---
title: "Appcues delivers personalized customer engagement with ClickHouse Cloud"
description: "Appcues replaced an analytics stack that included Snowflake and Airflow after rising scale pushed queries to 20 seconds or more and limited reports to 10-minute windows. The company evaluated ClickHouse Cloud on AWS for real-time segmentation, exporting Snowflake data to Parquet in S3 and testing complex email segments with nested conditions, historical events, and negative logic. Production events now pass through Kafka and a clickwriter consumer, while a clickclient library built on Elixir’s Ecto generates controlled ClickHouse queries; SharedReplacingMergeTree, table engines, and materialized views were tuned during a one-year migration. Across 1.31 PB and 410 billion events, P95 queries fell below two seconds, ingestion latency dropped to about five seconds, and analytics costs decreased 23%, while new funnel reports and AI-powered insights were added."
---

# Appcues delivers personalized customer engagement with ClickHouse Cloud

[Clickhouse](https://yomu.fyi/company/clickhouse) · Jun 18, 2026

**Type:** Problem & solution

## Summary

Appcues replaced an analytics stack that included Snowflake and Airflow after rising scale pushed queries to 20 seconds or more and limited reports to 10-minute windows. The company evaluated ClickHouse Cloud on AWS for real-time segmentation, exporting Snowflake data to Parquet in S3 and testing complex email segments with nested conditions, historical events, and negative logic. Production events now pass through Kafka and a clickwriter consumer, while a clickclient library built on Elixir’s Ecto generates controlled ClickHouse queries; SharedReplacingMergeTree, table engines, and materialized views were tuned during a one-year migration. Across 1.31 PB and 410 billion events, P95 queries fell below two seconds, ingestion latency dropped to about five seconds, and analytics costs decreased 23%, while new funnel reports and AI-powered insights were added.

## Context

Appcues needed lower-latency analytics as its data volume and product requirements grew. Its Snowflake and Airflow setup supported batch reporting and dashboards, but query times reached 20 seconds or more, reports were limited to 10-minute windows, and the company needed to evaluate complex, real-time segmentation for email and other customer-facing capabilities.

## Approach / What changed

Appcues exported Snowflake data to Parquet in S3, loaded it into ClickHouse Cloud on AWS, and tested complex email segmentation queries. Events flow through Kafka into a clickwriter consumer for light deduplication and fast inserts, while a clickclient library built on Elixir’s Ecto generates ClickHouse queries. The team tuned schemas, table engines, materialized views, service sizing, and autoscaling during a one-year migration with continuous verification and staged customer cutovers.

## Takeaways

- ClickHouse ran Appcues’s email segment queries 73% faster than its previous cloud data warehouse during the proof of concept.
- P95 query latency decreased from more than 20 seconds to under two seconds, while ingestion latency fell from 10 minutes or more to about five seconds.
- Analytics costs decreased 23% even after Appcues added new segmentation, email, funnel-reporting, and AI-powered insight workloads.

**Tags:** [AWS](https://yomu.fyi/topic/aws), [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Kafka](https://yomu.fyi/topic/kafka), [Performance](https://yomu.fyi/topic/performance), [Scalability](https://yomu.fyi/topic/scalability)

- Source: [Clickhouse](https://clickhouse.com/blog/appcues-customer-engagement-analytics)
- Source URL: https://clickhouse.com/blog/appcues-customer-engagement-analytics
- Ingested by Yomu: 2026-08-29T01:35:45.700Z

[Read original post](https://clickhouse.com/blog/appcues-customer-engagement-analytics)