Engage-Digital

I see that Eric Peterson has written a nice white paper on what he refers to as ”The Coming Revolution in Web Analytics”, it can be downloaded  here. In it he talks about the future of web analytics and in particular what he calls 3rd generation web analytics.

I won’t paraphrase what he says about 3rd gen WA (really better to read the white paper) except to say that he no longer describes it as  web analytics as it starts to move into the realm of general business and customer intelligence.

Among the thoughts that occurred to me whilst reading his white paper, two stood out:

1. What he says about 3rd generation web analytics / business / customer insight at a practitioner level sounds a lot like the kind of thing econometricians have being doing for years although initially they weren’t including heavy weight web data. But the kind of modelling he talks about is already being done by econometrics units in many media and advertising agencies.
2. What he describes as being 3rd gen (web) analytics is no doubt advanced stuff but I think the problem will be uptake. I think that there will be a small cadre of trail blazers who will get heavily into all the mechanisms he outlines such as cross channel data mining, predictive modelling, confidence analysis of customer segmentation etc. but the trouble is this will be for the big guys who can afford it and even then precisely because it is complex it will probably only be taken up by organisations who have the right people in the driving seat. i.e. despite having a highly capable team of analysts and statisticians, a C level director who’s head is filled with many things may find it hard to champion this stuff unless s/he has a reasonable to good understanding of it his or herself. For the rest of the world – i.e. the majority of small and mid-sized businesses it’s still a long way off.

None of that is to say that A) Peterson isn’t correct in his overview or B) it isn’t what should happen, just that the main obstacle may be one of human resource more than technology, much as it has been up until now.

IMPLEMENT AND CONFIGURE YOUR ANALYTICS TOOL PROPERLY

Bad data is worse than no data at all. Decisions made using bad data can have a negative impact on performance and revenue and that can often be worse than sticking with the status quo.

Thanks to Google and Microsoft a wealth of potentially valuable data about how visitors interact with your site is now available for free. Implementing Google Analytics or Microsoft Gatineau takes little more than ½ an hour and from the moment they are implemented they will start collecting data about traffic to your site including a range of useful metrics to help guide the decision making process.

However, to get the best from these web analytics tools they should be correctly configured post implementation. This involves filtering out unwanted traffic such as your own visits which can skew the data; setting up campaign tracking to understand which elements of acquisition strategy are the most cost efficient; setting up funnels which will identify which levels in the visitor journey through your website have the highest attrition points – knowing this helps focus attention on the most critical points and reduces the chances of wasting precious budget on design updates made on the wrong areas. Additionally setting up internal search tracking is a key part of understanding what visitors are specifically looking for when they arrive on your site, it cam also help inform you pay per click marketing strategy again reducing wastage.

Web analytics data is often the starting point from which wider analysis stems so making sure configuration of your web analytics tool is as good as it can be is critical.

In The Sunday Times (a leading Sunday newspaper in the UK) on 11^th Nov. 2007, there was a supplement devoted to the Customer Experience Awards 2007. On page 4 of the supplement there was an article written by Andrew Stone and based on work by David Jackson, the MD of Clicktools, a firm specialising in customer feedback. In it the article outlines the top 10 most important lessons for creating a positive customer experience. Whilst it doesn’t directly reference online it seems to me there are clear correlations to be drawn between the two.

With permission from The Sunday Times I am referencing David Jackson’s 10 lessons to draw these comparisons.

1. David Jackson: Three questions form the foundation of customer intelligence: Who are our chosen customers? What are their needs and expectations? How are we meeting their needs?

Online translation: Knowing your target audience is the central tenet of communication on or offline. Knowing what the needs and expectations of your audience is especially important online since the web is both a research medium and a sales and distribution medium. Therefore potential customers find it much easier to shop around if they don’t find exactly what they are looking for initially. Knowing if you are meeting those needs and expectations is first expressed in the Bounce Rate metric which is why it has become one of the flagship metrics in click-stream web analytics.

2. DJ: Understand how customers think.

Online translation: One of the advantages of doing business online is the relative ease with which customer insight can be gathered. There are many techniques for gathering insight online some of which have already been written about on this blog. Web analytics clickstream data, usability studies, online exit surveys, competitor data are just a few areas in which data can be gathered using existing technologies and, in most cases, without having to purloin unsuspecting members of the public who fall in to the relevant target segment.

3. DJ: Trust in your people.

Online translation:In web analysis, and especially click-stream analytics, it is important to give people their lead. It’s very hard to identify what visitors are thinking when they arrive on a site and while there are some fundamental performance indicators that should always be considered when looking at click-stream data, the analyst should always be allowed to disappear down rabbit holes to see what can be flushed out. You may be surprised by what you find out from your web insight team but you should always take it seriously until it can be reasonably refuted.

4. DJ: Work with people who believe in service excellence.

Online translation: Passion for a product or service and the way it’s delivered translates well and can help enormously in putting across a message online. This is all the more valuable on the web where the visitor / potential customer is in control. But, online where service excellence is translated through the web page, it’s important to remember that you design your site for your customers and not for yourself – an easy trap to fall into. So while it helps to have a passionate team it is important to make sure that belief and passion is channeled in the right direction.

5. DJ: Master the art of organisation

Online translation: It is critical to make sure there are strong lines of communication between the web insight team and all the key stakeholders. The first task is always to establish the objectives of the site in the eyes of the stakeholders, in doing so it will provide a clear goal to aim for. This will remove ambiguity and should result in better output internally and so a better experience for the customer. Additionally, mastering the art of organisation within the web insight team can be applied to the disparate techniques for gathering insight which need to be combined to provide a coherent impression of customer need – this as oppose to conducting research using techniques (mentioned in point 2) in isolation. Finally it is important that the insight can be translated into a clear set of actions that everybody involved can identify with.

6. DJ: Make the link to the bottom line

Online translation: This applies in the exactly the same way online as it does offline. In most cases it is standard theory online, in practice many are doing it but because the pace of change is so rapid it’s important to be able to identify as cleanly as possible the level of contribution an individual element will have. When reporting back on performance, filtering out noise from other concurrent efforts can often make proof harder to demonstrate.

7. DJ: Make everything a little better every day

Online translation: Never stop looking at how you can improve the customer experience online. Analysing your performance online isn’t a one-off exercise to be carried out every quarter, it should be an ongoing and iterative process. Some organisations may feel there is neither the time nor the budget to operate in this way so scaling the approach to fit the primary objective is important. Using dashboards which can be easily updated every week or two with the 5 most important performance indicators is the starting point for this. Making sure this is always tied to action that will improve the customer experience is the goal.

8. DJ: Understand that the future will be different

Online translation: I don’t think anybody in the online world has a problem with this, except that sometimes change and new technologies can be bought into with alarming ease and little thought as to how they will really help the customer. The current debate regarding web 2.0 technologies and content is a point in case.

9. DJ: Learn from your mistakes

Online translation: Make changes to the customer experience online but if they go wrong don’t go around wringing your hands and covering your back, learn from them and turn them to your advantage by making sure customers benefit from your learning.

10. DJ: Make things easier for customers

Online translation: This might almost come before #9 in that making life easier for customers online is all about ease of navigation and presentation of important information. This is where changes need to be made either to supporting technologies or to site design. Craig Menzies of Forrester research said during a recent speech in Barcelona that while so many tools and research technologies are available to online marketers, unless used to drive design changes that generate demonstrable improvements the insight they provide is really not much more than a form of customer voyeurism. In the pursuit of insight it’s important that we don’t loose sight of the actual goal.

If you use Google Analytics and you’re hot on your SEO and your site has an internal search functionality then you may be faced with a dilemma.

This post is about the apparantly confilcting relationship between URL rewriting and tracking internal search using Google Site Search and why it’s an issue worth addressing.

SEO & Dirty URLs
Everybody wants to optimise their site so it appears high in Google’s SERPs. “Dirty URLs” produced by dynamic web pages are considered harder for search engine bots to read, additionally they are harder for visitors to understand so there is also a usability argument for cleaning them up.

URL rewriting and associated best practice rules effctively turns a dynamic URL with all its query strings into a nice clean easy to understand URL. For a (very basic) example: www.site.co.uk/?page=SearchResult&SearchInput=widget&search=yes into www.site.co.uk/searchresult/widget . As a result the rewrite URL will obviously mask the original URL. The practice of using URL rewrites has been gaining some momentum over the past few months so keeping this in mind now consider internal search on your site.

The importance of internal site search
Internal search is one of the primary ways a visitor will find something on a site. Collecting insight from internal search can help inform and drive action; it’s as if the visitor is marching up to the e-store keeper and asking where to find to ketchup – or whatever. Internal search gives valuable insight on what visitors are actually looking for, in their own words. As a result it can drive tactical and strategic decisions ranging from words and phrases used in the pay per click aquisition strategy to product placement and stock control.

So, both URL rewrites are popular as part of the grander SEO strategy and internal search analysis is important for gaining deep insight into a visitor’s wants and needs as they browse a site.

Google Analytics has recognised the importance of internal search by introducing an internal search tracking function to its product, it’s called Site Search. But like the funnels in GA it needs to be set up using elements from the page URL. In this case it is the search query parameter that GA needs and therein lies the rub. If dynamic URLs are hidden using URL rewrites in order to optimise for SEO, then it is not going to be possible to see the search query parameter in Google Analytics – even though the rest of the data will be easier to understand. GA support confirm the proplem. In their own words:

“To set up Site Search, you’ll need to enter the query parameter. You may not be able to set up Site Search if the query parameter is masked using a URL rewrite.”

The tyranny of the “or”…
It seems like a choice needs to be made, but naturally the best of both worlds is most desired. The answer should in fact be relatively simple. Avoide using URL rewrites for internal search results. By doing this the search query parameter will remain exposed and therefore should be visible in Google Analytics. As a result it can then be used in setting up Site Search tracking.

Getting this sorted out will depend on how the rewrites are set up and the content management system in place but in most cases it should be possible to achieve.

The object of this post has simply been to draw attention to an issue that some may already be facing and which may become more prevelant given the ubiquity of Google Analytics and the increasing usage of URL rewrites on sites with dynamic pages.

The solution should be fairly simple and although it means that uniformity across all URLs will have to be forgone, the ends will most definately justify the means. If there are any other alternatives to solving this please feel free to comment.

I’ve worked with quite a few e-commerce sites over the past few years and in our dashboard of metrics visits reaching a product page has always been there. This is for two reasons:

1. In (almost) all cases its possible to add a product to the shopping cart page from the product page
2. Because the product page naturally contains the greatest volume of information relating to a product.

So it has always been considered good to get visitors to the product page because shop owners stand a better chance of selling from the product page.

On that basis marketers often have a fairly obvious eureka moment and decide what a good idea it would be to land traffic directly on their product pages. No!

Product pages are just that, they aren’t landing pages. There is a significant difference and it is hard to find a page that will do both.

Because of this strategy it is possible for the “Get to product page” conversion metric to vary from anything between 40-75%. I think there are two problems with this.

Firstly this standard way of viewing this conversion metric doesn’t take into account traffic that was just dumped there by an adword campaign or a banner of affiliate campaign.

Secondly, by including visits that enter on product pages it masks the ability of the site to actually drive traffic through to these pages.

Funnels
Ah, but what about using a funnel to accurately track conversion to product page?

It depends on which analytics tool you use but many now, including Google Analytics, quite correctly (in my view) do not base their funnels on rules that require a visitor to have passed through the previous step in order to be counted I the next step. That means all visits reaching each step are counted – including those that enter directly.

Don’t forget bounce rate

How could anybody forget bounce rate. In this instance bounce rate queers the pitch because not only do these visits enter on a product page but they also disappear again immediately without doing the one thing we want them to. In other words not only did the site play no part in driving these guys to a product page but also they were totally disinterested in what they saw. Definitely not to be included in a performance metric like “get to product page”.

In short then, first know what you want to measure.

1. Is it the site’s ability to convert visits to a product page or…
2. …just the total number of interested visits that arrive at a product page.
3. Or do you really want to know the total figure, warts and all.

If it’s 1 then look at total volume of visits reaching the product page less the total volume of visits that enter on a product page.

If its 2 then look at the total volume of visits that reach a product page less the total volume bounce visits on a product page.

If it’s 3 then send an email, I’d love to hear from you.