Oyster Customer Data Platform

Customer Data Platform
| 02 Feb 2022

Introduction Of Oyster CDP (Customer Data Platform)

Oyster is an artificial intelligence/machine learning-powered analytic customer data platform (CDP) and digital marketing analytics platform offered by Express Analytics that provides intelligent prescriptions that go beyond traditional CDP to help brands attain exceptional returns on investment (RoI) — from profitable acquisition to predictable retention.  Oyster is a single-point platform that integrates all business data across advertising, marketing, sales, commerce, and service channels.

Integration Architecture of Oyster CDP

Architecture:

Currently, Oyster is available on Amazon Web Services. Later versions of Oyster will be available on Microsoft Azure and Google Cloud Platform.

Source System Connectors:

Oyster has connectors to most source systems. The criteria to include a connector to a particular source system is the market share of the source system. The visitors’ browsing data from e-commerce sites or mobile apps is streamed in real-time, whereas the data from other sources come in on an hourly or daily basis. All the raw data from the sources are brought to the AWS S3 bucket.

Data Processing Engine:

Oyster ingests the raw data (both unstructured and structured) in AWS S3 and does multi-step processing before it loads data to AWS in a massively parallel columnar database. The data processing engine of Oyster is like a data refinery. At each step of the way the quality of the data improves significantly. 

Oyster data processing primarily involves 6 steps:

        • Data extraction
        • Data cleansing 
        • Data enrichment
        • Identity resolution
        • Data transformation
        • Data loading

Data Warehouse (AWS Redshift):

After the processing of data, the ingestion engine loads the data to AWS Redshift (columnar storage, massively parallel processing (MPP) database). This enables fast execution of the most complex queries operating on petabytes of data.

Oyster User Interface (UI):

Oyster UI is the front-end interface for Oyster users. Oyster UI primarily consists of the following modules:

  • Customer 360: Customer 360 visualizes all of the multi-touchpoint data where the customer interacts with the brand  – from customer profile to all of their browser data, their response to the promotions, and their interaction with customer services as well as social media behavior.
  • Work boards and action center: Each workboard is a collection of metric leaderboards and gadgets. Each is designed to keep the particular department in focus. Workboards further allow for customization of gadgets and metrics. Oyster has pre-defined intuitive workboards created for each of the key departments of an enterprise, namely:
    • Sales
    • Marketing
    • Customer Support
    • Product Management
    • Supply Chain
    • Information Technology
    • Human Resources
  • Campaign management: Through a diverse range of channels, content, and media, every marketing campaign targets specific audiences in an attempt to acquire new customers, and engage and retain existing customers. Oyster Campaign Management Screen allows planning, execution, and analysis of marketing campaigns, customer profiles, their potential touchpoint, and marketing content. 
  • Customer segmentation: When trying to reach customers with a marketing message or an ad campaign, targeting the right market with the right message is essential. If you aim too broadly, your message might reach a few people who end up becoming customers, but you’ll also reach a lot of people who aren’t interested in your products or services. When your messaging isn’t optimized for your audience, you’ll end up with a lot of wasted advertising dollars. Market segmentation can help an enterprise target only those most likely to become satisfied customers, or enthusiastic consumers of your content. To segment a market, you split it up into groups based on common characteristics. You can base a segment on one or more qualities. Slicing an audience in this way allows for more precisely targeted marketing and personalized content.
  • Scoring: Oyster uses a number of predictive machine learning models to score each customer. Each model provides a different score for the customer profiles based on the buying behavior of each customer. The scores help the marketing team in prioritizing leads, achieve higher lead qualification rates, and reduce the time that it takes to qualify a lead, etc.
  • Attribution: Oyster uses advanced deep learning models to attribute a conversion to a specific interaction by a specific channel. For online channels, there are two types of attribution models available. The standard rule-based models are available in the basic subscription to Oyster. For an advanced approach to multi-touch attribution (MTA), we use Markov Chain and Deep Learning MTA. These approaches are available in Oyster’s professional and enterprise versions. For offline channels such as TV advertising, direct marketing, snail mail, radio, etc. the marketing mix modeling (MMM) is available.

Stage File Types And Structure

Express Analytics accepts files in a flat-file format such as:

    • Pi