Why the number of clicks (clicks) is the wrong metric

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1, the industry display ad click-through is the name of insignificant, less than 0.1%

2, after the display optimization of the profit increment than the click after the optimization is 10 times times higher

3, in the control test, see IMVU (a virtual world name) advertising customers, whether they click on the ads, there are 10% people are more likely to become paid users

A century ago, John Wanamaker said, "Half the money I spend on advertising is wasted, and the problem is I don't know which half." "Today, network marketers are still trying to overcome the same problems in metric analysis.

The answer seems to be simple, because in the Web world you can track the number of clicks (clicks). The problem is that the number of clicks and the analysis based on the clicks are not tenable. Not only does the number of clicks not show the whole truth, it even reverses the fact, especially when using it alone.

Because of the application of Web analytics tools, many marketers simply attribute the activity of the site (participation, conversion –semwatch) to a click based marketing activity, such as a click on a display ad. However, this is a very limited approach.

Because the display ad hits (CTR) is very low, less than 0.1%, most people who see online ads will not click on it. In addition, the number of clicks and clicks is disproportionate. About 85% of the clicks came from 8% of the people. Many industry studies are focused on this issue.

However, a low click-through rate does not mean that the ad does not work-the opposite is true. Consumers often see ads soon after buying, but did not click ads.

In a recent test, a virtual social network, called IMVU, which buys virtual goods, tries to find out if free IMVU users (who have received marketing emails and see ads in the virtual world) are more likely to become paid users when they see IMVU online ads in the real world.

In control tests, customers who saw IMVU ads, whether they clicked on the ads or not, were 10% more likely to become paid subscribers. Compared to the control group, this 10% increment is an additional effect beyond all existing marketing efforts. The control group saw that the opportunity for other marketing activities was the same as for the test group. The only difference between the two groups is whether they actually see the ads. The Test team saw IMVU ads, while the control group saw unrelated ads.

IMVU used the same method to test if paid users would be willing to spend more money in the real world to see the ads that stimulate consumption. On average, IMVU members who see promotional virtual product ads, whether they click on the ads or not, will consume more than one more than those who see the irrelevant ads. Similarly, this promotion is done outside of the promotional activities of the mail and virtual world. For companies like IMVU, selling virtual goods is like printing money.

Let's focus on an E-commerce company. The company relies heavily on web analytics tools to analyze the clicked User behavior data (to optimize the marketing campaign effect-semwatch edit note) (Post-click date) (the site's traffic and revenue are brought by advertising). Advertisers want to rely only on the user's behavioral data after clicking on the basis of optimization, because the customer does not track the relevant revenue (Post-view revenu), so it can not be optimized.

Review these two scenarios: optimizing the allocation of transformation contributions (Post-clicks) based on post-click Data to allocate transformation contributions (Post-view) based on post display data. After the display, the optimized profit increment is 10 times times higher than the after-click Optimization. When analyzing revenue from a clicked perspective, we call the best ad a, the worst of which is C. But when analyzed from the perspective of the display, the results are just the opposite. C is the best, and a is the worst. This leads to a completely different optimization scheme.

Some people may put forward the contrary view, that the analysis after the display exaggerated the credit of online advertising. Because a potential consumer, whether or not they see an online ad, is likely to buy a product, and these ads are likely not to affect their decision. However, after verification over and over again, we found the opposite result. We analyzed the time window between seeing ads and buying products. Data show that the rapid increase in transformation occurs in a short period of time when the consumer sees the advertisement, which embodies the effect of the post attribution. In the following example, half of the conversions occur within six hours of the ad display, while 70% of the conversions occur within 24 hours of presentation. If there is no such effect on the post attribution, then we should see that the conversion rate is randomly distributed over time to follow the linear pattern rather than the curve pattern.

The bottom line is that every ad campaign is different. They should all be optimized based on as much data as possible. Don't just rely on a click based analysis. It's best to make the most of your strengths and resources.

Day Bank Commentary:

The effectiveness of the display of advertising analysis should be more reference to a variety of factors, such as:

1. What is the purpose of the display of advertising? is to do the brand or do sales promotion, this advertisement wants in the consumer decision which stage to have the influence?

2. What are the characteristics of the industry? How long is the consumer's decision cycle?

In fact, display ads are paid media, paid media, and the websites that you click on are owned by owned, but in fact both are media that advertisers can control, and they can decide what to show, how and when. Click, for advertisers, just from the display of an information module to the display of another information module.

From the consumer's point of view, both are sending him information, the difference is only the amount of information and his reading center. Clicking represents a relatively strong degree of participation, but presentation is also a message. The impact of this information delivery is the "top of mind" in decision making. For example, if I look at an ad for a fitness device, maybe I glanced at it but didn't click it, but if I had a similar need, and once again searching for fitness equipment through the search engine, I saw the same name because it was the second show, so I might have a sense of familiarity/trust to create a click.

The present status of the analysis of advertising data may be largely constrained by the difficulty of data acquisition. In its own media, with the popularity of web analytics technology, click Data is easy to collect and can be easily applied to practice, and in the pay media, especially in the domestic display advertising market media environment, a lot of data can not be collected or not to be shared by advertisers, which is doomed to analysis and optimization is ignored. What's more, we need to get the above conclusions, and we need to analyze a lot of data. This is the industry's development in the process of a pain bar.

Original: http://www.imediaconnection.com/content/29020.asp

The author Jarvis Mak's biological origins, serving both Yahoo and Neilson to perform customer analysis including the Megapanel project. is focused on the retail industry digital media and marketing work. 、

Source: http://semwatch.org/