Companies live or die based on their ability to predict and, to an extent, control customer perceptions. They need to understand what beliefs and preferences their customers have, so they can design better products and refine their marketing strategies. They need to make a reasonable prediction on how customers will react to certain news items, so they can plan a PR strategy around those reactions. And of course, they need to predict how customers will see their brand when compared to the brands of their competitors.
But even though we have access to more data-gathering and data-analyzing tools than ever before, it’s actually getting harder for businesses to make accurate predictions about their customers.
The Age of Data
There are thousands of tools available to businesses hoping to learn more about their customers’ behaviors, beliefs, and habits. Big tech companies like Facebook have built their entire business model around collecting pieces of data on their userbase and selling those data to other companies for marketing and advertising purposes. And companies like datapine have created tools specifically to help companies make more accurate measurements of their customers’ behavior, including hard-to-measure factors like customer satisfaction.
More data and better analytical tools are usually a good thing. Casting a wider net and getting more detailed information should hypothetically lead you to more accurate conclusions. So why are customer perceptions and behaviors actually getting harder to predict accurately?
The Core Problems
These are just some of the factors making it harder for companies to make accurate predictions on customer perceptions:
- Unpredictable variables. Technology has given us greater insight into customer variables that were previously unknowable, but it has also made our lives astoundingly more complex. For example, we live in the midst of a 24-hour news cycle; news outlets are constantly reporting new stories and new information, which introduces complexities to PR planning. The length of time a story stays in the limelight depends heavily on how social media users initially react to that story, and whether or not exciting, newer stories arise to replace it. For example, if your company announces a recall, it could quietly slip under the radar in a matter of hours, or get overblown as it cascades through social media channels. In a controlled environment, it would be much easier to predict how customer perceptions change in response to new variables, but we hardly live in a controlled environment.
- Overreliance on data. Data analysis is a good thing, but there’s something to be said for our overreliance on data. Marketers have shifted to conduct fewer interviews and spend less time on qualitative research so they can spend more time on quantitative research; they want to convert every human emotion or reaction to a data point, so they can make a more objective assessment of their target demographics. This isn’t inherently a bad thing, but objective data can never fully or adequately summarize how people are going to behave. Plus, we need to consider the fact that data alone can’t give us an accurate conclusion; instead, we have to be the ones to ask the right questions of the data we gather, and challenge our biases so we don’t misinterpret things.
- Ignorance of outliers. Gathering more data on customers and prospective customers means you have more objective information to work with. But there’s an unintended side effect; after gathering information on 100,000 people instead of 1,000 people, the outliers become less noticeable. It’s much easier to see the average or most frequent behavior, thought or preference, and much harder to take the fringe groups seriously. However, those outliers can play a massive role in predicting the behavior of the overall group.
At the end of the day, we have to keep in mind that human beings are inherently difficult to predict. Gathering more data and relying on better analytics tools can help us overcome some of the hurdles of customer perception prediction, but they also introduce new challenges into the environment.
Marketers and business owners need to remain prudent and humble, using data analytics as a tool to better understand their customers—but not as a perfectly reliable prediction machine.
