Completing the telecom analytic using SAP PA, I used a different data set for doing Association Analysis and Forecasting.
For Association Analysis I used the prepaid dataset which had subscriber plans. For compliance sake I changed the names of the plans.
Association analysis, sometimes also referred to as market basket analysis or affinity analysis, being used to find the strongest product purchase associations or combinations. Here the prepaid customer data was used identifying the prepaid recharges they have done over last 6 months. The idea was to find the association between recharge patterns. I had to create a custom R component to delete duplicate data and get unique data as Association Analysis needs to be done on unique data only.
Using the Apriori algorithm in SAP PA it was found that users buying the 3G Monthly plan and SMS plan frequently opted for a Net 49 plan. Hence these 3 plans
can be combined in the future to create one single comprehensive plan. Another thing that can be noticed here is that people option for corporate plan along with
video calling facility rarely used the 3G quarter plan. While people opting for Corporate along with 3G quarterly opted for Video Calling. This certainly indicates some
sort of pricing issues in the plan that can be sorted.
Forecasting Subscriber Base using Winter’s method:
Winters method sometimes also referred to as triple exponential smoothing, was used to forecast the future subscriber base for the company using SAP PA. The historical data of subscriber base from 1985 to 2010 was used to predict the foretasted subscribers.
The green line indicates predicated values of subscribers. While the blue bars represents actual number of subscribers. The prediction vs.
actual for the years 1985 to 2010 shows that the analysis is pretty close to actual. It also forecasts the future demand 2011 onward. By
looking at the graph we can say that the model we designed is pretty good, but how do we ensure quantitatively that the model is actually worth.
For this we need to refer to the “Algorithm Summary” window in the Predicted output. The Goodness of fit is 0.94 means that the fit
explains ~94% of the total variation in the data about the average indicating that the model is indeed a good one.