As quality CX becomes a key industry battleground, predictive analytics offer a growing competitive edge to businesses.

For decades, businesses have relied on customer feedback to improve and optimize their journeys, but in the digital era, this reactive approach falls short. To stay ahead, companies must be able to predict customer behavior, needs, and issues, to enable more proactive responses.

Enter predictive CX. Leveraging historical data from digital channels to forecast trends, sales, and potential problems, this analytical method helps paint a well-informed picture of industry trends. With recent advancements in technology and journey optimizations, predictive CX is now more accessible and poised for exciting growth in the coming year.

Why is Predictive CX More Viable Today?

Although predictive CX has been around for some time, businesses have struggled to implement it effectively until more advanced automation technology emerged.

The rise of AI and machine learning (ML) during digital transformations has made it far easier for businesses of any size to embrace predictive CX. Efficiently gathering and organizing customer data, these tools provide CX professionals with accurate models without requiring a team of analysts. Thanks to the growing shift towards omnichannel experiences, the interconnectivity between channels help reduce the amount of data silos, giving CX teams and AI tools an improved comprehensive view of customer behavior and feedback.

How Will it Evolve in 2024?

Current predictive CX can provide valuable insights to businesses that invest in it, but the process has the potential to enhance operations even more in the coming months. Here are three trends that are set to make predictive CX even more valuable in 2024:

1. Generative AI

An evolution of traditional AI and ML tools, generative AI has already shown massive potential in enhancing predictive CX, while also taking the provided insights one step further.

As an advanced self-learning technology, generative AI can offer more in-depth predictive models for businesses to act upon—and often faster than traditional ML tools—while also increasing the overall accessibility of predictive CX by providing recommended next steps to CX teams.

However, the most exciting aspect of generative AI integration comes with its ability to create brand-new content. For example, if a predictive model notifies a business of a possible issue with a product, CX leaders can use generative AI to quickly generate an article or FAQ section addressing this problem in a matter of seconds for customers to use. As the technology evolves, this could result in even more personalized content that customers can use to ensure a smooth journey.

2. Real-Time Insights

Precision is key to ensuring that predictive CX is accurate. To make this precision possible, businesses have leveraged digital transformation and innovative technology to glean real-time insights from their customer interactions.

This means that predictive CX models can analyze much more relevant and timely data to provide businesses with a more accurate idea of the incoming needs of their customers.

Understanding these needs will help brands discover and solve potential issues in the customer journey faster than if they relied on historical data analysis and also deliver greater levels of personalization that will be much more relevant to customers. Generative AI tools will also thrive from real-time data, utilizing the info to improve upon the synthetic data it creates, as well as offering more insight into emerging customer trends via the predictive models it produces that can help businesses continue to deliver quality CX ahead of time.

3. Synthetic Data

Data is the blood that keeps predictive CX pumping, but for some industries, training effective models can be challenging. To address this, many companies use synthetic data—digitally generated data based on real-world sets—and with generative AI, this approach can enhance predictive CX regardless of data quality.

By training generative AI on real-world data, organizations can produce high-quality synthetic data in minutes to leverage in their predictive models. The self-learning nature of this technology often yields data that will soon rival real-world data, with nearly 60% of businesses reporting improved efficiency and accuracy in their models after adopting it.¹

In industries like pharma, where privacy regulations are strict, the quick generation of synthetic data based off existing patient data enables brands to simulate customer outcomes and adverse events faster than ever for upcoming products. Even companies with quality real-world data can benefit from synthetic data, using it to test upcoming customer journey modifications like generative AI assistants to gain an idea of any potential changes in customer satisfaction.

Turn Your Insights into Action

Using customer data to plan out your company’s future is something many organizations are striving for, but it can be hard if your journeys are not optimized to yield the data first.

At eClerx, our Analytics team have made it their mission to deliver pertinent information on customer satisfaction, leveraging our domain experience and award-winning AI technology to help our clients better understand their business outlook.

Learn more about how our products and services can help you achieve your business goals.

Reference

1. Gartner Peer CommunityTM. “Generative AI for Synthetic Data.” Gartner, 19 June 2023.