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CASE STUDY

AI Optimized Keyword Advertising Bidding

AT A GLANCE

The CPC prediction model analyzed web-scraped Amazon product data to optimize keyword selection for ad campaigns. Automated pipelines reduced average CPC by 3-6% and achieved a 10% reduction in Mean Absolute Error (MAE) with a custom hybrid model. These advancements streamlined ad targeting and enhanced campaign performance.

Client information

Company Name
SaaS platform provider
location
Toronto
SIZE
Mid sized
INDUSTRY
Advertising
Services Provided
Marketing campaigns optimization
3-6%
Percentage reduction in average CPC
10%
MAE reduction with custom hybrid model.

CHALLENGE

The project aimed to predict the Cost Per Click (CPC) using web-scraped Amazon product data, which includes various keyword-related metrics. The primary goal is to identify the most cost-effective keywords that maximize advertising reach while minimizing costs, ultimately enhancing ROI for digital marketing efforts.

SOLUTION

Implemented LSTM and hybrid models to predict and update average CPC daily, leveraging temporal trends for real-time bidding optimization.

IMPACT

  • The hybrid model exhibits a 10% in Mean Absolute Error (MAE), and Mean Squared Error (MSE)
  • The LSTM-enhanced models demonstrated their ability to capture and predict changes in CPC, helped with optimizing keywords bids in real-time, increasing the efficiency of advertising budgets and enhancing overall campaign performance

TOOLS

MYSQL
PYSPARK
DOCKER

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