Estimated actual registered voters in this area, extrapolated from your survey sample using census population data (57% voter registration rate).
Priority score is calculated from: swing voter density (35%) + opposition softness (30%) + anger index (20%) + LDF gap to majority (15%). Red = urgent action, Amber = important, Green = lock & protect base.
These are data-derived scripts specific to this area. Customized from survey responses on what would flip each voter type. Print and hand to your volunteers.
Recommended deployment sequence for this panchayat, based on the data.
This shows what fraction of each party's supporters are truly locked in vs. still open to persuasion.
Counts show actual surveyed respondents. Higher swing % in an age group = bigger opportunity for LDF.
"Open to Change" = people in this group who haven't fully decided yet.
This score combines voter dissatisfaction with government services, low MLA rating, and anti-incumbency sentiment. Anything above 6 out of 10 is a red flag that needs immediate action — a candidate visit or local resolution can convert anger into votes.
Run your ads and campaign content on these platforms to reach the most people.
These are the specific channels to target for maximum conversion impact on swing voters.
Each percentage comes with a margin of error. Smaller panchayats (Koruthodu n=91) have high uncertainty (±10%). Only trust a number if its error range doesn’t cross party boundaries.
Tests whether the relationship between each demographic factor and party alignment is statistically significant, or could be explained by random chance in sampling.
Which factors most strongly predict whether a voter is undecided? Odds Ratio > 1 means this factor increases swing risk; < 1 means it reduces it.
Pearson r: +1 = perfect positive, -1 = perfect negative, 0 = no relationship. Spearman r handles non-linear patterns.
Machine-learning clustering of voters into natural segments based on age, anger, MLA rating, life satisfaction, religion influence, and household voting pattern. These are data-derived personas, not assumed ones.