With quality CX becoming the latest battleground for many industries, predictive analytics have become an increasingly viable way to gain a competitive edge, and one that is primed to evolve even more in the near future.
For decades, businesses have analyzed customer feedback to properly gauge and optimize their journeys, but today, that is no longer enough to stand out from the competition. The digital era has raised the bar for CX, and modifying the customer journey after a problem occurs is often too little, too late. With new technological innovations and customer analytics practices, businesses need to turn their attention to predicting customer behavior, needs, and issues to act proactively instead of reactively.
Enter predictive CX. Given the vast information that digital channels provide, brands can utilize both historical and current data to paint a well-informed picture of what trends are forming in the near future—from forecasting sales numbers to discovering product issues before they occur. And thanks to emergent technology and customer journey optimizations made over the years, predictive CX has become more accessible, as it continues to evolve in intriguing ways over the next year.
Why is Predictive CX More Viable Today?
Predictive CX has existed for quite some time. However, despite the increase in customer data, many businesses struggled to make this practice a reality until the introduction of smarter technology and customer journey enhancements.
The continued adoption of AI and machine learning (ML) technology during digital transformations has made it far easier for businesses of any size to start looking into predictive analytics. Not only do these automated tools help gather and organize customer data far more efficiently, but these solutions can also be deployed to give CX professionals detailed and accurate models, all without the need for a team of analysts to make sense of the information.
While ML technology has helped gain more relevant insights from customer behavior, it also wouldn’t have taken off without the growing shift towards omnichannel experiences. Previously, most channels largely operated independently from one another, but the addition of new self-service digital channels made many industries realize that this practice was no longer sustainable. With an omnichannel approach, the issue of data siloing is far less common due to the interconnectivity provided, allowing CX teams, analysts, and AI tools to all get a much better 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, it is not always easy to train—or even have the ability to run—a predictive model and get desired results. To get around this, many companies have introduced synthetic data—digitally generated data based on real-world sets—into their predictive models, and thanks to generative AI, this type of data has the potential to elevate predictive CX for businesses regardless of their data quality.
Utilizing real-world data sets, organizations can now train generative AI to create realistic synthetic data in a matter of minutes for use in predictive CX models. This generative AI created synthetic data is also much higher in quality due to the self-learning nature of these tools, possibly rivaling real-world data, with nearly 60% of businesses reporting accuracy and efficiency improvements in their predictive models since introducing it into their workflow.
In an industry like pharma that is heavily impacted by privacy regulations, the quick generation of synthetic data like patient records allows these businesses to fully utilize their predictive models to simulate customer results and possible adverse events for their upcoming products. This means that companies can spend less time in testing phases and deliver high-quality products to market before their competition. Even organizations that have quality real-world data on hand can utilize synthetic data to create robust and targeted points for their predictive models, with CX leaders being able to test out customer journey modifications like generative AI assistants before fully implementing them to gain an idea of customer feedback and CSAT changes.
Turn Your Insights into Action
Using customer data to plan out your company’s future is something many organizations are striving to accomplish, but it can be hard if your journeys are not optimized to yield the data and information first.
At eClerx, our Analytics team has made it their mission to deliver pertinent information on customer satisfaction, agent performance, and more to help our clients better understand their business outlook. Additionally, our suite of proprietary AI and automation tools allow brands to access and act upon their data quicker to fully take advantage of predictive CX within their organization.
Click below to get in touch with us and learn more about how our products and services can help you achieve your business goals.