APPROACH

              We forecasted the total spend on two types of coupons – inflight and out-of-box coupons. We did this by:

              • Creating a data architecture in Azure to automate data extraction process
              • Multiple forecast techniques using coupon parameters such as face value, duration, days in market of the coupon
              • Transformation of ML prediction to data structures that can be consumed by PowerBI

              KEY BENEFITS

              The solution provided a coupon stimulator which predicts the duration and face value of the coupon to optimize spends

              It also provided an interface to user with report on coupon spend forecast to identify drivers of change and deep dive at a coupon level the reason for change from previous month

              RESULTS

              • Our solution was able to reduce the time required for data extraction process from ~800 hours/year to less than 100 hours/year
              • Our solution was able to achieve ~94% accuracy for In-flight coupons and ~80% accuracy for out-of-box coupons

              亚洲图