Let’s begin with a deceptively simple question: What happens when the user of your product is also the consumer of your analytics?
That’s really what customer-facing analytics is about.
We’re not talking about quarterly dashboards tucked away in BI portals. It’s about giving users—advertisers, sellers, operators, creators—a live, built-in window into what’s going on. Did that campaign perform better today? How are your sales trending this hour? Who’s interacting with your post right now? These aren’t things you want to wait until tomorrow to find out.
As products become more data-driven, users expect that data to be embedded right where they work—not hidden in a separate tool. And once it’s there, it fundamentally changes how people interact with the product. It’s no longer just a tool—it becomes a source of truth, decision-making, and action.
What Is Customer-Facing Analytics?
Customer-facing analytics is what happens when data becomes part of the product experience—not a separate destination.
It’s about delivering insights directly to people who use the product, whether they’re external customers, internal users, or partners. That could be an advertiser checking campaign stats, a seller tracking orders, or even a support team using built-in reports to help clients in real time. The key is that the analytics are embedded into the product experience—not tucked away in a BI tool or weekly email.
This kind of analytics goes by a few different names, depending on context:
-
User-facing analytics – A broad term that includes any analytics surfaced directly to end users of the product, whether internal (e.g. sales teams, customer success) or external (e.g. advertisers, merchants).
-
Customer-facing analytics – A more specific flavor of user-facing analytics, usually referring to features designed for external customers or partners.
-
Embedded analytics – A general term for integrating analytics within a software interface; can include both internal and external use cases.
-
External or partner analytics – Emphasizes that the insights are intended for people outside the organization—clients, suppliers, partners.
-
In-product dashboards – Refers to analytics that live directly inside a product, often alongside other operational features.
-
Interactive reporting for end users – Highlights use cases where users can explore, filter, and manipulate data on demand—without needing a data analyst.
And here’s the important part: customer-facing analytics plays by a different set of rules.
You’re not designing for a handful of analysts who can run queries and wait. You’re building for real users—often many of them—who expect things to be instant. Whether it’s one customer or ten thousand, they want answers now, not later.
That means:
-
Sub-second query speed
-
Fresh, real-time (or near real-time) data
-
High reliability—because it can’t just break during peak hours
-
Support for high concurrency—dozens, hundreds, even thousands of users clicking around at the same time
In short, it has to feel seamless. No spinning wheels. No stale results. Just fast, responsive analytics that work like any other part of your product.
Where Customer-Facing Analytics Shows Up (and Why It Matters)
Customer-facing analytics isn’t one-size-fits-all. It shows up differently depending on the product and the people using it—but the thread is the same: you’re helping someone make smarter, faster decisions right where they already work. Here’s how that looks in the real world:
Self-Service Reporting: Giving Customers the Wheel
Imagine you're a B2B SaaS company. You provide software to hundreds of marketing teams. Each of them wants to know:
Which campaigns worked? Which regions are converting? How is our usage trending week over week?
Instead of emailing spreadsheets or sending weekly PDFs, you give them a live dashboard embedded right into your product. They can filter by cohort, segment by product line, compare this month to last month—all without filing a ticket.
Examples:
-
A product analytics tool lets teams break down daily active users by feature.
-
A marketing automation platform shows pipeline movement across multiple accounts.
-
A security vendor gives customers insight into their own event logs, filtered by severity or geography.
It’s not just about visibility—it’s about empowering users to answer their own questions.
Live KPIs and Real-Time Operational Metrics
Some users don’t just want data—they need it right now, because the value evaporates if it’s late.
Think about:
-
An advertiser watching CTR drop in real-time and pausing spend before wasting budget.
-
A delivery manager checking route efficiency and seeing one driver stuck in traffic.
-
A mobile game developer tracking live monetization events during a new feature launch.
This is the heartbeat layer—the live pulse of what’s happening now.
Use cases:
-
AdTech dashboards: impression delivery, spend pacing, conversion rates per creative.
-
Logistics platforms: vehicle ETA, delay alerts, cost per route visualizations.
-
Gaming platforms: in-game event triggers, real-time cohort progression, revenue tracking per level.
Real-time isn’t just nice to have here. It’s the whole point.
In-Product Personalization and Recommendations
This is where analytics moves from observation to action.
Instead of showing a user a chart, you change the experience based on what you know. The analytics run behind the scenes—quietly, constantly—but the effect is personal.
Examples:
-
An e-commerce site recommends items based on past purchases and session behavior.
-
A fitness app adjusts weekly goals based on how much sleep and exercise you’ve logged.
-
A financial tool suggests better savings products after tracking your transaction patterns.
Here, analytics isn’t a dashboard—it’s the logic behind what the user sees next.
Proactive Alerts and Smart Triggers
Sometimes, the best analytics isn’t visual—it’s invisible until something important happens.
You don’t want to stare at a dashboard waiting for a problem. You want the system to tell you—and fast.
Think:
-
A banking app that pushes a low balance alert when your account drops below a threshold.
-
A fraud detection system that flags unusual activity in real-time.
-
An ad platform that pings the user when campaign performance drops below target.
Done well, this feels magical. Like the product is watching your back.
Real-World Examples
Let’s ground this with a few examples:
-
Pinterest offers advertising customers real-time visibility into campaign performance through its Partner Insights platform. Advertisers can monitor metrics like impressions, CTR, and conversions in embedded dashboards with interactive drill-downs.
-
Xiaohongshu (RED) provides brand advertisers with dynamic dashboards to track ad engagement across dimensions like age, location, and content type—keeping advertisers informed as their campaigns evolve.
-
Demandbase gives B2B marketers access to personalized reports and real-time data about account-level activity. Instead of waiting for daily ETL cycles, users can filter and explore metrics with millisecond feedback.
-
Eightfold AI serves real-time analytics to recruiters and hiring managers—surfacing insights like pipeline velocity, diversity ratios, and conversion rates within their talent platform.
How Is Customer-Facing Analytics Different from Traditional Analytics?
Customer-facing analytics changes the game entirely—because the audience, expectations, and operational requirements are totally different. Think of it like this: traditional analytics is the back office. Customer-facing analytics is the main stage.
Here’s how that plays out:
| Aspect |
Traditional/Internal Analytics |
Customer-Facing Analytics |
| Who’s using it? |
Analysts, ops teams, finance |
Your customers, partners, end-users—in your product |
| How fast does it need to be? |
A few seconds? Fine. A minute? Still okay. |
Instant. Sub-second latency is the expectation, not the exception. |
| How fresh is the data? |
Often updated daily via batch jobs or ETL pipelines |
Needs to be real-time or near it—especially if tied to live user behavior (ads, transactions, engagement) |
| How many users? |
Tens or maybe hundreds inside the company |
Could be thousands to millions of users hammering dashboards, all at once |
| How predictable are the queries? |
Fairly fixed—predefined dashboards and canned reports |
Wildly dynamic—users filter, pivot, drill, and slice however they want |
| Can it go down sometimes? |
A little downtime during a deployment is fine |
Absolutely not. This is part of the user experience—if it’s slow or broken, your product is broken |
Put another way: building customer-facing analytics isn’t like building BI dashboards. It’s like building part of the product.
And that means thinking like an application developer—not just a data engineer.
You're not just delivering insights. You're delivering interactions that need to be fast, reliable, and seamless—at scale.
What Makes Customer-Facing Analytics So Demanding?
You can’t treat customer-facing analytics like a batch report with a pretty UI. Once you’re exposing data to users—inside your product—everything gets harder. Expectations are higher. Feedback loops are tighter. And the engineering requirements stack up fast.
Let’s walk through what actually makes this challenging.
Sub-Second Queries, Even With Joins
Your users don’t just want totals. They want to filter, group, compare, and drill down—often across multiple dimensions. That means JOINs. And those JOINs can’t take three seconds to resolve.
Example:
A seller on an e-commerce platform wants to know how her discounted shoes are performing—broken down by referral source, region, and time of day. She clicks into her dashboard expecting answers now—not after a coffee refill.
Under the hood? You’re JOINing sales → products → traffic sources → promo codes → geography. That’s multiple large tables. High-cardinality dimensions. All within 300ms.
High Concurrency at Real-World Scale
You’re not building for five analysts on Looker. You’re building for thousands of customers poking around their dashboards, filtering reports, launching campaigns—sometimes all at once.
Think about:
-
10,000+ QPS during peak usage
-
Bursty traffic: morning check-ins, end-of-month reports, product launches
-
Multi-tenant separation: one customer shouldn’t slow down another
This isn’t just a performance issue—it’s a resource isolation problem too.
Real-Time Data, Not Last Night’s Batch
When your product revolves around real-world decisions—like ad spend, fraud risk, or user engagement—stale data breaks the experience.
Where freshness matters:
-
Adtech: A campaign burns $20K/hour. Pacing errors hurt.
-
Fintech: Fraud signals can't lag behind transactions.
-
Logistics: ETA alerts are meaningless if they’re delayed.
-
Social platforms: Comments, reactions, posts—these evolve constantly.
If your data is 15 minutes old, it might as well be 15 hours.
Schema Changes and Evolving Models
Here’s a brutal reality: your product team will ask for “one more field” every week. A new dimension, a new flag, a new tag.
If your data stack breaks every time someone says “Can we also segment by...”, you’re sunk.
What you actually need:
-
Lightweight schema evolution (no week-long backfills)
-
Support for semi-structured formats like JSON
-
JOINs that don’t require denormalizing your universe every time
This isn’t just about flexibility. It’s about staying agile without creating tech debt.
Granular Access Control
Your users aren’t supposed to see all the data—just their slice. That slice might vary by account, by role, even by metric.
So you’ll need:
-
Row-level permissions (e.g., only see your own customers)
-
Column masking (e.g., hide sensitive fields unless privileged)
-
Multi-tenant partitioning (e.g., physically or logically isolate customer data)
This has to be baked in at the data layer, not duct-taped at the API level.
And Then There’s the Infrastructure Reality Check
It’s not just about product requirements—it’s about what the database or engine can actually handle under pressure.
Challenge #1: JOIN Performance (and the Denormalization Tax)
Some OLAP engines—like ClickHouse or Druid—struggle with complex joins. To compensate, teams denormalize everything up front.
But here’s the cost:
-
10× storage blow-up
-
Long, brittle ETL pipelines
-
Massive compute to rebuild data on schema changes
-
Inflexible queries and slow iteration cycles
You’re essentially precomputing everything because your engine can’t keep up.
Challenge #2: Mutable Data in Real Time
ClickHouse, Druid, and others rely on merge-on-read to support updates. That’s fine if you’re refreshing hourly.
But try to keep up with sub-minute freshness—and it starts to choke.
Merges pile up. Queries slow down. Data gets inconsistent. You’re back to batch pipelines or stale results.
Challenge #3: Concurrency at Scale
Serving 10 dashboards? Easy.
Serving 10,000 dashboards concurrently—each with dynamic filters, aggregations, and sub-second latency?
That’s where most systems hit a wall.
You get:
-
Query queues
-
Resource contention
-
Node bottlenecks
-
Frustrated users
That’s why most “analytics-ready” engines don’t actually cut it for CFA workloads in production.
TL;DR:
Customer-facing analytics isn’t a reporting problem. It’s a distributed systems problem—with real users on the other side.
To do it right, you need a system that can handle live JOINs, real-time updates, high concurrency, schema evolution, and access control—all without falling apart.
Enter StarRocks: A Database Built for CFA
Let’s say you’re building analytics into a product—not just dashboards for analysts, but real-time insights for your users, customers, or partners. You want sub-second queries, live data, rich filters, and thousands of concurrent users.
But you also want your data model to make sense. You don’t want to flatten everything into a giant table just because your engine can’t handle joins. You don’t want a week-long backfill every time someone adds a column. And you definitely don’t want users waiting 8 seconds because someone else ran a heavy report.
This is where StarRocks comes in.
StarRocks is a high-performance analytical database designed for exactly these kinds of problems—fast queries, fresh data, and scale. Let’s walk through how.
1. Fast JOINs
Most analytics engines struggle with JOINs at scale. The usual workaround? Denormalize everything—pre-join all your tables into one wide blob upstream. That means massive storage costs, slow pipelines, and a lot of pain every time someone changes a field.
StarRocks avoids that trap.
It was built from day one to execute JOINs on the fly. That includes:
-
Broadcast joins for small-to-large table scenarios
-
Shuffle joins for distributed many-to-many joins
-
Colocated joins when you control the table distribution keys
These aren’t bolted-on features—they’re core to how StarRocks runs. That means you can model your data cleanly, keep your events, users, accounts, and entities in separate tables, and still get blazing-fast performance.
Real-world example: Demandbase
Demandbase used to run 49 separate ClickHouse clusters. Why? Because they had to pre-join all their data to make queries fast. When they switched to StarRocks, they ditched denormalization entirely. Now, queries span events, accounts, and people tables with predictable low latency—and zero backfill pain.
2. Real-Time Updates, Natively
Many analytics engines use a merge-on-read approach for updates: new rows are written in fragments, and queries stitch everything together later. It works—until you need second-level freshness. Then things break down.
StarRocks takes a different approach.
Its primary key model supports row-level UPSERTs. You can stream updates in real-time, overwrite old values, and still get fast queries. No background merges, no surprise performance cliffs.
Used at: Xiaohongshu (RED)
Xiaohongshu powers its ad exposure dashboards using StarRocks with primary key tables. Real-time data, frequent corrections, and rapid ingestion—all without query degradation.
3. Blazing-Fast Execution, with SIMD and Caching
Every operator in StarRocks is vectorized, meaning it processes batches of rows in parallel using SIMD (Single Instruction, Multiple Data). This makes a huge difference for aggregations, filters, and GROUP BYs.
And for extra speed, StarRocks adds:
Together, these reduce latency and boost throughput—especially in high-concurrency scenarios.
4. Scalability That Fits Your Architecture
StarRocks gives you two deployment modes:
-
Shared-nothing: Storage and compute co-located, great for consistent workloads and local disk performance.
-
Shared-data: Compute and storage decoupled, with BE nodes pulling data from S3. Perfect for variable workloads, multi-tenant setups, or just minimizing cost.
Used by: Pinterest
Pinterest runs Partner Insights—one of their highest-volume analytics services—on StarRocks. With shared-data mode, they handle over 10,000 QPS using less than one-third the instance count previously required.
5. Built-In Access Control
If you’re building B2B analytics, you can’t let users see each other’s data. StarRocks supports:
-
Row-level filters: Different users see different rows
-
Column masking: Hide or obfuscate sensitive fields
-
Role-based policies: Define who can see what
This is all built-in—not a patchwork of external tools.
6. Ready for the Lakehouse Future
As more companies move to open table formats like Apache Iceberg or Delta Lake, CFA systems need to follow.
StarRocks can query these formats natively—no need to copy data into a separate warehouse. You can:
-
Run fast queries directly on Parquet files
-
Use materialized views to accelerate hot paths
-
Keep all your data in one place
Used by: Xiaohongshu again
They now run StarRocks directly on top of Iceberg, replacing ClickHouse. The result? 3× faster queries, 50% less storage cost, and a single source of truth for all teams.
Closing Thoughts: Rethinking Analytics as a Product Surface
Customer-facing analytics isn’t a backend problem—it’s a user experience. When people use your product, they expect the analytics to be as fast, intuitive, and polished as every other feature.
That means:
If you want that, you need infrastructure that’s designed for it. That’s what StarRocks brings to the table: an engine built not just for analysts—but for the end users they serve.
Let’s build analytics people actually want to use.
Frequently Asked Questions
What is the difference between customer-facing analytics and embedded analytics?
“Embedded analytics” is the broader umbrella—it just means analytics built into a software product. “Customer-facing analytics” is a specific flavor where the end users are your customers, not your internal teams. That distinction matters, because external users bring higher expectations around speed, availability, and polish.
How is customer-facing analytics different from traditional BI?
Traditional BI tools like Looker, Power BI, or Tableau are typically used by internal analysts or operations teams. They tolerate higher latency, are usually updated in batches, and support a smaller number of users. CFA, on the other hand, is part of your product. That means it needs to be real-time, highly concurrent, and always on—because your customers are using it like any other feature.
Is customer-facing analytics only for B2B products?
Not at all. CFA shows up in both B2B and B2C contexts. In B2B, it might be a usage dashboard for enterprise clients. In B2C, it could be a portfolio tracker in a fintech app or a health trends chart in a fitness app. The key is that it’s embedded, real-time, and designed for non-technical users.
Do I need real-time data for CFA?
Not always—but often. The more the user’s decision depends on “what’s happening now” (ad performance, fraud detection, ETA tracking), the more real-time matters. In cases where latency is less critical (e.g. weekly usage summaries), freshness can be more relaxed. But your stack should be able to support both.
Why can’t I just use my existing data warehouse?
You can try—but most warehouses weren’t built for CFA workloads. They might struggle with:
-
JOIN performance across large tables
-
Real-time updates or high-frequency mutations
-
High QPS and concurrency
-
Sub-second latency requirements
-
Row- or tenant-level access control
You’ll likely end up building brittle ETL pipelines, caching layers, and access filters on top—unless you choose a system designed for CFA from the start.
How does StarRocks compare to ClickHouse or Druid?
ClickHouse and Druid are excellent for certain OLAP workloads, especially when data is denormalized and updated infrequently. But when you need fast JOINs, mutable data, sub-second freshness, and high concurrency all at once—StarRocks pulls ahead.
It was designed for real-time, user-facing workloads:
-
Supports real JOINs on normalized schemas
-
Handles UPSERTs with a primary key model
-
Works well with both shared-nothing and shared-data deployments
-
Integrates natively with Apache Iceberg and other open lakehouse formats
.jpg)
