The most pivotal advances in marketing have been simple discoveries. A small piece of data -- or an insight into the human condition -- often sheds light on a nearly ubiquitous truth about the human condition.
Until recently, the process of determining the wants and needs of potential and current customers was instinctive and marketers often had little beside intuition to guide them. However, thanks to exponential leaps and bounds in software and data processing, markets are able to use data to guide their strategies and new initiatives.
Unfortunately, the available tools have outpaced the skillset of the average marketer and many are left clueless as to how best to use, manipulate, and present the slew of new facts that they are able to collect. This means that many insights are overlooked and many campaigns may never live up to their potential. While this skills gap will take years to fill, and it will likely be an iterative process with a few steps backwards, there are several things any marketing team can do today that will improve its big data efforts.
One of the most tempting things when dealing with data is to give everyone all of the facts, even if they aren't necessarily important. New and robust data processing programs, an obvious example being Excel or IBM's Infosphere BigInsights, make it fairly easy to manipulate data in thousands of different ways.
Most of the time, the only limiting factor for a data demonstration is creativity.
However, these great capabilities beget the need for restraint. It is now the responsibility of the marketer to create presentations that paint clear pictures, not overload clients and CEOs with extraneous facts. Be critical and be sure to edit yourself.
Graphs: the best friend (or worst enemy) of most marketing presentations. Executives and clients love them because they have the potential to succinctly summarize huge data tables into a digestible visual aid, but if not properly made, they can be misleading.
This means that, before putting a graph into a slideshow, be sure to check its accuracy. Really evaluate if you've chosen the correct axis variables and if the graph's meaning is clear. Additionally, don't be tempted to cram too much information onto one table -- having a few graphs side by side is much better than a master graph that confuses your information.
Once again, the graph is both a blessing and a curse for many marketers. As technology advances, there is most definitely a greater opportunity to create stunning visual presentations -- cutting edge stuff your boss has never seen before.
This is also, once again, a great opportunity to shoot yourself in the foot.
While it's tempting to make 3D data presentations that move, make noise, and model information, it is far better to give your audience something that they can easily understand. It cuts down on meeting times and lowers the stress levels of confused executives.
A company needs to be interested in collecting three types of data: proprietary, public, and purchased. The first type of data is information your company is generating without your knowledge about it; you just need to take advantage of the fact that it's there.
The second is free information published by reputable sites that can help you see what others are doing and compare your business to current market trends. The third is probably what most people think of when they think of big data: the information you pay to find out.
This is potentially the biggest pitfall for most marketers, but organization is absolutely crucial when developing and executing a successful data project. It is also sometimes difficult because, as programs develop, it's not uncommon for companies to change how data is stored, which program processes it, and how it is collected.
The best way to avoid organizational nightmares is to constantly revisit your data infrastructure. It's also a good idea to take a day and reorganize once a quarter.
If you want to determine what percentage of kindergartens can actually spell "kindergarten," you can't just ask ten and take it from there. You have to ask a hundred, a thousand, maybe even one million.
Then you have to take into account a million other factors (point in the school year, SES, home life, etc.) and only then might you have a somewhat accurate representation.
The point is you need to keep collecting data, while doing your best to create an equal playing field and adjusting for differences at separate points in time. You have to be in it for the long haul.
Data like this doesn't exist in a vacuum. You have to shift the way you think from top to bottom. Executives and employees alike have to be on board with these kinds of strategies.
You can't just crunch the numbers and leave it at that. Entire companies are being built on a foundation of big data, so established companies have got to get in the loop, too, or they'll never catch up.
As data progresses and becomes more sophisticated, it is obvious there will be tons more to learn about the field. This means that it will be necessary to stay on the cutting edge, do your research, and constantly evaluate the quality of your information and reporting.
It will often be a hard task, but those who dream of being extremely effective marketers will need to do it.
Improving and augmenting your knowledge of data analysis can be done in both a structure and unstructured way, so be sure to find a method that works for you. Some people will thrive in a class whereas others will be fine studying independently.
Anna Johansson is a freelance writer from the Olympia, Wash., area.
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