Project

BETTER FACEBOOK ADS RETARGETING PERFORMANCE

Client

AdEmotion

General info

Client

AdEmotion provides a SaaS service to digital agencies looking to increase conversion through tailored, personalized creative.

Project

DLabs.AI developed an AI tool that reviews how specific target groups use Facebook. The algorithm analyzes the social network to glean information around what type of imagery a user group prefers. Then, it leverages the insights to deliver a hyper-specific look across different websites.

About the project

Problem

Creativity doesn’t account for the preferences of the individual user. This leads to brands delivering the wrong aesthetic to target groups and so failing to capture their interest.

Solution

An AI tool that analyzes the aesthetic preferences of different user groups, using psychological profiling to determine the correlation in preferences based on Big-5 theory.

Results

The tool links target group details to keywords, using predictive algorithms to determine the psychological traits of a particular group, then producing a creative brief per segment.

Project duration

12 months

  

Technologies used

Python

Django

Pandas

Docker

Nltk

Angular

Nginx

Scikit - Learn

  

The path to success

Step 1: Data collection

Create a survey to determine the psychological traits of a given user

Use Facebook data (inc. posts, likes, shares) to enhance findings

Step 2: Data integration

By combining insights around user behavior on Facebook with personality models based on the Big-5 theory, we devised a custom classification methodology

We based the methodology on levels of neuroticism, extraversion, openness to experience, agreeableness, and diligence

We added data points that assessed aesthetic preferences, helping understand both the user’s psychological traits and the kind of creative content they enjoy (e.g., abstract, organic elements, people, symmetry, abstraction, etc.)

See it in action

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