Artificial intelligence (AI) and machine learning (ML) continue to fundamentally change the way companies manage the customer experience (CX). Reacting to customer questions or issues only after they arise has been the norm for years, but now the tables are turning, thanks to AI and ML. Now, instead of waiting for customers to ask for a new product or schedule needed services such as maintenance, companies can proactively use AI and ML technologies to engage customers at just the right moment. The result is more efficient, personalized customer interactions, which leads to more satisfied and loyal customers. 

Indeed, AI and ML are marking a new beginning in the way companies deliver the customer experience today. And that’s good news for businesses that have struggled for years—and invested millions of dollars in solutions—to keep customers happy. A recent McKinsey & Company article explores the topic further, including how new data analytics technologies are ushering in the future of customer experience.

The McKinsey & Company article looks at customer satisfaction surveys and how companies measure and take action on the results. In short, survey-based customer satisfaction measurements are failing to meet companies’ CX needs. In fact, in a survey of 260 CX leaders from US-based companies, McKinsey found that “Ninety-three percent of respondents reported using a survey-based metric (such as Customer Satisfaction Score or Customer Effort Score) as their primary means of measuring CX performance, but only 15 percent of leaders said they were fully satisfied with how their company was measuring CX—and only six percent expressed confidence that their measurement system enables both strategic and tactical decision making.”

So, how are today’s more innovative companies doing things differently? The answer, in a word, is simple: data. As customer data proliferates from every direction, including smartphone and website interaction data, financial data, and operational data, forward-thinking companies are using it to deliver improved experiences and more efficient operations, from the contact center to the back office. According to the article, “Those with an eye toward the future are boosting their data and analytics capabilities and harnessing predictive insights to connect more closely with their customers, anticipate behaviors, and identify CX issues and opportunities in real time.”

The McKinsey & Company article also highlights four steps that leaders can take to jumpstart their CX transformations, which we’ve outlined for you below:

​​1. Work on Changing Mindsets

Success requires that business and technology teams work together to realize the impact that AI, ML, and predictive analytics can have on the customer experience and the overall business. For CX leaders, this requires repositioning themselves around sound communication skills and the ability to generate “buy-in and excitement” for data analytics initiatives with key stakeholders from across the company.

2. Break Down Silos and Build Cross-Functional Teams

The article cautions CX leaders not to create their own silos within the company. Predictive CX initiatives require access and use of data that may reside across the company and even with external sources. As a result, CX leaders need to build strong relationships with senior managers and those areas where the required CX data sources are located.

3. Start With a Core Journey Data Set and Build to Improve Accuracy

Data quality and availability challenges are inevitable for virtually every company. To overcome this, CX leaders should initially focus on using basic customer-level data, including operations and financial data. These sources, combined with customer interaction data, are “usually a solid jumping-off point,” according to the article.

4. Focus First on the Use Cases That Can Drive Quick Value

When getting started, CX leaders should focus on a limited number of use cases that can quickly show tangible business value. Leaders can achieve this by implementing a simple framework that helps the organization review opportunities as well as pain points and their proposed solutions. By connecting your efforts to value from the start, you can overcome internal challenges and position those efforts for long-term business impact.

You can find the complete McKinsey & Company article here. If you’d like to learn more about AI, ML, predictive analytics, and how these technologies are impacting specific industries, see these recent APEX of Innovation posts on banking, energy, healthcare, farming, and public services.