Contextual personalization use cases

These examples show how businesses across different industries use contextual personalization to optimize campaigns for real business outcomes — not just clicks and opens.

For a general overview, see Contextual personalization. To get started, see Configure contextual personalization.

Batch campaign (retail)

The problem: Optimizing a one-time email or SMS blast for opens and clicks often incentivizes clickbait — high engagement, zero sales. Connecting a batch campaign to downstream revenue traditionally required exporting data, matching it with transaction logs in a BI tool, and manually updating the campaign for the next send.

The solution: Set the goal to purchase_item and let Loomi AI do the work. Within the attribution window, if Variant A drives more clicks but fewer purchases than Variant B, Loomi AI shifts traffic to Variant B — automatically, within the running campaign.

If a customer clicks the email and then completes a purchase, both interactions contribute to the variant's value, ensuring full-funnel optimization rather than surface engagement.

Business impact: Campaigns maximize revenue per send, not just traffic. Loomi AI targets buyers, not window shoppers.

Goal settings: purchase_item (for example, filtered to category = 'summer')

KPIs: Revenue per email, conversion rate.

Automatic campaign (banking and finance)

The problem: In automated lifecycle journeys, a click on a welcome email is a weak signal. A customer might click "Learn More" about a mortgage calculator but never use it. Optimizing for clicks can lead new users to dead ends rather than the features that drive retention. Connecting marketing automation to product analytics typically requires a costly custom integration.

The solution: Configure the contextual personalization node in your welcome series to optimize for app_downloaded or feature_used. Loomi AI tracks which variant actually drives customers to activate their account — not just open the email. For automatic campaigns, you don't need to worry about wait nodes in your
scenario — Loomi AI automatically starts tracking after the message is delivered, with a full 24 hours to capture downstream behavior.

An email interaction can be optimized based on an event that happens entirely inside a different environment — such as the mobile app — without any additional integration.

Business impact: Your welcome series automatically evolves to serve the content that drives digital adoption for each new customer.

Goal settings: app_downloaded, feature_used, or first_deposit_complete

KPIs: Activation rate, feature adoption.

Weblayers and banners (gaming and SaaS)

The problem: Optimizing banners for clicks leads to dark patterns — pop-ups that are hard to close or overpromise. If a customer clicks "Play Now" but drops off at registration, the banner failed — but a click-based model considers it a success.

The solution: Set the goal to registration_complete or level_up. Loomi AI captures behavior immediately after the banner interaction within the same session and learns which variant drives users past the click and into the product. Standard CRO tools track clicks — they rarely have access to deep backend events like level_up that define true success.

Example: A gaming company runs a homepage banner with two variants — "Join the Battle" and "Claim 500 Gold." Loomi AI learns that while "Claim 500 Gold" gets more clicks, "Join the Battle" attracts users who actually complete sign-up, and prioritizes it accordingly.

Business impact: Banners become a reliable source of qualified leads — not just traffic.

Goal settings: registration_complete or level_up

KPIs: Sign-up rate, quality of acquired users.

Mobile push (gaming)

The problem: Push is a high-risk channel. A poor email gets ignored — a poor push leads to an uninstall or system-level opt-out. Optimizing for opens encourages clickbait that frustrates users and permanently cuts off a key communication channel.

The solution: Set the goal to match_completed and let Loomi AI select variants based on each player's historical play style. If "Come back for a free chest" brings users to the app but they close it immediately without playing, no match_completed event fires and Loomi AI registers no value. If "Your troops need you!" inspires users to actually play a round, Loomi AI learns to prioritize that variant.

A successful re-engagement via push also updates the customer profile in real time — triggering an exit from any parallel email re-engagement flow, so you don't message a user who has just become active again.

Business impact: Higher daily active users who actually engage with your product — with lower notification opt-out rates over time.

Goal settings: match_completed or purchase_item

KPIs: Daily active users, retention rate, push opt-in rate.

App Inbox and in-app messaging (fintech and SaaS)

The problem: App Inbox messages are often ignored. If a bank sends a message about mobile deposit, what matters isn't whether the user opened it — it's whether they actually deposited a check. Measuring this typically means exporting message logs to a separate analytics tool.

The solution: Set the goal to mobile_deposit_complete and trigger the message on login. Loomi AI tracks each user for 24 hours after the message is delivered and identifies which message style drives the actual transaction for different customer segments. Because the App Inbox is part of the unified scenario builder, it's aware of what the customer did on the web or in email previously — not an isolated channel.

A successful in-app interaction feeds back into the customer profile, instantly updating their segment for future email or SMS campaigns.

Business impact: Faster feature adoption and higher upsell conversion — with no additional analytics integration required.

Goal settings: mobile_deposit_complete or subscription_upgrade

KPIs: Feature adoption rate, upsell conversion rate.

Cross-channel optimization (email versus push)

The problem: Push notifications often have higher click rates than email — but overusing push leads to opt-outs and app uninstalls, permanently damaging your addressable audience. A model that compares email and push purely on click-through rate will over-index on push for everyone, maximizing short-term engagement at the cost of long-term retention.

The solution: Assign a higher value to email to create a safety buffer. Loomi AI only sends a push notification when the customer is significantly more likely to engage with it than with an email.

Example: A fashion retailer runs a "New Collection Drop" campaign across email and push:

• Variant A (push): "The Fall Collection is here!" — high visibility, higher opt-out risk.
• Variant B (email): "Explore the new Fall Lookbook." — rich content, lower risk.

With email set to value 2 and push to value 1, Loomi AI makes the following decisions:

• A casual browser with a 3% likelihood to click email and 5% to click push → Loomi AI sends email. The engagement probability is similar, so the safer channel wins (email score: 3 × 2 = 6, push score: 5 × 1 = 5).
• A mobile-first user with a 2% likelihood to click email and 10% to click push → Loomi AI sends push. The high engagement probability justifies the notification (email score: 2 × 2 = 4, push score: 10 × 1 = 10).

Because Bloomreach manages both email and push in one platform, Loomi AI shares the same customer profile and frequency data across both channels — making this decision instant, with no data sync required.

Business impact: High engagement rates with significantly lower push volume to less receptive customers — preserving your push channel for when it really counts.

Value settings:

• Push: 1
• Email: 2

Goal settings: purchase_item or click

KPIs: Push opt-in retention rate, daily active users.

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Note

For step-by-step setup of cross-channel scenarios, see Contextual personalization for scenarios.