Competitive Survey Information

Key Highlights

  • Optimized Burial/Cremation price or sales volume w.r.t. competitor.
  • Bucketing of location based on correlation of Burial/Cremation change w.r.t. competitor.
  • Optimized return and operating profit by change in Burial/Cremation prices business decision.

Challenges

The client didn’t consider their competitor’s Burial/Cremation prices as a metric for analyzing their sales volume. The objec1ve is to predict an op1mized Burial/Cremation sales price w.r.t compe1tor and also predict the change in sales volume

Approach

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  • Analyzed the client’s exis1ng compe1tor data for both burial/cremation.
  • Segmented the location as Posi1ve/Negative correlated with each location.
  • A polynomial (quadratic) equation is fit to the demand curves for each location.
  • Optimum Burial/Cremation price and predicted volume is obtained with respect to competitor(s).
  • Optimum return/operation profit is obtained with respect to competitor(s).
  • Graphically visualize the effect of change in Burial\Cremation price on sales volume with it’s relative correlation coefficient.
  • Guided client in integrating/consuming the machine learning results as part of business model/process

Tech Stack

Python, Spyder, Sta1s1cal and Machine learning libraries/packages numpy.polyfit, Scipy (Pearson – correla1on),TFS,Jira

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