12 Amazing Things You Can Now Do With Customer Preferences

For years, customer preferences were treated as static – captured through surveys or stored in CRM fields – and, whilst many contact centres still operate in this way, the latest advancements have raised the bar for what can be done in this space.

That’s why we asked our technology experts for the lowdown on how the most innovative contact centres are utilizing these insights right now. Ready to be inspired?

1. Measure Effort Signals Across Key Touchpoints

Do customers really have channel preferences? Or do they have effort preferences? Someone might “prefer” email, but if they switch to phone when the problem is really urgent, that’s telling you something more interesting than simple channel preference.

They’re telling you exactly where they stop caring about convenience and focus only on getting that resolution. This is their “effort threshold”.

With conversation intelligence, we can now measure effort signals across touchpoints (repeat contacts, escalations, channel-switching, resolution times, etc.), so we can map where customers go when they really want something fixed ASAP.

And understanding where the effort threshold varies lets you redesign customer journeys to match it. For example, if customers are jumping to the phone when a chat takes more than three exchanges, maybe you could limit chat interactions and send complex queries straight to your voice channels.

Contributed by: Derek Corcoran, CEO, Scorebuddy

2. Predict “Optimal Contact Windows” at Different Times of the Day

Modern AI allows us to move beyond stated preferences (e.g., “I prefer email”) to inferred preferences.

By instantly analysing the entire CRM and interaction history, it can provide agents with a “Golden Thread”, revealing not just what the customer bought, but their unresolved frustrations and unspoken needs.

This enables the “wow factor” of anticipatory service, such as hyper-personalized forecasts – so, instead of staffing based on generic historical averages, AI can predict “preferred channel spikes” and “optimal contact windows” that customers prefer to use at different times of the day.

Also, intelligent call routing, where AI can match customers based on personality, interests, or current mood with agents having similar interests, or technical proficiency in that specific context, or even a specific agent that customers prefer to speak to.

Contributed by: Ray Agar, WFM Expert, Peopleware

3. Determine Where to Personalize and Where to Fix

When one AI analyses data across all customer touchpoints simultaneously, it surfaces themes that determine where to personalize and where to fix.

As one of our customers experienced when their agents scored high on a recurring policy query, screen recordings showed them navigating to the same knowledge base article on every call, and VOC data confirmed high frustration upstream.

The source was one ambiguous sentence on their website. They updated it and contact volume on that topic dropped materially within weeks.

Removing friction before it generates a call is personalization at scale! The same data, applied at the individual level, briefs agents on a customer’s specific interaction history before the call begins.

Cross-channel interaction data reveals the conditions that produced the experience, and those conditions are the raw material for personalization that holds up across the full customer base.

Contributed by: Ashish Nagar, CEO, Level AI

4. Tailor Interactions Based on Factors Such as First Language

Contact centres are both contributing to and acting on granular customer preferences with greater precision than ever before, tailoring interactions based on factors such as first language, channel choice, and optimal time of day, and even other behavioural elements, such as whether an individual prefers self-service or human contact, written or spoken, and in which circumstances.

For example, some younger customers default to written digital interactions, with a second choice of voice communication with machines – rather than voice conversations with human agents.

A customer data platform (CDP) can help to accurately capture and guide these hyper-personalized interactions. This is because – unlike a traditional CRM – a CDP is a living system of action: unifying multiple systems of record into a single, real-time environment.

CDPs handle large unstructured field items such as AI-powered transcription and summarization output – vast quantities of raw data from which insight is derived.

Crucially, not all this data belongs in core systems of record, such as CRMs or electronic health records (EHRs).

A CDP can unify and interpret the data in a CX context and then dynamically update the appropriate CX process or system of record, ensuring every interaction reflects evolving customer preferences. 

Contributed by: Martin Taylor, Co-Founder and Deputy CEO, Content Guru

5. Surface Signals Fast Enough to Be Useful During the Conversation

Most preference data ends up in routing logic: which channel, which queue, which tier of support. That’s a start – but it leaves most of the value on the table.

The bigger opportunity is using preference insight to change how the interaction itself is handled. The tone. The level of guidance. Whether a customer needs efficiency or reassurance.

AI makes it possible to surface those signals fast enough to be useful before or during the conversation, not just in a post-call report. Quite simply, routing gets a customer to the right place, but insight gets them the right conversation.

Contributed by: Chris Mounce, Product Training & Enablement Specialist, evaluagent

6. Resolve Issues Before a Customer Even Reaches Out

Modern CCaaS platforms have transitioned from simple interaction hubs to real-time orchestration engines.

By leveraging AI and unified data layers, organizations can now move beyond static profiles to capture and act upon dynamic customer preferences at scale.

Predictive proactivity, for example, whereby CX AI anticipates the next best action.

When matching historical data with real-time intent, brands can provide hyper-relevant recommendations or proactively resolve issues before a customer even reaches out, fostering deeper trust and long-term loyalty.

Contributed by: Matthew Clare, VP, Product Marketing, UJET

7. Identify How Someone May Want to Get in Touch and When a Human Needs to Step In

Not long ago, “knowing your customer” meant remembering their name at the top of a call. Today, it can mean much more – if you let it.

With analytics tools connected across the customer journey from front to back office, preference stops being a form field and becomes a live signal.

It can identify how someone may want to get in touch, which channel they might abandon, and when a human needs to step in.

That context travels with them from chat to voice to video – so no one needs to repeat their account number for the third time. And when a hand-off to a live agent is needed, the full history and context goes too.

Connected CX powered by AI means every interaction informs the next – creating alignment, not fragmentation, and turning insight into coordinated action across the organization. 

Contributed by: Ben Neo, Head of Zoom Contact Centre and CX Sales, EMEA , Zoom

8. Capture Preferences in Conversations – Rather Than in Tedious Forms

Many contact centres are pointing AI at the customer, to predict what they want next. But the bigger opportunity to attach your customers is in augmenting the agent with AI, giving every agent the memory of a great human.

Capture preferences in conversation, rather than in tedious and inadequate forms. Support engagements by briefing the agent before they answer, not after.

Accumulate customer understanding by ensuring every interaction adds to the record, rather than starting each time from scratch.

Personalization isn’t going to get more impressive than that, and it’s also exactly what your customers care about!

Contributed by: Steve Nattress, VP Product, Enghouse

9. Deliver Proactive Engagement Tailored to Individual Needs

Customer expectations are shifting faster than ever, making personalized, channel-flexible interactions the new norm.

AI in CCaaS platforms allows contact centres to respond in real time, with intelligent routing, intent prediction, and proactive engagement tailored to individual needs.

The key is aligning AI with actual customer preferences rather than adopting technology for its own sake. By using data driven insights, organizations can deliver meaningful personalization at scale.

Agentic capabilities can leverage and act on a range of stored data, while feedback capture and AI-based analytics provide a continuous improvement loop, helping identify both common trends and individual customer preferences.

This enables businesses to reduce friction, improve satisfaction, and enhance the human experience rather than replace it.

Contributed by: Viren Patel, Technical Solution Consultant, Route 101

10. Understand Common Preference Behaviours Across Customer Segments

Beyond individual preferences, organizations should analyse aggregated data to identify broader patterns and trends.

This is because understanding common preference behaviours across customer segments allows businesses to proactively design experiences that better align with expectations.

For example, patterns may reveal preferred communication channels, common friction points, or expectations around response times. These insights can then inform decisions related to staffing, channel strategy, communication styles, and overall service design.

By shifting from reactive personalization to proactive experience design, organizations can create journeys that feel more intuitive and efficient from the start.

Instead of adjusting one interaction at a time, they can build systems and processes that consistently reflect what customers value most, improving both satisfaction and operational performance at scale.

Contributed by: Jonathan Kenu Escobedo, Customer Success Manager, MiaRec

11. Understand Where Each Customer Is in Their Lifecycle

True value comes when preferences inform how, when, and where organizations engage with customers, based not just on channel choice, but on where someone is in their lifecycle and what they are trying to achieve.

When preferences are used with intent and, importantly, applied in real time, they can actively guide better decisions – boosting performance and experience.

For CX leaders, it becomes a shift from tactical use of preferences to preference management as a capability. One that enables more relevant engagement, reduces friction, and supports better outcomes at scale.

Contributed by: Lewis Gallagher, Senior Solutions Consultant, Netcall

12. Personalize Every Step of the Customer Journey as It Happens

Using interaction history, behavioural signals, and real-time AI insights, businesses can personalize every step of the customer journey as it happens.

Most contact centres already hold this data – and many have strong AI tools generating it. The problem is it’s not being surfaced at the right moment. That has a direct cost: 55% of customers are frustrated when agents don’t know their history, and nearly three quarters (73%) have switched channels mid-conversation, expecting each agent to pick up where the last one left off.

The fix isn’t replacing the tools you have – it’s connecting them. When your CRM, AI models, and agent desktop share a unified view of the customer, personalization stops being a project and becomes the default.

Contributed by: Rodney Hassard, Head of Product, Applications, Vonage

What Exciting Things Are You Doing With Customer Preferences in Your Contact Centre?

Click here to join our Readers Panel to share your experiences and feature in future Call Centre Helper articles.

For more great insights and advice from our panel of experts, read these articles next:

• How to Use AI to Connect the Dots – Not Create More Silos
• Get Your AI Pilot Off to the Best Possible Start
• Upgrade How You Listen to Customer Feedback

Author: Megan Jones
Reviewed by: Xander Freeman