We need data to do our jobs. We can't plan content projects based on our gut feelings anymore, nor feel our way into a great marketing strategy. Data helps us to determine what's working, what isn't, and what people are looking for.
But depending solely on data, especially data just for the sake of data, can be a dangerous move for content marketing teams. This is perfectly illustrated by how the idea of "big data" blew through the marketing world like an F5 tornado, changing the landscape forever.
The amount of data collected on the internet is staggering. There is so much being generated that special harvesting and analytics techniques had to be developed just to cope. This is where the phrase “big data” came from. Think of companies that use big data as those diehard collectors that just can't rest until they have every edition, version, and variety of what they are collecting. Companies became almost data-bloodthirsty, trying to squeeze as much insight from the data as they could.
But when you have this much data, it can mislead you as much as guide you. Companies began relying on data too much and didn’t get the results they wanted. Also, much like clouds, if you look at data for too long you'll eventually see whatever you want to see. This leads to skewed decisions.
Most companies have shown that they are better at collecting data than they are knowing what to do with it. For this reason, the whole idea of big data has recently gone from industry hot topic to an outdated idea companies are running from. So, what can be done differently?
Let's clear something up here: big data was never the problem. The amount isn’t the issue. The problem is making sure it’s used smartly instead of blindly trusting it. Let’s call this smart data. Smart data is different from big data in two ways. First, it is strongly filtered so you can keep what is important. Second, it is put in a proper context.
Let's see how those two things work.
Remember those word problems you used to have to do in math class? One of the primary things you have to do with a word problem is deciding what information you need and what information isn’t important.
It is much the same way with data collection. Collecting large amounts of information, such as social shares, bounce rates, or click-throughs, can be distracting if you don't know what data is good for your research and what data is merely a distraction.
What to focus on is completely dependent on what you’re wanting to discover. Marketing assets designed for the top of your sales funnel will have different metrics to consider (such as social shares, likes, or comments), while assets designed for the middle or bottom of your funnel should be centered around different data points (like leads generated, ebook downloads, or free trials).
Filtering data is one place where AI is coming to the forefront. AIs are used to sift through the data quickly and intelligently so you can collapse a mountain of data into something a human can grasp. When your lump of big data has been shaved down in a smart way, it's less likely to mislead you into making the wrong decision.
Data analysis in a vacuum doesn't help very much, right? Smart data analysis software solutions can help you place your data analysis in a proper context. The really good packages can allow you to change that context on the fly and reveal different conclusions. Here are a few examples:
The time: The time of year, or even the time of week or day, will slightly alter the meaning of certain data points. People behave differently at different times, and they are more or less likely to take a risk at different times.
Who you're tracking.: If you're looking at lead behavior, you have to consider who you're tracking. Leads from different niches and within different internal positions will act differently as they move down the purchase path.
Which industry you're marketing to, and how that industry is doing: If you are selling to steel companies, for example, the way you interpret data points will change depending on how the steel industry is doing.
Here’s an example of a lack of context. If a company saw their online marketing metrics going down, they might think it was something they did and spend more money on their efforts and blame the marketing team. But if there’s an overall downturn, there may be nothing wrong with the marketing efforts at all. You must consider the context of any particular analysis so you won’t be wrongly influenced by the data.
Call it big data, call it smart data, or call it anything else. How useful that data will be for you will depend completely on how smart you are (or how smart your AI is) with the information you have. When planning your next company content strategy, remember to use smart filters and the right context on your data set. That will make data truly work for you, rather than lead you on a wild goose chase.
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