How Do You Know Its Accurate
The author's views are entirely his or her own (excluding the unlikely issue of hypnosis) and may non ever reflect the views of Moz.
Big Data and analytics has been called the "next big thing," and it tin certainly make a strong case with the explosion of easily accessible, high-quality information available today. In the entering marketing world, we have access to backlinks and ballast text, traffic and click stream data, search volume and click through rate (CTR), social media metrics, and many more than. There is huge value in this information, if we can unlock it.
But, in that location's a trouble: existent world data is messy, and processing it can be catchy. How practice nosotros know if our data is accurate, or if we can trust our last conclusions? If we desire to apply this information to find a ameliorate manner to practice marketing, we take to exist careful most accuracy.
There are no hard and fast rules when it comes to data analysis. At that place are some all-time practices, but even these tin can become a piffling murky. The nigh of import thing to do is to put on your detective cap and dive into the data. The more than familiar you are with the data, the easier information technology is to spot something that seems foreign. More than likely, your findings will exist quality problems that need to exist improved.
Throughout this post, we will use a data gear up from Google Webmaster Tools of keyword search referrals as a case study. Here'south a snippet of the data:
Nosotros also put all of our keyword analysis code on Github and then you tin run our analysis on your ain site's data.
The rest of this mail service discusses six best practices and suggestions for ensuring your data and results are accurate. Savor!
ane. Split data from analysis, and make analysis repeatable
It is best do to split the data and the process that analyzes the data. This also makes it possible to repeat the assay on unlike information, either by you or by someone else. For this reason, nigh data scientists don't apply Excel since it couples the data with analysis and makes it hard to echo. Instead, they often use a high-level statistical oriented scripting language, like R, Matlab/Octave, SAS, or a general-purpose language like Python.
At Moz, the data scientific discipline team uses Python. Our Big Information squad also uses information technology heavily, which makes information technology easy to integrate our algorithms with their production lawmaking.
2. If possible, cheque your data against another source
In many cases this stride may be impossible, but if you can, it's the all-time way to make sure you data is accurate. In Moz's case, we were able to cheque the Google Webmaster Tools data against data from Google Analytics.
Some pieces to focus on when you're comparing information include total aggregate counts, counts in sub-categories, or averages. In our case, nosotros checked both the full search visits and spot check the number of visits for a few different keywords.
3. Get down and muddied with the data
This is the fun office where nosotros go to play with the information and practice some exploratory data assay. A good place to offset is by looking at the raw data to run across what jumps out. In the case of the Google Webmaster Tools data, I noticed that they don't always give the search volume in long-tail cases with simply a few searches. Instead, the data has "<10" or "-" instead of numbers that will demand to exist handled advisedly since they volition result in missing values.
This is as well the time to put on your detective cap and start asking questions about the data. We looked at some keywords like "seomoz" and "page authority" that are branded, and some like "writer rank" and "schema testing tool" that are not. Afterwards checking out the data, I asked myself, "Hmmm, I wonder if there is any departure in Click through rate between branded and non-branded keywords, or in average search position?"
Usually by this point I'k amped to start answering hard questions, but I try to resist the temptation to jump off the deep end until I run a few more sanity checks. Univariate analysis is a bang-up tool to help y'all check yourself before going besides far, specially since nigh software packages provide an easy way to exercise it and it often produces the beginning interesting results. The thought is to become a picture of what each variable "looks like" by plotting a histogram and calculating things similar the mean.
The above chart shows an example of univariate analysis on our data. In each panel, we have plotted the distribution of one of the four variables in our data: Impressions, Average Position, Clicks, and CTR. Nosotros as well included the hateful of each distribution in the title. Immediately, we can see a few interesting comparisons.
First, almost all of our keywords are "long-tail" with less then 100 searches/month. However, much of our traffic is too made upwardly from a few high-volume keywords (>m searches/calendar month). The boilerplate position is concentrated in the peak 10 equally expected (since results off the kickoff page send very piffling traffic). This is also practiced cheque of our data. If we had seen a significant corporeality of keywords sending traffic at ranks lower then #ten, we should investigate further. Finally, the CTR in the lower right is interesting. Most of the keywords have CTR less then 40%, but we do have a few high volume keywords with much higher CTR.
By now, I usually experience pretty comfy with the data and tin jump in. At this signal, I've found that asking specific questions is often the most productive style to reply bigger questions, but everyone works differently, so yous'll need to find what works best for y'all. In the instance of the Google Webmaster Tools data, I'thousand curious nigh the impact of branded vs non-branded keywords.
One fashion to examine this is to segment the data and then repeat the univariate analysis for each segment. Hither's the plot for impressions:
We can see that, overall, branded keywords have a college search book so non-branded words (ways of 380 and 160, respectively). It gets more interesting if we look at boilerplate position and CTR:
We encounter a huge difference in Boilerplate Position and CTR between the branded and non-branded words. Most of our traffic from branded words is in the top two or 3 positions, with non-branded queries sending traffic throughout the top 10. The CTR is also significantly different with a few branded keywords having very high CTR (60%+).
We might likewise wonder about how the CTR changes with the search position. Nosotros await that lower-ranking keywords will have a lower CTR. Can we see this in the data?
Indeed, the CTR drops off chop-chop after the top five. At that place is an interesting crash-land up at position 15, but this is a data sparse region and so this may non be a real signal.
4. Unit test your lawmaking (where it makes sense)
This is a software development best exercise, just can get a piffling sticky in the data science world and often requires judgement on your part. Unit testing everything is a peachy way to catch many problems, simply it will really slow y'all down. Information technology's a skilful thought to apply unit of measurement test lawmaking that you think volition be used over again, has a general purpose exterior the specific project, or has complicated enough logic that it would be piece of cake to get wrong. It's often not worthwhile to examination lawmaking apace written to check an thought.
In the case of the Google Webmaster Tools data, nosotros decided to test the process that reads the data and fills missing values because the logic is somewhat complicated, only didn't test our lawmaking to generate the plots since it was relatively simple. We used a small, synthetic data set to write the tests since it is easy to manage. Check out some of our tests here.
5. Document your process
This pace can be annoying, but y'all will thank yourself a few months later when you need to revisit it. Documentation also communicates your thoughts to others who tin check and validate your logic.
In our example, this web log mail documents our process, and we provide some additional documentation in the README in the lawmaking.
vi. Get feedback from others
Peer review is one of the cornerstones of the academic earth, and other people'southward insight is almost e'er beneficial to improving your analysis. Don't hesitate to ask your team for feedback; nearly of the time, they'll be happy to requite information technology!
Practice you accept any other helpful testing tips? What has worked for you and your team? I'd love to hear your thoughts in the comments below!
Source: https://moz.com/blog/how-do-you-know-if-your-data-is-accurate
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