Learning How to Use Insights for Successful Advertising Choices
Making real insights requires thinking about data.
Data Needs Insights and Interpretation
You see so many businesses out there that claim to be “data driven”.
And for good reason, because working with data can give you a lot of useful insights.
Data is an unusual creature because it can be so useful, but in other instances, irrelevant. The problem is when gathering the data is more important than using it to make inferences.
And that is why data is there. It’s not there to drive completely accurate decision making. It’s there to guide decision making based on insights, because taking action based off of data sometimes requires critical thinking. You can’t have numbers completely guide a service.
You see some people, mainly data scientists, saying that businesses who are not data-driven are not fully optimized. And, to a degree, this is true. However, getting a tunnel vision from data is not something that is beneficial. You still need to make inferences and look beyond the scope of the data.
What does that mean when a company says that they are data driven? It means that their products are able to provide actionable services for you using algorithmic binaries, or models that outsource “true or false” decisions on complicated topics. Qualitative data can be rendered into a numerical value through using models that are able to do so through NLP, or Natural Language Processing. This is how modern software is able to make more detailed computations about data. The problem is when companies rely on it, or are dependent on data to the point where they believe that expertise is irrelevant. Everybody wants to be “data driven”.
Many companies are using this as a selling point when it shouldn’t be. It’s often called “data washing”. Most of the time, data comes after the conclusion is developed, and not before.
However, it does leave a lot of room for biased use of data and ignoring relevant data that contradicts the insights. What is wrong is that when the desired action drives the data, and not the data driving the desired action. Data makes the service sound like it is maneuvering using the scientific method, which is bogus.
So, you could have a software generate a list of headlines for you. Let’s say that the algorithms reveal that 70% of headlines within a particular industry that have a particular word will sell, and so that seems obvious, right? Not quite. Context is always more important, and you have to be able to be skilled enough to see through what the data indicates and determine whether it is appropriate.
The Map is Not the Territory
Often, advertising and marketing problems are complex systems. I’m always looking at data and stats when it comes to researching markets. But, that simply gives me a map. The map is not the territory. That’s where insights come in.
If you are trying to come up with a successful email marketing technique, you can go look for the data. You can get software to automate things from data.
The problem is that the market is really not predictable right now. There are a lot of unusual variables moving around.
For instance, people do not think quite as simply as the way data suggests. These figures about how people make choices can guide us to make an educated action about what to do, but they can’t do the work for us. We can use findings to understand what kind of actions people take, but you can’t make people take action based on a number. You can’t make lemons out of lemonade. You make lemonade out of lemons.
Demographics aren’t predictable. For instance, Gen Z are thought to be “digital natives” because they don’t remember older technology. But, that doesn’t mean they don’t desire to use or acquire “old” technology.
Anybody who is in marketing is probably used to the younger generation being more receptive of technology, and the older generation being opposed to it. The old findings indicate that, but not the new data.
We’re ignoring something important. More Gen Z consumers are interested in using older technologies like flip phones, iPods, and vintage CRT televisions. So, obviously there is a market there that isn’t being pursued by many companies. They simply want to keep their boats going in the same direction even when some people are going in different ones.
Data Changes Its Meaning
Data changes meaning over time. Some data insights that were true at one time, change over a period of time. For instance, if you were born in 1984, then you were used to senior citizens who grew up during the early 20th Century. Today, senior citizens have a totally different set of characteristics. These differences can be cultural, social, and physiological. Therefore, you do not advertise to senior citizens in 2025 or 2025, the same way that you did in 2004.
A generational perspective is a much more valuable insight than an age group. You can’t predict the behavior of an age group as well as a generation. Predicting the behavior of an age group relies on old data from past age groups, which is irrelevant because each generation ages differently. A generation is more likely to make choices that are similar to what other people in their generation make, and not based on their age. For instance, a 40-year-old male today is more likely to make lifestyle choices based on being a Millennial, and not on being 40 years old.
According to Statista, more Gen Z consumers are interested in using so-called “dumb phones” than older Millennials. The reason transcends nostalgia, but the idea that having different devices for different purposes, rather than a device that does it all, seems to make things less stressful and more appropriate to some people who are Gen Z. Whereas, more baby boomers have adapted to the smartphone age and are attracted to how a smartphone can organize everything on one device. That is, the demographics become less predictable, because it used to be that younger people were less likely to adopt older technology.
Responding to Data
Therefore, some old tech companies are relaunching some old technology for this very reason. The problem is that smaller companies don’t listen to the current culture, and they set out to find data that affirms their preconceived ideas. Companies who do not observe the status quo and simply rely on making insights from old information will not succeed.
This is not an undocumented paradox. This is something known as concept drift. Concept drift is when old data (or an old idea) is still used to make decisions even though newer, more relevant data is available. For instance, we still believe that people in their 60s have an aversion to new technology. We look at the age over the generation. The generation is more relevant. While it was true at one time that people in their 60s didn’t understand new technology, a new generation has reached their 60s.
Concept drift is more than an anomaly. Anomalies are random outliers, while concept drift is a much larger trend.
Key Points
- There is helpful software that can help you collect and understand data, but use them wisely.
- Data needs insights and interpretation to be the most useful.
- Many companies simply use “data driven” as a selling point.
- Collect information and data, but look at it with an educated understanding.
- Look for quality data that is relevant.
- Don’t ignore concept drift.
- Demographic data can change in its attributes.
- Data that drives decisions is often better than decisions that drive data.
- Advertise with an open mind, understanding the big picture.
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