As pollsters continue to explore theories of what went wrong, an interesting picture is emerging. So far, little statistical support was found for the “shy Trump voters” theory (see this 538’s analysis) . Opinions seem to converge on polls underestimating the unity of the republican base and the eleventh hour splash in enthusiasm that the polls were slow  to capture. Strategies focused on fragmenting the republican base didn’t work as expected. This goes well with the conclusion we reached above: in case of republican women party identification was a much stronger factor of their opinions and voting behavior than gender identification. Similarly, post-election analysis shows that an attempt to influence larger numbers of Latino voters  – another target group – wasn’t too productive either, despite earlier expectations that GOP party, and its base, would splinter .

An additional issue that we also noted in the previous post is that horse race polls impose a potentially misleading identity framework, don’t sufficiently support micro-targeting and also are slow to reflect shifts in the electorate. Broad characteristics of the electorate measured by such polls (gender, party affiliation, ethnicity) cannot compete with detailed user profiles accumulated by social networks and search engines – which allow for a much more precise targeting. Neither can traditional polling compete with the speed of measuring feedback in social networks – in particular the effects of micro-targeted fake news, which appear to have been a factor in boosting or depressing enthusiasm. Analysis of larger surveys, such as ANES, can help with micro-targeting – but certainly won’t help with the need to measure responses at the Internet speed. The three types of analysis – large infrequent surveys, horse race polls, and social network analysis – should be done in combination.