Contact Center Pipeline July 2026 | Page 17

As examples, predictive systems can:

• Forecast spikes in call volume and adjust staffing in advance.

• Identify customers who are likely to churn based on interaction patterns.

• Alert agents when customers may need support before they contact the company.

According to McKinsey( cited by Worxpertise), organizations adopting predictive customer support have reported efficiency improvements of up to 30 % and a reduction in average wait times of 20 %.

Further, research studies by Deloitte( also cited by Worxpertise) found 88 % of contact centers leveraging predictive analytics report significant improvements in customer satisfaction.

The goal is simple: resolve issues before the customer experiences friction.

AI AGENTS: WHERE AUTOMATION WORKS BEST

As predictive insights grow, so does the role of automation.

AI agents, sometimes called virtual agents or conversational bots, are now capable of handling many high-volume customer interactions. These systems can answer common questions, provide order updates, troubleshoot basic issues, and route customers to the right human agents.

But the most effective deployments treat AI as a complement to human service rather than a replacement.

AI works best when it:

• Handles routine requests.

• Provides 24 / 7 availability for simple support.

• Routes customers to the right resource.

• Assists human agents with real-time insights.

Many organizations are also deploying AI agent-assist tools, which analyze conversations in real time and provide agents with knowledge suggestions, summaries, and next-best actions. The results? Faster resolution and reduced cognitive load for agents: without removing the human element from complex interactions.

THE TRUST CHALLENGE IN PROACTIVE OUTREACH

While proactive service has clear benefits, it faces a new obstacle: customer trust. With fraud and scam attempts rising globally, many consumers hesitate to answer calls, texts, or emails, even when they come from legitimate companies.

A proactive outreach strategy must address this concern directly. Organizations are increasingly using tools such as:

• Branded caller identification

• Verified SMS messaging

• Secure in-app notifications

• Authenticated customer portals

Just as important, companies must respect communication preferences and clearly explain why they’ re reaching out.

Proactive service works best when customers feel confident the message is legitimate and valuable.

STRATEGY

Proactive customer engagement looks different depending on who you ' re serving. Here ' s how the strategy shifts between business-to-business( B2B) and business-to-consumer( B2C) environments:

Relationship depth

• B2C organizations manage millions of individual customers, making AI-driven segmentation and automation essential to scale proactive touchpoints.

• B2B relationships involve fewer, higher-value accounts with named contacts and dedicated support teams. Proactive outreach is more personalized and account-specific.

Predictive signals

• In B2C, behavioral data— purchase history, app activity, support volume— drives predictive models.

• In B2B, signals are more relationship-based: contract milestones, usage patterns, renewal timelines, and stakeholder engagement levels all inform when proactive outreach is warranted.

Communication channels

• B2C customers are typically reached through SMS, email, app notifications, and automated voice.

• B2B outreach relies more heavily on direct account manager contact, email, and scheduled check-ins through CRM-triggered workflows.

Stakes and timing

• A B2C churn event may involve one customer.

• A B2B churn event can mean losing an entire account worth significant recurring revenue, making early intervention even more critical.

The common thread: in both models, proactive outreach must be relevant, timely, and trusted. The technologies differ, but the principle does not.

BOX JULY 2026 17