In the last decade, Big Data technology has been extremely disruptive with open source playing a dominant role in shaping its evolution. It has led to a complex ecosystem where new frameworks, libraries and tools were being released pretty much every day, creating confusion as technologists struggled to understand the intricacies of the systems. In 2015, I made an attempt in addressing that (see original article) by demystifying the space. Well a lot has changed in the last 5 years with new ecosystems like Spark and Flink that have disrupted the 1st generation systems, advances in AI and ML and…
Customer Data Platforms(CDP) have recently received a lot of attention as they are becoming an essential part of the digital strategy for an enterprise. This article provides an introduction to CDPs to help demystify their functionality and usage. Basically, a CDP is a marketer-managed which creates a persistent, unified customer database that is accessible to other systems. In general, it is built or bought infrastructure operated by marketers who act on real-time signals that the customers provide to in-turn deliver delightful personalized experiences back to the same customers.
Marketer-managed means that marketers can operate a CDP with minimal assistance from…
Back in 2015 I had written an article on 100 Big Data papers to help demystify landscape. On the same lines I thought it would be good to do one for AI. The initial part is about the basics and provides some great links to strengthen your foundation. The latter part has links to some great research papers and is for advanced practitioners who want to understand the theory and details.
In Part 2 we looked at how Personalization is a message or content that is relevant to the individual user, built on top of Globalization and interacts closely with Experimentation. In Part 3, we will look at how we have built out a Personalization platform that scalably solves for the scenarios we covered in Part 1.
There are 5 foundational blocks to Personalization namely Globalization, Experimentation , ML , Profile and Tracking & Instrumentation. Globalization and Experimentation are covered in detail in prior blogs, ML and Profile are below, while Tracking will be covered in a subsequent blog.
In Part 1 we looked at some of the scenarios on how we Personalize to create customer delight in their interactions with us. In Part 2 we will look at a functional framework in how we go about solving for those scenarios.
Let’s start with the basic definition of Personalization. Personalization enables suggestion of message or content that is relevant to the individual user, based on user’s implicit behavior (Optimization) and/or explicitly preferences (Customization). Optimization uses implicit interests and learns what you like from your actions. Customization on the other hand is driven by explicit preferences. Optimization usually is at…
Our mission at Intuit is to Power Prosperity Around the World for small businesses, self-employed and consumers. We want to generate more money, more time, and more confidence for 50+ million people. By leveraging our data we want to build delightful experiences and unlock the power of many for the prosperity of one. A key capability that powers our mission is Personalization.
In Part 3 we covered the execution engine that supports serving thousands of concurrent experiments to millions of users. In Part 4 we will look into the basics of experimentation analytics.
If you recall from Part 2, OEC refers to what measure, objective or goal we trying to achieve. It is a metric used to compare the response to different treatments.
For QuickBooks, the primary business metrics are:
In part 2 we covered the design and setup of experiments to support thousands of concurrent experiments. In Part 3 we will look into the execution engine that serves experiments.
Let’s look at the logical architecture — steps 1 to 3 are design time, while steps 4–10 are runtime, when the user interacts with the product.
In Part 1 we looked at how Intuit’s culture of design thinking has evolved to embrace rapid online experimentation. In Part 2 we will look into Design of Experiments.
Any experiment is a continuous process of design, execute and analysis. Let’s take a closer look at each.
There are some basic concepts in experimentation
This is a 4 part series on experimentation at Intuit. Part 1 covers the culture of experimentation @Intuit while Parts 2 to 4 cover how we have built a powerful and scalable experimentation platform to power data driven decisioning.
Our mission at Intuit is to Power Prosperity Around the World for small businesses, the self-employed and consumers. To achieve this we use design thinking to deeply understand our customers and solve their problems through Design 4 Delight (D4D).
D4D is ultimately about evoking positive emotion throughout the customer journey by going beyond customer expectations in delivering awesome product experiences.
Vice President, Data Platforms Walmart