Smarter Segments in Amazon Connect Customer Profiles

Most contact centers don’t struggle with data. They struggle with precision.

By December, teams are usually juggling peak volumes, year-end retention plays, and budget planning for 2026—all while trying to personalize service without slowing agents down. And that’s where segmentation quietly becomes the difference between “we have customer data” and “we can act on it.”

AWS just pushed that line forward: Amazon Connect Customer Profiles now includes new segmentation capabilities (Beta) powered by Spark SQL, with an AI assistant that can generate segment logic from natural language prompts. If you’re building AI in customer service & contact centers, this is the kind of feature that turns personalization from a buzzword into an operational system.

Why segmentation is the engine behind modern contact centers

Segmentation is how you operationalize personalization at scale. Without it, your AI chatbot, your routing rules, your outbound campaigns, and your agent guidance all end up treating customers too similarly.

In a contact center, “segment” shouldn’t mean a static marketing list built once a quarter. It should mean living groups that reflect what’s happening right now:

• Customers who bought in the last 7 days and have already contacted support twice
• High-value customers trending toward churn (reduced purchase frequency, rising complaint rate)
• People stuck in a delivery loop who need proactive outreach

When those groups are accurate and timely, you can:

1. Route smarter (VIPs to senior agents, repeat callers to specialized queues)
2. Deflect better (self-service tuned to the customer’s situation)
3. Proactively resolve issues (outbound campaigns before tickets pile up)
4. Measure what matters (segment-level CSAT, containment, handle time)

Here’s the stance I’ll take: If your segmentation can’t be expressed in data logic, it’s not segmentation—it’s a guess. This beta release is interesting because it makes “data logic” both more powerful (Spark SQL) and more accessible (AI-assisted generation).

What AWS launched: Spark SQL segmentation + Segment AI assistant

Amazon Connect Customer Profiles segmentation (Beta) lets you build advanced segments from your complete Customer Profiles dataset using Spark SQL—with optional AI assistance to generate the SQL.

The announcement highlights four practical capabilities that matter in the real world:

Access complete profile data (standard + custom objects)

You can segment on both standard objects and custom objects, which is crucial because most organizations store meaningful signals outside a default schema.

Examples of custom objects that often decide outcomes:

• Subscription status changes (pause/resume/cancel)
• Product registration and warranty events
• Loyalty tier and point burn patterns
• Refund reasons and fraud flags

When segmentation can “see” those objects, your contact center stops acting blind.

Use SQL joins, percentiles, and date standardization

Spark SQL enables joins across objects and advanced filtering, including statistical functions like percentiles.

That unlocks segmentation patterns that teams commonly want but rarely implement well:

• “Top 10% lifetime spenders” (percentile-based) instead of a hard-coded dollar threshold
• “Customers with 3+ contacts in 30 days about a new purchase” (multi-object join + time window)
• “Customers whose last three orders were delayed” (sequence-based logic)

The practical win: you can express business intent as deterministic logic, then test it.

Build segments with natural language prompts (AI assistance)

The Segment AI assistant can generate segment definitions in Spark SQL from natural language prompts.

This isn’t about replacing analysts. It’s about reducing the cycle time between:

• a business stakeholder describing a target group
• an analyst translating it into SQL
• a team validating it
• operations deploying it

If you’ve ever watched a “simple segment request” take two weeks because everyone’s busy and requirements keep shifting, you know why this matters.

The best practice here is straightforward: treat AI-generated SQL as a first draft. It’s a productivity layer, not an approval stamp.

Validate before deployment (estimates + explanations)

You can review the generated SQL, see natural language explanations, and get automatic segment estimates.

That validation step is underrated. In contact centers, bad segments cause real damage:

• Wrong customers get outbound calls
• VIP routing rules misfire
• Agents see irrelevant guidance
• Campaign budgets get burned

A built-in “sanity check” workflow reduces those risks—especially when you’re iterating fast.

Snippet-worthy truth: A segment isn’t useful when it’s clever. It’s useful when it’s correct, explainable, and deployable.

How this fits into AI in customer service & contact centers

AI in contact centers fails when it’s trained or triggered on the wrong context. Segmentation is how you fix the context problem.

In this series, we talk a lot about chatbots, voice assistants, sentiment analysis, and automation. Those tools perform better when they know who they’re dealing with and what’s happening around the interaction.

Segmentation feeds that context into:

• Flows and self-service: Different prompts, authentication paths, or escalation rules
• Agent assist: Different scripts and next-best actions by segment
• Outbound campaigns: Targeted outreach based on recent contact history or spend
• Customer experience personalization: Tailored offers, apologies, and service recovery

And from the campaign angle—AI in cloud computing & data centers—this is also about making heavy customer data workloads manageable:

• Spark-based segmentation centralizes logic instead of scattering it across tools
• SQL-based definitions are auditable and portable across teams
• AI assistance reduces repeated manual effort, which reduces operational drag

Practical segments worth building first (with examples)

Start with segments that reduce cost or prevent escalations within 30 days. Those are easiest to justify and easiest to measure.

1) Repeat callers after a new purchase (service recovery)

Business goal: reduce churn and complaints.

Segment idea:

• Customers who contacted support more than 3 times in the last 30 days
• Filtered to contacts related to purchases made in the last 45 days

Operational action:

• Route to a specialized “new purchase recovery” queue
• Trigger proactive outreach with a resolution offer
• Show agents a tighter troubleshooting checklist

Why it works: repeat contact is a strong indicator of unresolved friction. Catch it early.

2) High-value customers by percentile (VIP handling that’s actually fair)

Business goal: improve retention and CSAT for top customers.

Segment idea:

• Customers in the 90th percentile of lifetime spend
• Optionally exclude those already managed by an account team

Operational action:

• Priority queue and lower hold thresholds
• Faster escalation paths
• Proactive notifications during known incidents

Percentile-based VIP logic avoids a classic mistake: setting a static spend threshold that stops matching reality as your business grows.

3) “Likely to churn” based on service signals (not just marketing)

Business goal: prevent churn using contact center indicators.

Segment idea:

• Increase in contacts over the last 60 days
• Decrease in purchase frequency over the last 90 days
• Spike in negative outcomes (refunds, complaints, cancellations)

Operational action:

• Route to retention-skilled agents
• Offer service credits or tailored save offers
• Flag for follow-up if the issue isn’t resolved in one interaction

Even without a full ML churn model, strong rule-based segmentation can produce immediate wins.

How to deploy segmentation without causing chaos

Treat segmentation like production code: version it, validate it, monitor it. The contact center is not forgiving when logic breaks.

A simple operating model that works

1. Define the business outcome first

   • Example: “Reduce repeat contacts for new purchases by 15% in 60 days.”

2. Write the segment definition in plain English

   • Include time windows, thresholds, and exclusions.

3. Generate the first-pass SQL (AI assistant or manually)

   • Then refine it with someone who knows your data quirks.

4. Validate and estimate membership

   • If your “VIP” segment returns 48% of customers, something’s off.

5. Dry-run in a non-customer-impacting way

   • Start by surfacing the segment in dashboards before routing or outbound actions.

6. Deploy with guardrails

   • Caps on outbound volume
   • Fallback routing if segment evaluation fails

7. Monitor drift

   • Track segment size over time
   • Track outcome metrics by segment (CSAT, FCR, AHT, churn)

Common pitfalls (and how to avoid them)

• Segments that are too broad: You’ll dilute outcomes and lose trust.

  • Fix: tighten with time windows and intent filters.

• Segments based on unreliable fields: Garbage in, confident garbage out.

  • Fix: create a short list of “gold” fields and standardize them.

• AI-generated SQL treated as final: Small logical errors become big operational mistakes.

  • Fix: review + test + estimate membership every time.

Getting started: what to do this week

If you’re already using Amazon Connect Customer Profiles, the fastest path is to enable the Data store and pilot one segment with a single downstream action. Keep it contained and measurable.

A strong first pilot looks like:

• One segment (repeat callers after purchase)
• One action (priority routing or a targeted outbound campaign)
• Two metrics (repeat contacts per customer, segment-level CSAT)
• A two-week review cycle

Once the workflow is stable, scale out to higher-impact segments like percentile VIPs and churn-risk groups.

This beta release is a clear signal of where contact centers are heading: AI isn’t only answering customers; it’s shaping the operational logic behind who gets what experience, and when.

If you’re planning your 2026 contact center roadmap, what would happen if you treated segmentation—not the chatbot UI—as the main control plane for personalization?