If you have ever been exposed to the website analytics or digital marketing world, you are probably familiar with Adobe Analytics, Adobe Campaign, or Adobe Target. Now, there is a new Adobe product at the forefront of emerging Customer Data Platforms—Adobe Experience Platform (AEP).
Adobe describes AEP as:
"The foundation of Experience Cloud products... an open system that transforms all your data —Adobe and non-Adobe — into robust customer profiles that update in real-time and uses AI-driven insights to help you to deliver the right experiences across every channel," (Adobe Office Website).
This tool is a platform that provides services for data warehousing, building and deploying advanced machine learning models, and perhaps most importantly, deploying insights and results back to the business world.
To discover this product’s true capabilities, we completed a proof of concept (POC) project that leverages AEP to create and publish a machine learning model. In the POC, we ingested Salesforce data and built a model that predicted an opportunity’s probability to convert into a sale. We chose this use case based on our experience working with many of our clients in the B2B space, who often have longer sales cycles consisting of multiple digital and personal interactions.
We will walk you through our entire process as we explored AEP from a Data Scientist’s perspective. We will discuss challenges and surprises we encountered, and provide learnings and tips for using this product.
The homepage of AEP after logging in
The Three Main Stages
To make things simpler, let’s break down this process into three main stages: Discover, Build, and Activate. This will make it possible to select the most impactful and feasible solution for your business.
This stage aims to gain a solid understanding of both business goals and data. It requires strong communication between the domain experts and data experts, and good collaboration with clients. For any model to be useful, we must be constantly aware of the business context that we start with and end with, and identify an activation plan upfront.
This stage is when the Data Science team will be heads-down working on all the technical pieces. We want to create contextually, meaningful features for a contextually, meaningful high performing model.
The goal of this stage is to act on the model and create a measurable impact. It’s also a collaborative phase that requires the input of all teams. Some sample outcomes may be an increase in click-through rate, revenue, or new site visitors, or increased efficiency measures through internal processes or site dwell time.