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ValueMap Adoptimization

Quantify commercial upside based on machine learning algorithms. Implement changes quickly at scale based on recommendations.


Customers: large ecommerce companies
Challenge: Ad testing is given low priority due to high manual effort involved, but also difficulty to estimate the commercial upside
Objective: Identify actionable recommendations for high performing ads + patterns and implement at scale
Solution: Hybrid approach combining the power of machine learning and expert input. Recommendations are implemented with simple click, rather than painful manual processes 

What were the key challenges?

Challenge #1: Ad rotation algorithm by Google is a black-box. Optimization is geared towards CTR rather than conversions, revenue, or margins.

Challenge #2: Learning and optimization by Google does not take into account customer insights or business know-how by experts.

Challenge #3: Rolling out new ad ideas and testing different hypotheses is painful due to high manual workloard

Key Recommendation: Use a hybrid approach of machine learning (with transparent criteria) and expert input / creative input.


How does our solution differ?

The Ad Challenge module will provide you with ML-recommendations on which ads to pause/ eliminate

The Ad Mining module provides you with learnings about high performing patterns across different ad groups

The Ad Operation module allows you to easily rollout and label ads across multiple accounts and campaigns

Customers who use our products and services

Ready to get started?

Set-up 30 min consultation

We offer free consultation in order to see if and how we could help. Do not hesitate, the least you can expect is that we point you in the right direction!

Set-up free demo

We have a demos for our application with sample data. Wether you want to automate process, improve your SEA, or get a feel for HR Analytics, just get in touch.