Customer experience initiatives are getting stuck with quite a few weighty labels lately: next key battleground, primary competitive differentiator, table stakes. And momentum for this topic is not slowing anytime soon. 

My ongoing personal survey of CMOs and industry analysts continues to land customer experience within the top three marketing and digital transformation priorities this year, as it did last year and the year before that. So it's no wonder companies are looking for every advantage to make their customer experience processes “smarter.” 

Enter machine learning (ML) and cognitive computing. Several years ago, IDC posited that by 2018 half of all consumers would interact with services based on cognitive computing on a regular basis — and we are well on the way to validating that prediction. ShortStack projected that machine learning and the associated automation technologies would get significant attention from marketers this year with an estimated 300 percent increase over last year in marketing spend and this is holding true as well.

Machine Learning in Customer Experience: Put Customers F.I.R.S.T.

ML really can make a difference to CX initiatives. My favorite acronym for ML in CX is the combination puts the customer “FIRST.” First, meaning ensuring service processes and customer experiences are Fast, Intelligent, Relevant, Satisfactory and Trouble-free for the customers that use them. What follows are some examples of how CX leaders are using ML to achieve these objectives:

Real-time intervention in negative experiences

Machine learning can identify negative experiences as they happen (too much time, too many screens, frustration in tone of voice or typed language, multiple call-backs within a certain time period, repeated access to help applications, etc.). Early detection allows companies to inject real people into automated service processes to mitigate bad situations or help resolve complex problems, which in turn facilitates interactions that are both satisfactory and trouble-free.

Intelligent routing

Intelligent routing can modify menu options in an interactive voice response (IVR) or web/mobile app and route calls to the right representative based on the context of the interaction. ML helps by inferring what the customer is trying to do and presenting options relevant to that intention first. If contact centers can apply intelligence in their routing processes, it will decrease resolution time, reduce call time and increase one-call resolution — making contacts fast and satisfactory.

Automation of repetitive tasks and FAQ responses

With natural language processing, chatbots can better interpret meanings and intent. Combining these capabilities with an understanding of historical communications and purchases will speed time to resolution. Using ML with chatbots makes the interactions more like human contacts, which improves satisfaction for the customer and frees up costly call center representatives to deal with more complex problems.

Contact optimization 

Using ML-type analytics to combine historical purchases and behaviors with real-time information about events and digital channel interactions makes it possible to deliver product offers and other communications. This represents the ultimate in personalization and provides intelligent and relevant communications to customers.

Learning Opportunities

Content optimization

Machine learning shines when managing large volumes of structured and unstructured content, determining which content will be most relevant to present based on historical behaviors of like-minded searchers, and offlaying labor-intensive content management processes. In a business-to-business environment where prospects can transverse up to 68 percent of the buy cycle before they ever consult a sales representative, ML can be invaluable in keeping them in the sales funnel. This helps companies present content to customers that is timely, relevant and easy to navigate.

What’s Next for Machine Learning and CX?

We believe CX leaders will increasingly turn to emerging data sources such as beacon and sensor data (IoT), video, location and weather data in combination with more sophisticated analytics (artificial intelligence) and technologies such as robotics to fundamentally change the way we go about our daily lives. 

We are starting to see some of these innovations today, in the form of cars that park themselves, printers that order ink automatically, wearable medical devices that alert doctors to health issues and smart stores where shoppers can use kiosks to virtually try-on clothing and accessories.

Things to come include:

  • Simplified shopping experiences: Stores without cashiers eliminating the check-out line waits and speeding up the shopping experience. Shoppers simply select their items, bag them and leave the store. Payment is automatically withdrawn from pre-selected accounts.
  • Connected customer: Extending this concept from appliances and digital devices to repetitively purchased consumer goods such paper products. Sensors embedded in the packaging let the manufacturer know when supplies are running low and replacements are automatically shipped out and delivered to the consumer’s home.
  • Personalized billboard messages: Customizing displays shown to individual consumers. Sensors will be connected with the billboards that can read the license plate, determine the make, model, and age of passing cars, as well as understanding weather and traffic conditions. Billboard messaging will be customized accordingly, targeting messages to the type of consumers in passing vehicles. For example, adult males of a certain demographic might see an advertisement for a high-end car while young mothers could see baby products or pre-school displays.

One thing is clear — the triumvirate of increasing computing power, decreasing storage costs and exploding data volumes guarantees that CX leaders will continue to innovate when it comes to delivering superior experiences and enhancing products and services.

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