What and how to measure Media and Content websites

By media and content websites, I am talking about pure online media properties like CNET as well as companies (e.g. traditional newspapers like New York Post) who must somehow, either be in the midst of an involuntary transitioning of readers, from offline content consumption (their paper) to online content consumption (their website) – or in the fortunate circumstance that they managed to set up a business model that compliments both channels at the same time. (I am of course talking about any other website where the product is content – thus your typical affiliate website!)

Studies by analysts in general and in particular Jupiter Research concludes that - we are seeing an increased hunger for personalization and thereby a disaggregation of the website - and at the same time the emergence of an online-only audience who at the same time demand participation (social media as we konow it today).

Which makes me conclude on a progressive path and journey most media and content website have to take:

1. Media
2. Online Media
3. Online Social Media

This seems straightforward and as understandable as in taking the newspaper (Media) and reprocess the content on a website (Online Media) and then throw in a couple of “post a comment” widgets (Online Social Media). I do hope that we all agree that is takes a lot more than that! ...and that a lot more is at stake. As online social media continues its striking invasion into the overall media landscape and seize valuable mindshare, particularly from the younger demographic, the degree to which a traditional media company move towards the final goal of social integration of its audience is rapidly becoming a proxy for its ability to survive the future.

If we partly agree on the above I think we also agree that It is simply NOT enough to just randomly report on the effects of this transitioning – in vague hopes of moving towards this goal of social integration. One have to accept that however sexy Online Social Media or even just your Online Media (your basic content website) sound, there MUST be a set of measurable KPI’s across all three steps from above - and they should very much be aligned to your business objectives.


Content websites business objectives:

• Increase Advertising Revenue
• Increase Subscriptions
  

Having in mind that I am allowing for a broad interpretation of the term “subscription” – the above clearly defines the business objectives for most Media and Content websites. Assuming that you agree with me, we then have the opportunity to define a set of specific KPI’s and accompanying important metrics to drive those objectives forward:


Content websites advertising revenue KPI’s:

• Advertising Revenue
• Visits per week
• Ad units per visit*
• Ads served*
• Ad CTR
  

* The “Ad units per visit” KPI is a replacement for the old "page views per visit" as a way of indicating the size of your Ad inventory and the “Ads served” KPI is the actual number of revenue generating banners or other media type served in those units.

Remember, that when talking about these 5 important KPI’s (and no business should really have more than 5 KPI’s on a management level) – we are not just talking about a set of basic reports on their performance in retrospective – we are talking about collecting data on a granular level so that we can both report AND more importantly do analysis on these – by segmenting, slicing and dicing them any way imaginable.


Content websites subscription KPI’s:

• Subscribers
• Anonymous visitors to subscriber conversion rate*
• Cost per new subscriber (CPA)
• Subscriber churn rate
• Articles viewed per visit
  

* "Subscribers" is not necessarily the same as or equal to that of an off-line subscriber, this could be something as loose as a RSS subscriber to something as tangible as an online paying subscriber (who get’s access to premium content by login and password).


Content Websites KPI control elements:

• KPI Targets
• KPI Indexes
• KPI Competitive Intelligence
  

I am sorry to add to the list of tasks, but one simply have to create a credible target for every single content websites KPI that we discussed above (credible as in utilizing competitive intelligence to set realistic goals) so that there is a clear driver within the organization on where it is going. At the same time while working towards those targets one need to create a sensible content website KPI index that can be used when doing analysis for the KPI in question (sensible as in taking into consideration how the KPI fluctuates due to season, campaigns and other factors). Finally utilizing competitive intelligence to spot market opportunities for optimization and general KPI improvement.

NOW - having clear business objectives, 10 well defined content website KPI’s and a set of KPI control elements in place - we get the opportunity to put all this to work, as in nobody should go to the extent of setting up a framework as the above without aggressively pursuing a performance improvement in ones business objectives. It's really not worth measuring something if you cannot or will-not take any action on it, and to be worth taking action on, it has to have some kind of measurable monetary value. There is no magic 5 points to optimize, but given the framework as described above one would have an unlimited number of opportunities to make more money! - find a couple of suggestion below:

But before starting to think about optimization (however basic) – you need to make sure that the Web Analytics tool that you have deployed can collect and not only report on the mentioned KPI’s – but more importantly: That you are given the technological opportunity to do analysis!


Content websites optimization opportunities:
(7 basic and to some extent obvious suggestions)

My first comment is that before trying to optimize anything for the better - some sort of potential monetary valuation should be put on the effort as in; are we looking to increase revenue EUR 10.000 per year or are we looking to increase revenue EUR 2.000.000 per year and with what certainty can this be determined.

As an example, let’s say that we have 15 Ad units served per visit and that we have an Ad unit CPM value of EUR 3 and that we have 1.000.000 visits per week (notice how all of these are KPI’s) – this leaves us with a weekly Advertising Revenue on = EUR 45.000 (1.000.000 visit * 15 Ad units per visit / 1000 (CPM) * 3 EUR). The optimization opportunity in question estimates that we can increase the number of Ad units served to 18 by adding a small additional unit AND that there will be a 0.30 EUR decrease in Ad unit CPM value due to this (notice that we utilize our KPI’s index here). Therefore a monetary opportunity on EUR 3600 per week or in perspective approximately an opportunity per year of an additional EUR 188.000 in revenue!

Here goes is my 7 basic and obvious suggestions (very specific and less strategic though) on what you should look into when optimizing a content website:

1. Segment articles based on ad unit placement to create insight on what ad unit placement drive the biggest Ad CTR KPI without decreasing the Ad units per visit.
   Opportunity: Increase Advertising Revenue by optimized ad unit size and placement.
   
   
2. Segmenting the Anonymous visitors to subscriber conversion rate KPI by campaign channels (in my world ALL incoming traffic should be part of a campaign, this including SEO activities) to find and focus on better performing channels and campaigns.
   Opportunity: To increase the Anonymous visitors to subscribers conversion and thus if visits are constant the number of new subscribers
   
   
3. Reporting on the Subscriber churn rate KPI over a given period and drill through (on spikes) to articles (or specific HTML pages) - to determine and create insight of less compelling content. Although it is nearly impossible to determine why subscribers end their relation with you (as basic as stop coming to the Website) – it is still possible over time with repeatedly insight on the same fact to drive a conclusion on what type of content is badly chosen.
   Opportunity: Decrease the Subscriber churn rate KPI and thus increase the numbers of total subscribers.
   
   
4. Setting up basic external campaign tracking on ALL channels from PPC to Email to affiliate networks and compare not only channels, but compare campaign to campaign across channels, sorting out high performers and low performers to Decrease the Cost per Subscriber (CPA) KPI.
   Opportunity: To decrease the average cost per new subscriber and thus an the opportunity to re-invest that into new external campaigns
   
   
5. Utilizing the articles viewed per visit KPI together with the Visual Overlay tools from your Web Analytics package to define Homepage content blocks -- not unique articles -- (this works for content category front pages as well). These content blocks typically on e.g. a newspaper website being; “Main article”, 2 sets of “Sub articles” and a list of “Most popular articles” and so forth. Then determining a CTR (Click trough rate) baseline for each of these content blocks – Now having an Index on the expected click through on e.g. the main article on the homepage on, say, 14% - any underperforming articles (as compared to the index and thus below 14%) can be replaced.
   Opportunity: Increase the Articles viewed per visit by pulling underperforming articles away from prominent positions.
   
   
6. Tracking ALL your Internal Promotions (essential campaign tracking for banners or links on your site promoting other sections on your site) and then segmenting your visitors by these Internal Promotions and looking at the Ad units per visit KPI – you will to begin with, find out which internal promotions actually drive more Ad units (potential revenue) and more importantly which internal promotion does not work and at worst drive DOWN Ad units per visit according to your index.
   Opportunity: Increased Ad inventory by better internal promotions.
   
   
7. Try to increase the “quality” of your Ad units per visit KPI by using External Data Sources and relate your CRM user database to the Member-ID tracking done by your Web Analytics package (this will give a plethora of opportunities), but one obvious is generating a simple report by content group and thereby get insight into the demographics and sociographics for every specific content group, whether that be sports, politics or tech.
   Opportunity: Increase the Ad unit CPM value by demanding higher pricing for better targeting and at the same time expect higher Ad CTR
   

Even though these 7 basic suggestions (and both you and I could come up with 50 others) somehow seem simple – then I am confident that anybody running a content site would be far better off looking at and concentrating on the KPI’s framework described.

My overall conclusion in this post about media and content websites is that; one simply cannot be successful -- in the long run – as a media company moving towards embracing Online Social Media, if not one or more activities around enterprise optimization analysis is deployed. I hope that the above somehow indicated and inspired which direction to go in.

N.B.
I recently wrote a post about what and how to measure Online Social Media websites, this including 7 online social media optimization opportunities (which I suggest you take a look at – should you be on the path towards becoming an Online Social Media). You will see that the KPI’s in that post to some extent are a progression of the KPI’s for Media and Content websites. Which is of course only natural and expected.

What and how to measure Social Networking websites

By social networking websites, I am talking about the likes of Facebook, Myspace, Linkedin, Xing and StudiVZ (and of course any other web property who are engaged in Social Media activities – like any decent blog you know).

Social Networking is the prime headline for Web 2.0 and highly successful so far. This fact only leading towards an even greater responsibility in trying to measure ROI on ones social networking activities, especially as it is somehow agreed upon that only a limited number of players can win (having Metcalfe's law in mind). Remember how quickly Friendster somehow lost momentum, I bet you that they did not deploy detailed analysis, at the level required in this game, within their organisation! – if so, they would have spotted the decreased user engagement and its relating metrics in time.

First I would like to conclude that one have to accept that however sexy social networking is as a trade, there MUST be a set of measurable KPI’s that are strongly aligned to ones business objectives, like any other organisation that operates with the web being a channel of theirs. Hereby concluding that KPI’s are meant to be acted on and not just reported on. Most of the social networking sites that I have been working with somehow work to optimize the following two business objectives.


Social Networking business objectives:

• Increase Advertising and/or premium member-ship Revenue
• Increase User Engagement

Now that we have clear business objectives defined – and some that in my humble opinion are pretty much spot on in regards to the overall success of any social networking business. Assuming that you agree with me, we now have the opportunity to define a set of specific KPI’s and accompanying important metrics.


Social Networking advertising revenue KPI’s:

• Advertising Revenue
• Visits per week
• Ad units per visit*
• Ads served*
• Ad CTR

* The “Ad units per visit” KPI is a replacement for the old "page views per visit" as a way of indicating the size of your Ad inventory and the “Ads served” KPI is the actual number of revenue generating banners or other media type served in those units.

Remember, that when talking about these 5 important KPI’s (and no business should really have more than 5 KPI’s on a management level) – we are not just talking about a set of basic reports on their performance in retrospective – we are talking about collecting data on a granular level so that we can both report AND more importantly do analysis on these – by segmenting, slicing and dicing them any way imaginable.

In the end for any Social Network to succeed and survive it’s users have to be engaged with the website - and that engagement actually turns into positive revenue as they spend more time, take more action and convert on a higher rate than non-engaged users. Concluding that we are looking at a win-win-win situation for the user, the social network and finally it’s advertiser. We of course have to agree on what “user engagement” is to begin with and inspired by Eric Peterson I would say:

“Social Network User Engagement is an estimate of the degree and depth of visitor interaction on the website against a clearly defined set of measurable goals.” – and with that in mind we can define our KPI’s.


Social Networking user engagement KPI’s:

• User Engagement*
• Anonymous visitors to members conversion rate*
• Active member length
• Time since last login
• Total time spent on site

* The “User Engagement” KPI is a custom session metric designed and calculated from a set of basic metrics such as for example; pages viewed, time spent on site, time since last login, comments or other content submitted, subscribed to a feed or alert and so on – the calculation of this KPI is highly dependent on the structure of the Social Network in question). The “Anonymous visitors to members conversion rate” is very much a growth KPI that is used until the Social Network reaches its final plateau.

I think it is of the utmost importance (as in any other media company) to understand what benefits the advertiser and how he get the best possible ROI. Social Networks have the opportunity to create value for the advertiser far beyond the click -- if the Ad creative and Ad content showed allow this -- so it is also important to track the actions taken beyond the click (and in general for the website). Such accompanying social network user engagement action metrics like:

- Forwarded advertiser content
- Endorsed advertiser content
- Rated advertiser content
- ..and other obvious types of engagement with an advertisers content

As you can see the secondary audience is as important as the immediate audience and somehow one should be able to illustrate that value to ones advertisers - as in being able to increase revenue based on the fact that the advertiser gets proven value increase by word of mouth with the secondary audience derived from the immediate audience.

I also find it important that one set up specific custom fields for tracking such accompanying social network user engagement metrics like:

- User profile completion level
- Premium member level

Both the accompanying set of metrics for advertiser and user engagement are super specific - so the above two small lists are only a couple appetizers. More importantly how do we “work” the KPI’s


Social Networking KPI control elements:

• KPI Targets
• KPI Indexes
• KPI Competitive Intelligence

I am sorry to add to the list of tasks, but one simply have to create a credible target for every single social networking KPI that we discussed above (credible as in utilizing competitive intelligence to set realistic goals) so that there is a clear driver within the organization on where it is going. At the same time while working towards those targets one need to create a sensible social networking KPI index that can be used when doing analysis for the KPI in question (sensible as in taking into consideration how the KPI fluctuates due to season, campaigns and other factors). Finally utilizing competitive intelligence to spot market opportunities for optimization and general KPI improvement.

NOW - having clear business objectives, 10 well defined social network KPI’s and a set of KPI control elements in place - we get the opportunity to put all this to work, as in nobody should go to the extent of setting up a framework as the above without aggressively pursuing a performance improvement in ones business objectives. It's really not worth measuring something if you cannot or will-not take any action on it, and to be worth taking action on, it has to have some kind of measurable monetary value. There is no magic 7 points to optimize, but given the framework as described above one would have an unlimited number of opportunities to make more money! - find a couple of suggestion below:


Social Networking optimization opportunities:
(7 basic and to some extent obvious suggestions)

My first comment is that before trying to optimize anything for the better - some sort of potential monetary valuation should be put on the effort as in; are we looking to increase revenue EUR 10.000 per year or are we looking to increase revenue EUR 2.000.000 per year and with what certainty can this be determined.

As an example, let’s say that we have 15 Ad units served per visit and that we have an Ad unit CPM value of EUR 3 and that we have 1.000.000 visits per week (notice how all of these are KPI’s) – this leaves us with a weekly Advertising Revenue on = EUR 45.000 (1.000.000 visit * 15 Ad units per visit / 1000 (CPM) * 3 EUR). The optimization opportunity in question estimates that we can increase the number of Ad units served to 18 by adding a small additional unit AND that there will be a 0.30 EUR decrease in Ad unit CPM value due to this (notice that we utilize out KPI’s index here). Therefore an monetary opportunity on EUR 3600 per week or in perspective approximately an opportunity per year of an additional EUR 188.000 in revenue!

Here goes is my 7 basic and obvious suggestions (very specific and less strategic though) on what you should look into when optimizing a social network:

1. Segment users based on their community activity (this could be anything from viewing pictures or videos to commenting and participating in forums, this is essentially all the defined actions in your web analytics package) - and list this according to the Ad CTR KPI and comparing the different segments to your Ad-CTR index. This giving you an opportunity to do better internal promotions (as basic as a link in the right place) for better converting content (that above your index) on poorer performing content pages.
   Opportunity: To increase the Ad CTR and thus the Ad unit CPM value.
   
   
2. Segmenting the Anonymous visitors to members conversion rate KPI by campaign channels (in my world ALL incoming traffic should be part of a campaign, this including SEO activities) to find and focus on better performing channels and campaigns.
   Opportunity: To increase the Anonymous visitors to members conversion and thus if visits are constant the number of new members
   
   
3. Perform single-page FORM Analysis (tracking every single FORM field) on the sign-up page and cross-reference this to the Anonymous visitors to members conversion rate KPI - getting to know which fields make Anonymous visitors abandon the sign-up form.
   Opportunity: To increase the Anonymous visitors to members conversion and thus if visits are constant the number of new members
   
   
4. Create a custom report with the metric Time since last login KPI and a grouping by entry pages. Filter out all user who have visited the site the last 5 weeks so that you end up with a list of somehow disengaged users who returned to the site. The most successful entry page for disengaged user should be the one heavily used in e.g. Email Marketing. (A common page on this list is the “Your Friend x added you as a friend”).
   Opportunity: To decrease the Time since last login and thus increase the numbers of Visits per week and thereby increase Ads served and finally increase Advertising Revenue as a result. At the same time increasing Active member length.
   
   
5. Run an out of the box standard Action Participation report and add the User Engagement KPI – creating an easy an instant understanding of what content triggers a passive visitor to become a active contributing user and member of the community. As of now you probably do not really know whether the highly engaged users connect to the community by watching user submitted videos, read gossip, chat with friends or what have you.
   Opportunity: To increase the User Engagement compared to our index and thus we typical see an increase in Visits per week and thereby an increase in Ads served and finally increased Advertising Revenue as a result.
   
   
6. The Total time spent on site KPI is not only important because Nielsen//NetRatings has officially replaced "page views" with the "time spent" metric for their official ranking -- whether you agree with Nielsen or not -- it is still very important to know which content groups that users spend their time on - the most valuable resource they have. Create a report that shows Total time spent on site divided per content group and conclude the individual standing on these content groups weighed against your competitors using competitive intelligence. Not as a relative result, but as in looking at the fact that if a user is willing to spend 15 minutes watching user generated videos in general, this is the actual number to target). Optimizing content groups that deliver below industry standard time consumption.
   Opportunity: Increase Total time spent on site and thus an increase in Ads served and finally increased Advertising Revenue as a result.
   
   
7. Try to increase the “quality” of your Ad units per visit KPI by using External Data Sources and relate your internal user database to the Member-ID tracking done by your Web Analytics package (this will give a plethora of opportunities), but one obvious is generating a simple report by content area and thereby get insight into the demographics and sociographics for every specific content area (network of people).
   Opportunity: Increase the Ad unit CPM value by demanding higher pricing for better targeting and at the same time expect higher Ad CTR

..and a bonus optimization comment/suggestion inspired Jim Novo on why using the “User Engagement” KPI on a management level can save your business (or Job). Let say you have a Social Network for students and the number of members and total number of actions in general is increasing (up and to the right), but the “User Engagement” KPI and the related “Time since last login” is decreasing in value more rapidly – then at some point the disengaged users overpowers the increase in members and the website starts to spiral downward in volume. One can predict this downward spiral will happen by monitoring these KPI’s (even as a basic management Alert in your Web Analytics tool) so one can try to take action before it's too late. Most people look at volume as a measure of popularity and growth and by the time volume starts dropping, it is already to some extent too late to save the website. The audience has already disengaged. The user engagement drops off before the traffic volume does, and that is why user engagement is predictive and directly addresses future value.

Even though these 7 basic suggestions + bonus comment (and both you and I could come up with 50 others) somehow seem simple – I am confident that anybody running a social network would be far better off looking at and concentrating on the KPI’s framework described.

My overall conclusion is that one cannot be successful -- in the long run -- running any social media activities if not one or more activities around enterprise optimization analysis is deployed - and I hope that the above somehow indicated and inspired which direction to go in.. sorry for the partly long post (essay) :-)

9 Ways to Make Money on Analytics

Eric Enge had a great post over at Search Engine Watch and with a title like this: 9 Ways to Make Money on Analytics - I just had to reference it here. This being a blog about how to increase publisher revenue through analytics! :-)

..And on top of that; I am of course proud that he chose to add my “The Long Tail (drooping tail theory)... and how to calculate missing Revenue” as tip #5:

“Spot a missing long tail: There was a great post recently by Dennis Mortensen about Spotting a missing long tail (another person whose blog you should read on a regular basis). Dennis explains it really well, but the basic idea is that the traffic on your highest volume page should be matched by the traffic on a larger number of lower volume pages on your site. If this is not the case, then you are missing out on an opportunity. Check out Dennis' post for more details on this suggestion.”

Go check out his post.. it is always worth having a look at what Eric has to say.

Cheers... off to Emetrics San Francisco.

Competitive Intelligence. Why?

Why?: There is a revenue opportunity using competitive intelligence!

I think it’s naive to believe that you control your own web setting. You might control your website, but numerous factors way beyond site optimization have impact on your Revenue! – and more often than not, a revenue impact is due to competitor actions. It’s time to recognize that your own silo of data is only half the picture. You are dangerously putting your revenue at risk if all your decisions are based on inward looking data, as in e.g. clickstream data - you have to count in the overall industry setting.

The good thing however; is that there is everything from FREE competitive intelligence tools to high end tools, such as:

• www.alexa.com (-)
• www.compete.com (-)
• www.ranking.com (-)
• www.chart.dk (- local Danish CI portal, I am quite sure you could find one in each country)
• www.hitwise.com ($)
• www.comscore.com ($)

But it should not be discounted that the Search Engines provide some very valuable competitive insights as well (for free).

www.adwords.google.com
www.adcenter.microsoft.com

Let me try to illustrate, by using an example, what I am talking about when I say that there is valuable actions to take, analyzing competitive intelligence. As a very basic example using the infamous Alexa – the competitive intelligence feature: “related links” – which in reality is upstream analysis and downstream analysis (of course not on a level as we know it from enterprise tools like e.g. HitWise). Forgetting for a moment that this data might be flawed – it’s super interesting to see that Clicktracks (a IndexTools Web Analytics vendor competitor) have visitors visit the following links before and after they visit ClickTracks.com

Sample Alexa “Related Links” for clicktracks.com:

• www.webtrends.com (Log Analysis vendor)
• www.sitelogz.com (Log Analysis vendor)
• www.newmanservices.com/logjam (Log Analysis vendor)
• www.urchin.com (Log Analysis vendor)

Where this initial finding, that visitors to the ClickTracks website are searching the web for "log analysis software", can be further confirmed by using the “Google AdWords - Related Keywords” competitive intelligence feature.

Google Related Keywords for clicktracks.com:

• log file
• log
• logs

CI CONCLUSION:
And based upon the above, one could (or at least for sake of this blog post) conclude that the visitors have a focus, or at least stronger focus than my initial stance, on log analysis software, when visiting clicktracks.com. And with that in mind we could conclude that they (hi John) have more revenue coming from software licensees than from ASP subscriptions.

CI ACTION:
I should remove ClickTracks from my immediate top level competitor list – as I do not provide a log analysis software package (or have any plans of doing so). I should investigate whether there really is two very differently customer segments - by looking at competitive intelligence for a pure on-demand (ASP) vendor as well.

I would like to dig even deeper into the competitive intelligence subject later, but I hope the above gave you an indication on WHY I think CI is important - You can read some in depth post about the subject as well from both Avinash’s and Marshall’s blogs.

The Long Tail ... and how to calculate missing Revenue

Inspired by Avinash’s “long tail” post today I thought it would be appropriate to take it a step further and indicate how you as an affiliate with revenue generation based on content can calculate how much revenue you are leaving on the table - spotting a drooping tail. I know this is going to be a bit long haired, but stay with me for a second.

First lets draw our own graph (based on an anonymous dataset from the perfect IndexTools client).

Graph: "The number of visits per page" - the homepage got 16241 visits in the given period and was the most popular page




This is the traditional Long Tail graph, telling us that a few pages generates a lot of traffic, BUT the same amount of traffic is generated by a large pool of lesser visited pages. Without going into detail, I think we can agree that it is fair to assume (just by looking at it) that a standard long tail distribution as the above is inversely proportional and thus follows Zipf's law. With that said, let’s try to plot the same information into a double-logarithmic chart, to confirm that we indeed have a straight line.




I think it’s fair to say (from this perfect dataset) that we do indeed have a straight line and thus a distribution that follows Zipf’s law. However; let try to look at a less perfect dataset, another IndexTools client, who will not know how much revenue they are leaving on the table, if they are using traditional long tail linear graphs visualizations. I will have Sales call them up tommorow ... :-)

Graph: "The number of visits per page" - the homepage got 319270 visits in the given period and was the most popular page



And by looking at it (using a linear graph) – everything looks "normal" and I could assume that this is the traditional long tail distribution. But using a double-logarithmic chart, we all of a sudden see that there is a drooping tail.





And if we keep the assumption that the long tail should be inversely proportional and as indicated follow Zipf's law – we are missing something here. And what we are missing is more CONTENT! – this client simply do not have enough content to support the long tail.

We could therefore say that additional content would increase Revenue. With the above as an example, the perfect distribution would roughly add an additional 1.7M visits per week, it would also mean that they had to move from 3600 something content pages to more than 300.000+ content pages.

For the fun of it, let’s assume (based on my latest Google AdSense experience) that you have an average ECPM on $2 and that each visit resulted in 5 page views. That’s a $17000 per week revenue increase!! – whether this offset the cost of creating 300.000+ pages is another debate.

But we should have in mind that this approach is not just for Content Pages – it might as well be for keywords (as Avinash used in his post), referring URLs and other metrics generating the typical long tail distribution.

Conclusion:
There is a Revenue opportunity in the dropping tail that most content sites miss out on – because they do not have optimal reporting on their data.

NB:
If you are an IndexTools client and this does not make sense – Add a comment (including your account name) – and I, ..probably outsourced to someone else. :-) will show you how to do the right exports and Excel setup’s to see if YOU are missing out and whether you have a drooping tale opportunity.

.

Q. Name an example of when analytics can increase an affiliates revenue

I was recently asked whether I wanted to answer a set of questions in line with the topic of my blog – which I am of course delighted to do. The first question goes like this:

Q. Ingrid:
“Kindly give us an example of when analytics could increase an affiliates revenue drastically – indicating why analytics was important and how it was used”

A. Dennis:
No problem Ingrid. First, let me point out that I think there is multiple Analytics Strategies that an Affiliate (on any level) can use to increase revenue, but to answer your question on a very actionable level, I will use one of my favourite affiliate metrics as an example; The “Conversion Participation Metric”. (I talked a bit about conversion funnels on my blog last month, and this is what I described as a “Non-Linear Path towards conversion”).
Most affiliates (or Publisher as some call it) write valuable content that are aligned with their keyword strategy and use these pages as part of their SEO (Search Engine Optimization) strategy. However; ranking well and driving in organic traffic on a specific set of keywords, based on a well optimized content page, is not the same as concluding that this is the page that contributes the most towards conversion.

Conversion of course being a bit tricky when you are an affiliate (I briefly touched the subject in my outline of conversion tracking for affiliates post). Complicated because you typically do not convert the visitor on your own site, this is done on the retailers site. But let’s just conclude for the sake of the example that conversion is a “On Click conversion” (clicking the retailers banner). As an example, let’s imagine an affiliate website with the following pages:

• CarPage01.html
• CarPage02.html
• CarPage03.html
• CarPage04.html
• ...
• CarPage99.html

Best performing affiliate programs for this imaginary website is:

• TradeDoubler’s “AVIS Car Rental”
• TradeDoubler’s “Hertz Car Rental”

The content pages all drive relevant incoming traffic for wanted keywords, BUT given the question on how to optimize your content to increase “Car Rental” conversion (clicks on banners) – we simply do not know which of our pages contributes the most towards this conversion. So we would not be able to conclude where to optimize. This is where the Web Analytics Conversion Participation Metrics comes in handy. Running a report on the pages that contributed the most towards the “Car Rental” conversions, we would be able to find out that it is in fact e.g. CarPage37.html and 5 other pages that contributes the most when talking about these conversions (Remember we might even find pages here that do NOT have the AVIS or Hertz banners). This conclusion gives us a clear actionable insight of where to optimize for even better conversions in the future, it might even enlighten us on what behaviour drives “Car Rental” conversion.

I hope this straight forward example gives you and your readers an indication of how powerful analytics can be. :-)

Go see the original post here: http://www.rabbitblog.hu/2007/03/13/webanalitika-novekvo-bevetel (Hungarian)