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We’re down. We’re up. We’re down again. If you have been following the roller coaster of a ride that are media reports about online retailing, you may be getting motion sick by now.
If you look a bit further than the media reports, however, you can find steadier voices. One of those is Sucharita Mulpuru from Forrester Research. As a disclaimer, Forrester is a key research partner for Shop.org.
I agree with Sucharita that, “eCommerce is insulated from the worst of it because there continues to be channel shift because it’s just easier to shop online and perhaps most compelling, it’s a channel where consumers can easily find the best price for anything they want to buy.” Sucharita’s comments were partly in response to a Shop.org/Forrester survey of more than 90 online retailers of their November through December 1 sales (detailed findings require Shop.org membership).
There is now one more piece of evidence released today by NRF that supports this idea. According to NRF’s 2008 Holiday Consumer Intentions and Actions Survey (there’s some great data here and, best of all, it’s free), conducted by BIGresearch, a large majority of last-minute shoppers are planning to skip the crowds and visit the web, with 40.2 percent of people planning to shop online, up substantially from 34.9 percent a year ago. Even more encouraging is the fact that the average person had completed 47.1 percent of their holiday shopping by the second week of December, about ten percent less than the 52.6 percent average completed by this time last year.
As the holiday season runs its course, Shop.org will continue to bring data to its members to help them navigate through these unprecedented times.
As with all data, statistics are like bikinis (an old professor’s words, not mine). What they reveal is interesting, what they hide is essential. Key questions to ask when sorting through the loads of holiday data out there: is it based on retailer or consumer responses? Are overall figures actuals or inferred? Is it consistent with other findings? If not, what biases in sampling or modeling could account for the variances? What assumptions would have likely changed the findings? Qualifiers are critical to drawing the right conclusions.
At Shopatron, we operate hundreds of stores. We think of trends in terms of sames store sales year over year. When comparing historical averages, recent trends, and very recent data, we have been able to say whether things are “up” or “down.” All year, we’ve seen same store sales trend downward, although a good September kept Q3 in line with Q2, much to our astonishment. Q4 started out badly and got worse in November. November was a weird month, though, with the election and a late Thanksgiving. We saw sales “up” since then, with continued strength through this week. We will see how much gas is left in the tank this week and, where the risk really seems to be, in Janaury.
Scott and other commenters:
Very interesting. We’ve noticed the same oddities. My husband is a Six Sigma Green Belt and we still had difficulty reconciling all the different stories, because they all were based on different data sets.
On a lighter note, we opened our December 7, 2008 blog on “ShopWorthy – A Blog About Online Shopping” with a first-story headline of: “THINGS LOOK BAD; THINGS LOOK GOOD. OH, JUST GO SHOPPING!”
You can see it at http://DreamWorthyGifts.blogspot.com, if interested.
Thanks for some great free data sources and for validating that while we may well be crazy, it’s not because this confused us.
Kali
_________________________
Kali Kunkle
Owner and Chief Shopping Advisor
DreamWorthy Gifts LLC
Kali@DreamWorthyGifts.com
http://www.DreamWorthyGifts.com