This article covers key use cases for Reverse ETL that RudderStack customers use to drive business outcomes and make their data teams more efficient:

• Push AI/ML outputs to marketing tools
• Quickly respond to marketing data requests
• Drive personalization with warehouse data
• Decrease churn
• Enable personalized search results
• Enrich contact records for sales
• Update millions of customer records from your warehouse

Easily push AI/ML outputs to marketing tools to power better customer experiences

Use cases:

• Customer engagement/personalization
• Increase sales and subscriptions
• Team efficiency

Challenges & solutions

• Challenge: Wyze had limited visibility and data on the customer journey. Personalized recommendations were ineffective and inefficient.
• Solution: Wyze uses RudderStack to inform their AI/ML models with better data, build complete customer profiles in their warehouse to feed their computed metrics like churn score and customer LTV, and send those metrics to Braze via Reverse ETL for personalized customer engagement and product offers.

Results

RudderStack’s Reverse ETL was the final piece for Wyze to achieve a customer 360. Now Wyze can send enriched data and computed metrics to Braze, route it to their member services platform, or use it to trigger emails, text messages, and push notifications.

Read the full case study: Wyze ships 3x more campaigns driven by ML models, leading to increased conversions

Quickly respond to marketing requests for new data points

Use cases:

• Customer engagement/Personalization
• Marketing efficiency

Challenges & solutions

• Challenge: Joybird was spending too much time on low-value integration work. They didn’t have a complete customer view, and data sent to marketing was limited and delayed.
• Solution: RudderStack Reverse ETL to push Snowflake data to Iterable and their CRM.

Results

The marketing team is now able to spin up new campaigns in an hour which is down from 2 weeks. Additionally, data engineering reduced the time spent on building new integrations and managing data pipelines by 93%

Read the full case study: Joybird Reduces Engineering Time Spent on Customer Data Integrations by 93% with RudderStack

Drive personalization with data from your warehouse

Use cases:

• Personalization
• Increase conversions

Challenges & solutions

• Challenge: Grafana had limited visibility into the customer journey and needed a solution that allowed them to remain compliant with security and privacy laws. Marketing and product needed better insight into the customer journey and product usage.
• Solution: RudderStack Event stream to aggregate user profile and event data in BigQuery, and Reverse ETL to push BigQuery data to Customer.io, Intercom, and BI tools.

Results

Marketing is able to rapidly test new signup funnels and variants, and trigger personalized campaigns that drive web conversions. Product teams can identify product experience issues faster and fix them before they affect the bottom line.

Read the full case study: Grafana Increases Conversion Rates with RudderStack

Decrease churn by sending the right offer at the right time

Use cases:

• Reduce churn/increase retention
• Drive revenue

Challenges & solutions

• Challenge: Wynn needed to track billions of user events a month. Information was siloed and marketing was spending three to six hours a day gathering data manually and writing queries, delaying campaign delivery and limited their ability to personalize.
• Solution: RudderStack Reverse ETL to send data from BigQuery to Amplitude.

Results

Wynn has a deeper understanding of player engagement. Marketing can use the warehouse data sent to amplitude to run churn campaigns and create churn prediction models that leverage machine learning. 

With RudderStack, Wynn increased revenue per payer by 25% and predict that the company can retain 80% of Wynn Slots payers for the next 30 days

Read the full case study: Wynn Slots Increases Retention with RudderStack and AWS

Enable personalized search results

Use cases:

• Personalization
• Site performance

Challenges & solutions

• Challenge: Loveholidays’ unique search engine serves 20 million users a month. The company also wanted to improve personalization by offering a customized list of hotels based on user ID without compromising response times.
• Solution: RudderStack Reverse ETL to transfer data from BigQuery into the company’s Redis in-memory data store.

Results

With RudderStack Reverse ETL, loveholidays has their own in-house personalization engine that can serve customized hotel recommendations to customers in 20 milliseconds. Reverse ETL also accelerated the data transfer frequency from 24 hours to 15 minutes. All while reducing the risk of non-compliance

10x speed of in-house personalization engine (200ms > 20ms) which led to 2% uplift in conversions and saved $500k a year in SaaS costs.

Read the full case study: loveholidays Takes Ownership of Its Data and Brings Personalization In-house

Enrich lead and contact records for sales

Use cases:

• Personalization
• Sales effectiveness

Challenges & solutions

• Challenge: Pachyderm generates gigabytes of data by tracking their users’ product interactions and data from their cloud sources. Data was siloed in cloud tools like Hubspot, and the data team was spending a lot of time moving data across marketing and sales tools.
• Solution: Reverse ETL to send Sigma data in the warehouse to Hubspot, and sync with Salesforce and Outreach for personalized email and campaigns triggered by user behavior.

Results

Pachyderm is now able to leverage enriched, transformed warehouse data for analytics and product optimization while also routing enriched data back to downstream tools for inbound and outbound marketing and sales.

Read the full case study: How Pachyderm Uses RudderStack to Master Lead Qualification

Efficiently update millions of customer records from your warehouse

Use cases:

• Customer record enrichment
• Data-driven customer engagement
• Integration efficiency

Challenges & solutions

• Challenge: Iterable’s event/track rate limit was exhausted after a couple of weeks of sending 5 million user records per day
• Solution: Use Iterable’s lists/subscribe endpoint with RudderStack Reverse ETL and Transformations to send updates in batches

Results

A 5 million row job was reduced to a 10 thousand row job, saving run time and downstream API bandwidth.

Read the full case study: Sending 5 million users from Snowflake to Iterable once a day for customer engagement campaigns