Personalization @Intuit Part 3 — Platform 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 […]
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.
ML is to critical component for our personalization strategy.
Background — A ML algorithm uses example (training) data to create a generalized solution (a model) that addresses the business problem that needs to be solved. After you create a model, you can use it to answer the same business question for a new set of data. This is also referred to as obtaining inferences. A typical ML model cycle is shown below.
The model evolution is a continuous cycle. After deploying a model, you monitor the inferences, collect performance data and evaluate the model to identify drift. You then increase the accuracy of your inferences by updating your training data to include the newly collected performance data, by retraining the model with the new dataset. As more and more example data becomes available, you continue retraining your model to increase accuracy. A core part of the lifecycle is the ability to manage all the artifacts associated with a model. This includes the source code, environment metadata, feature-sets, training sets and trained models. This allows us to tie the various components together into a seamless platform and support end to end automation of the model lifecycle.
A model can predict an outcome during a user interaction in one of the following ways.
A user profile needs to encapsulate the view of customer with all profile attributes such as behavioral, social, mobile, demographics, transactional, contextual and location data, with the ability to aggregate & cross link 1st party (mobile, web) 2nd party & 3rd party data sets seamlessly across distinct data sources. The data model needs to be flexible to support extensibility where new attributes can be added dynamically.
The profile service can Personalize either on a strong identity like logged in user, phone, email or a weak identity like cookies,mobile identifiers or social identifier. The profile backend needs to supports highly parallelized ingestion of new identities and profile maps with inbuilt data security, privacy and compliance. Any Personalization technique has serious consequences to privacy and needs to be carefully evaluated around website’s data collection, data usage and sharing policies. It needs to be either disclosed in T&C or driven by explicit user consent. The profile service needs to support both deterministic segments or predictive scores.
With all the concepts defined, a simplified architecture for serving of Personalization content is shown below.
To conclude, at QuickBooks we leverage Personalization to create user delight . There are 5 essential components to a successful Personalization strategy namely Globalization , Experimentation , ML , Profile and Tracking.
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