Over the last two weeks, there’s been a lot of chatter about the presidential election, and as there was in 2016, plenty of discussion about the shortcomings of pre-race polls.
Presidential Election 2016 vs. 2024: Similar Takeaways
Here on the SiteSeer blog we wrote about this very topic eight years ago. Many of the points we made then still stand today:
- Confirmation bias on the part of poll takers can impact polling. This could have impacted polls iindicating a likely Kamala Harris victory.
- The media’s misunderstanding of Donald Trump as a viable competitor to Harris could also have impacted the polls. This may have been due to a combination of incorrect assumptions about the gender divide—women voters showing a strong preference for Harris and women and men for Trump— plus overestimates of the Democratic turnout (and a lot of other things).
- Data collection was flawed. The 2016 election revealed problems with polling, which pollsters tried to address for the 2024 election. They shifted to a combination of email/phone/online polling and refined their sampling techniques. The practice of weighting polls to align with the broader population on key characteristics is supposed to result in greater accuracy. Still, this year proved that even the best efforts to gather accurate poll results can still fall short.
As we all know by now, polling has its limitations and it certainly is not perfect. It’s impossible to predict with 100% accuracy the public’s political preferences when it comes to presidential elections, and thus, it is impossible for polls to be completely accurate.
There are many theories floating around about why many pollsters got this election wrong. Two possible reasons have come up frequently since November 5th, summarized by Pew Research:
- First is the fact that it is difficult to estimate who will vote. Pew Research shares that “since pollsters often use past turnout to predict who will vote, it can be difficult to anticipate when irregular voters will actually show up.” NPR echoed that sentiment, saying "in the debate over whether demography is destiny, the 2024 presidential election showed clearly it is not." Some call this a political realignment. Certain ages and races that have been historically considered "solid" as a voting bloc for the Democratic or Republican party weren't during this election.
- Second is a theory: that some voters are more likely to take surveys than others. Despite using different modes of communication to contact a representative sample of the U.S. population during polls, some groups--like Republicans who supported Trump--might still be underrepresented in many polls. Thus, pollsters underestimated his support.
What can retailers learn from all this?
Perhaps the biggest lesson about polls is this: their results must be interpreted knowing that they come with a degree of uncertainty. Polls can be biased and influenced by a number of factors. They have their limitations and every poll includes a margin of error.
At SiteSeer, we see retailers looking for answers behave similarly to pollsters trying to predict the outcome of the presidential election. There are lessons here for retail chains and others in the business of market research and analytics:
Gut-feel analytics are dangerous.
Retail teams might have a gut feel about a market or location and ignore data that indicates that market/location might not be a top performer.
It's important not to over-rely on the data.
At the other end of the spectrum, retailers sometimes put too much faith in the data. Retail research must account for risk and differences in consumer groups in different geographic areas. One store’s customers may have behaved in a way that led the retailer to assume that all future customers would be willing to drive to a location further than a closer-to-home competitor. But that might not be true everywhere, and could lead to a bad location decision in another market with entirely different customers.
Incorrect assumptions about competitors can lead to bad decisions.
Retailers often make incorrect assumptions about their competitors in a market. This can cause them to overlook certain competitors and underestimate the strength of others. This leads to inaccurate analyses and ultimately, site selection that disregards the all-important weight of competitors.
Misunderstanding customers and their preferences and priorities can lead to underperformance.
Similarly, retailers might misunderstand their customers and their shopping behaviors and priorities. That can lead a retail chain to open locations with trade area profiles that don’t match their target customers well, and stores that underperform.
Time will tell how the pollsters adjust their methods after this election to avoid their mistakes from the 2024 election. In market research and site selection, we must do the same when choosing a less-than-optimal location based on rushed research. Data and analytical tools are helpful in making location decisions, but it’s critical to understand them and recognize their limitations. The quality of your data and thoroughness of your site selection methodology are critical.
Learn more about how SiteSeer can help you make the most accurate location decisions possible. Call us today at 866.524.2804 or schedule a demo.
