iBOOD Achieved 16.6X ROIwith AI-Powered Automation

How iBOOD, Europe's top daily deal platform, went from zero retention to 6.3% revenue share with Replenit's AI-powered replenishment engine.

Decision Objective

Replenishment

Industry

Multi-category eCommerce

Upstream

MongoDB

Downstream

Insider

Expansion

5 New Countries

16.6X

Return On Investment

6.3%

Revenue Share

+27%

Higher AOV

164,038

Data Field Enriched

54-Day Turnaround

From Zero to #1 Automation

iBOOD wasn't built for retention—their flash sale model had no static product feed, no recurring inventory, and no product availability guarantees. Replenit changed everything.

Zero Tech Lift No Product Feed

WEEK 01-02

Integration

Zero engineering effort. Connected to existing CRM stack.

WEEK 03-04

AI Learning

Behavior analysis identifies 100+ repeat-worthy SKUs.

DAY 54

#1 Revenue Flow

Top-performing automation across all marketing flows.

The Partner

Europe's Leading Daily Deal Platform

iBOOD is one of Europe's top daily deal platforms, built on flash sales and discovery-driven purchases. Their unique model posed serious challenges to traditional lifecycle marketing.

With no static product feed, no recurring inventory, and no availability guarantees, the question was: Can you drive predictable, repeatable revenue in a model built for one-time purchases?

The Flash Sale Dilemma

Retention in a Non-Retention Model

iBOOD's unique structure posed serious challenges. Retention messages were reactive, based on promotions rather than customer readiness. There was no system to identify repeat behavior.

Flash-Sale Driven TimingMessages sent only when deals reappeared—reactive, not predictive.
No Signal for Repeat BehaviorNo system to identify or act on behavioral replenishment patterns.

BEFORE_REPLENIT

FLASH SALES

No product feed • No recurring inventory • No retention

Reactive

One-time

Deal-based

RETENTION_NOT_POSSIBLE

When AI Took the Wheel

Behavior, Not Catalogs

Rather than relying on static product data, Replenit's AI analyzes customer behavior to answer: What to offer next? When is the optimal time? Through which channel?

AI-Timed Readiness

Detects when each customer is likely to reorder.

Omni-Channel Scale

Email, App Push, and Web Push automated.

SKU Discovery

100+ repeat-worthy items identified automatically.

Zero Tech Lift

Works with existing stack, no engineering needed.

AI_BEHAVIOR_ENGINE

LIVE

Customer Behavior

What

to offer

When

optimal time

Where

which channel

100+ SKUs identified

16.6X_ROI_ACHIEVED

AI-Timed Replenishment

From Inbox to Lock Screen

AI predicts the perfect time to remind each customer. Dynamic product details auto-filled. Runs on autopilot—no campaigns to build.

ibood.com/replenishment-showcase

iBOOD Multichannel Replenishment - Email, App Push, Web Push

Product Grouping

Multiple items in one reminder

Dynamic Content

Name, price, image auto-filled

Web & App Push

All dynamic attributes

Beyond Campaigns

From Automation to Intelligence

Replenit became iBOOD's core intelligence layer, shifting their mindset from CRM execution to strategic retail marketing.

Prioritize High-CLTV Deals

Focus on lifetime value, not just high-margin products.

Negotiate with Suppliers

Use real lifecycle data to drive better deals.

5 New Countries

Expanding to Belgium, Germany, Poland, France, and Austria.

"Working with Replenit has been a game-changer for our retention strategy. The setup was incredibly smooth—no tech lift required—and their team truly understood our challenges from day one."

Hannah van den Berg

Sr. CRM Marketer, iBOOD

LOG_01VERIFIED

“We were dreaming about an automated solution that could cover all of our products and customers and Replenit jumped in with a fully automated and full coverage of our product portfolio.”

Jennifer Blease-Williams

Digital Marketing Lead, Faith In Nature

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“I am thoroughly impressed with Replenit for several reasons. Their alignment with our vision was immediately apparent, making the decision to work with them a natural choice. Particularly, their capability to handle complex data sets and develop customized solutions to meet our specific requirements has been outstanding.”