maandag 26 mei 2014

Elections’ Epilogue: What Have We Learned?

First the good news: a MAD of 1.41 Gets the Bronze Medal of All Polls!

The results from the Flemish Parliament elections with all votes counted are:

 Results (source: Het Nieuwsblad)
SAM’s forecast
20,48 %
18,70 %
Green (Groen)
8,7 %
8,75 %
31,88 %
30,32 %
Liberal democrats (open VLD)
14,15 %
13,70 %
13,99 %
13,27 %

Table1. Results Flemish Parliament compared to our forecast

And below is the comparative table of all polls compared to this result and the Mean Absolute Deviation (MAD) which expresses the level of variability in the forecasts. A MAD of zero value means you did a perfect prediction. In this case,with the highest score of almost 32 % and the lowest of almost six % in only six observations  anything under 1.5 is quite alright.

Table 2. Comparison of all opinion polls for the Flemish Parliament and our prediction based on Twitter analytics by SAM.

Compared to 16 other opinion polls, published by various national media our little SAM (Social Analytics and Monitoring) did quite alright on the budget of a shoestring: in only 5.7 man-days we came up with a result, competing with mega concerns in market research.
The Mean Absolute Deviation covers up one serious flaw in our forecast: the giant shift from voters from VB (The nationalist Anti Islam party) to N-VA (the Flemish nationalist party). This led to an underestimation of the N-VA result and an overestimation  of the VB result. Although the model estimated the correct direction of the shift, it underestimated the proportion of it.
If we would have used more data, we might have caught that shift and ended even higher!


Social Media Analytics is a step further than social media reporting as most tools nowadays do. With our little SAM, built on the Data2Action platform, we have sufficiently proven that forecasting on the basis of correct judgment of sentiment on even only one source like Twitter can produce relevant results in marketing, sales, operations and finance. Because, compared to politics, these disciplines deliver far more predictable data as they can combine external sources like social media with customer, production, logistics and financial data. And the social media actors and opinion leaders certainly produce less bias in these areas than is the -case in political statements. All this can be done on a continuous basis supporting day-to-day management in communication, supply chain, sales, etc...
If you want to know more about Data2Action, the platform that made this possible, drop me a line: 

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on all levels of your organisation

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