NZ Retail stats: How is Ferrit doing?

I’ve updated the New Zealand Retail site daily stats referred to in a previous post. Once again it is Nielsen NetRatings data, for domestic traffic.

First and foremost- Trade Me is still the only online retailer in New Zealand (aside from airlines)

Net Nielsen data

Let’s look at that by hours spent on site – this is Unique Browsers x Frequency x Avg Session duration, and is measured in hours per day. Trade Me received over 80,000 hours of browsing time in the last 10 days measured, while Ferrit received just 185.

Net Nielsen data

But the news is not all bad for Ferrit – traffic recently has picked up.

Net Nielsen data

That’s come from an increase in the number of unique browsers to Ferrit, probably from recent ad campaigns.

Net Nielsen data

Those UB’s have led to rising Page Impressions.

Net Nielsen data

but that increase in Page Impressions has not matched Trade Me’s natural PI growth rate.

Net Nielsen data

While Ferrit’s Page Impressions per Unique Browser has actually fallen since launch, as shown in this log scale chart. Time per page has risen by about 10% though.

Net Nielsen data

Published by Lance Wiggs


6 replies on “NZ Retail stats: How is Ferrit doing?”

  1. But – uhh… trademe is *not* a -shopping- site, it’s an auction site and thus the whole interaction with it’s content is different in terms of page views (let’s reload every 30 seconds during the last minutes of that auction we’re trying to win) and time on site (again, we’re going to hang out there for longer in the last minutes(hours?) of our auctions, just to stay up with the action).

    The function of a *shopping* site must be to present the products as logically as possible so I can find what I want, make the purchase decision – then go through the checkout procedure to get them on their way… isn’t it?

    I’d be interested in seeing the metrics of time on site vs. unique sessions vs. completed transactions as I think this would be a more valuable comparison for displaying the effectiveness of a shopping site. In terms of auction sites then perhaps page impressions and time on site are good measures to compare them with each other. Is there a way to capture the number of sucessful auctions vs. failed auctions vs. buyouts vs. offers accepted?

    Unfortunatly, I can’t think of any simple way of capturing enough those stats (at least not in a site agnostic, consistent manner), given that you’d need broad buy-in across the ‘big’ shopping sites to make it a worthwhile comparision. Can anyone else suggest a better way to measure the sucess of shopping/auction sites?


  2. Trade Me is indeed a shopping site – a substantial proportion of TM sales are for new, buy-now items. It is, as you point out, also an auction site.

    Trade Me spends a huge amount of effort on making the process of finding and buying as smooth as possible. It’s hard (sadly) in NZ to find an easier buying process, and with over 800,000 items on sale it is a continuous challenge to keep ‘finding’ easy.

    Unfortunately the Trade Me buying $ metrics are not published, but I for one would tend to judge shopping sites on a simple ‘how much do they sell?’ basis.


  3. Hi Lance,

    You may be interested in our Monthly Online Retail Monitor. We survey around 750 Kiwis each month on actual spend and report via category, retailer, local vs. offshore, etc. A link some findings from the latest release can be found Here. There are a couple of large e-commerce sites in New Zealand that you probably wouldn’t think of but they are there.



  4. Glen Barnes,

    Just curious about your NetRatings system, because it appears to me that it uses very simple statistical functionalities (descriptive statistics) for its analysis. Correct me if I am wrong here, that NetRatings only does simple analysis and not the advanced methods that can give your clients a deep understanding of their data. I can pick perhaps 3 obvious analytical techniques that are missing from NetRatings :

    #1) It can’t do time-series sub-sequence matching of surfing behavior (matching one time-series to another one or to a group of other time-series to find out which is the closest match). Looking at those graphs above, there is no way to tell which time-series that have the same co-movement with which other series. This is important as the analyst can pick out important patterns.

    #2) It can’t do sequential pattern extraction of users surfing sequence in a website. This is important that you can track the behavior of users. It gives the analyst the capability to see if there is a need for improvement of the website design, since there is emerging pattern from the log data that shows a high rate of a repeated sequence.

    #3) I don’t know whether you system cluster users according to their browsing behavior. It is important to know this, since you can automatically predict what similar users going to be doing when they come to the website. For e-commerce , you can recommend products to such users who belong to same clusters (same browsing & buying behavior).

    I understand that your system is developed in Australia, if you want more info on advanced data analysis, then I am happy to point you out to more info regarding those techniques.

    SPSS has a web-analytic (web-mining) product, which they are moving in to those territory that NetRatings is occupying. All the functionalities I have listed above and other advanced analysis methods are already implemented by SPSS. SPSS is a data-mining vendor so, they will implement the latest state-of-the-art web analytic algorithms that appear in literatures.


  5. Thanks for the reply Falafu.
    The great thing about NetRatings is that it exists, it is an industry (ex Yahoo!xtra) agreed standard, and it is simple. Advertisers need to know how much traffic is going where, and the currency is pageviews and unique browsers. It does not pretend to be a statistical tool – just a deliverer of data. Once you have that data, it is up to you what to do with it.
    If a website wants more robust numbers, there there are several solutions out there ranging from the free (Google analytics) to the wildly expensive. The bottlenecks preventing people using these, I see, are the complexity, the external cost and the internal resources required to do so.


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