the task of establishing They told me recently a “best of the best” report for the Asia-Pacific region. Best of the best ratios are average yield analyzes of websites, as well as the ranking of the top 20 percent of performers in specific countries and industries on a variety of key performance indicators. Thank you to the Adobe Digital Insights data repository, I had access to a huge amount of data. I had brought four of these reports for other regions, so that the enormity of the data set was not new to me. However, the area was, and I am happy to explore and learn about its diverse cultural and regional distinctions.
As an analyst, the challenge is to translate the data in the results an action for your audience. The data can show you the figures. This is science. But the development of the story behind the numbers is the art and where the real work of a good data analyst begins.
The strong set of data available to Adobe Digital Insights allows us to get ideas from the real world that marketers can take to their own businesses. Here are my thoughts on how to approach the extremely robust data, unique cultural settings, and sometimes your own sense of being small.
Dealing with and Enjoying-Extremely robust data
By 2015 Asia Pacific Best of the Best, we pulled aggregate, anonymous information to over 100 billion hits 3,000 sites in Asia Pacific for all of 2014 and 2015. One of the first challenges that I ran into was the size of the data. Our go-to standard tools could not handle a report of this magnitude. The data does not fit into our framework and we had no choice but to develop methodologies.
As analysts, we are responsible to both the significant production of useful information, and provide guidance and strategic direction to our audience can come with their own ideas and strategy. For example, watching Country smartphone visits, I live in Japan, an average of 38 percent total website traffic comes from smartphones, against only 28 percent in the US In addition to the 20 sites visited nearly 60 percent of reported traffic comes through a smartphone. Clearly, not only Japan’s leading companies in this field, they were ahead of the curve.
Although the size of the data set kept me give specific examples of how top performers were the commitment of experience or attract traffic, I was able providing marketers with the data they need to draw conclusions and make judgment calls on how to apply the results.
take Faced with reliable data, do not let the tools limit your results. Search and identify other methods to obtain the data. Finally, be sure to qualify your story line and speak appropriately.
Look at your own experience to Insight Guide and recommendations
Analysts are often expected to know every bit of data and everything that goes into it. The truth is, this is impossible. Sometimes it is helpful to take a step backwards when deciding what is most important to include and what should be left to the readers to determine and investigate on their own. How do you make those decisions? In this case, I drew on my experience as Director of Retail Strategy and e-mail to determine the kinds of things that were important for traders. Since the data showed that consumers were moving towards smartphones, I knew a discussion of the winners, or even those underperforming in this space would be interesting and useful.
Knowing from data that companies successfully attracted traffic on smartphones, I wanted to understand what it meant for the lowest traders in the funnel. It could be something of a metric retention on a website (visitors are leaving or staying?) In a metric of engagement (how many pages they are interacting with?), All the way to a metric conversion. It was important not only where traffic came from, but also how visitors are interacting with the site content.
take :. Take a step back from the data and research on your own experiences can help to highlight the recommendations that are important and meaningful
Discover the “Why” behind the data
Although I did not know exactly what Japanese companies did, I knew that the data was important. I also knew that companies in India showed increased activity. Yet, curiously, the data showed the smartphone traffic in India to 28 percent down compared to the other. The data was right, but I had to explain input from someone more familiar with the Asia-Pacific market. Discussions with my manager, I learned that India as a country has bandwidth problems that cause them to lag behind their counterparts. It was a moment “a-ha” for me. Knowing bandwidth problems and look at some of the other measures, I am able to tell the story.
take with the underlying issues were explained, the perspective changes you help identify strategies for your audience can use. It would have been easy for me to leave at a declaration that India is underperforming. Instead, I chose to try to discover the reason behind the poor performance of India. In doing so I am able to discover what is really happening, and it helped me to tell a more complete story.
Help your audience to create individual strategies with a macro-Level View
As traders, which makes the sense of relationships at the macro level often requires consideration of internal data. An increase of a metric for company A could be positive, while the same increase for Company B may be negative. Marketers often find macro level views to be applicable and good guide. But we must determine where failures of a company occur within its own purchase funnel and optimization to these failures. At the end of the day, it means identifying individual strategies based on the external input your own data.
Analysts decide how to present the assembly to arrive at ideas that tell a story data. Good but sometimes perceived indicative of cutting means of a data set in two different ways. If you want to outline that fit the information that is useful, develop strategies beforehand. We create a starting point for what we would like to be using a pre-analysis form. Next, we examine the data to see what it reveals and how it corresponds to our expectations. If it does not match, it’s also a glimpse, and we include because it is unexpected.
There were some surprises, which were the direct result of looking at the data differently.
For example, the automobile industry ranked lower on some parameters of engagement in the Asia-Pacific region than other industries. This finding could simply reflect a US based company tried without success in the market to people in a different geography. However, instead of looking at the data vertically, I looked horizontally, highlighting the differences and similarities more, which makes possible to pick invisible macro models.
take If you know your audience, a pre-analysis can help you make more efficiently what you mean, build rapport, and identify ideas that would be more to your audience. Remember to look at the data in different ways to reveal unexpected patterns.
How do you approach a set of robust data and transform your knowledge into conclusions translatable to your audience?
In this case, facing incredible amounts of data, we have taken care not to let our tools limit our conclusions. We also made sure to qualify our story line with data and talk properly. I looked for expertise when needed, and spoke through a number of questions that I could not explain.
When I finally had the a-ha hour when I found the resources I had available and applied the entrance, I am able to more effectively deliver meaningful recommendations. A preliminary analysis helped prepare the report, the identification of ideas that might be useful to our audience. Finally, discover the “why” behind the data was a big part of understanding what was going on behind the numbers. And like any great analysis, explanation made all the difference in telling the story.