Why are offer impressions a better metric than a catalogue page view?
Offer Impressions are a better metric than a catalogue page view for many reasons that can best be appreciated when looking at the short history of the web. Initially, the metric that was used as common currency for doing apples to apples comparisons of web real estate was the “hit” – the number of client requests to a web server. This was quickly outdated as pages became more sophisticated and “hits” were being registered when every asynchronous element refreshed (widgets etc), tracking servers were called and a myriad of other activities resulting in “hits” took place.
Then, to replace this came the Page View metric. Marketers decided to disregard how many “hits” there were since it was no longer one page = one “hit” and it was more valid to see how many page views there were. However, this metric soon became outdated as well when AJAX (asynchronous JavaScript and XML) became the standard for usability in the Web 2.0 world. Now, more than half of a page’s content could change without a new “Page View” ever being registered since the URL remained the same.
So, the impression was born to be the apples to apples comparison metric for marketers. Regardless of what all of the information on a page was doing, how many servers where “talking” to produce a complete page of information, the one thing that would remain constant and relevant to marketers across all mediums was – how many times did my offer appear in front on the eyeballs of a consumer.
Catalogue Page view’s suffer the same drawbacks as page views with regard to performing accurate analysis for a marketers spend – despite being the common currency in the analogue world where “offer impression” granularity is hard to come by since offers per page vary from page to page. However, ad space inside catalogues is still sold at the offer level, indicating that more granular metric is the more relevant one to marketers.
We can track catalogue page views, yet, if this is all that is looked at, then the offer impressions that appear in search, and not on a catalogue page view is never counted, despite this being the more relevant and targeted advertisement. Some may think you could just measure both and report this, but now you are dividing what would otherwise be a consistent metric, making it harder to understand and do accurate analysis on. What is the point of looking at a report that may say
100 Catalogue Page Views
13 Search Listings
813 Offer Impressions
Rather than
813 Offer Impressions
13 Search listings
Where catalogue pages are evident inside the more granular metric?
The result of this approach is that when comparing to other media and you are looking at
Print: Circulation
Print: Readership
Print/ catalogues: Distribution
Viewership: TV
Listenership: Radio
Visits: Web
You have one universal metric to apply to your ROI calculations.
NB: This will become much more important when we look forward to a more convergent Internet as described here by Kevin Kelly.
Part 2. Using a universal metric when calculating ROI? The offer Impression
Reducing offline media to a universal metric – the offer impression, is fairly intuitive, but has some intricacies in finding the correct estimates for certain variables.
Offer Impressions = (Circulation x (wastage factor)) x (probability of reaching ad page depth) x Offers per page
Offer Impressions = (Readership x (wastage factor)) x (probability of reaching ad page depth) x Offers per page
Offer Impressions = (Distribution x (wastage factor)) x (offer impressions per person)
Offer Impressions = (Viewership x (wastage factor)) x (offer impressions in TVC)
Offer Impressions = (Listenership x (wastage factor)) x (offers in radio commercial)
Offer Impressions = Offer Impressions
Now you have an apples to apples metric to begin calculating ROI (In fact, you could even work out offer impressions per unique visitor – however, this is a different yardstick compared to the other media that are largely absent of any tracking).
The next thing required is to establish the quality of an offer impression in each medium with respect to sales. This is the responsibility of the marketer, not the ad network. The job of the ad network is to generate the highest quality offer impressions in the greatest quantity possible for the lowest possible price.
The general formula then for establishing media effectiveness would look something like
Media Effectiveness = Sales per offer impression = Sales / Offer Impressions
I.e. Sales = Offer Impressions x Media Effectiveness
This can now be ascertained per media channel.
ROI = Sales/ Dollars Invested
Hence, your ROI can be predicted and measured by
ROI = (Media Effectiveness x Offer Impressions)/ Dollars Invested
Now, to get a relevant statistic for this you really need to run a control set of offers, probably simultaneously across different media that is unique to each state – eg Radio in Melbourne, Search in NSW, TV in Queensland, and then assess what the performance was in each medium so that you can ascertain what the Media Effectiveness was for each channel. If you have eCommerce enabled, you can get an idea of the effectiveness of online channels by seeing the quality of different traffic streams on online sales. However, this totally ignores web to store conversion which research has indicated counts for up to 95% of online traffic on the eCommerce enabled sites.
Measuring the effectiveness of offline campaigns or even how prominent your brand is in consumer mindshare can be tricky. Traditionally, this had to be measured purely from sales data, or through research companies. Finding a quick gauge just to measure the pulse of your brand has always been a little bit harder.
However, as with pretty much everything digital, tracking these traditionally unquantifiable things is becoming more of a concrete science. There are a few handy tools you can use to assess you brand equity.
Google Trends and Hitwise (both pictured below) are fantastic for measuring how many people are searching for your brand. Considering about 17% of all searches are “navigational” (by people who know where they are going), you can get some idea from this trend line of what your brand is doing in the collective consciousness.
Another interesting way to look at how much penetration your brand has gained is through a little known tool called Lexicon (from Facebook), which allows you to see not how many people are searching for your brand, but how often it is mentioned in conversation – and surprisingly this is a very different story:
Looking at the dips and spikes will give some indication of the impact of certain events. However, just because you are being talked about, doesn’t necessarily mean your brand value is going up – but usually it is better than not being talked about. The best way to see exactly what your brand stands for among the populous is with this little tool named Brand Tags – however currently it is only focusing on US brands.
Many people wonder about how accurate many of the online competitive intelligence tools are since they all pull their data from such an expansive array of sources. Based on my own experience, I’m going to make a quick comparison of the online traffic tracking tools to try and divine exactly where the truth lies - pardon the pun.
For this exercise I’ll do a comparison of some websites that have an amount of traffic that is much smaller than the behemoths like Ebay and Google (but not necessarily small). This will show weaknesses of some tools.
First, we have Hitwise - an expensive product choice, but the data, in my experience is fantastic - they track traffic from the ISP level and have a great team of analysts and techies to make sure this is accurate. They currently catch about 3 million of the 16 million Australian web surfers, which is great for achieving an accurate sample size.
The data is updated regularly (almost daily), you can see it at a daily granularity and break it down by a host of metrics like visits, page views, time on site….the list goes on - and that is just website traffic measurement. Since we are doing a comparison, that is all I’ll look at, but a lot of the value of Hitwise comes from the rest of the capabilities of the tool.
Now, looking at the same cross section through Compete.com, a free web tool with some paid services, we can see that the trend lines are dramatically different for Ticketek.
Compete get their data from several sources; from what I can tell it mainly comes from their own Compete toolbar. This is used as a statistical sample to generate the numbers of “People”we see here. As you can see, they acknowledge they do not have enough data here for accurate estimates except in the case of mytickets, which they seem to have done a fairly good job on. Hitwise on the other hand opt for accuracy with percentage market shares, rather than absolute numbers.
As a general rule, unless you are looking at sites that have massive amounts of traffic, compete can be OK for assessing relative trends, but for absolute numbers I wouldn’t trust it at all.
Compete also provides some top level keyword analysis which can give a couple of nice hints (I just hope “boners in speedos” is a band.)
Next we have Google trends for website. In Australia, and globally, Google has access to massive amounts of data on user behaviour. From what I can tell, the information for these trends comes from what Google sees through its toolbar users that share information, what it sees from people travelling through search, and what it sees from the users that share their analytics information.
It seems that Google also produce estimates on actual visitor numbers, rather than go the Hitwise route of reporting market share as the default metric. It has picked up the Ticketek decline well, but missed the spike in mytickets seen by Compete and Hitwise in January. This would correlate with New Years gigs and I think there was also some integration with SMH to explain this. There is some top level click stream data (also provided in great depth from Hitwise) here which is great to see at a glance which sites people are moving between.
Finally, we have Alexa, the pioneer of competitive analysis for the masses of the web. It seems that Alexa disagrees with everybody about inthemix, as they have inthemix doing almost as well as Ticketek. However, Alexa also seems to have missed the spike for mytickets in January. Alexa relies on Alexa toolbar users which has a significant amount of Australian users -my guess is that these are skewed to more web savvy users that are more likely to be interested in inthemix than the other two sites that were developed at later stages.
So, what does this all mean?
Well, you need to be doing competitive tracking to complement your on site analytics. The tools you choose for this may change depending on your needs - but I do think for data reliability, you can’t go past Google or Hitwise. However, if you want to use Hitwise, it will cost you and you are going to need to find information to strategically pay that off - it is possible to do this, but you need to find the right person, and be in the right industry to make that work.
In the mean time, I’d say Google is enough for starters, do some crosschecks against other tools for validity and aspire to Hitwise when you find that those trends aren’t enough, and the cost of not knowing is too great.
looking at the short history of the web. Initially, the metric that was used as common currency for doing apples to apples comparisons of web real estate was the “hit” – the number of client requests to a web server. This was quickly outdated as pages became more sophisticated and “hits” were being registered when every asynchronous element refreshed (widgets etc), tracking servers were called and a myriad of other activities resulting in “hits” took place.
