24 Feb 09 Offline retailers are reclaiming online market share

 

Traditional retailers are reclaiming the ground lost to ‘e-tailers’ (online only retailers) over the past few years, with over 3.5 million unique visitors searching catalogues online for in-store offers over the December shopping period.

Bricks-and-mortar retailers reclaimed market share from e-tailers over the Christmas shopping period, with more than 3.5 million unique visitors searching the catalogues of retailers published on Salmat DigitalForce’s Lasoo.com.au and Dynamic Catalogue platforms during December.

The figure is an aggregated total of unique visitors throughout the month to Lasoo.com.au and the 20-plus Dynamic Catalogues hosted within the sites of individual retailers, including major brands such as Target, BIG W, Myer and Dick Smith.

Previous Christmas retail periods indicated a trend towards consumers engaging more with e-tailers’ at the expense of the bricks and mortar retail brands. This was largely attributable to the fact that time-poor consumers were easily able to find exactly what they were looking for on the internet from the online retailers, while traditional retailers struggled to make their brands and offers discoverable online.

Since the November 2007 launch of Lasoo.com.au, which allows consumers to search catalogues and offers from various retailers; and Dynamic Catalogue, a managed catalogue hosting solution that sits within the retailer’s own site; offline retailers have had an effective way of making their catalogue offers visible to consumers online. What’s more, the technology behind Lasoo.com.au and Dynamic Catalogue allows these offers to appear towards the top of search results from search engines such as Google. Until Lasoo and Dynamic Catalogue, these search results pages were previously dominated by e-tailers.

Pre-Shopping has become a growing trend for online shoppers seeking instant gratification, as witnessed by e-tailers with conversion rates around 1% to 4%. Retailers have capitalised on the behaviour in 2008, clocking up a 24% conversion rate from online–to-offline transactions.

Lasoo analysed Hitwise data by comparing a custom category of the Top 25 Retailers (traditional bricks and mortar retailers) against a custom category of top 25 E-tailers (pure online retailers) based on market share of Australian Internet visits. As a result the year-on-year comparison of the market share of traditional retailers v e-tailers[1] during Q4 clearly demonstrates this trend, with the retailer Christmas traffic surpassing the benchmark previously set by e-tailers as they now seem to have reached a ceiling over the last 2 years.

E-Tailers vs Retailers  Q4 06

Etailer vs Retailer Q4 07

Etailer vs Retailer Q4 08

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[1] Using custom categories (see endnote)  comparing weekly visits against traffic in “All Categories”

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E-tailer traffic included:

shop.lego.com www.1-day.com.au www.abebooks.com www.allposters.com.au www.altech.com.au www.amazon.com  www.ap.dell.com www.apple.com.au/itunes  www.avon.com.au www.bargaindeals.com.au www.bigpondmusic.com www.bodybuilding.com www.booktopia.com.au www.cafepress.com www.catchoftheday.com.au www.crazysales.com.au www.dailydeals.com.au www.deals2u.com.au www.dealsdirect.com.au www.dinosaurdeals.com.au www.dstore.com.au www.ezibuy.com.au www.ezydvd.com.au www.factoryfast.com.au www.fishpond.com.au www.graysonline.com.au www.hp.com www.isubscribe.com.au www.itvsn.com.au www.latestbuy.com.au www.lenovo.com www.liteneasy.com.au www.modyourcar.com.au www.moshtix.com.au www.mwave.com.au www.oo.com.au www.ozstock.com.au www.play-asia.com www.redbubble.com.au www.shoppersadvantage.com.au www.shoppingsquare.com.au www.simplymobile.com.au www.soldsmart.com.au www.strawberrynet.com www.streetsmartshopper.com.au www.techbuy.com.au www.ticketek.com.au www.ticketmaster.com.au www.topbuy.com.au www.wickedweasel.com.au www.winemakerschoice.com.au www.zazz.com.au www.zazzle.com.au www.zodee.com.au

Retailer Traffic included:

catalogues.bigw.com.au shop.telstra.com.au store.three.com.au store.vodafone.com.au www.abcshop.com.au www.aldi.com.au www.angusrobertson.com.au www.bcf.com.au www.bigw.com.au www.bigwentertainment.com.au www.binglee.com.au www.borders.com.au www.bunnings.com.au www.coles.com.au www.crazyjohns.com.au www.davidjones.com.au www.digitalcamerawarehouse.com.au www.digitalhome.com.au www.dse.com.au www.dymocks.com.au www.ebgames.com.au www.fantasticfurniture.com.au www.flightcentre.com.au www.freedom.com.au www.game.com.au www.harveynorman.com.au www.ikea.com www.jaycar.com.au www.jbhifi.com.au www.jbhifionline.com.au www.kmart.com.au www.loadit.com.au www.mitre10.com.au www.msy.com.au www.myer.com.au www.officeworks.com.au www.petersofkensington.com.au www.rebelsport.com.au www.retravision.com.au www.rivers.com.au www.sanity.com.au www.spotlight.com.au www.supercheapauto.com.au www.supre.au.com www.target.com.au www.target.dynamiccatalogue.com.au www.teds.com.au www.thegoodguys.com.au www.toysrus.com.au www.umart.com.au www.videoezy.com.au www.westfield.com.au www.witchery.com.au www.woolworths.com.au

23 Feb 09 Dear Dame

This question is a great one that confuses many – even the Wikipedia page it references could be made a little clearer…. maybe in my spare time I’ll update it

“This information was referred to me but it has sort of confused me more. Its called The Hotel Problem. http://en.wikipedia.org/wiki/Web_analytics

I understand what it is saying but why doesn’t it apply to First Time Unique visitors which gives you the same total for the month in comparison to adding up the value for every individual day?

It will explain how Prior Unique Visitors is different depending on the date range.

Any further thoughts?”

This is a common question and basically it comes down to the measure of “uniqueness”.

Prior unique visitors seems to be a “return unique visitor” while the Ffrst time unique visitor is an absolute unique visitor.

Your total unique visitor count should be a combination of Prior Unique Visitors and First Time unique visitors.

If you look at the table below;

 

Day	1	2	3	4	5	6	7	8	9	10
Visitor A	1		1		1		1			1
Visitor B		1				1				1
Visitor C			1		1				1

Then you get this result,

FTUV	1	1	1	0	0	0	0	0	0	0
PUV	0	0	1	0	2	1	1	0	1	2
TUV	1	1	2	0	2	1	1	0	1	2

When you look over different time period and add these visits together you see a lot of variance that seems contradictory.

1 Day granularity	10 Day Granularity
3	3
8	0
11	3

FTUVs will never change with granularity because a visitor can only be new to your site once (unless they delete a cookie, and then they are a new visitor).

21 Feb 09 Dear Dame

I recently got asked a good question about calculating unique visitors to a particular page without actually having the UV data.

“Can I determine the number of visitors to this page in the following way?

Total Unique Visitors Jan = 100

Total Page Views Jan = 1000

Avg Page View per visitor = 100

Notebook page Jan = 600 page views / 10

Therefore 60 unique visitors visited this page in Jan?

60% conversion.”

It isn’t uncommon to have to extrapolate some things from data that doesn’t quite answer what we want to know, and we can do this by using proxies from what we are measuring, or using relevant trends and applying it to the data we have.

Unfortunately, you can’t really make this assumption – unless you want to be a bit fast and loose with the truth.

The only real way to know this is to actually measure it, and without knowing the specifics of the tool, I’m not sure what it’s capabilities are. In Omniture, you can just turn on a unique visitor correlation to pages. In Google analytics, you can just drill down to the content page and it will tell you the unique views and other juicy info.

I would suggest that if you can’t get this level of detail, you should implement Google analytics as well as what you are currently using, or totally migrate.

As for logic in the question, you can’t say that “Therefore 60 unique visitors visited this page in Jan” because you just don’t know that for a fact. It would be largely dependent on your site structure. For example, if the notebook page was 4 pages deep, then to construct the average of 10 page views per person, you would make up those 10 pages of something like 5 at one page deep 3 at two pages deep, 1 at three pages deep and 1 at 4 pages deep (5 + 3 + 1 + 1 = 10). If that was the makeup of the average visit and the page resided at 4 pages deep, then 600 page views would correlate to 600 unique visitors.