How to correctly understand black Friday data?

With the end of Black Friday, I received an article called "2009 Black Friday coremetrics standard report." The article analyses the difference between this year's black Friday and last year's, and finds some interesting clues. For people in other parts of the United States, Black Friday is the most popular shopping day of the year in the United States, ending after Thanksgiving. It was called Black Friday because it broke the retail year and made the profit rise for the year.

Coremetrics is a http://www.aliyun.com/zixun/aggregation/13143.html "> Network analytics Tool Provider with data from more than 500 leading retailers in the United States." Its data spans many different retail industries and can quickly see trends in development. In its report, it reveals trends beyond retail customers.

The most interesting include the following: Average consumer online consumption rose by 35% compared to last year, and apparel retailing is the leader. In the current economic climate, this is clearly the result of sacrificing offline sales. It's not surprising that there are more people hoping to reach a deal and look for better promotional opportunities, especially in black Friday.

The number of consumers buying goods has increased by 18.3% compared with last year. There is a reason behind this figure, which is closely related to the 1th. Do consumers want to reduce purchases from a single retailer and buy more?

--Consumers spend less time browsing retailers ' websites, and they have increased the time they used to compare research before Black Friday, and they chose to shop from the list: the volume of browsing dropped by 5.4%, the number of users who left the site after browsing a page grew by 39.4%, and the volume of each page dropped by 30.4%. This suggests that consumers know what they are looking for, that they have specific intentions to enter the site, and that if they cannot find something at the right price, they will quickly leave. To my surprise, the bounce rate increased from 22% to 31%, which means that almost three users have a user who logs on to the site and then leaves without browsing more than one page.

So now if consumers are landing on your site and can't take their rise, they'll leave quickly and forever. Consumers know that they have a choice and can easily find alternatives. If this is not the importance of customizing and orienting landing pages, I don't know what else.

Getting these data quickly, and seeing some trends, reminds me of other things: the speed of analysis. Coremetrics published the report in less than 12 hours. I don't know how many retailers have done a comparative analysis of this year and last year's data? After Black Friday, if this trend continues, how can you, as a retailer, have the opportunity to be successful during shopping? It's not easy, but the report shows us that you can quickly use the Web analytics data to make judgments and analyses of changing behavior and potential opportunities.

-Social impact. Given the explosive growth of Twitter and Facebook since last year and the sharing and ranking of other social networking sites and black Friday deals, I don't know how social media affects sales results. If Coremetrics analyzes this and shares the analysis of the retail industry, there may be some interesting findings. But as marketers, we can based on their own web site through the analysis of the relevant site data, to discover the different performance of these users.

--The importance of related performance. This is yet another example of coremetrics standards and understanding competitive situations (Compete, ComScore, Hitwise). The report notes that apparel and gemstone retailers reflect their online orders have grown 25%--30%. Gem retailers can analyze black Friday data and are pleasantly surprised to find that average orders have grown by 8% compared with last year. But once they see industry figures and find their rivals to grow more than three times times their own, they will have a completely different overall understanding and will find ways to improve performance during holiday shopping. You have to understand your own performance, and at the same time you need to understand the relevant performance.

I have to thank Coremetrics for publishing this data and letting others share it quickly. As I mentioned earlier, this is the best example of an important day's activity and a quick analysis of what happened this day. While this is a macro-level analysis, this is what retailers should know. However, this analysis is more of a proposal to improve future development.

(Original: December 1, 2009; compiling: Song XI)