Predictive Analytics – The Next Big Thing in Customer Service

“It's not like the customer is picking up the phone and the customer service representative has no idea why the customer is calling anymore,” says Brandon Purcell, VP and Principal Analyst at Forrester. 

Thanks to AI and GenAI, predictive analytics is making inroads in transforming the overall customer experience (CX). Enterprises can now leverage AI-led predictive analytics to inform and engage the customer in a series of contextually relevant moments across every touchpoint. 

What is a contextually relevant moment, you ask? 

It is when a customer receives information that is relevant to them at the right time on their preferred touchpoint. 

Brandon breaks it down further by explaining the concept of “The Anatomy of a Moment.” 

Brands must ask themselves a few questions before interacting with the customer to ensure the interaction is effective and the overall customer experience, unforgettable.   

Let’s understand this concept a little better:  

Why is predictive analytics so important for customer service today?

Predictive analytics is a game-changer for customer service. It helps your brand to anticipate customer needs, personalize interactions, and boost satisfaction.  

By using data-driven insights, your brand can proactively address issues and choose the best timing and channels for engagement. Plus, 45% of high-performing companies are already leveraging predictive analytics and GenAI to stay ahead of the competition.  

There’s no doubt that it's a must-have tool to enhance customer experience and drive loyalty. 

How does predictive analytics help in delivering contextual CX?

Predictive analytics helps a customer service representative orchestrate the interaction to truly benefit the customer. 

Understanding and addressing the four aspects of a contextually relevant moment equips your contact center agent to deliver personalized and meaningful experiences to your customers. 

Now, let’s understand what each aspect of a contextually relevant moment entails: 

💡Hot tip: Use AI to segment your data based on behavior, preferences or demographics. Gain deeper insights into who your customers and what they need.

💡Hot tip: Lean on AI-based smart prompts to choose the most appropriate responses tailored to your customers’ needs. AI can analyze customer sentiment and intent in real-time, helping your agents choose the most suitable responses with the right words and tone.

💡Hot tip: Once you know which channel to reach your customer, ensure you carry the context of the conversation across every channel. Allow customers to drop off and pick up from any channel they choose.

💡Hot tip: Proactively resolve customer issues before they reach out. Use AI to identify customer patterns based on contact history.

Once you have addressed the four aspects, the next step is to measure how effectively your contact center is delivering these contextually relevant moments to the customers.

One key metric to measure this success is – Customer Lifetime Value. It indicates if the customer is going to stay and what value their loyalty will bring to your business.

Conclusion

As seen above, customer lifetime value (CLV) is a key indicator of the success of your predictive analytics endeavors. By improving CLV, your contact center can demonstrate the value of efficient customer service to the broader organization, impacting both the top and bottom lines favorably.

Predictive analytics isn’t just another buzzword; it can become a game-changer for customer service. By answering the who, what, when and where of customer interactions, your contact centers can deliver effective customer service, improve the overall customer experience and drive business growth.

Know what more you can do with AI and data in Brandon’s latest video.