The modern customer expects businesses to be responsive and anticipate their needs before they arise. Predictive analytics in customer experience (CX) has the potential to transform how businesses operate by improving proactive engagement, reducing friction, and personalizing interactions at scale.
Companies that leverage predictive analytics are seeing tangible benefits, from improved customer satisfaction to increased efficiency and revenue growth. The growth of the market reflects this also, forecasted to be worth $41.5B USD by 2028.
New research conducted by Forrester Consulting on behalf of Avaya reveals that analytics and automated quality monitoring technologies will drive continuous improvement in customer service with AI. The study findings indicate that participants recognize AI-driven analytics as valuable tools for extracting performance insights and understanding customer sentiment, enabling organizations to make evidence-based adjustments as situations evolve. Additionally, the implementation of automated oversight systems, such as augmented quality monitoring platforms, delivers ongoing evaluation of service quality standards, helping to maintain performance consistency while identifying specific improvement opportunities.
David Funck, Avaya’s Global Vice President and Chief Architect, underscores this shift, “There’s a real opportunity in using analytics to identify the right path for a customer before they even realize they need assistance. This can significantly improve customer satisfaction and contact center efficiency.”
Proactive, Not Reactive
Predictive analytics harnesses customer and employee data, AI, and machine learning to forecast customer behavior and preferences. By understanding patterns in customer interaction data, businesses can get ahead in the following areas.
- Proactively Address Issues—Businesses can anticipate problems and resolve them before they escalate. For example, AI can detect frustration signals in a customer’s journey, in some cases in real-time, and proactively offer support before they reach a breaking point. According to the Forrester Consulting study real-time sentiment analysis (56%) and real-time transcription and multilingual support (59%) highly valuable according to decision-makers.
- Enhance Personalization – In the same Forrester Consulting study, 52% of business decision-makers prioritize AI to increase customer support efficiency, including personalization.
- Optimize Contact Center Efficiency – Predictive analytics also streamlines operations. Contact centers can use analytics to anticipate call volumes, optimize staffing, and route inquiries to the best-equipped agents.
Funck explains, “Predictive analytics isn’t just for the customer’s benefit; it’s also about making the agent’s job easier by ensuring they have the right tools and insights at their fingertips.”
For Funck, engagement levels are also crucial agent happiness and performance measures. He continued, “An agent becoming less engaged can be a predictor of them leaving your business. With workforce engagement, you can get in there and try to intercept that engagement drop off with some human or even automated action. That might be sending them a break or offering them some kind of incentive.”
Enterprise Benefits
Enterprises are constantly prepping for the next ‘what if scenario’ to ensure they can adequately maintain operations amid unexpected events or even known high-traffic occasions. Power companies might prepare for a storm event, or a financial services organization might prepare for a security breach for example. The added context analytics can bring to these situations is invaluable.
Some other key use cases include:
- Financial Services: Fraud detection and anticipated customer inquiries related to banking transactions improve security and trust.
- Retail & E-commerce: AI-driven recommendations increase conversion rates by predicting what customers will buy next.
- Healthcare: Providers use predictive analytics to remind patients about follow-ups, reducing missed appointments and improving care outcomes.
- Telecommunications: Predictive models help providers anticipate service disruptions and proactively communicate with customers before issues escalate.
So, what types of contact centers are investing in predictive analytics?
Funck says, “I think it’s a medium and large enterprise endeavor. I don’t see your 200-agent contact center investing in predictive analytics; the data just isn’t meaningful enough to warrant that type of investment.”
The Future of Predictive Analytics in CX
Predictive analytics continues to improve, becoming more accurate and impactful as AI technology evolves. Businesses will move toward real-time analytics, leveraging data as it is generated to enhance decision-making instantly. Additionally, AI will improve sentiment analysis, allowing companies to gauge customer emotions and respond with increased empathy and precision.
Funck highlights this growing trend: “The ability to process and analyze massive amounts of customer data in real-time is game-changing. It allows businesses to transition from reactive to truly proactive, creating seamless and satisfying customer journeys.”
Here and Now
Predictive analytics is a critical tool for businesses looking to stay ahead in the CX game. Companies that invest in AI-driven analytics can anticipate customer needs, enhance personalization, and optimize operations, leading to stronger customer relationships and improved business outcomes.
As Funck puts it, “The businesses that succeed in CX will be the ones that use data intelligently. Predictive analytics is about making every interaction more meaningful, seamless, and proactive.”
With predictive analytics, businesses are not just responding to customer needs but staying one step ahead.
