Probabilistic Matcher

Zeta brings the power of our leading martech software directly to your Snowflake instance. Using Streamlit and Snowpark, the Zeta Probabilistic Matching Application enables you to enrich customer records stored in different systems that are not linked or not associated with customer records. Unlinked data can result in an incomplete customer view, redundant data entry, and impact the quality of care provided while also missing out on potential sales opportunities.

Zeta Probabilistic Matching combines multiple data sources with complex fields through automated probabilistic matching algorithms. It has an intuitive UI for non-technical users to customize algorithm parameters and output.

Zeta Probabilistic Matching Application; an advanced technique is used that utilizes statistical algorithms to match data from multiple sources. For this process two data sources are involved, i.e. unmatched data and matched data. Unmatched data contains complex data such as credit card transactions that cannot be easily matched with standard identifiers. Matched data refers to the CDP data that should be linked with the unmatched data.

Typically deterministic techniques are widely used in industrial record matching. However, with advanced data linking algorithms such as probabilistic matching, we can better match the records based on a provided score. This score determines how strictly one needs to match the records; we will get higher accuracy with a higher score.

Zeta Probabilistic Matching Application requires the below parameters to run:
- The main column on which the data should be matched
- A threshold score that indicates the quality of matched records.
- Additional optional parameters which contain extra columns for the algorithm to run on

First, in the Zeta Probabilistic Matching Application, upload your data to the application pointing to the Snowflake tables to be matched and check the data quality in the UI.

Select the parameters and perform the matching. The algorithm compares rows of data and calculates how similar the values are. Then a final table with the pairs of matched records between the customer data and the unmatched data is created. A low threshold means less accurate pairing. Users can change these parameters and examine the different matching metrics and graphs in the UI. Finally, users can discover additional insights based on matching.

The app with the demo data will provide details on the custom logic used to incorporate matching restrictions based on the store and the store zip area, which allows for more targeted and specific matching.

Zeta uses Snowpark UDF, Streamlit, and the Native App framework to enable this probabilistic matching capability. A preview of the application using Zeta sample data is immediately available by specifying the table names mentioned within the relevant help. To experience the application using your own first-party data please see the expected workflow below.

Expected workflow for client-specific data outputs:
1. Customer sends email to Zeta at: [e-mail] with the following information.
— Account ID
— Account Locator
— Industry Vertical
2. Customer creates two tables
2.1 Unmatched data
2.2 Matched data
2.3 One column in each table which contains first name and last name
3. Zeta will access client data and run it in the Zeta application environment to generate client-specific charts. (Estimated time to deliver is 3 business days)
4. Zeta will notify the client when they can run the application in their account to generate their client-specific outputs.

Businesses Need Data to be linked
Understand the best data sources to be linked:
- This will give the insights of the additional record information that can be linked

Accelerating Advertising Revenue:
- Maximize your ad spend by focusing additional linked attributes
- Improve revenue attribution, data quality, quantity, and match rates with minimal effort
- Measure and attribute ROI with greater accuracy

Audience Segmentation:
- Strengthen marketing execution with more complete insights

Security
Consumer data is kept private and secure. After the app is installed, it is recommended by the provider to grant the following privileges as needed.

Account level privileges:
Account level privileges : This app doesn't need any account level privileges. Privileges to objects: This app doesn't need any privileges to objects.

Diagnostics:
This app doesn't collect any logs.

搜集汇总

背景与挑战

背景概述

该数据集描述了一个基于Snowflake平台的Zeta概率匹配应用程序，它采用统计算法整合多源客户数据，提供可视化界面供用户自定义匹配参数。该方案能解决企业数据孤岛问题，提升营销效果和客户洞察，同时确保数据安全隐私。