Why Choose Customer-Facing Analytics Over Traditional BI?

Customer-facing analytics (CFA) is what happens when your users—not your analysts—become the primary consumers of analytics. It’s the practice of embedding data insights directly into your product interface, not tucked away in a BI tool or monthly report. Whether it’s an advertiser monitoring campaign performance, a recruiter tracking pipeline health, or a user checking their engagement stats, CFA puts insights exactly where they matter—inside the product experience itself.

Unlike traditional BI, which serves internal stakeholders through tools like Tableau or Looker, CFA delivers interactive dashboards, reports, and alerts to end-users: customers, partners, merchants, or external teams. And this shift changes everything.

It’s not just about data access. It’s about real-time, secure, high-concurrency insights that feel like part of the app—not an afterthought.

Core Features of Customer-Facing Analytics

Real-Time Data Access

Real-time feedback is a defining trait of modern digital products. Users no longer tolerate stale reports or overnight batch jobs. They expect to act on what’s happening right now—and that means the analytics layer must update in sync with the system of record.

From a technical perspective, this demands streaming pipelines and engines that can ingest and query fresh data with sub-minute latency. Traditional batch warehouses struggle here. Engines like StarRocks, with native support for streaming ingestion and real-time upserts, are purpose-built for this use case.

Interactive, Embedded Dashboards

In CFA, dashboards aren’t separate. They’re woven into the product. They allow users to:

• Filter by timeframe, geography, campaign, or product

• Drill into specific segments or trends

• Compare performance across cohorts

• Export insights or trigger workflows

These dashboards must be intuitive, low-friction, and responsive—requiring frontend frameworks that support embeddability, and backend systems that can support low-latency, ad hoc queries at scale.

Personalization and Role-Based Views

One-size-fits-all analytics no longer work. Whether through user-specific filters, cohort views, or saved dashboards, CFA should reflect what each user cares about. Under the hood, this often means implementing row-level and column-level access controls.

Systems like StarRocks support this natively, letting you enforce fine-grained access without rewriting query logic or adding brittle application-level workarounds.

How Customer-Facing Analytics Differs from Traditional BI

Customer-facing analytics isn't just traditional BI with a new coat of paint. It plays by a different set of rules—starting with the audience, but extending deep into performance, reliability, and design expectations.

It’s Not Just About Who Uses It—It’s About How It’s Used

Traditional BI tools like Looker or Tableau are designed for internal teams: analysts, operations, executives. These users are typically trained to explore data, interpret charts, and tolerate slower load times or scheduled refreshes. A missed SLA might be frustrating, but it doesn’t break the product.

Customer-facing analytics is fundamentally different. It’s part of your product surface. Users may not even know they’re “using analytics”—they’re just expecting real-time feedback, fast load times, and accurate, secure data as part of the overall experience. If it’s broken or slow, the product feels broken or slow.

Here’s how the two compare:

Why This Matters

When you embed analytics into your product, you're not building for internal experimentation or ad hoc slicing. You're building production software—used by customers in the critical path of their workflows. The expectations are higher:

• Queries need to be fast, even with JOINs and filters

• Systems must scale with your user base, not just your analyst team

• Data access must be fine-grained, secure, and always available

In short: customer-facing analytics isn't a dashboard. It's a product feature—and one that needs to behave like the rest of your app: fast, resilient, and intuitive.

Business Benefits of Customer-Facing Analytics

1. Stronger Customer Engagement

When users have access to meaningful data about their own experience—how their campaigns are performing, which users are engaging, how they’re trending—they become more invested in the product.

Analytics turns the user from a passive recipient into an active participant.

2. Differentiation in Crowded Markets

Offering transparent, customizable, in-product insights gives you a competitive edge. Customers increasingly expect data visibility, and products that lack it can feel dated or opaque.

Platforms like Pinterest, Xiaohongshu, and Demandbase have made CFA a core part of the product—not a premium add-on.

3. Deeper Loyalty and Trust

The more value your product provides through data, the harder it is to churn away from. Especially in B2B, where reporting and analytics often feed into decision-making and executive communication, being the source of truth cements you as a long-term partner.

Key Considerations for Implementing CFA

1. Data Security and Access Control

Any analytics exposed to external users must enforce strict controls:

• Row-level security: Users only see their own data.

• Column masking: Sensitive fields hidden or obfuscated based on role.

• Audit trails and permissioning: All access must be traceable and manageable.

These controls should be implemented in the engine, not the application code. StarRocks offers built-in RLS and column masking, which simplifies compliance and reduces risk.

2. Backend Performance: Real-Time, Scalable, Reliable

Your backend must meet real-world demands:

• Fast JOINs: Support multi-table models without flattening everything.

• Real-time upserts: Handle mutable data without hurting performance.

• Concurrency at scale: Serve 10K+ simultaneous queries.

• Schema evolution: Add fields or dimensions without breaking the pipeline.

Warehouses like Redshift and BigQuery are great for reporting, but often fall short under these conditions. That’s why many teams deploy a system like StarRocks behind their CFA stack—it handles these constraints natively.

3. Seamless Frontend Integration

The experience needs to feel native—not bolted on. Whether you build a custom frontend or embed tools like Metabase or Superset, the key is usability:

• Responsive performance (no spinners)

• Useful defaults (sensible filters, saved views)

• Help text and hover tips for non-analysts

Remember: your users aren’t data professionals. They’re trying to get a job done—your analytics should help, not confuse.

Case Studies: How Leading Companies Deliver CFA at Scale

Pinterest: Partner-Facing Ad Performance

Pinterest delivers real-time analytics to advertisers through its Partner Insights dashboards, which provide metrics such as:

• Impressions and click-through rates (CTR) over time

• Conversion rates segmented by creative

• Spend pacing and performance benchmarks

These dashboards are embedded directly into the ad management interface, offering near-instantaneous updates. To support this at scale—handling over 10,000 queries per second (QPS) during peak hours—Pinterest migrated from Druid to StarRocks. This transition enabled:

• A 50% reduction in p90 query latency

• A 3x improvement in cost-performance efficiency

• Data freshness with a latency of just 10 seconds

• Operation with only 32% of the infrastructure previously required

Xiaohongshu (RED): Real-Time Engagement Monitoring

Xiaohongshu (also known as RED) delivers real-time, multi-dimensional ad performance dashboards to brand advertisers across its platform—used by over 200 million monthly active users. These dashboards support dynamic filtering and breakdowns by:

• Region, gender, age group, and device

• Keyword and campaign metadata

• Engagement types (clicks, impressions, likes, purchases)

Originally built on ClickHouse and Flink, Xiaohongshu faced performance and maintainability issues at scale:

• JOIN operations were not feasible at high cardinality, forcing heavy denormalization in upstream Flink jobs.

• High ingestion volume (60B+ records/day) and real-time mutable data led to instability due to ClickHouse’s merge-on-read design.

• Flink clusters became overloaded with pre-aggregation and business logic, increasing development and operational overhead.

• ClickHouse’s lack of native scaling required manual rebalancing across 20+ clusters and 1000+ data templates.

After migrating to StarRocks, Xiaohongshu was able to:

• Eliminate denormalization pipelines by leveraging StarRocks’ real-time JOIN engine

• Consolidate serving into a single OLAP system with native support for mutable data (via primary key tables)

• Achieve sub-second latency (P99 < 200ms) and 10K+ QPS even under complex multidimensional filtering

• Significantly simplify their Flink architecture—delegating business logic and aggregation to StarRocks

The result: faster ad performance insights, more responsive dashboards, and drastically reduced infrastructure complexity. By embedding StarRocks into their advertiser analytics stack, Xiaohongshu turned real-time ad monitoring from an operational burden into a competitive advantage.

Demandbase: Account-Level B2B Insights

Demandbase, a leading B2B go-to-market platform, faced challenges with their ClickHouse-based analytics infrastructure, including:

• Inefficient multi-table JOIN operations necessitating extensive data denormalization

• High storage costs due to data duplication

• Complex and resource-intensive ETL pipelines

• Operational overhead from managing 49 ClickHouse clusters

To address these issues, Demandbase transitioned to a modern data architecture combining Apache Iceberg with CelerData Cloud, powered by StarRocks. This new setup provided:

• Robust multi-tenant support with data isolation

• Real-time data updates with row-level mutation support

• Efficient distributed SQL execution across the cluster

• High-performance JOIN operations with sub-second query latencies

As a result, Demandbase achieved:

• A 90% reduction in storage costs

• A 60% decrease in hardware resource usage

• Elimination of heavy ETL pipelines

• Simplified operations with a consolidated 45-node StarRocks cluster

This transformation enabled Demandbase to deliver real-time, account-level insights to B2B marketers, enhancing their ability to monitor web activity, campaign engagement, and detect anomalies promptly.

Eightfold AI: Recruiter-Facing Talent Analytics

Eightfold AI provides embedded analytics for enterprise talent teams, surfacing insights like:

• Pipeline velocity by role and department
• Offer acceptance and decline rates
• Diversity ratios across hiring funnels
• Time-to-hire and recruiter performance metrics

These dashboards are embedded directly into Eightfold’s platform and are used by both recruiters and AI agents. After outgrowing Redshift due to concurrency bottlenecks and high-latency JOINs, Eightfold migrated to StarRocks.

With StarRocks, they were able to:

• Retain a normalized star schema (fact + dimension tables) without denormalization

• Support real-time analytics across multi-tenant clickstream and profile data

• Leverage shared-data architecture with S3 for scalable storage, while caching hot data in EBS volumes

• Eliminate Redshift’s leader node bottleneck and serve high-QPS queries from both users and LLM agents

The result: sub-second analytics experiences delivered at scale—empowering recruiters with instant visibility, while laying the foundation for conversational and agentic analytics capabilities.

Final Thoughts

Customer-facing analytics isn’t a reporting tool—it’s a product surface. When implemented well, it becomes part of how users engage, explore, and make decisions inside your product. But to deliver this experience at scale, you need infrastructure that’s built for it:

• Real-time updates

• Sub-second JOINs

• Secure, dynamic access control

• High concurrency and low operational overhead

That’s what separates CFA from traditional BI. And that’s why systems like StarRocks are emerging as foundational pieces of modern analytics architecture—not just for performance, but for flexibility, usability, and scale.

FAQ: Getting Started with Customer-Facing Analytics

What’s the difference between customer-facing analytics and traditional BI?

Traditional BI tools are designed for internal use—by analysts, operations, or executives. These users are typically trained to work with dashboards, interpret charts, and wait a few seconds (or minutes) for a query to return. In contrast, customer-facing analytics (CFA) is embedded directly into the product and serves your external users: customers, partners, merchants, and occasionally internal roles like account managers or support teams.

CFA must deliver real-time insights, handle thousands of concurrent users, and feel seamless within the product interface. It’s not just “BI for users”—it’s a production-grade, always-on component of your application.

Can I use my existing data warehouse for customer-facing analytics?

You can—but there are trade-offs.

Warehouses like Redshift, BigQuery, and Snowflake excel at batch analytics and internal reporting. But they often struggle with sub-second latency, real-time ingestion, and high concurrency without layering on caching systems or complex data pipelines.

Many teams use their warehouse for storage or historical analysis, and pair it with a real-time OLAP engine like StarRocks for powering interactive, in-product dashboards. This dual approach allows you to combine reliability with responsiveness.

What kind of query performance should I aim for?

For customer-facing use cases, anything above 500ms feels slow—especially if users are applying filters, clicking through dashboards, or drilling into segmented data. You should aim for sub-second latency under normal load and predictable response times under peak traffic.

The best systems (like StarRocks) handle this with:

• Vectorized execution engines

• Materialized views

• Intelligent caching

• Optimized JOIN strategies

What’s the biggest mistake teams make early on?

Over-denormalization.

Many teams flatten their entire data model into wide tables to avoid JOIN performance issues. This creates:

• ETL bloat (massive pipelines with complex transformations)

• Storage explosion (duplicated dimensional data)

• Slow iteration (schema changes require backfilling)

A better approach is to use a query engine like StarRocks that can handle JOINs on normalized tables efficiently. This keeps your models flexible and your pipelines manageable.

How fresh does the data need to be?

It depends on your use case:

• AdTech, trading platforms, fraud detection → Sub-minute freshness

• Usage summaries, engagement metrics → Near real-time (5–15 minutes)

• Weekly or monthly reporting → Hourly may be enough

The key is to set clear expectations with users. Real-time doesn’t always mean “milliseconds”—but the data should never feel stale or outdated in context.

When should I implement row-level security?

As soon as users start seeing data that isn’t theirs.

Even in a single-tenant application, you’ll likely need:

• Role-based visibility (e.g., admin vs. standard user)

• Field-level redactions (e.g., hiding sensitive metrics)

• Multi-tenant isolation (if customers share infrastructure)

Implement this in the engine, not the application logic. Engines like StarRocks offer native row-level filters and column masking, which reduce risk and complexity.

Can I support thousands of concurrent users?

Yes—but only with the right architecture.

Traditional BI tools often hit limits when multiple users run queries at the same time. For true CFA, your system must:

• Handle spiky workloads (campaign launches, report downloads, login surges)

• Scale horizontally

• Isolate tenants and avoid cross-user contention

That’s why engines purpose-built for CFA—like StarRocks—focus heavily on high QPS, parallelism, and shared-data modes that decouple compute from storage.

Do I need real-time data ingestion?

If your product relies on fast feedback loops, yes.

Real-time ingestion allows users to:

• See the impact of an action immediately (e.g., a campaign just launched)

• Respond to anomalies or alerts without delay

• Monitor high-frequency events in live dashboards

StarRocks supports real-time upserts, meaning you can ingest and reflect new data in seconds without performance trade-offs or query degradation.

How does personalization work in CFA?

Personalization typically means showing:

• User-specific dashboards (e.g., only see their own campaigns)

• Saved filters or views

• Context-aware defaults (e.g., region, role, or team)

You’ll need:

• Row-level security at the data layer

• Parameter-based filtering in dashboards

• Embedded session context (user ID, role, etc.) passed to queries