Comments on: Making Sense of the 2008 Online Holiday Season http://blog.shop.org/2008/12/16/849/ This blog is for the members of Shop.org Mon, 08 Mar 2010 15:21:36 +0000 http://wordpress.org/?v=2.9.2 hourly 1 By: Kali Kunkle http://blog.shop.org/2008/12/16/849/comment-page-1/#comment-155923 Kali Kunkle Tue, 23 Dec 2008 04:59:31 +0000 http://blog.shop.org/?p=849#comment-155923 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 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

]]> By: Ed Stevens http://blog.shop.org/2008/12/16/849/comment-page-1/#comment-153573 Ed Stevens Tue, 16 Dec 2008 17:36:03 +0000 http://blog.shop.org/?p=849#comment-153573 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. 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.

]]> By: Sucharita Mulpuru http://blog.shop.org/2008/12/16/849/comment-page-1/#comment-153556 Sucharita Mulpuru Tue, 16 Dec 2008 15:45:24 +0000 http://blog.shop.org/?p=849#comment-153556 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. 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.

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