There is no single way in analyzing and interpreting data. But, there is one secret that will key it manageable, and that is with ‘perfect information‘ in mind.
In the presence of perfect information, there is for analyzing and interpreting data, nor for analytics. In fact, the whole aim of analytics, with all its methods, models, algorithms, and statistics, is to get as close to perfect information as possible (or, better said, as close as necessary). Analytics professionals keep this aim for perfection information in mind, as it has a way of focusing the analysis and and interpretation of data. Thus, they successfully get rid of any wasteful processes. In simple terms, it makes analyzing and intepreting data more focused.
Analyzing and Interpreting Data Example
Let’s take an example on analyzing and interpreting data through perfect information. One that illustrates well the value of aiming for perfect information. We will have a look into the field of web analytics.
Suppose we were just given access to a large set of web analytics data for a company that sells pugs (yes, the animals), and they want you to ‘optimize their website for pug sales’. The novice analyst, without any regard to what the phrase ‘optimize for pug sales’ even means, would no doubt jump right into the boundless data set and get lost in a fog of pageviews, unique visits, time on site, and convoluted navigational summaries. That is not how you should be analyzing and interpreting data.
This is not what is going to happen to us, because we are going to pause, consider what the concept of perfect information can tell us, and then dive into an effective session of analytics with the aim of selling lots and lots of pugs. We are going to use perfect information in analyzing and interpreting our data.
How Should You be Analyzing and Interpreting Data
Before you start on analyzing and interpreting data, first, we’ll consider a question. If we could know everything we needed to know in order to optimize pug sales, what would we want to know? Don’t be realistic here; remember, we’re discussing the inherently unrealistic concept of perfect information.
Perfect information example
Here’s what my pug selling informational paradise for analyzing and interpreting data would look like:
I would know every single person in the United States of America who wants to own a pug. I would know them by name, where they lived, how much they were willing to pay, and how many pugs they would want. I would know the best time to reach all of these people, and I would know what method they would prefer to be communicated with, whether that be phone calls, direct mail, digital media, personal visits, smoke signals or carrier pigeons. I would know what motivates their love for pugs and what need having a pug would fulfill in their lives. And remember, I would know all this about each person individually. Further, I would know, by name, each person in the United States that could be persuaded into buying a pug. I would know all of the above information about them, along with what exactly it would take for them to make the decision to take home a pug, whether that be an Internet advertisement, a informational video, a seminar, a mailer piece, a discussion over a croissant lunch, or whatever else. I would know this about each and every one of them.
Using perfect information to your advantage in analyzing and interpreting data
Phew! What a paradise! Unfortunately (or rather, we might say, fortunately), it is impossible for me to know all of these things. There is no such info that you can use in analyzing and interpreting data. Moreover, getting all this information would take an infinite amount of time, and that’s to say nothing about keeping it updated.
So what are we to do in order to improve analyzing and interpreting data? We’ve taken the proverbial journey into pug selling informational heaven, only to realize we’ll never achieve it in this life.
But we don’t need to. The picture of everything we would want to know, in a perfect situation, is a useful framework to use as we start to list out all of the things that we can know.
Check indications from perfect information and start analyzing and interpreting data from there
We can’t know by name everyone in the U.S. that currently wants to own a pug. But what indications can we use to find and get to as many of them as possible?
One – people searching for anything related to ‘pugs’, especially ‘buying pugs’, in search engines, will be an attribute of these people. Start analyzing and interpreting data from there. Then, optimize for these terms and put up some relevant search engine advertising.
Two – people looking at dog sites or pet sites, like petfinder.com, are also in the market, perhaps, for a pug. Advertise on these sites.
Three – people that find your site and actually take time to browse the ‘pugs for sale’ pages or start entering in information on your shopping cart. Optimize this process for them. And on and on.
Analyzing and interpreting data from your own existing resources
Aside from checking outher resourses, you can also use your existing resources. Start analyzing and interpreting data from it and you’ll find useful trends.
Above, we talked about persuading people to buy pugs – about knowing what it is that will convince them to make the buy decision. We can’t know this for every single person, but we can know it for certain types of people. Start analyzing and interpreting data about them.
I’d start by doing analyzing and interpreting data from the various past blog posts that the company has put up – which ones have been the most popular, induced the most checkouts, and brought the highest level of engagement? What were they about? You may notice that they were about finding companionship, or about giving a dog a good home, or about guarding your house from intruders (a stretch for a pug, I know, but perhaps they’d be effective at keeping away small cats). These are key findings into what is motivating people to buy pugs. (And if such a blog with diverse topics doesn’t exist, perhaps it’s time to start one. . .)
Final words on analyzing and interpreting data using perfect information
We can go on and on here. But I think the point is clear – start with what you would know in the perfect scenario, and work your way backwards into what you can know with the available resources and data. As you do this, you’ll notice that the individual information we knew in paradise will manifest itself as understanding certain groups of people with similar tendencies, or customer segments. These segments are the currency of effective analytics. This is how you should be analyzing and interpreting data.
On final tip in analyzing and interpreting data. Take note that we did not actually stay within the realm of web analytics click-stream data as we were doing our analysis. This is important! Just because you have endless web analytics data doesn’t necessarily mean it is the most useful way at getting the information you need to know. Don’t let yourself get cornered – understand what you need to know, and then use the best resources available to you, whatever they may be, to know it. Finally, use it in analyzing and interpreting data for sales.
Perfect information to knowable information to the data resources available for knowing it – that’s the thought process. If you do it the other way, you’ll soon find yourself spending many hours sifting through a sea of data, only to come up thirsty for insights. Don’t let that happen to you. Learn how to be smarter analysts and follow our secret tip in analyzing and interpreting data. Learn more about how to do data analysis here.