How Polling Software Disproves that Polls Don't Work

How Polling Software Disproves that Polls Don't Work

Polling has a bad reputation, but polling software, when used correctly, proves that polls are predominantly accurate. 

In order to receive responses that truthfully reflect the views of a certain population, there are a few things to keep in mind on the nature of polls and survey research. When you take these matters into consideration and couple them with robust polling software, you will be poised to glean useful data that you can use to power any market research campaign.

This article will cover polling software, including instances of it having made accurate predictions, along with the key aspects to keep in mind to reap the most out of your polling efforts.

How the Right Polling Software Predicted the 2016 Presidential Election

The Pollfish polling software was able to predict Trump’s lead in several key swing states back in 2016. While most national and state polls projected Hillary Clinton to win the presidency, Pollfish was able to forecast Trump’s victory in several key states.

The platform discovered Trump’s favorability in purple states including Florida, North Carolina, Pennsylvania and Ohio. It also reported Trump winning in blue states such as Wisconsin and New Hampshire, which ran counter to many state polls. These findings painted a far more accurate picture of the direction of the 2016 presidential election. 

This is due in part to the sentiment we studied using the Pollfish platform, which included measuring attitudes around each candidate’s stance on key issues. The platform showed that many of the states with Trump’s victory held his opinions on salient topics, like immigration. 

This polling software was also able to hyper-localize opinions, by amassing respondents based on their US census region, city and zip code. These capabilities, along with calculating the margin of error allowed Pollfish to extract data that was closely aligned with the 2016 presidential election.

Avoiding Inaccurate Data: The Sampling Error & the Margin of Error

Regardless of the polling software you use, by their very nature, polls do not reflect the views of everyone in your target market or subject of interest. The reason is self-evident; it is impossible to survey everyone who fits within your targeted population.

Even those who would not object to participating in a poll may not happen upon one, whether it is over the phone or online. This is especially true in random sampling, in which respondents partake in a poll by chance. As such, polling is subject to several errors.

The most common error found in polling is known as the sampling error, which comes about from using a sample of a population instead of its entirety. The sampling error itself is unknown; it relies on the margin of error for a calculation. 

The margin of error is an estimate of how much of the results of the sample may deviate from chance as compared with what the results would be like if the whole population was polled. It shows the maximum possible size of the sampling error. 

In doing so, researchers can understand the range of data, rather than one number for a truer read. For example, if polling finds that 35% of people use their phones to listen to music and the margin of error is at 5%, the statistic is then expressed as 30 to 40%.

Accurate polling requires using the correct methodology. This involves calculating the margin of error to gather more precise findings. Being able to calculate the margin of error helps researchers pinpoint the degree of variability to expect around responses. This will help gain more accurate insights — the kinds that make accurate predictions.  

Avoiding Polling Biases 

Regardless of how researchers choose to conduct polling, biases exist inadvertently. In order to disprove the myth that polling doesn’t work, you ought to understand the various biases that you are sure to encounter in the polling process. 

Avoiding these biases will help refine your polling efforts. Most importantly, using the proper polling software will help you avoid these biases by way of its capabilities. Here are the most common biases to avoid and how an online polling tool can help you circumvent them. 

Sampling Bias 

Also called the sampling error, as aforementioned, sampling bias arises when only specific portions of a population take part in a poll. Sometimes, leaving out certain segments, or only polling one is intentional, as it may be part of a targeted effort towards one segment of a target market. 

But when it’s not, it creates polling results that don’t accurately reflect all the views of the segments that make up a population.

To stamp out sampling bias: use polling software that allows you to zero in on your audience, with multiple subcategories. Make sure the software allows you to add quotas on each subcategory; that way you won’t miss the respondents who belong to this category. If you want to study a wide group of people, opt for a tool that allows you to incorporate multiple audiences. 

Non-Response Bias

This bias refers to the inadequate responses or a low response rate from the respondents who you’re targeting. This can include customers who have made purchases from a long time ago or those who simply do not wish to take part in polling research.

To stamp out non-response bias: Make sure the timing of the survey is not too far away from an interaction with your brand, such as a chat, a call with a customer service agent or a digital experience. You can also send it through various ways of distribution such as via a web page, social media or email. 

The proper polling survey can remedy this without trouble. This is because this kind of software (the right kind, that is) should be equipped with a response-generating capability. This will ensure that not only is your poll sent out but that it doesn’t stop making the rounds online, until it receives the preset amount of responses. 

Often, the software achieves this with quotas and an option for the total number of completes. 

Survivorship Bias 

This type of bias emerges when your polling is only completed by retained customers, clients and longstanding employees. This kind of bias is present specifically within a business survey. It gravely limits your results to people who generally have a favorable opinion of your business. Their opinions are going to differ in a number of other ways from customers who churned or bounced.

To stamp out non-response bias: software can wipe out this bias with the use of screening questions. These allow researchers to ask for specific questions, as they would in the questionnaire portion of a survey; however, the answers to these questions qualify or prevent a respondent from taking part in a poll. 

A potent online polling tool will include several screening questions so that you can diversify your survey respondents. 

Acquiescence Bias

This bias occurs when respondents repeatedly and consistently answer with positive responses or connotations. This can arise due to boredom, so rather than entirely reading a question and thinking about it, respondents just answer with “yes” by default. This can also come about out of politeness or fear of retribution in non-anonymized surveys. 

To stamp out acquiescence bias: Keep surveys relatively short and ensure your questions and answers are 

The best way to minimize the chance of acquiescence bias is to use thought-out questions and answers. You should avoid yes or no questions for this purpose as well.

Polling software is your best bet to clamp down on this bias, as it allows you to create scaled questions. This gives respondents a diverse set of answers, so they won’t feel that the answer they have in mind simply isn't there and then resort to the positive or “yes” answer. A strong polling tool will allow you to create a wide variety of scaled and ratings surveys such as the NPS, CSAT, CET and other such surveys.

Testing Your Marketing, Market Research and Other Business-Related Campaigns

Polls have a bad rep, especially those of the political variety. However, a well-built polling software can overcome any challenge present in market research, which includes gathering responses that are accurate to a specific population.

Aside from making predictions, polling software is also useful for many other market research needs, such as testing the images and messaging of an advertising campaign that’s underway, or seeing if new product releases have been useful for your customer base. 

At any rate, it is crucial to pay heed to the features of the software you seek to use. These should empower your research and help you avoid skewed results and other pitfalls. 

You should also bear in mind the various biases that exist within polling and keep track of the margin of error of any poll. When you couple these practices with robust polling software, you will be on the right track towards obtaining bias-free information that accurately reflects the thoughts of your subjects. 

by lorraine