How to avoid the costly data analysis mistakes that most marketers make
If you're a digital marketer, you can't failed to have noticed—there’s been a seismic data shift over the last several years that encourages empirical marketing based on data and analytics. The sentiment underlying this shift seems to be that if you think your art and copy are good, then you better have the stats to prove it.
This evidence-based approach is heartening in many ways, but many people are still learning how to apply its principles. Just because you have the numbers, doesn’t mean you know how to apply them. In this short article, I want to look at three 'boneheaded' ways that people are looking at their data and offer some suggestions to help put your data analysis back on track.
You’re Not Doing It At All
Sadly, a lot of people are in this category. But I get it. Again, the major emphasis on data in marketing is still relatively new, and this means many people are still learning what good data is and how to apply it to their marketing strategies.
Even so, it’s frustrating how little some marketers understand the difference between data reporting and analytics. Here’s the deal: it’s great that you’ve managed to collect a huge data set about an area of concern for your company. But, if that’s as far as you’ve gotten, the data is completely useless. Data only becomes meaningful once you give it parameters that organize it in ways to address key questions. Basically, these parameters are your KPIs (key performance indicators), and if these aren’t defined, there is no real analysis happening, just a cacophony of information that doesn’t mean anything yet.
Your KPIs are Immature
And this leads to another dumb way of analyzing data. Namely, your KPIs are incomplete or immature. For example, a lot of rookies still think page views are the be-all-end-all metric, and while it’s important, it’s sort of an unsophisticated KPI that needs to be nuanced with other metrics like bounce rate.
It’s a mistake to get tunnel vision with one KPI and treat it as the only important factor. Look at this way. When reviewing a product, there are all kinds of factors coming into play to help you make your decision about whether to buy it. The factors for something like a mattress, for instance, are things like firmness, feel, durability, price, etc. All of these factors contribute to a cohesive picture of the product and its usefulness to you. If you only looked at one of these factors, you would probably be misled into making an unwise purchase.
Marketing data is no different. If you’re only looking at one KPI like page views, you’re not getting the full, informed story about the data and its usefulness to you is severely limited.
You’re Favoring Big Data Over Small Data
This is a dumb way to analyze data that seems very popular right now. For marketers, grappling with big data is a major preoccupation—trying to suss out large general trends from massive sets of data to improve customer engagement, retention, and all other kinds of outcomes. This is standard best practice for big-league marketers, and it makes perfect sense.
One undesirable consequence however is that, with this emphasis on big data, small data gets overlooked. Small data is a term coined by Martin Lindstrom that signifies: “seemingly insignificant behavioral observations containing very specific attributes pointing towards an unmet customer need. Small data is the foundation for breakthrough ideas or completely new ways to turnaround brands.”
Small data helps understand individual customer motivations, which don’t necessarily fit neatly into general trends. If big data is the what, then small data is the why. And understanding both is key to reaching your customer segments. Don’t ignore one in favor of the other.