Overview
To Improve Marketing Strategy and Budget, Pharmaceutical Marketers’ reliance on informed decision-making is becoming increasingly complex. The measurement exercises are happening more frequently owing to a digital-heavy omnichannel environment. Pharma organizations face challenges while assessing the performance of their marketing efforts. Speed, accuracy, and adoption of new techniques are of utmost importance, and hence, an accelerating intervention is needed to swiftly white-box the marketing effort outcomes.
With this spirit, Fractal offers Intelli Market, a one-stop solution for all the Market Mix decision-making. The accelerator catalyzes nearly all MMM/budget planning exercise processes through its state-of-the-art User Experience. It comprises all the necessary components of a traditional MMM but does that within a single suite, thereby decreasing the effort and hassle.
The platform is highly elastic and can accommodate data from all possible sources and stages in AWS native services. Harnessing the removal or replacement with a more specific adjective technology of aws, data management, data quality, and democratization of user base are achieved. Moreover, the accelerator houses nearly all possible ML algorithms that might help measure, simulate, and allocate budget to promotional efforts up to the practical execution level. Also, leveraging the disruptive Gen AI of Fractal, which has been tested across industries, the tool offers its users many controls despite technical novelty.
Hence, Fractal encourages Pharma organizations of all sizes and goals to catch up with the competition by pivoting around the ultimate marketing Catalyst- the “Intelli Market.
Intelli Market is built using Amazon Athena, Redshift, and Glue.
Highlights
- Democratization “Decision science for Decision makers”- Faster, highly configurable, and no code accelerator that is enabled by Gen AI that empowers brand managers to make decisions through modeling without any technical barriers
- Graphical white boxing to choose from the choice of model outputs with the least manual effort, leading to the measurement that is both statistically robust and viable for business
- Continuous-practical execution level measurement and optimization.
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