Visualizing Website Page Component Performance

Earlier this year, I did a bit of work with an analytics team working on an e-commerce site. The site allowed for a wide variety of layouts, which gave the retailer a lot of flexibility to experiment in finding the optimal page composition to maximize conversion. One of the challenges was how to evaluate the performance of components based on their page placement; some solutions included a dashboard placing numerical data on top of illustrative page layouts, but a limitation of this was that you could only see the performance for components of a single page layout at a time.

After thinking about the challenge, I came up with this potential visualization:

It color-codes components based on their relative performance (green = good, blue = average, red = poor) and places components in their relative spot on the page. For example, the layout in the top left shows the performance of five components of varying widths and height that were positioned in the top left section of the page. By distilling detailed numbers down into colored boxes representing performance and placement, one can visually identify the areas and component sizes that tended to outperform others (e.g., larger components in the top left corner outperformed components in the top right corner).

Had fun with this exercise, maybe at some point I'll get a chance to put it to use.

Confidence Pool: Leaderboard Visualizations: Part III

I had recreated the leaderboard visualization I had seen on Kaggle, and kept playing with the data set a little bit more. I started wondering how individuals performed week-to-week as compared to the weekly averages and quartiles. So I plotted this on a graph that showed the maximum, minimum, average, and 1st and 3rd quartile ranges. This was somewhat interesting, with some people oscillating wildly above and below the average (TW1, who placed 11th):

... and a couple others staying fairly close to the mean throughout the season (YO1, who placed 4th):

But I still felt that it was difficult to see how close (or far) participants were from winning, so instead of doing a week-to-week chart, I made it cumulative. This became interesting, and I saw how the winner, BL1, ran away with the pool pretty quickly:

For comparison, you can see how far away the fourth place participant, YO1, was from first place:

So this was definitely a fun data set to work with. In an upcoming post, I'll provide the Excel files along with an explanation of some of the VBA used to make the spreadsheet interactive, so people who are interested can play around with it.

Confidence Pool: Leaderboard Visualizations: Part II

Right when I started, I did some basic analysis and found that the average win percent (correct picks vs all picks) of all participants for the season was 50%, and that the average points per week for each participant was 4.5, right at the midpoint (confidence points were assigned from 1-8). So as a whole, we did no better than if picks and points were assigned randomly. The distribution for both fell fairly close to the normal distribution, with slightly higher clustering within a deviation of the mean for the average points.

I first implemented a visualization that tried to capture both the overall position of a participant, as well as their individual performance for that week. In this visualization, participants are ordered by their final finish, with color coding for their weekly and cumulative performance. For each week, the small square on the left indicates how they compared to others for that week (green = good, red = bad) and the rectangle on the right indicates their overall rank based on cumulative points. In this visualization, you can see that the top two finishers had built up enough of a lead to retain their top two spots despite bad finishes in weeks 14 and 17, and that strong performances in weeks 15 and 16 by the number six finisher (FA1) allowed him to take over the spots occupied by CR1 and JA1 (who finished 7th and 8th respectively).

I also tried implementing a visualization similar to the Kaggle visualization I liked. As in that visualization, each week represents the leaderboard for that point in time, with the shading corresponding to the final finish of the person in that position. I also added the ability to see where a particular participant finished for each week. You can see that in this case, the overall winner quickly climbed to the top couple spots and held on to that position from week 8 onward. You can see other participants with fairly steady positions as well as some others who came in to the top 10 in the final weeks.

So the visualizations were a success and I also played around with some alternate visualizations as well, including one I'll share in my next post that showed how the winner ended up running away with the pool.