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Referral Traffic – an Essential Metric for Judging Digital Ads

Dr. Rüdiger Paschotta

One of the advantages of digital advertising, for example comparing with print ads, is that you can monitor the effectiveness more easily.

Curiously, however, many advertisers seem not to do that, and consequently waste a lot of money on ineffective digital marketing offers. In the following, I explain what metric is most important, how to get the required data and how to interpret them. The required efforts are actually not excessive.

Page Views

It is of course of interest how many times a certain ad is displayed; that is e.g. the number of page views of those web pages where the advertisement is included. Unfortunately, it is usually not possible to verify such data as claimed by website operators because you do not have access to their web server. A simple solution may be to trust their data, but unfortunately it appears to be an alarmingly widespread kind of ad fraud that utterly inflated page view numbers are presented. According to my own experience, that can happen even when dealing with organizations which one thought to be prestigious and to care about their reputation. However, particularly those having poor data in reality (too poor to present to an advertiser!) appear to be tempted to present inflated data, also considering it unlikely that it will be discovered.

Inflated traffic numbers may not always be generated purely by invention, but also result from other dishonest tricks, such as counting views of pages which are not at all relevant for the advertising. In fact, in media data it is often not made sufficiently clear to what pages exactly certain numbers apply. Another possibility is that yearly numbers are erroneously taken as monthly data…

You may wonder how I can tell that some others are dishonest about their traffic; I will come back to that later on.

One should also emphasize that even reliable page view numbers do not tell you that much, as long as it is not known (a) what kind of audience looks at those pages, and (b) with what likelihood they will recognize the advertisement. The latter depends on its visual appearance, but also on other page content (e.g. dozens of other advertisements?), the intensity of interaction with those pages etc. – quite difficult to assess in detail. Considering all that, one may conclude that it is not worthwhile spending a lot of time on such analysis. However, what applies to page view data does not equally apply to other data:

Referral Traffic

A very relevant metric is the quantity of obtained referral traffic. Such traffic results when people do not only read an advertisement but click on an HTML link in it (in the text or associated with an image), so that they get e.g. to the website of the advertiser.

It is easy to understand why referral traffic is substantially more relevant than page view data. After all, it only occurs when people do not only recognize an advertisement but engage by clicking on it. That removes various uncertainties related to page views; for example, you will not see much referral traffic if the ad is not sufficiently well visible or if you got the wrong audience.

At the same time, referral traffic is much easier to check because it leaves marks on the web server to which interested people are sent – and that one is usually under your control as an advertiser. If the marketing person does not have direct access to the web server statistics, it should usually be an easy thing to find the appropriate IT person providing such data. For example, that person could simply be asked to provide the number of page views in certain months where the referrer was from a certain web domain (e.g. rp-photonics.com) where the ad has been placed. A more refined analysis is also possible, for example retrieving the countries of the users from the IP addresses, or relating clicks to specific target pages.

Usually, that method is quite reliable. Only a minor part of the referral traffic may be lost (i.e., stay unrecognized) e.g. if the web browser of a user does not transmit referrer information. In principle, genuine clicks can be simulated e.g. by paying some people to do that, or even in an automated fashion, but that is probably not that common. A closer analysis of referral data may actually uncover such practices, e.g. when recognizing that many clicks come from few IP addresses or from a single country only.

So if you get referral traffic data for example from the RP Photonics Buyer's Guide or some other buyer's guide, you should be able to cross check it with own server data. Personally, I very much welcome people to do such checks (and regret that only few people appear to be doing them) because the results very much support my claim that we have the absolutely superior offer for the digital marketing of photonics products:

  • First of all, people find a good match of their data with ours. (Often, they measure even somewhat more traffic than we report because we do particularly strict filtering in an attempt to suppress any effects of robot activities. Also, for example we count only one click when a user causes identical clicks in a sequence.) So the trust is confirmed.
  • Second, if they do that check with other digital marketing offers, they surprisingly often find that there the real numbers are far smaller than what has been claimed. One long-term advertising customer of us actually once told me that we are the only ones where the referral traffic data can be confirmed!
  • Third, I always hear (i.e., with no exception) that the amount of referral traffic which our buyer's guide generates is far larger (e.g. 8 times more) than that of any other digital resource in photonics. Well, that's not so surprising, given that no other buyer's guide is linked to something so attractive as my RP-Energie-Lexikon.

So those people who have done that simple test know got confirmed that their money is well spent at RP Photonics, while they can save money by dropping other ads without losing much in terms of results.

Plausibility of Page View Data

Quite a few times, I have seen a situation where the measured referral traffic from some other digital resource was far lower than that generated by us, although the page view numbers claimed for the other resource were similar to ours. It is theoretically possible that these things are all true, e.g. if poor visibility of advertisements led to weak referral traffic despite a large number of page views. However, my impression typically was that the page view data are just not credible. Well, in the end it does not matter that much concerning the results; basically one can say: no referral traffic, no results!

Audience Match and Traffic Quality

I believe that the audience match is pretty good for any kind of photonics buyer's guide, simply because only the relevant audience can be expected to use such a resource; nobody else has a reason to go there. The resulting quality of referral traffic (in the end, the likelihood that they order products) must thus be expected to be quite similar for all those resources; so I would not claim superiority in this particular area. There is a big difference, however, to more general resources such as Google Ads, where the audience match is relatively poor, and the traffic quality is thus substantially lower. For reasonable comparisons concerning the value for money, this absolutely needs to be taken into account. Cheap referrals don't help you much if many of them come from people who e.g. just accidentally clicked on some link, or just out of curiosity with no real interest. Generally, you don't want ads to appear on many pages with poor audience match, but you can hardly prevent that with Google Ads and the like.

Measuring traffic quality is actually not so easy, particularly in the area of photonics, where people usually don't end up ordering straightaway from some online store (as the end point of a sequence of clicks), but first need to go through some sophisticated purchase decision process and thus do the purchase on some other day. It is then often very hard to relate that to the original visit to a supplier's website. I guess that by then they will often even have forgotten how they first got there. And tracking visitors sufficiently well to solve that problem can (if at all) hardly be done without substantially violating privacy. (We would never try that since we fully respect privacy.)

Trying to measure such things, one can also easily be mislead. For example, if one measures the average number of page views after a referral (e.g. within one hour), one should not interpret that activity as an indication of traffic quality. If users are led to the exactly fitting page on the supplier website (describing the relevant product), they may not cause much additional traffic, simply because they got exactly what they need. One would obtain more page views by sending them all to the supplier's homepage, from which they would have to search for the relevant product information, but that would hardly be in the interest of the supplier: people may easily get lost without finding the right target page.

Therefore, I think the best one can do is to estimate the audience match and traffic quality simply by considering what kind of resource the ads are on: is it one which is likely to be used only by photonics professionals, who might order the relevant products, or rather some more general website, used mostly by people who would never order? In other words, referral traffic numbers are probably the best metric one can use to judge the results of the campaign, already taking into account audience match since the clicks on advertising links already indicate an interest.

Conclusions

The conclusions are easily drawn:

  • Referral traffic data are a very important and useful metric for judging the effectiveness of an online advertising campaign – more relevant than page view data.
  • In contrast to the latter, it is quite easy to verify referral traffic data, since you can use data from your own web server.
  • If you do it, you will find good agreement for our data, while others may present much higher numbers than you can verify. That tells you something about credibility.
  • The audience match is already confirmed by good referral traffic, and you do not need to worry about traffic quality in addition, since a good audience match more or less guarantees that.

And the final conclusion should be that you should use the advertising package for the RP Photonics Buyer's Guide for the marketing of photonics products, and can expect much better results than with any other digital resource.


This article is a posting of the RP Photonics Marketing News, authored by Dr. Rüdiger Paschotta. You may link to this page, because its location is permanent.

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