Test and Learn Platform (TALP): An End-to-End Experimentation Solution by Tredence

por Tredence Inc

Test and Learn: An AI-powered experimentation platform to design experiments & exploit winning ideas

Objective: Develop an end-to-end experimentation solution for designing and executing experiments in marketing, digital, and retail. Reducing the time spent on designing, executing, and measuring experiments from weeks to hours

Enterprise User challenges:

  • Legacy testing platforms using decades-old analytics limit the enterprise value of developing an experimentation culture.
  • Learns only within the context of a given experiment, limiting the adoption of a learning culture and reducing the enterprise impact of insights.
  • The current marketing team has a broken experimentation process and no holistic view of how each initiative is affecting the purchasing behavior of customers.
  • Limited visibility and understanding of the effectiveness of marketing experiments for campaign owners
  • Dependency on campaign analysts for standard campaign effectiveness read outs

How do we address challenges?

TALP is a lightweight, configurable platform for designing marketing innovation experiments and obtaining holistic 360-degree performance. It augments the decision-making of campaign owners, store managers, product managers, and marketing managers by providing a 360-degree view of experiment performance by highlighting critical metrics, analyzing lift, and providing cross-sectional insights.

These insights are used to improve campaign design and audience selection processes, as well as new product feature launches, promotional plans, and so on.

  1. Design your new experiment: proactive system recommendations & ML techniques help you build the optimal experiment design, test it, and match it with the control.
  2. Measure tests & their impact across variables. Enable a full 360-degree real-time view into experiment performance across all key measures, such as sales, profit, shopper response, traffic, etc.
  3. Learn from the adjacent experiments. Utilize adjacent learning to drive enterprise-wide insight adoption and a virtuous cycle of experiment improvement.

Implementation uses these native Azure components: Azure Data Factory, Azure Data Lake storage (raw and curated data), Azure SQL DB, Azure DB, Azure Analysis Services, Azure DF, and Azure DevOps

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