Blå elefant

How we shifted focus from product to purchase intent and scaled Blå Elefant's sales on Meta Ads by 807%.

growth

+807%

sales on meta

profit

+404%

meta roas

conversions

+564%

meta orders

The
problem

Traditional Targeting Missed Gift Intent: Blå Elefant was drowning in generic product advertising targeting the end user instead of the actual buyer.

Rapid Ad Fatigue: Severe ad fatigue and skyrocketing CPCs during peak season.

Signal Loss Post-iOS 14: Severe degradation of tracking signals post-iOS 14, leaving Meta blind to who was actually buying for whom.

CATALOG

—

CREATIV

—

Blå Elefant

—

Gifts

—

OPTIMERING

—

STRATEGI

—

DATA

ROAS

—

CATALOG

—

CREATIV

—

Blå elefantt

—

Gifts

—

OPTIMERING

—

STRATEGI

—

DATA

—

ROAS

—

The solution

01 / Gift-Intent Segmentation

We stopped selling products and started solving the gift dilemma. By re-engineering the creative strategy, we built dedicated entry points for parents, grandparents, and friends, giving the algorithm the exact emotional context needed for optimization.

02 / Algorithmic Creative Volume

To crush ad fatigue, we produced high volumes of unique hooks and visual angles. We fed the platform an unrelenting stream of creative assets, allowing Meta to match the right message to the right audience in real time without ad spend stalling out.

03 / Structured Catalog Synergy

We tore down the generic product feed and built bespoke product sets based on age, price, and occasion. This transformed Advantage+ campaigns from blind sales engines into intelligent, personalized recommendation systems. By shifting focus from product to purchase intent, we scaled Blå Elefant's sales on Meta Ads by 807%.

import { MMM } from 'zkond-core';
const engine = new OptimizationEngine();
// Analyserar Google Ads Performance...
engine.analyze('ROAS', '7.2x');
if (engine.status === 'ready') {
  engine.applyScale(Math.PI * 2);
}
console.log("CR: +12.5% | Sales: +185%");
function syncPixels(cid) {
  return cid.map(t => t.value);
}
// Running deep-link validation...
let data = MMM.fetchMatrix('Q3_DATA');
const config = { mode: 'aggressive' };
data.process(config);
// Fetching GA4 Realtime API stream...
const ga4Stream = await fetch('/api/ga4/stream');
const metrics = await ga4Stream.json();
if (metrics.bounceRate < 0.25) {
  budget.allocate('Meta Ads', 0.40);
}
/* Executing Machine Learning Attribution Modeling */
const model = new DataDrivenModel();
model.train(metrics.historicalData);
console.log(`Attribution confidence: ${model.score * 100}%`);
const webhookUrl = "https://zkond.se/hooks/v1";
await axios.post(webhookUrl, { status: "OPTIMIZED" });
// Cleaning temporary caching matrices...
Cache.clear(['temp_roas', 'temp_cpa']);
let activeCampaigns = engine.getRunning();
activeCampaigns.forEach(c => c.refreshBid());
console.warn("High CPA detected on TikTok Ads - adjusting.");
engine.throttle('tiktok_pixel_01', 0.85);
// Optimering slutförd. System stabiliserat.
window.dispatchEvent(new Event('ZKOND_SYNC'));
import { MMM } from 'zkond-core';
const engine = new OptimizationEngine();
// Analyserar Google Ads Performance...
engine.analyze('ROAS', '7.2x');
if (engine.status === 'ready') {
  engine.applyScale(Math.PI * 2);
}
console.log("CR: +12.5% | Sales: +185%");
function syncPixels(cid) {
  return cid.map(t => t.value);
}
// Running deep-link validation...
let data = MMM.fetchMatrix('Q3_DATA');
const config = { mode: 'aggressive' };
data.process(config);
// Fetching GA4 Realtime API stream...
const ga4Stream = await fetch('/api/ga4/stream');
const metrics = await ga4Stream.json();
if (metrics.bounceRate < 0.25) {
  budget.allocate('Meta Ads', 0.40);
}
/* Executing Machine Learning Attribution Modeling */
const model = new DataDrivenModel();
model.train(metrics.historicalData);
console.log(`Attribution confidence: ${model.score * 100}%`);
const webhookUrl = "https://zkond.se/hooks/v1";
await axios.post(webhookUrl, { status: "OPTIMIZED" });
// Cleaning temporary caching matrices...
Cache.clear(['temp_roas', 'temp_cpa']);
let activeCampaigns = engine.getRunning();
activeCampaigns.forEach(c => c.refreshBid());
console.warn("High CPA detected on TikTok Ads - adjusting.");
engine.throttle('tiktok_pixel_01', 0.85);
// Optimering slutförd. System stabiliserat.
window.dispatchEvent(new Event('ZKOND_SYNC'));



Catalog segmentation



Time-automated bidding rules for peak season



Server-side tracking



Advanced catalog filtering and custom feeds



creatives

Up to 52 unique creative assets running simultaneously.

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