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.
- Design your new experiment: proactive system recommendations & ML techniques help you build the optimal experiment design, test it, and match it with the control.
- 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.
- Learn from the adjacent experiments. Utilize adjacent learning to drive enterprise-wide insight adoption and a virtuous cycle of experiment improvement.