Recipes for CX engagement on the fly

Amazon’s Alexa knows my playlist content, she curates it and gets it right 90 percent of the time. Waze handles a 50-fold traffic increase on my local roads during apple-picking season, suggesting optimal driving routes to each visitor based on a combination of predictive models. Apple’s Siri analyzes thousands of movie showings and recommends the best times and theaters based on my location within seconds. IPsoft’s Amelia responds to bank account holders with individualized account setups that take minutes instead of hours.

It’s not enough anymore to really know your customer — to understand individual customer preferences, sentiments and spheres of influence. Personalization has matured with customer analytics, and customers expect immediate recommendations relevant only to them.

The next horizon is to actually unlearn what we think we know about customers and dynamically configure a customer response “on the fly” — optimally done with artificial intelligence (AI) and machine learning

Why AI in CX?

Machines learn on the fly from any interactions (human or chatbot) and can dynamically reswizzle a response based on the customer’s needs at that very moment. Machines can probe further with deep learning, which is AI that uses complex algorithms to perform tasks in domains where it learns the domain with little or no human supervision. In essence, the machine learns how to learn.

While AI is changing the way we interact, the big game-changer in customer experience (CX) for an enterprise is combining ingredients (predictive models, algorithms, past behavior, use cases, industry and social attributes) into automated recipes that enable companies to score, predict and recommend how to interact with a customer on the fly to create the best outcome.  Recipes are created by starting with a business outcome in mind and thinking of the recipe providing something about that outcome you did not already know.

Recipes can provide:

  1. Customer scoring — Score a client prospect and provide the origin of that score. For example, predictive lead scoring gives each sales lead a score representing the likelihood it will convert into an opportunity. You also get the reasons underlying the score — for example, the lead source, the industry, or some factor you may be unaware of that’s an especially strong indicator that a lead will or won’t convert.
  2. Predictive recommendations for agents and consumers — Anyone who shops online knows that AI makes suggestions for retail purchases, but it can also make smart recommendations for any other product or service category you might buy. “Push” recommendations at the point of interaction (while on a call, text or chat) provide a next suitable service to a consumer.
  3. First interaction resolution — Provide automated responses based on channel preference and “impatience” rating. An automated channel offers immediate access and answers for customer questions while also driving call volumes to alternative channels to increase first-contact resolution.

But, like your favorite family recipes that taste good only when your mom cooks them, the outcome doesn’t just depend on the right ingredients, but also on the way they’re prepared and cooked. The key to making CX automation produce successful outcomes is not just knowing your customer and kicking off automated recipes, but also using an omnichannel strategy to engage with your customer.

Here’s an example from the travel industry. Airlines are clear leaders in travel and hospitality loyalty programs. But when weather cancels flights, all bets are off.  What can automation do? By using a virtual agent and a “disrupted traveler recipe,” passengers can receive friendly, accurate and consistent service 24×7. This provides the sensation of communication with a “real” person who can answer users’ questions, not just FAQs.

A passenger can communicate immediately with a rebooking agent from mobile device, asking interactive questions to rebook flights in minutes from any location, and with one-click escalation to customer service agent. The airline saves money with the disrupted traveler recipe, using a cost-efficient omnichannel strategy with an automated virtual assistant at a cost of 25 cents per interaction — vs. $15-$18 for a telephone call with a human being. Automated recipes are kicked off for rebooking based on traveler profiles.

Visit DXC at Dreamforce to see how we’re leveraging the power of Einstein AI together with a full spectrum of automation technologies and methodologies to better know and engage customers.


Lisa Lovas is a product manager in DXC’s Business Process Services, Customer Experience group, with a focus on leading CX digital transformation and automation. She has held corporate officer and CX leadership positions at Aon, IBM and Spherion, and developed award-winning CX products such as REPeValuator™. Connect with Lisa.

 

 

RELATED LINKS:

Personalization at global scale

Client conversations – is anyone listening?

 

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