Why Pie Charts are Evil

Pie charts are evil in the same way that the devil is evil: through mental trickery they beguile you, clouding your judgment while giving you the sense that you are making perfectly rational decisions.  Pie charts obscure the very data story that they are meant to tell.  And that – for visual data representation – is truly evil.

pie chart evilness

Breakdown of Pie Chart Evilness

Everybody knows that pie charts look great – especially 3D! – and no doubt there are some situations in which they can be used to get the message across.  But they come up short on a critical component of data visualization: context.  The only context present within a pie chart is the relationship of individual data points to each other. But this is usually only part of the picture. To gain an understanding of what is going on in our data universe, we almost always need to be able to monitor trends over time. And this is where pie charts fall apart.

It is interesting to know, for example, the distribution of website traffic among various sources, as represented in a pie chart.  But this is not a static relationship – it is likely to be changing and the important thing is to be able to understand how it is changing.

Recent example:

At a presentation to reviewing the progress of search marketing efforts, one slide included the following chart:

google analytics traffic source pie chart

All well and good, provided out-of-the-box by Google Analytics so it is easy to grab, and it gives a snapshot of where things stand.  But it tells us NOTHING about any progress that has been made (or not) in driving traffic from search engines to the site.  Not quite as sexy, but your basic bar chart can tell us what has been happening much more effectively.  Here are a couple different bar chart options, depending on the aspects of the story that you want to emphasize:

Bar chart alternatives to pie chart

Alternatives to pie chart - ah, now I see it!

Bar chart on the left shows percentage breakdown and now we can a) clearly see the relationships, while b) observing that traffic from Search Engines and Referring Sites is growing, as a percentage of total traffic, at the expense of Direct traffic.  Bar chart on the right shows us more clearly the degree of change from one month to the next in each of the categories. Now we can see what’s going on and base our decisions accordingly. Each one takes up about as much space as the pie chart, yet delivers much more valuable information.

More often than not, when you’re tempted to use a pie chart, a less glamorous visual tool may do the job better. This is why hard-core data visualization masters such as Edward Tufte and Stephen Few (.pdf link) renounce pie charts in all but very particular circumstances. And so should we all.

Choosing KPIs: Visitors or Visits?

Recently had a client situation where we were providing the client with monthly organic visitor numbers to their ecommerce site. One of my colleagues showed me a report received from the client, which had the same data, but marked as ‘visits‘.  So that got me thinking…what do they really want to measure here: visitors or visits? And do they know? And have they thought about what difference it makes? And, of course, what recommendation can we provide?

Both numbers are important (although they may not be critical – depending on the outcomes you need to measure) and they provide similar information, but there are some important differences. Leaving aside the argument over whether either of these satisfies the criteria to become a real KPI, let’s consider the uses of each metric in the context of this client.

Visitors

I’m pretty sure that since the reporting was done on a one month period, that the tool the client is using reports ‘unique visitors’. (i.e. People – or at least browser cookies – that are only counted once during the period.)

[Side Note on ‘visitors’ in Google Analytics:

For Google Analytics, apparently the term ‘visitor’ is not enough and they even go beyond ‘unique visitor’ to insist they are reporting  on ‘absolute unique visitors’.  Of course, this over-states the case, given the limitation of cookies. But, ok, we get it, this is your best count of individuals visiting the site during a given time period.  More confusing terminology is used in the ‘New vs Returning’ report.  This is reporting visits, rather than visitors (as explained on the Google Analytics Blog) but the term ‘visitor’ is also used.  So maybe it would clear things up to refer to ‘New Visits vs Return Visits’. ]

It is good to know how many people have come to your site, just as it is good to know how many people walk into your store in the mall. It gives you an idea of the total number of customers/potential customers that you are drawing in, and allows you to compare trends over time to spot opportunities or problems.

But there’s a big difference between a person poking their head into your store on their way to the food court, then never to returning again, and a person who repeatedly makes the trip to your store, even if they don’t purchase something every time. And this is where I think visits may provide more relevant, actionable information than visitors for this client.

Visits

As always, metrics that warrant attention vary depending on the nature and goals of a site. The client I’m talking about has a B2B ecommerce site that sells a broad mix of commercial products, including many that represent ‘repeat‘ or ‘modified repeat’ purchases in Buyersphere terms.  So, yeah, it is interesting to know how many people visit the site and to hopefully see this grow over time, but more critical in this case is the number of visits.

We are looking at organic traffic, and we are trying to use search engines to drive as many visits on as many relevant search terms as possible to the site. New visitors, certainly, but if we can capture visits from searchers who already know the site, so much the better, giving us the opportunity to further build on a relationship already established.

Further, we are already using this logic in measuring paid traffic, by counting ‘clicks’.  Not necessarily the same as visits, but likely to be closer to visits than it is to unique visitors.  So comparing organic visits to paid clicks may not quite be apples-to-apples, but it is at least apples-to-pears and pears are more like apples than oranges. (Visitors being oranges…you get the idea.)

Where the Rubber Meets the Road: Conversions

We all know – because Avinash has drilled it into us with his trinity approach 🙂 – that it is essential to move from clickstream data to outcomes. (And, to be fair, virtually all leading web analytics advocates promote a similar philosophy.) So the number of visitors is interesting, the number of visits may be more so, but we need to get to the real reason our site exists: conversions. In this case, purchases.  And to make decisions about optimization and resource allocation, we need to understand the efficiency of various channels bringing visits to our site and this means: conversion rate.  And to get a conversion rate that makes sense, we need to have the most appropriate denominator.

Which brings us back to visitors vs visits.  Yes, it can be useful to know what percentage of unique visitors in a month made a purchase, but wouldn’t it be more useful – in the case of this B2B ecommerce site selling repeat purchase products – to know the percentage of visits that resulted in a purchase?  For a lot of B2B sites, the purchase pattern may resemble that of a car dealership: long consideration phase involving multiple visits, probably multiple decision-markers, (hopefully) culminating in a purchase that will serve the buyer’s needs for a lengthy period. This particular client has a site that is more like an industrial grocery store.

So in terms of organic traffic, it is quite possible that the same visitor may return to this site several times during a month searching for different products (in fact, there could be several different searches during the same visit, so visits are not the same as searches, but probably close).  If we really want to understand how efficient our site is in converting organic traffic, we should be calculating conversion rate = orders / visits.

This also helps us compare organic search engine traffic with paid search traffic, where conversion rate = orders / clicks.

Conclusion

Focus on visitors or visits, as appropriate to site type and objectives, but do so consciously. Recommendation for this client: switch from a focus on organic visitors to organic visits.

It might even be worthwhile to consider tracking and analyzing conversions against visits for some keywords (‘repeat’ purchase) and against visitors for other keywords (larger, less frequent, or ‘blank-slate’ purchases).

For other sites, visitors – or a visitor segment – may be more relevant.  With reference particularly to non-ecommerce sites, Anil Batra has a great blog post on how to dive in and select the appropriate denominator for your conversion rate.