Engage-Digital

Bounce rate is a kind of standard bearer metric for measurement in web analytics, it’s up there with conversion. Even novices in web analytics know what bounce rate is, and when asked what should be the objective regarding bounce rate you’d have to be a lunatic to say anything other than “try to reduce it”. But increasingly, I think there are some misconceptions about this metric. Remembering that bounce rate applies to both entry pages and referring sources of traffic, two thoughts that come to mind are as follows:

1. When thinking about bounce rate in the context of entry pages it is hardly surprising that it’s lower on the home page than on a product page. I’ve seen very few sets of data in which the situation is reversed. I think this is because the home page offers a bigger target, i.e. there are more options for a visitor when they arrive on the home page than if they arrive on a highly specific product page. Trying to reduce the bounce rate on a product page is worthy and will yield results for sure but don’t expect to get it down to home page levels.
2. Lower bounce rate = more actual conversions. I’m not disagreeing with this but increasingly as I look at weekly and monthly trended data for various clients I see examples where BOTH bounce rate and conversion actually go UP against a particular referral source. This in turn quite often results in higher yield volumes as well.

This isn’t to say that it’s OK to let the bounce rate metric rocket up, retaining more visitors at the same conversion rate WILL of course produce greater conversion yield volumes. It’s simply to say that it’s not necessarily worth freaking out the minute a bounce rate shows signs of edging upwards. I would always look at the surrounding metrics to build a bit of additional context before deciding on a course of action.

The old adage ”quality not quantity” comes to mind here. A higher bounce rate might mean lower visitor retention but if more of those visitors are converting and the overall conversion yield is going up then that might prompt another old adage “if it isn’t broke….”

This is an article I wrote for issue 176 of .net magazine in the UK.

I used to be Head of Online Planning and Buying at a London based media buying agency. I was there for 3 years between 1999 and 2002. In my first year our nascent online media planning and buying department experienced a 1000% growth in billings and some growing pains. Of course overall spend was much lower then than it is now as online media was also in its infancy relatively speaking.

Then in 2001 things slowed dramatically. At the time, growth in online media had been fed by new internet start ups with lots of VC capital looking to advertise to help grow their businesses and drive inexorably towards IPO! Additionally it was driven by a growth in interest from mainstream advertisers looking to dip a toe in and check the temperature.

Advertising is often considered a bellwether of economic decline as it’s one of the first things to be cut from budgets as belts tighten and when the slow down came in 2001 billings pretty much flat lined in our corner of the online media world, but other channels fared worse.

Part of the reason why online advertising may have fared better is due to much greater levels of accountability compared with other forms of advertising. Now consider the level of accountability we have with web analytics.

Back then in the early “naughties” web analytics was barely a twinkle in a webmaster’s eye, now it is proudly sitting at the boardroom table.

Not only can web analytics bring even greater accountability to on and offline advertising (if set up correctly) but it also completely opens up the level of business accountability for the website itself. It can be used to drive growth and cut costs through improved efficiencies across the whole spectrum of online communication.

If we are truly staring down the barrel of worsening economic conditions, especially looking forward into 2009 then arguably the worst thing any organisation could do would be to cut its web analytics budget.

Back in January I was working with a client that operates in an industry that is itself suffering but the saving grace for this particular client was their new website which had proved a great success in the face of a generally poorer trading climate.

If economic conditions deteriorate web analytics and the insight that it provides should be safeguarded and pored over with even greater intensity in the same way that normal business reporting and results are.

I whole-heartedly agree that visitor engagement is a concept that needs to be considered as an aggregate of several elements covering both data and, crucially for me, context. I also think it is quite subjective.

I have recently done a piece of work for a client who put a new site redesign live at the beginning of January. Looking at the data before and after the live date there were three very clear changes:

1. Performance to the required goal has dramatically increased

2. Average time spent per visit has increased by c. 50%

3. Average pages viewed per visit has more than doubled.

I was only interested in comparing the data within this one particular site and not with others in the same industry since I accept that competitors design their sites slightly differently, may have different goals and different acquisition strategies – and so may view engagement differently. For this purpose I was interested in our little world only and I will try to justify that later.

Looking at the post redesign data I was initially tempted to think that if overall site performance, as measured by conversion to one specific goal, had increased at exactly the same time as a change occurred in the average visit length and pages viewed per visit and that the occurrence of that change was at the time of the site re-launch then it could be said there is a correlation between the three.

In an effort to try and filter out as much noise as possible, I looked at one referring source which has been a constant over the past 10 months – pay per click marketing. I also know from looking back over the ppc performance data in this particular business that seasonality in market demand appears to have a limited impact on conversion.

Looking at just pay per click (from Google only) the results were the same – a marked increase in conversion occurring at the same time as a marked increase in average visit length and PVs/visit.

Theo Papadakis talks about the idea of positive and negative engagement in an article recently posted on Occam’s Razor by Avinash Kaushik. I like this idea and would consider what I have seen here as an indication of positive engagement.

Looking further into content popularity it became clear that the new internal search function had started receiving much more traffic and now forms the backbone of the site’s navigation. This element of the site functionality was given much greater prominence in the new re-design.

So, what can be observed?

1. It’s easy to see that all 3 key changes occurred at the time of the site re-launch (in this respect we were lucky to have such a marked even to punctuate the data)

2. The same behaviour appears to be the case with a single source of referring traffic.

3. The increased conversion, average visit length and PVs / visit appears to be linked to a change in the sites primary navigation

4. Seasonality in the business cycle can in the main be discounted

This all points to the suggestion that visitors who convert tend to spend more time on the site and view more pages per visit. Given that the conversion goal is a positive outcome for us, then a simultaneous increase in average time on site and average pages viewed per visit must also be positive suggesting that visitors are more positively engaged with the content.

What do we do with this engagement?

I don’t propose to use it as an indicator to drive change in its own right. I see it as a “soft”  indicator; I prefer to think of it as a stalking horse. One which will prompt further investigation should a significant change occur. Additionally, where we have other referral sources I would like to use it to help assess relative value.

One final factor that will have to be taken into consideration and which cannot be accounted for so soon after the site launch is the novelty factor of the new site itself. This particular site sees a high proportion of returning visitors and customers, because of that we will have to see if the new re-design has prompted repeat customers and visitors to stay and look around partly out of curiosity. This should be born out in time.

My view is that engagement should largely be considered on an individual site by site basis. That is why I prefer only to look at engagement in the context of one particular site over time. It may be interesting to compare with other businesses but brand recognition and loyalty will most likely skew results to some degree regardless of site design.

I may get shot (down in flames) for writing this post.

Web analytics is not just about data, this is well documented and blogged by far greater minds than mine – so I won’t get shot for that I hope! Web analytics is simply the engine behind driving better performance online. Better performance online for most organisations that actually engage in web analytics is usually about driving more revenue and improving cost efficiencies- and of course improving conversion.

99% of companies in the UK are SMBs and I think this is the great challenge for the web analytics industry. Many SMBs have websites and many of those websites perform a function, but the hard reality is that amazingly they don’t have the same size budgets as the average blue chip fortune 1000. They still need to invest to improve performance so they must approach their performance optimisation from a different perspective.

Usability is arguably a part of web analytics (2.0 as it has been labelled). There are many great usability experts out there and several different ways of approaching usability; these range from individual lab based usability tests, remote sample based usability tests using services such as Ethnio to journey replay solutions like Tealeaf.

To be clear, after looking at click-stream data and having identified where a problem might lie, if usability is what’s needed to unearth the truth then the methods just mentioned should be the preferred route; but they aren’t cheap.

A more cost efficient option would be to use a click based heat mapping product such as ClickDensity or CrazyEgg. These are not new products, they’ve been around for a while and they’re like click maps on steroids. They record clicks regardless of the presence of a link or not. They show the results either as actual clicks on the part of the page where the click was made or aggregated as a heat map. The advantage here is that where a standard link overlay will only record a click if it occurs on a link (assuming the tag is set properly) these tools will record click activity regardless. In other words, if a visitor reaches a page and attempts to click on something that looks like a link but isn’t, it will be recorded and show up.

So how do you get the most out of these tools in 5 ½ steps?

1. Assume a customer journey based on a task – making a purchase or signing up to an email
2. Replicate the customer journey as best as possible using a funnel or scenario in your analytics tool
   • Start with the most popular entry page
3. Allow enough data to collect
4. Identify main points of attrition (try and think why this might be happening i,e, form a hypothesis for each page where there is considerable drop off)
5. Look at the offending page using the heat mapping tool. The heat map will of course only show where your users have clicked but because it records every click there may be some surprises regarding where activity has and has not occurred and this could lead to action resulting in improved performance. For example there may be a high volume of clicks on a piece of text which has been mistaken for a link, this is potentially lost traffic and could go some way to explaining the drop off.

Any tweaks to the page that are implemented can subsequently be A/B tested to verify performance.

Cons

• Again, I should state, this is not the real deal in usability circles (don’t shoot!)
• You can’t talk to the people viewing the page and you can’t hear their thoughts as they navigate the page
• You can’t see cursor movements
• You can’t run the test with users instructed to carry out specific tasks

Pros

• For the budget conscious business it is much cheaper and more cost efficient. Even for large organisations it is a good practice
• The sample size includes everybody that interacts with a given page
• You can run A/B tests using these tools and compare your results instantly and run the best performing page.

This final point is perhaps the crux of it. The objective here is to amend the page design so that it makes life easier for the visitor and thereby unblocks the path to customer satisfaction.

This post has been written in the hope that it will prompt the more budget conscious business to think about how they can approach usability from a standing start. It’s not an attempt to provide a definition.

Please feel free to comment with your own thoughts and experiences.

Analysing performance of a web site is only as useful as the results it achieves. If insight isn’t acted upon and changes aren’t implemented then progress can’t be made and the analysis becomes nice but pointless.

Failing to act on insight that will yield results is probably a bit like panning gold, finding a rock with a rich seam in it and then being too knackered to break it up to gain access to the loot.

Time(ing) and money are often the reasons given for resistance to change. The advent of Google Analytics and shortly Microsoft’s Gatineau means that good quality web analytics data is available free of charge to all that want it. This removes part of the expense in acquiring visitor insight. Much of the remaining expense (depending on the methods used in doing the analysis) is down to resource both in conducting the analysis and implementing the changes.

Additionally, interest is normally around the actions rather than the insight, this is not surprising but it’s important to remember that unless the site is a mess its less likely that actions will present themselves without some level of quantitative and / or qualitative site analysis; a classic chicken and egg situation for many site owners but one which shouldn’t be difficult to resolve.

Before embarking a specific piece of analysis it is worth asking the question:

“If change is recommended, what financial and human resource is available to implement it?”

This should have two effects:

Firstly it will prevent money being wasted on an analysis from which no action can realistically be taken.

Secondly it will help concentrate the analysis on the areas where change can actually be effected. Again, this will help focus resource and avoid waste.

In a large organisation where resource is available timing may be an issue because changes to the site could be restricted to scheduled site update periods. There may also still be departmental budget issues which can act as constraints.

In small organisations that are more agile and where timing may be less of an issue budget availability could prevent action being taken especially if some changes are particularity expensive to implement.

In order to make sure that valuable resource is not wasted at the analysis stage, whatever the size of the organisation, it worth considering the following:

1. What are the objectives for the site? This is obvious but always the first thing to consider whatever your intentions.
2. Are there any specific areas that need to be investigated? Although it may be preferable “on paper” to start with a blank sheet and let the analysis guide the output, in practice it generally helps reduce cost and focus resource if there is already some idea of where the problem may lie. i.e. acquisition and retention, site stability, navigation, page design etc
3. Assuming there is neither time nor budget available to afford the luxury of using all available analytical techniques (quantitative, qualitative, competitor and so on), which one or combination is most likely to yield the desired results, how quickly can the insight be obtained and at what cost?
4. If changes need to be made who will make them? Consider the possibility of changes to the design, marketing and back end of the site and think about who will actually implement these changes. Check their schedules over the next few weeks to see if they have any available time.
5. Budget availability. This is perhaps more of an issue if any part of the process is outsourced to agencies or other suppliers but can still have an impact if not as some changes might involve buying in new or extra technologies.

Points 4 & 5 are the two main ones. Knowing these opperational parameters in advance should really help concentrate effort.

When the analysis is done it will still be necessary to run a cost benefit analysis to see what kind of revenue uplift can be expected, this is the final stage in persuading the FD – or whoever holds the purse-strings; but, knowing in advance if the resource if even available on all levels will avoid wasted effort in the first place.