Summary: Not caring about the effectiveness of your advertising usually means you are wasting money. The efficiency can be measured best on the internet, but offline advertisement can be also measured.
The aim of advertising is either increase in sales or building brand. In this article, I write mostly about tracking the impact on sales, as this is the aim of most small companies.
Measuring advertising effectiveness is not easy. Sometimes, the results of measuring are just better guesses. Still, it is much better this way than not to adress this problem at all. There are dramatic differences in the effectiveness of various forms of advertising. If you pay for advertising, then it is probably important for you see some results. But if you waste money on inefficient advertising, you are missing better opportunities and the results may not come at all.
Our main objective in measuring advertising effectiveness is to determine the effect of each advertising campaign from the results of our measuring and compare it with its price. Then we can decide which campaigns bring the best value for the money spent.
It is also important to realise the various factors influencing advertising. The medium, ad copy (exact wording), the format, audience (is the ad well aimed to the people who use our products?) – all of this effects the final success of the campaign. Therefore, it is necessary to judge the effectiveness in context.
Before we start, we need to decide which criteria are we going to monitor. These will differ with respect to the medium used, our possibilities, the purpose of the ad etc.
Examples of possible criteria are:
It is best to combine several criteria, because a customer can for example either contact you by calling your line or by sending you an email. Also, accept the fact, that we are not going to be able to measure everything. Especially if you run several campaigns in various media simultaneously, it may be difficult to ascribe the measured effects to a specific campaign. This can be helped by careful choice of criteria or by running campains seperately (though it is not always desirable). Contrary to traditional media, online campaigns are usually very easily traceable and can be measured well.
Small companies will probably not use the methods of big corporations (ad recognition or recall) which are based on questioning samples of people once the campaign has ended. This would be too costly for small advertisers. Instead, you can simply judge the impact by how many people has the medium reached (viewers, readers, listeners) and comparing how much did it cost to reach thousand people (this is called CPM).
Monitoring the effect of your internet advertising can be easy and cheap when you use quality tools. I have good experience with Google Analytics, which is a free website analysis tool. When you implement it in your website and set up your campaings properly, you'll have all the information you need to decide which advertisement you should drop and which brings you good return on your investments.
Besides basic functions like monitoring number of visits and pageviews, it offers you a variety of statistics regarding visitor segmentation, traffic sources etc. Google Analytics also allows to set up goals on your website and track conversions – goals accomplished by visitors.
Examples of conversions:
I would definitely recommend to set up the analytics for your website proffesionaly before it is launched, because it can provide so much valuable information for marketing purposes.
Then you can mine for data and see for example what is the average transaction value for visitors from London, how many page do they view per visit etc. If you set value for your goals, you can also see return on investments (ROI) for the money spent on advertising. This is possible because of tags, which are added to the links pointing to your website.
Another advantage of this tool is that it helps you to test the different versions of either website or ad copy. Thus, you can create much better landpage or more effective ad. This is called A/B testing.
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