Eric T. Peterson, author of Web Analytics Demystified and general sage on all things to do with web measurement, speaks to us about the benefits of ‘Web Analytics 2.0’ – the insights to be gained by integrating site data with other sources of online intelligence, such as online surveys, customer experience management, and email marketing stats.


Could you sum up what you mean by Analytics 2.0?

Sure. Web Analytics 2.0 is a reflection of the fact that the measurement landscape is maturing and the number of the types of things we need to measure is increasing quite quickly.

It used be that page views and visitors would give us almost everything we needed. But the same cannot be said now, with the rise of Web 2.0 technologies like Silverlight, RSS, Ajax etc. There’s also a deepening relationship between the website and visitor.

So Web Analytics 2.0 strives to address the fact that you cannot just look at the quantitative data alone.


Could you give a real-life example of how this wider view of Analytics is helping e-commerce companies and other websites?

One of the fundamental technology shifts in Web Analytics 2.0 is the inclusion of qualitative data – not just using the clickstream data, but also watching visitor behaviour through ‘Customer Experience Management’ (CEM) and ‘Voice of Customer’ (VoC) technologies to analyse engagement and satisfaction.

So, when you put this measurement stack together, site operators are suddenly able to answer complex ‘how?’ and ‘why?’ questions. It leads you inevitably to action.

When you roll in CEM with analytics, you can look at sessions that are abandoning the checkout process at a particular step and understand what the problem is – high shipping costs, for example. VoC then enables you to take that a step further and ask people why they abandoned – whether there was a problem or they were planning to use an offline channel.

By doing this, what you can learn about customer behaviour becomes increasingly robust.


For clients, why should integration with CEM and online surveys be a higher priority than developing a more multichannel approach to Web Analytics?

There has been a lot hype about Multichannel Analytics being the future. My theory is that companies are better served by getting their online channel in order, before embarking on what will inevitably be very complex, expensive and political projects.

Web Analytics 2.0 is not about bringing in offline data – it is about bringing in more robust data about the online experience. I suppose that when you get really good at that, then those same technologies will help you bridge the gap to multichannel endeavours. But I’ve talked to very few companies that are really, really good at analytics today. Web analytics is hard.

Companies that have deployed these technologies still have a lot to do to drive web analytics into a centre of excellence in their organisation. I don’t know that complex Multichannel Analytics efforts are necessarily worth the time until that happens.

Those companies also already have research and data warehousing and business intelligence groups.


What are the cost implications of Web Analytics 2.0?

There are cost implications, but an interesting thing that I have noticed is that in large companies, these technologies have often already been deployed in other parts of the organisation. In those cases, the cost implications are very low – there’s still data integration and learning costs, but you don’t need to add these technologies.

If you don’t have these technologies, there will be an additional cost associated with getting them up and running and integrated. But there are other ways to start – there are less expensive examples of VoC and CEM technologies.

Also, the more important thing to find out would be the percentage of time your analysis come up with the answer ‘we don’t know, based on the available data’.


Have companies with legacy systems reported any trouble with integration of these data sets? And what implications are there for education and skills?

So far, folks I have talked to aren’t talking about integration hassles. Part of this is the fact that it is early days. The types of companies doing this are particularly well-endowed in terms of IT resources and budget.

Also, some of these integrations are made dramatically more simple because of existing vendor partnerships. Omniture is a good example of this through its Genesis programme (through which application partners can integrate with Omniture’s products).

In terms of skills, it’s not a particularly high bar with CEM. The VoC stuff is a little more complex because you need to know how to design good surveys and questions. But the best vendors have services groups to help you with survey design and methodology.


How big an impact do you expect these partner programmes and APIs to have on the analytics space in the next couple of years?

I think APIs are critical to the future of the sector. The work that WebTrends has done with Marketing Labs in open data architectures; CoreMetrics’ informal API that it has had for years; and all the work Omniture and its competitors have been doing – all this is crucial for bringing in the data you need to answer your business questions.

I was brought up with exporting CSV files and massaging that so they could be put into a relational database, and that was just painful.

So all this work is very welcome – so much so that I was frankly shocked that the folks at Google Analytics didn’t announce an API at the eMetrics Summit in Washington last month.

There is this reflection that everything Google does is built around APIs – its recent social networking announcement, for example. I’d expect that in subsequent releases of Google Analytics – perhaps at the San Francisco eMetrics release – we will see that as well.

If that happened – and I fully expect it will in the next six months – I would immediately dust off my old JavaScript and PHP skills and create an iPhone interface for Google Analytics. I have a feeling a lot of people around the world would write novel data output interfaces for the Google Analytics data collector.

Also, from a data input perspective, they would all be about your campaign cost data, your metadata for campaigns and so on.


Related research:

Web Analytics Buyers Guide 2007
Online Surveys Buyers Guide 2007

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Eric T. Peterson on the difficulties of Web 2.0 measurement

Web Analytics ‘still widely misunderstood’

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