.
  • Customers who move into a chat session can be greeted by an agent who is informed on who the customer is, and their activity.
  • Live chat agents, and voice agents, can be provided with recommended resolution paths for the customer, before the customer interaction even begins, all generated automatically by the fusing together of data from across all other channels ‘behind the scenes.’

This ‘behind the scenes’ activity that makes it happen involves:

  • Real-time analysis of prior and current chat transcripts
  • Tracking online page visits and views
  • Calculating propensity of accepting offer to chat 
  • Tracking of mobile activity
  • Shopping cart status data
  • Purchase history data
  • Customer profile data

Now is the time to meet and exceed the new expectations of today’s customers by combining natural language and predictive technologies. And [24]7.ai is your partner for making it happen. Contact us today to see how we’re making these technologies easily deployable across your enterprise, with proven solutions that deliver measurable results faster than you think.
 

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