RisingWave enables cross-channel marketing analytics by continuously joining event streams from paid ads, email, social, organic, and CRM into unified materialized views. Marketing teams get a single source of truth for channel performance, customer journeys, and attribution — updated in real time — without maintaining separate pipelines for each channel.
The Cross-Channel Analytics Problem
Modern marketing runs across dozens of channels: paid search, paid social, programmatic display, email, SMS, organic search, referral, and direct. Each channel has its own data format, reporting API, and latency. Building a unified view of performance across all channels is one of the hardest problems in marketing data engineering.
The typical solution — pulling data from each channel API into a warehouse on a schedule — creates a fragmented picture. Data from different channels arrives at different times. Channel A reported conversions may overlap with channel B due to different attribution windows. Reconciliation is manual, slow, and error-prone.
RisingWave addresses this by providing a single streaming SQL layer that ingests events from all channels simultaneously, applies consistent attribution logic, and maintains unified metrics as materialized views that update continuously.
Multi-Channel Event Ingestion
Each channel feeds into a common event schema through Kafka. This normalization layer is the key to cross-channel analytics:
CREATE SOURCE marketing_touchpoints (
touchpoint_id VARCHAR,
user_id VARCHAR,
channel VARCHAR, -- 'paid_search', 'paid_social', 'email', 'organic', 'direct', 'referral'
campaign_id VARCHAR,
medium VARCHAR,
source VARCHAR,
cost_usd DOUBLE PRECISION,
event_type VARCHAR, -- 'impression', 'click', 'open', 'visit'
event_time TIMESTAMPTZ
)
WITH (
connector = 'kafka',
topic = 'marketing.touchpoints',
properties.bootstrap.server = 'kafka:9092',
scan.startup.mode = 'latest'
)
FORMAT PLAIN ENCODE JSON;
CREATE SOURCE conversion_events (
conversion_id VARCHAR,
user_id VARCHAR,
revenue_usd DOUBLE PRECISION,
conversion_type VARCHAR,
event_time TIMESTAMPTZ
)
WITH (
connector = 'kafka',
topic = 'marketing.conversions',
properties.bootstrap.server = 'kafka:9092',
scan.startup.mode = 'latest'
)
FORMAT PLAIN ENCODE JSON;
Unified Channel Performance Materialized View
Build the core cross-channel analytics view with hourly windows:
CREATE MATERIALIZED VIEW cross_channel_performance AS
SELECT
t.channel,
t.campaign_id,
t.source,
window_start,
window_end,
COUNT(*) FILTER (WHERE t.event_type = 'impression') AS impressions,
COUNT(*) FILTER (WHERE t.event_type = 'click') AS clicks,
COUNT(*) FILTER (WHERE t.event_type = 'visit') AS visits,
SUM(t.cost_usd) AS total_spend,
COUNT(DISTINCT t.user_id) AS unique_users,
COUNT(cv.conversion_id) AS conversions,
SUM(cv.revenue_usd) AS revenue,
-- Channel efficiency metrics
SUM(t.cost_usd) / NULLIF(COUNT(cv.conversion_id), 0) AS cost_per_conversion,
SUM(cv.revenue_usd) / NULLIF(SUM(t.cost_usd), 0) AS roas,
COUNT(*) FILTER (WHERE t.event_type = 'click')::DOUBLE PRECISION
/ NULLIF(COUNT(*) FILTER (WHERE t.event_type = 'impression'), 0) AS ctr
FROM TUMBLE(marketing_touchpoints, event_time, INTERVAL '1 hour') t
LEFT JOIN conversion_events cv
ON t.user_id = cv.user_id
AND cv.event_time BETWEEN t.event_time AND t.event_time + INTERVAL '30 days'
GROUP BY t.channel, t.campaign_id, t.source, window_start, window_end;
This single view replaces separate per-channel reports. Any BI tool querying cross_channel_performance gets an apples-to-apples comparison of paid search vs. email vs. organic — with consistent attribution logic applied to all channels simultaneously.
Channel Mix Optimization
Understanding which channel mix drives the best results requires a higher-level aggregation. Build a channel mix view that shows relative contribution:
CREATE MATERIALIZED VIEW channel_mix_analysis AS
SELECT
window_start,
window_end,
channel,
SUM(total_spend) AS channel_spend,
SUM(conversions) AS channel_conversions,
SUM(revenue) AS channel_revenue,
-- Share of total
SUM(total_spend) / SUM(SUM(total_spend)) OVER (PARTITION BY window_start) AS spend_share,
SUM(revenue) / SUM(SUM(revenue)) OVER (PARTITION BY window_start) AS revenue_share,
-- Efficiency vs. portfolio average
(SUM(revenue) / NULLIF(SUM(total_spend), 0)) /
NULLIF(SUM(SUM(revenue)) OVER (PARTITION BY window_start) /
SUM(SUM(total_spend)) OVER (PARTITION BY window_start), 0) AS roas_vs_portfolio
FROM cross_channel_performance
GROUP BY window_start, window_end, channel;
A channel with roas_vs_portfolio greater than 1.0 is outperforming the portfolio average — a signal to increase budget allocation. Below 1.0 signals underperformance. This metric updates continuously as new conversion data arrives.
Comparison: Cross-Channel Analytics Approaches
| Approach | Data Freshness | Channel Unification | Custom Attribution | Operational Cost |
| Native ad platform reports | Real-time (siloed) | None | None | Low |
| Marketing cloud analytics | 24-hour delay | Partial | Limited | High |
| Custom warehouse pipeline | 2–4 hour delay | Full | Complex | Medium-High |
| RisingWave streaming SQL | Seconds | Full | Custom SQL | Low |
Customer Journey Analysis
Beyond channel performance, cross-channel analytics enables customer journey reconstruction — understanding the sequence of touchpoints that leads to conversion. Use SESSION windows to capture journeys:
CREATE MATERIALIZED VIEW customer_journey_sessions AS
SELECT
user_id,
window_start,
window_end,
-- Ordered channel sequence (simplified as first/last touch)
MIN(channel) FILTER (WHERE event_time = window_start) AS first_touch_channel,
MAX(channel) FILTER (WHERE event_time = window_end) AS last_touch_channel,
COUNT(DISTINCT channel) AS channels_in_journey,
COUNT(*) AS total_touchpoints,
SUM(cost_usd) AS journey_cost
FROM SESSION(
marketing_touchpoints,
event_time,
INTERVAL '24 hours' -- 24-hour inactivity gap = new journey
)
GROUP BY user_id, window_start, window_end;
This view enables journey analysis queries like: what percentage of conversions involve 3+ channels? Which first-touch channel leads to the longest journeys? Which channel combination has the highest conversion rate?
FAQ
Q: How does RisingWave handle duplicate conversions reported across multiple ad platforms? A: Create a deduplication layer using a materialized view with DISTINCT on conversion_id. Each ad platform tags its own conversion events; the deduplication view retains only one conversion per ID, regardless of which platform reported it.
Q: Can I connect Google Ads, Meta Ads, and LinkedIn Ads data to RisingWave? A: These platforms expose API-based reporting, not event streams. Use a tool like Airbyte or Fivetran to pull data into Kafka or PostgreSQL, then source it into RisingWave. For click-level data, your own tag management system (GTM, Segment, etc.) produces the event stream directly.
Q: How do I handle different attribution windows per channel?
A: Parameterize attribution windows as a reference table: channel_attribution_config (channel, view_window_days, click_window_days). Join against this table in your attribution materialized view to apply channel-specific windows automatically.
Q: What is the best way to visualize cross-channel performance from RisingWave? A: Connect Grafana (via PostgreSQL data source) for real-time operational dashboards. For business user reporting, Metabase or Looker works well — both support PostgreSQL and respect materialized view freshness automatically.
Q: Does RisingWave support custom attribution models beyond last-touch? A: Yes. You can implement position-based, time-decay, or linear attribution by computing touchpoint weights in SQL using window functions over the customer journey. The materialized view pattern makes these models compute continuously without batch jobs.
Get Started
Build a unified cross-channel analytics layer in minutes with the RisingWave quickstart guide.
Connect with marketing analytics engineers in the RisingWave Slack community.
