With online marketing dollars getting wasted in advertising — (i) largely because ads were reaching the wrong audiences, (ii) reaching the right audiences at the wrong time, or (iii) ad spend was more than necessary to achieve the defined marketing goals, the client needed to automate bidding in a way which allows their customers to optimize their ad spend based on their marketing goal – site visits, impressions, conversions, etc.
The steps in the building of the predictive machine learning model process were:
Defining Outcomes: Determining what questions we wanted the model to answer, like “How many of my existing customers are likely to buy this product/service again in the next 12 months?” This helped us highlight the features that were most required.
Data Collection: Determining which data we need, how do we collect it, and the best ways to label/store it.
Data Analysis: We analyzed patterns based on historical data and performance through both manual and automated approaches to form conclusions about customer purchase behavior.
ML Modeling: We tested out those conclusions by predicting a future outcome with AI algorithms based on the past data available.
Deployment: We leveraged business analytics and predictions to help customers determine the best course of action.