Oyster CDP (Customer Data Platform)

Introduction of Oyster CDP (Customer Data Platform)

Oyster is an AI/ML powered CDP and marketing analytics platform that offers intelligent prescriptions that go beyond traditional CDP to help brands attain exceptional 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.

Integration Architecture of Oyster CDP

Source System Connectors:

Oyster uses connectors like RudderStack and Cyclr to connect to various source systems. The visitor’s browse data from the ecommerce sites or mobile apps area streamed in real time basis whereas the data from other sources come in at hourly or daily basis. All the raw data from the sources is brought to AWS S3 bucket.

Data Processing (IICS):

IICS takes the raw data at S3 (both the unstructured and structured) and does required processing before it loads data to AWS redshift.

IICS data processing primarily involves 5 steps:

Data Warehouse (AWS Redshift):

Data warehouse or an analytical database for Oyster is redshift. After the data processing, IICS loads the data to redshift. Redshift is a columnar database where it’s massively parallel processing (MPP) enables fast execution of the most complex queries operating on large amounts of data.

Oyster UI:

Oyster UI is the front end interface for Oyster users. Oyster UI is primarily consist of following components.

  • Customer 360: Customer 360 visualizes all of the multi-touchpoint data where customer customer interacts with the brand  – From customer profile , to all of their browse 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 of which are designed keeping 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 any organization namely.
    • Sales
    • Marketing
    • Customer Support
    • Product
    • Supply Chain
    • IT Administration
    • HR
  • Campaign management: Through a diverse range of channels, content, and media, every marketing campaign targets  specific audiences in an attempt acquire new customer, 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 [Copy edit] When trying to reach customers with a marketing message or 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 you to target just the people most likely to become satisfied customers of your company or enthusiastic consumers of your content. To segment a market, you split it up into groups that have similar characteristics. You can base a segment on one or more qualities. Splitting up an audience in this way allows for more precisely targeted marketing and personalized content.
  • Scoring [Copy edit]: Scoring uses a predictive machine learning model to calculate different score for all customer profiles. The score helps marketing team in prioritizing leads, achieve higher lead qualification rates, and reduce the time that it takes to qualify a lead, etc.
  • Attribution [Copy edit]:

Stage File Types and Structure

Express Analytics requires that files be provided in a flat, text-based format such as:

    • Comma-delimited files (CSV)
    • Tab-delimited files (TSV)
    • Pipe (|)-delimited files

These files can be column-based with delimiters or in key-value format. 

Non-text-based file types such as Microsoft Excel (.xls or .xlsx) or Word (.doc or .docx) are not accepted. As a general rule of thumb, if the file contents cannot be previewed within a command-line terminal or simple text editor (like WordPad or TextEdit), we cannot accept it.

Note: If exporting data from Microsoft Excel for Mac, choose the Windows Comma Separated (.csv) option. Do NOT use the MS-DOS or Macintosh CSV versions.

Compression

Express Analytics strongly recommends compressing files before transferring them to Express Analytics, as this saves storage space, reduces transfer time, and makes it simpler to detect when files are not completely transferred (incomplete files will fail to decompress). The exception to the is recommendation is for files that you intend to manually add to a Source in our self-service UI; these should be uncompressed and unencrypted.

Express Analytics can accept these file compressions:

  • gzip (.gz)
  • 7z (.7z)
  • ZIP (.zip)

All of these can be automatically decompressed once automated ingestion is set up, with gzip preferred but not required. We cannot accept password-protected compressed files – for additional security options see “Encryption” below. The original and compressed files should have the same name (minus different file extensions), i.e. filename. txt.gz when decompressed should result in filename.txt.

Archiving

Express Analytics accepts multiple files archived together for transfer. That is, any .zip or .tar.gz files should, when decompressed, result in one file.

Encryption

GPG/PGP-encrypted files using Express Analytics’ Public key can be automatically decrypted when automated ingestion is set up. If necessary, an RSA version is available as well but is not supported for automated ingestion. If you require use of the RSA key, please contact your Express Analytics representative.

If you would like to compress and encrypt your data, be sure to either:

  • Use the built-in compression functionality of gpg.
  • Compress the file, then encrypt it. This will produce a .gz.gpg file extension, for example.

File Naming

Filenames should use only ASCII characters and should not contain whitespace characters or special characters such as !@#$%. Underscores “_” are permitted, but should not be the first character of the filename.

Express Analytics does not mandate a particular naming convention. Please do include these elements in order to make differentiating your data easier and prevent any chance of overwriting files:

  • Type of data/description
  • Date/timestamp indicating either when you created the file or the time range of the data

Example: TwoButtonSuitsCampaign_impressions_2016-01-01.csv

Data Formatting

Regardless of whether your data is formatted as columnar or key-value, adhering to these general guidelines will produce best results. For all data types, We recommend using UTF-8 encoding. UTF-8 is in wide adoption and ensures maximum compatibility across different systems. ASCII encoding is accepted as it is included in the UTF-8 standard.

  • Put all data related to a given identifier on a single row. That is, if you have three different segments, put them all on one row either as three columns or three key-value pairs rather than listing the same identifier on three rows with one segment per row. The latter approach will make file processing take significantly longer. Exception: multi-valued data. If you have a column where there are multiple values for the same header, each column value should be on a separate row, along with the identifier.
  • When in doubt, use double quotes. Data containing punctuation characters is at risk of delimiter collision and thus data bleed, where the delimiter chosen (such as a comma or pipe) also appears as part of the data values. This can cause us to interpret data in a particular row as belonging to the wrong field. To avoid this, enclose each field, key, and value in double quotes, with delimiters such as commas, pipes, and equals signs outside the quotes. Please ensure all quotes are closed with an even number of double quotes characters per data row. If using quotes, best practice (but not required) is to quote all fields rather than only those with delimiter collision potential.  It is not necessary to quote empty/null fields. If a particular field value itself contains double quotes characters, they should be properly escaped and maintain the “even number of quotes” rule:
    • LCD TV,50″ becomes “LCD TV”,”50″””
    • “early-bird” special becomes “”early-bird” special”
  • Do not use placeholders for empty values. If a given field for a particular row of data has no value, leave it blank. Do not use a placeholder such as “NULL” or “N/A”.

3.2 COLUMNAR DATA

Express Analytics prefers and recommends that you provide column-based files, particularly when there is more than one identifier field (this will typically be offline/PII data with a name & postal, multiple email addresses, or some combination thereof).

Include a header row in every file. By including a header, we are able to flexibly accept columns in any order and can automatically detect and map much of the data without human intervention.

In a single file, every header must be a unique label. Files with two or more columns with the same header cannot be processed.

If you do not intend to use a particular data column, do not include it in the file. Each data column makes the file larger, and this size increases as a function of the number of rows in the file. Plus, we need to inspect that column for privacy compliance. The smaller the file and the fewer analysis operations Express Analytics needs to perform, the faster the file can be processed. If a given column will not be used as an identifier or as a segment to be distributed, do not include it.

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File Formatting Guide

Files must be rectangular. Every row should contain the same number of delimiters and fields. If a given field has no value for an identifier, simply leave that value empty for that row.

Express Analytics does not accept fixed-width files. Please use a delimited format.

3.3 KEY-VALUE DATA

Express Analytics accepts key-value files. These are best suited for single-field identifier files (this will typically be online data such as cookies or offline data tied only to a single email address or phone number).

When there is a single identifier, it should be the first field of each row and does not need to include a key:

<identifier>,k1=v1,k2=v2; not ID=<identifier>,k1=v1,k2=v2

When there are multiple identifiers, they should be in key-value format:

email=<email address>,phone=<phone number>,k1=v1,k2=v2

Separate keys and values by an equals sign (=). Please contact your Express Analytics representative if

this will be a problem for you.

Separate the identifier and each key-value pair with a comma or similar delimiter. Delimiter

options are the same as for columnar data.

Keys should be unique per row. That is, do not include something like <identifier>,k1=v1,k1=v2. In the case of multi-valued data, make a new row tied to the identifier for each value tied to the same key.

If using double quotes, do not enquote the equals sign or delimiters. Each key and value should

be separately double-quoted:

“<identifier>”,”k1″=”v1″,”k2″=”v2″; not “<identifier>”,”k1=v1″,”k2=v2″.

If there is no value for a given key for an identifier, do not include that key in that row. That is, do not include something like “k1=”.

(Optional but recommended): Include every possible key in the first row. This allows us to quickly detect every key that might show up in the file and reduces the chance of error. This row can be all “dummy” data: a placeholder identifier and all keys set to equal “1”.

4  IDENTIFIERS

Express Analytics accepts both offline (personally-identifiable) and online (anonymous identifiers). Offline and online identifiers should never be in the same file.

Note: Match data from our match partners excepted. Contact your Express Analytics representative for details.

File Formatting Guide

Files of the same type for ingestion in the same Source should always have the same set of identifier fields: names should be consistent from file to file. For offline Sources with multiple identifiers (for example, name & postal plus email), if a subsequent file contains data tied only to some subset of those identifiers, still include those headers and simply leave the row data blank.

4.1 OFFLINE

Express Analytics can accept one or multiple pieces of offline identifiers in a file: email address, names & postal, telephone number, or any combination thereof. All offline identifiers for the same person should be put on the same row.

In order to maximize reach and maintain accuracy, the following 14 bold standardized are highly recommended for every file of offline identifiers you upload to us. If you cannot supply a given field of data, you may include the respective header anyway and simply leave that column blank. Headers must be included in the first line of every import, and should match the contents of the file.

In order, the standard fields are:

  • Client Customer ID (should be unique and persistent)
  • First Name
  • Last Name
  • Street Address 1
  • Street Address 2
  • City
  • State
  • Zip Code
  • Zip Plus 4
  • Email1
  • Email2
  • Email3
  • Phone Number1
  • Phone Number2

 

  • Field1
  • Field2
  • Field25

The first field (“Client Customer ID”) should be unique and persistent across all audiences. This identifier will allow us to de-duplicate rows in the uploaded file, in case a file has multiple rows related to the same person. Please contact your support team if you are not using a Client Customer ID to identify records.

 

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