Close the Intent Divide With AI (Without Spamming)

A hard number from Cordial’s recent “Intent Divide” research should make any customer ops leader uncomfortable: 100% of marketers still rely on basic behavioral signals to infer intent, yet only 34% of consumers feel brands understand their needs. That gap isn’t a “marketing problem.” It shows up where it hurts most: your contact center queues, your return rates, your delivery exceptions, and your end-of-year service costs.

In December—right when holiday volume peaks and “Where’s my order?” tickets surge—misreading intent becomes expensive fast. If a customer is browsing replacement filters for an industrial compressor, are they planning a routine reorder, troubleshooting a failure, or pricing alternatives because the last shipment was late? A generic promo email won’t help. A contact center agent without context won’t help either.

Cordial’s newly announced capabilities—Customer Insights Dashboard, Advanced RCS Personalization, and First-Visit Personalization—are framed as marketing tools. I think the bigger story is this: they’re a practical blueprint for AI in customer service and contact centers, especially for companies running complex operations in AI in supply chain & procurement. When intent is understood earlier, service becomes calmer, faster, and cheaper.

The “intent divide” is really a service cost problem

Intent is the “why” behind a customer action. Most organizations still treat clicks, opens, and page views as the why. That’s how you end up with service experiences that feel tone-deaf.

Here’s what intent misread looks like operationally:

• A customer visits the returns policy page. You assume “buyer remorse,” but the real intent is delivery failure.
• A distributor downloads a spec sheet. You assume “purchase interest,” but their intent is compliance documentation for procurement approval.
• A shopper views the same product three times. You assume “high consideration,” but the intent is checking if inventory is back.

When intent isn’t captured, customers end up explaining themselves repeatedly across channels. That pushes:

• Average handle time (AHT) up
• First contact resolution (FCR) down
• Escalations and supervisor interventions up
• Refunds, reships, and goodwill credits up

And for supply chain-heavy businesses, it also impacts:

• Demand signals (you mistake “problem solving” for “demand”)
• Procurement planning (you mis-time replenishment because you missed true reorder intent)
• Risk (you miss early warning signals that vendors or carriers are failing)

Cordial’s release matters because it’s a clear attempt to operationalize intent: measure it, message on it, and personalize the experience before the customer ever contacts support.

Customer Insights Dashboard: segment shifts you can actually act on

Cordial’s Customer Insights Dashboard focuses on how customers move between states like Prospects, Active Customers, and Inactive Customers over time. That may sound like a familiar lifecycle view, but the operational value is in the movement, not the labels.

Where contact centers should copy this approach

If you’re running a service organization, you already segment customers—usually by tier, SLA, or contract size. What’s often missing is a living view of behavioral drift:

• Active customers quietly becoming “inactive” after repeated delivery issues
• Prospects showing high purchase research intent but abandoning at checkout due to shipping costs
• Loyal customers shifting to “replacement intent” because product quality slipped

If I’m advising a team, I push for a dashboard that answers these service-first questions:

1. Which customers are most likely to contact us in the next 7 days? (prevention)
2. Which topics are rising week over week? (root cause)
3. Which segments are turning from buyers into complainers? (retention risk)

Marketing lifecycle insights become service fuel when they’re shared with the contact center in a usable way: a short intent summary in the agent desktop, a reason code suggestion, or a proactive message that removes the need to call.

Subject lines are a small feature with a big operational lesson

Cordial also mentions an AI Subject Line Generator that learns brand tone and performance patterns to produce five options.

The contact center equivalent isn’t “subject lines,” it’s agent talk tracks and suggested next steps. The winning pattern is the same:

• Learn from what has worked historically (resolution outcomes, CSAT, containment)
• Match tone to brand and customer state (calm and direct during incidents; consultative during evaluation)
• Provide multiple options (not one “magic” script)

If your knowledge base and QA program can’t tell you what language reduces repeat contacts, you’re flying blind.

Advanced RCS Personalization: messaging that prevents tickets

Cordial’s Advanced RCS Personalization expands rich messaging experiences using product data, account history, and browsing signals, including reply-based follow-ups.

Even if you don’t care about RCS specifically, the strategic point is strong: rich, contextual messaging can remove the reason to contact support.

A practical contact-center use case (especially in peak season)

Consider a late shipment scenario in December:

• Traditional flow: customer notices delay → calls or chats → waits → agent checks status → customer still annoyed.
• Intent-led flow: system detects “shipping status” behavior + carrier delay in region → sends proactive message with updated ETA + options.

Those options are where rich messaging wins:

• “Confirm delivery window”
• “Hold at pickup location”
• “Replace items that are out of stock”
• “Switch to expedited shipping (fee waived)”

When customers can self-select the right path, you reduce:

• inbound volume
• recontacts (“I still don’t know when it’s arriving”)
• escalations

Why this ties directly to AI in supply chain & procurement

In supply chain, the difference between “informational” and “actionable” is huge. RCS-style experiences are actionable:

• Customers choose alternatives when inventory is constrained (demand shaping)
• Buyers confirm substitutions for backordered SKUs (procurement flexibility)
• Accounts select preferred replenishment cadence (forecast stability)

That’s not just better CX. It’s operational control.

First-Visit Personalization: reducing friction before identity is known

Cordial’s First-Visit Personalization tailors onsite experiences for visitors who aren’t identified yet, updating recommendations in real time based on browsing behavior.

This matters because a huge share of service and conversion friction happens before a customer logs in.

The best “first-visit” service win is deflection that doesn’t feel like deflection

Most companies get deflection wrong by forcing chatbots on people the second they land on a page. It’s annoying and it trains customers to avoid self-service.

A better approach:

• Detect intent from behavior (pages, scroll depth, repeated searches, comparison patterns)
• Adjust the site experience to answer the likely question
• Offer help only when it’s genuinely helpful

Examples that work:

• If behavior suggests compatibility checking (common in parts, industrial, and electronics), show a guided selector and compatibility confirmation.
• If behavior suggests shipping anxiety (shipping page visits, cart hesitation), show real delivery cutoffs, carrier reliability by ZIP, and “order by” times.
• If behavior suggests procurement approval intent (spec sheets, compliance docs), surface certifications, vendor onboarding docs, and contract terms.

This is exactly where AI in customer service and AI in procurement intersect: you’re reducing time-to-approval and preventing inbound “Can you send me the doc?” emails.

How to implement “intent-to-action” in a contact center (a simple model)

Tools alone don’t close the intent divide. The operating model does. Here’s a straightforward way to run it.

1) Define intent categories you’ll operationalize

Start with 8–12 intents that cover most volume and cost. In a supply chain-heavy environment, common high-impact intents include:

• Order status / delivery ETA
• Address change / delivery instructions
• Returns / exchanges
• Damaged or missing items
• Backorder and substitution approval
• Product compatibility and fit
• Invoice, PO, and payment questions
• Warranty and replacement

The key is to choose intents where you can do something: automate, route, or proactively message.

2) Build an “intent signal stack,” not a single score

Relying on one signal is why so many teams get this wrong. Use a stack:

• Behavioral: pages visited, search terms, repeat views
• Transactional: order age, shipment scans, inventory status
• Contextual: region weather disruptions, carrier exceptions, holidays
• Conversational: chatbot/IVR utterances, email text classification

If you’re only using page views, you’re stuck in 2015.

3) Connect intent to actions across channels

For each intent, decide the first-best action:

• Proactive message (SMS/RCS/email/push)
• Onsite personalization (banner, widget, guided flow)
• Self-service workflow (change address, reschedule delivery)
• Smart routing (send to the right queue with context)
• Agent assist (suggested resolution + next best step)

This is where Cordial’s approach is instructive: insight → message → experience.

4) Measure outcomes that finance will care about

Don’t overcomplicate it. Track:

• Containment rate (resolved without agent)
• FCR and repeat contact rate
• AHT for top intents
• Cost per contact by intent
• Refund/reship rate for delivery and damage intents

If intent understanding isn’t reducing cost or improving resolution, it’s just analytics theater.

People also ask: intent AI questions contact centers should answer

Can AI detect customer intent accurately without cookies or logins?

Yes—to a point. First-visit behavior, page sequences, search terms, and real-time context can identify probable intent. The right goal isn’t perfect prediction; it’s better routing and better self-service.

Does personalization increase or decrease trust?

It increases trust when it’s tied to an obvious customer benefit (faster resolution, fewer steps, clearer options). It destroys trust when it feels creepy, irrelevant, or overly salesy.

Where should you start: messaging, dashboards, or onsite personalization?

Start where your cost is highest. If inbound “Where’s my order?” volume spikes every week, prioritize proactive messaging and self-serve actions. If conversion drop-offs are driven by procurement paperwork, prioritize first-visit personalization that surfaces documentation.

What Cordial’s announcement signals for 2026 service teams

Cordial is packaging these capabilities for marketers, but the pattern is bigger: the winners in AI-powered customer service will treat intent as a shared asset across marketing, commerce, supply chain, and the contact center.

That’s especially true in AI in supply chain & procurement, where customers don’t just want “personalized offers.” They want certainty: accurate ETAs, clean documentation, and fast exception handling.

If you want to reduce ticket volume without irritating customers, stop thinking about AI as “automation.” Think about it as earlier understanding. When you act on intent before the customer has to ask, service feels human—even when it isn’t.

If you’re planning your 2026 roadmap, here’s the question I’d put on the whiteboard: Which three intents create the most avoidable contacts—and what would we need to detect and resolve them before a human agent gets involved?