Marketing heads and major decision makers within Fortune 500 companies are responsible for using consumer data to make better decisions.
You need to understand who your consumers are, how they think, and how they make purchases if you want to persuade them. This much is obvious.
But currently, we live in an age of hubris. That's because we live in the age of big data and AI. This combination of technologies entitles us to more data from more sources than ever before, and it enables us to crunch those numbers at record speed.
It's only natural that CEOs, CMOs, and other executive authorities feel like they're on top of the world. Now, every decision can be based on data.
But there's a problem.
There are massive consumer data blind spots holding Fortune 500 companies back.
And if you want to maintain your market dominance, you'll need to learn how to recognize and avoid them.
What do we mean by “consumer data blind spot?”
To put it succinctly, a consumer data blind spot is any error, inaccuracy, missing piece, or misinterpretation in your data sets or your interpretation of data that leads to a meaningful disconnect. Furthermore, because these are blind spots, you aren't aware they actually exist.
As an example, let's say you're buying T-shirts for a group of 10 people. You know the sizes of 9 of those people, so you know that there’s 1 data point missing.
But what you may not realize is that 3 of those people wrote down the wrong shirt size, or that 1 of them lives in a foreign country where shirt sizes work a bit differently.
These are blind spots.
Keep in mind that blind spots aren't just related to inaccurate or incomplete data. They also exist in the context of processing, analyzing, or interpreting data. Our example doesn't work here because T-shirt sizes are unambiguous and require very little high-level consideration. But more complex data sets for more abstract decisions are ripe with opportunities for misinterpretation and botched analysis.
If you want to make better decisions for your business, you need to be able to uncover and either eliminate or account for these blind spots.
So let's look at some of the common blind spots that plague modern business leaders.
Survey creators typically understand the importance of sampling – and getting responses from a representative audience. However, there are fundamental limitations to what surveys can tell you about customers.
Most notably, not everyone likes to participate in surveys. In fact, some people absolutely refuse to do it under any circumstance, even if you bribe them. You may receive enough survey responses to feel confident that you have a representative sample, but that representative sample will always disproportionately reflect the types of people who choose to participate in surveys. Even if it's only slight, this effect can become noticeable on larger scales.
This is especially noticeable in political arenas, where people with certain political beliefs are inherently less likely to participate in surveys. This is one reason why election polls are seemingly becoming less accurate with time, despite better tools and a better understanding of statistics to help us gather and analyze the appropriate data.
Also, we need to understand that customers participating in any kind of survey or feedback mechanism may be incentivized to give responses that don't necessarily align with their true thoughts and feelings. For example, an especially nice person might be unwilling to give a poor review of their experience because they don't want to get the customer service representative who helped them into trouble – or a person having a bad day may give a disproportionately bad review because of their emotional state at the time.
This effect becomes even more exaggerated when you attract more customer feedback through the use of incentives. For example, if you offer your customers a $5 gift card as a reward for completing a survey, you'll naturally attract many people who don't want to waste their time filling out a survey but do want the $5; they're not going to think about their responses very much, as they just want to get the survey done and over with.
This doesn't mean that you can't use surveys, focus groups, or structures for collecting customer feedback. On the contrary, these can be very good sources of consumer data. However, you need to account for this uncertainty. Just because most of your customers are leaving positive feedback doesn't mean you're making the best possible impact.
Most of you reading this will already be familiar with confirmation bias, but it’s an important concept that bears repeating for a very crucial reason: many people believe themselves to be exempt from this effect. In fact, many people believe themselves to be exempt from biases in general. It's only natural, as this is, in itself a bias: the illusory superiority effect.
As a refresher, confirmation bias is the tendency for people to exaggerate the importance of pieces of evidence that align with their preconceived ideas and downplay the importance of pieces of evidence that don't align with their preconceived ideas. It's a way of using the data to validate your preexisting conclusions, rather than to challenge them or naturally guide you to a new conclusion.
In our experience, confirmation bias is an insidious and notorious effect in the higher echelons of Fortune 500 companies. Instead of starting with a neutral view, leaders often form assumptions, then look to the data to try and prove themselves right.
In some ways, this is actually a byproduct of competence, knowledge, and experience. If you've spent the last 30 years in a certain niche, perfecting your professional skill set, you can and should feel confident about your abilities. You'll also develop a kind of sixth sense for the industry, giving you a legitimate way to “feel things out” even without data.
However, this can also lead to overconfidence and arrogance, causing leaders to assume that their assumptions are correct – and unintentionally ignore pieces of data that disagree with them.
Fortunately, there's a somewhat easy fix here. Even if it's only temporary, you have to assume the opposite of what you actually believe – and try to prove your initial assumptions wrong. In other words, you need to flip confirmation bias’s effects on its head, disproportionately weighing evidence that disagrees with you. If you can't possibly prove yourself wrong, you know you're onto something.
Organizations have also developed a tendency to ignore or underplay outliers. Outliers are data points that don't conform to trends or expectations. For example, let's say that 95 percent of your orders arrive at their destination between 3 and 5 business days after shipping. However, there are some orders that arrive in only one business day, and there are a couple of examples of orders that arrive after 25 business days.
In this example, it's easy to see that the outliers need to be examined. What caused such a massive delay in the shipments that took 25 business days? Why did that happen? How can you prevent it? Even if it only happened to one person out of thousands, it's still something that warrants looking into.
At scale, and in other matters, it's easy to write off outliers. And in some cases, it may be genuinely appropriate to do so. However, generally, it's important to at least acknowledge the presence of outliers and determine why they are outliers. Doing so can help you better understand the mainstream trends even better – and possibly help consumers who are otherwise neglected or ignored.
Black Swan theory, developed by Nassim Nicholas Taleb, seeks to examine and explain human behaviors related to major, surprising, transformative events that seemingly emerge out of nowhere. For example, nobody saw the 2008 financial crisis coming – and yet, with the benefit of hindsight, everyone becomes an economics expert who insists that the signs were all there.
The truth is, before a major, disruptive event, even the best experts in a given field are unsuspecting. After all, the status quo is the status quo for a reason, and the longer that status quo has existed, the longer we assume it will continue into the future.
But if you want to better understand your audience and make better decisions within your organization, you need to understand that Black Swan events can and do happen – and they tend to happen when you least expect them.
These events are, by nature, unpredictable, so don't bother trying to predict them. Instead, assume that major disruptive events can occur within your target demographics, such as a sudden shift to adopting a new technology or making significant spending cuts.
The proper way to approach this is to make your business agile and ready to respond to almost any conceivable crisis. Assume that you don't know and can't predict everything, and be ready to shift on a moment’s notice.
Blind spots can also emerge as a byproduct of overrelying on data. Yes, data is objective and it's the perfect foundation for decision making. However, if you treat data as a God and you assume that decisions can be made with raw data alone, you'll set yourself up for failure.
· The limitations of quantitative data. First, you need to understand that quantitative data does have limitations. Just because the solution looks good on paper doesn't mean that it works well in real life, and at the same time, not everything can be easily quantified. If you ask a person how happy they are on a scale of 1 to 10, and they say 6, what the hell does that actually mean? Different people can mean different things with the same number, so while it's not totally useless, it's also not perfectly accurate. We simply can't rely on quantitative data for everything.
· Historical data vs. future data. Second, you need to understand that historical data doesn't necessarily predict future data. Today, we have powerful data analytics engines fueled by machine learning and AI, but these tools can only help us project existing trends into the future; predictive analytics engines can't magically predict new technologies that emerge or consumer behavioral patterns that evolve. Just because something has trended a certain way in the past doesn't mean it will continue to trend that way in the future.
· Overconfidence. For the past couple of decades, writers and analysts everywhere have been extolling the virtues of “data-driven decision making,” and for good reason. But this has sort of poisoned leadership mentalities in Fortune 500 companies in various industries; leaders have come to assume that raw data is everything, and that it can be trusted inherently on every matter. This overconfidence makes blind spots worse, since you'll have no reason to actively search them out.
So far, we've looked at general and relatively timeless factors related to consumer data blind spots. But now, we need to turn your attention to an upcoming, specific event.
Despite several delays, Google and other major tech companies are preparing for the imminent death of third party cookies. If you've been relying on third party cookies to fuel your marketing and advertising campaigns, this is going to cause major ripples in your understanding of consumer data. If you haven't already been preparing for this shift, you're behind schedule.
Already, most organizations have begun to reoptimize their internal approach to gathering and analyzing consumer data. They're relying much more on first party data as well as internal financial data to better understand campaign performance and appeal to their customers more effectively.
We'll close out this article with a brief list of tips that can help you correct consumer data blind spots generally, as we only covered a small fraction of potential blind spots in this article.
· Rely on many independent sources of data. Blind spots shrink and eventually disappear when you gather data from more diverse sources. If a combination of surveys, focus groups, statistical analysis, behavioral analysis, and competitive research all tell you the same things, you can generally trust them.
· Blend quantitative and qualitative data. Quantitative data is great, but you also need qualitative data if you want to truly understand your consumers. Don't just ask them to rate their satisfaction on a scale of 1 to 10; prompt them for comments to explain their reasoning for the number they selected. It takes more time and it's more abstract, but it can lead you to better conclusions.
· Zoom in on outliers. Your natural inclination is to gloss over or completely ignore outliers. But instead, you should zoom in. Oftentimes, outliers hold the most critical pieces of information for better understanding a topic.
· Predict the unpredictable. You can't predict Black Swan events. But you can operate under the assumption that there's always a Black Swan event on the horizon. Consider the types of disruptions that could significantly influence your consumers and your organization, and develop strategies to respond to these disruptions with agility. Building more resilience into your organization is a useful auxiliary approach as well.
· Always estimate certainty fairly. Finally, approach your analyses and your decisions with a conscious, deliberate estimation of your certainty. How certain is your conclusion, based on what you know? This forces you to study areas of limited knowledge and more accurately assess your own understanding.
Are you interested in tackling your consumer data blind spots?
Do you need help better understanding your audience, your competitors, and the strategies you need to get an edge in the modern era?
Marketer.co is here for you. If you’re ready to take the next step, contact us for a free consultation today!
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