Engage-Digital

Solutions implemented to improve site performance will not always appeal to everybody.

The average conversion rate may rise (and fall) but really there’s no such thing as an average conversion rate. Performance and conversion rates become more useful when they are segmented based on audience type.

Audience types can be defined and segmented in many different ways and people who buy advertising space spend their lives thinking about this. In web analytics segmentation is based on actual observed behaviour and leaves less to guess work. Segmentation ranges from how a visitor found your site to what they did on site. Depending on the level of sophistication of the analytics tool, cross segmentation can be used to obtain even better insight. Segmentation can then be used to represent groups of visitors with different objectives and levels of motivation.
Developing some understanding of visitor motivation helps minimise the risk of losing customers and maximise the potential for retaining new and existing customers.

It also helps to give some kind of weighting to your segments. This makes it easier to know where to concentrate resources.

A 3 dimensional bubble chart can help visualise this (you can click on the chart to enlarge).

In this example the Y axis shows the conversion rate within each segment based on a specific KPI; the X axis shows, as a percentage, the contribution by each segment to the total volume of good outcomes; the size of the bubble for each segment effectively acts as the weighting metric – the larger the bubble the bigger the weighting in context of all site visits.

By creating this chart it’s possible to see which segments are the heavy hitters and which have the most potential. It also gives more meaning to scenarios when the segments are overlaid on them.
Segments and scenarios
Scenarios are created to understand visitor behaviour by looking at conversion through the scenario. If several different segmented visitor types are overlaid on a scenario it’s possible to get a much richer insight into levels of motivation.

In this simple two step example scenario, it’s possible to see how the various segmented sources of traffic convert on a comparative bases. (Click to enlarge)

As I’ve already mentioned, once you start looking at segments overlaid on to scenarios, it may help to cross reference some of them to gain deeper insight. For example, say 40% of your traffic entered on the home page and 40% entered on a product page and you’ve created segments for each to see how they convert; then assume 50% of your traffic comes from organic search and this source generally converts well. You may then want to know how does traffic from organic search that enters on the home page compare with traffic from the same source entering on a product page? More importantly even if one converts better than the other does that mean the poorer converting group has no value? Not necessarily – they may just be looking for something else – check the search terms they came in on, look at the bounce rate and so on.

As more on and off-line marketing is tracked by campaign tracking and bespoke landing pages segmentation will surely become increasingly important in assessing performance and value for money. I have already experienced this with several clients.

Any thoughts / comments please do post. Many thanks.

A butterfly beats its wings in the Mojave desert and there is a tornado in China

I’m not suggesting that web analytics is like Chaos theory but I am suggesting that seemingly small changes to a site can have big impacts.

It’s a happy but rare occurrence when one fix is so clear that by its implementation a site will be transformed from having poor performance to stellar performance. A cost / benefit analysis will help demonstrate expected return.

Grand solutions like fixing internal search, reducing basket leakage or improving retention through the checkout process are all quite obvious; alternatively driving more visits to a product page or improving conversion from product pages to the shopping basket are equally so.

These are important areas which everybody should cover off in the process of analysing a site and getting them right should yield good returns. But it is because they are so important and demand so much attention that we may be overlooking a number of other smaller, simpler and cheaper fixes that could have an equal effect on performance.

Example:
The site analytics data shows a relatively low conversion from product page to basket page and this has an impact on overall site conversion. When a customer adds an item to the shopping basket they should then automatically be taken to the shopping basket. “A bird in the hand is worth two in the bush”. Many sites assume that the customer will want to continue shopping or that he/she can be persuaded to continue shopping and so the customer is left on the product page. A look at the average number of items purchased per order or per visit will resolve that question.

I have seen conversion rates double by making this simple navigational change. In this case the decision process about whether to progress to the shopping basket or not was taken out of the customers hands. If a customer wants to continue shopping there is nothing to prevent it after arrival on the basket page.

We spend a good deal of time trying to effectively second guess what our customers want and then providing them with the options. On some occasions it may be preferable for both the customer and the e-tailer to have a decision taken by default.

There are more of these simple and extremely cost efficient fixes out there. Analysing site data gets us closer to them. Making sure we already have our analytics configured by default to measure performance on the big stuff gives us more time to explore the data for these little gems.