Insight, Impact, Innovation
HomeOur WorkCase StudiesCAE helped reduce time taken for market mix modeling by 50%. Executed 200+ projects already

CAE helped reduce time taken for market mix modeling by 50%. Executed 200+ projects already

Download PDF


Centralized Analytics Environment (CAE) is Fractal’s proprietary analytics platform which provides an analytics development infrastructure along with codified standard workflows and algorithms to develop analytics at scale with speed, thus making more capacity available for innovation.

The Business Challenge

Clients want to run market mix modeling to assess marketing performance on regular basis and at greater frequency than annually/ biennially

  • Long processing time (3-4 months) for a typical model, such delayed analyses make it difficult to respond to changes in the market in real time
  • Lack of consistency between studies in terms of the outputs, treatment of variables, model parameters, etc. makes comparisons difficult and validity of the models questionable and ambiguous

Desired outcome from CAE

Help generate analyses through a standard and aligned modeling process at speed
Enable end to end meta-analysis across studies in more real-time manner 
 

The Solution

Leveraging CAE platform, created a standardized market mix modeling solution with following features-
  • End to end standardized workflow for data processing, iterative model building and model result visualization in a single platform
  • Modular solution to customize as per input data and modeling assumptions
  • A web-based analytic collaboration layer to enable real-time collaboration to view model results
  • An insights-repository to enable cross-comparison and meta-analysis

The Results

Delivered market mix modeling in 50% less time and enabled meta-analysis across studies in real time

Insight

  • It is possible to bridge the gap between data availability and actionable business insights by building a standardized approach to enable scale, and customizing the parameters and assumptions to make it relevant to business
  • Real-time and active collaboration across all stakeholders (including decision makers and data scientists) enables much faster model development and higher confidence in model result.

Impact

  • Compressed data preparation and modeling process time to 50%, expected business impact of $150M for one of our clients’ emerging markets operations globally
  • Reduced errors by 100% through standardization of data handling/ transformation processes and elimination of multiple platforms/ interfaces

Innovation

  • Real-time collaboration through the web helped rapidly iterate between various model results and finalize the best one. This helped decision making become 2x faster.
  • “White box approach” to analytics helped build faith in the models and enabled insights adoption and driving action