Wednesday, December 23, 2009

Widget Analytics in Omniture – Part 2 of 3

National Geographic Photo of the Day Widget

In part one of this article, “Widget Analytics in Omniture – Part 1 of 3”, I discussed widgets, how they are used by Business, and the metrics used to track business goals. In this section will outline how to implement those metrics in Omniture.

Widgets are usually intended to support branding, reach, and acquisition. So widget metrics are for the most part campaign metrics, not much different than those used for emails, banner ads, or your direct mail promotion. And like other campaign metrics, you need to track activity on sites other than your own.

Again, here are the metrics we want to get:

1. Reach by widget (count of domains)
2. Which domains have placed the widget?
3. Widget placements per domain
4. Which domains have distributed the most widgets?
5. Specific places the widget was placed.
6. Acquisition by site (UU per domain where user clicked or interacted with the widget)
7. Repeat use for a Widget (user must have clicked or interacted) across all domains
8. Impressions for a widget across all domains
9. Impressions for a widget per domain
10. Unique user presentations for a widget across all domains
11. Unique user presentations for a widget per domain
12. Count of clicks (or whatever you deem an interaction) to a widget across all domains
13. Count of clicks to a widget per domains
14. Count of clicks to a given link in a widget.
15. CTR of a widget across all domains
16. CTR of a widget per domain
17. Site activity driven by a given widget

Implementation Approach

The implementation will require getting information on “clicks” and “impressions” for the widget overall and by domain. In addition to the hosting domain, we will want to know the specific pages the widget is on. We will need the Page View, Visit, and Monthly Unique Visitors metrics. To track the users that the widget drives back to your site, we will use a tracking code.

Here is a detailed list of each metric with the implementation approach:

Metric Approach
1. Reach by widget (count of domains)Pass a variation of the widget name for impressions and the domain for the third party page into variable 2. Use the page view metric.
2. Which domains have placed the widget?Pass a variation of the widget name for impressions and the domain for the third party page into variable 2.
3. Widget placements per domainA given domain may place the widget on many pages. Pass the URL for the third party page into variable 5. Correlate the widget name in variable 1 to the host page name in variable 5 then add the number of pages.
4. Which domains have distributed the most widgets?Set up the distribution to that it is always initiated by a click on a link in the widget. Use this click as a proxy for grabbing the widget. Get the counts per domain in variable 4. Use the page view metric.
5. Specific places the widget was placed. Pass the URL for the third party page into variable 5.
6. Acquisition by site (UU per domain where user clicked or interacted with the widget)Pass the widget name and the domain for the third party page into variable 2. Use the monthly unique visitors metric.
7. Repeat use for a Widget (user must have clicked or interacted) across all domains Pass the widget name into variable 1. Use the visit and monthly unique visitors metrics.
8. Impressions for a widget across all domainsPass the widget name + impression identifier for impressions into variable 1. Use the page view metric.
9. Impressions for a widget per domain Pass a variation of the widget name for impressions and the domain for the third party page into variable 2. Use the page view metric.
10. Unique user presentations for a widget across all domainsPass a variation of the widget name for impressions into variable 1. Use the monthly unique visitors metric.
11. Unique user presentations for a widget per domain Pass a variation of the widget name for impressions and the domain for the third party page into variable 2. Use the monthly unique visitors metric.
12. Count of clicks (or whatever you deem an interaction) to a widget across all domainsPass the widget name into variable 1. Use the page view metric.
13. Count of clicks to a widget per domainsPass the widget name and the domain into variable 2. Use the page view metric.
14. Count of clicks to a given link in a widget. Pass the widget name and the linkID into variable 3. Use the page view metric.
15. CTR of a widget across all domains Pass a variation of the widget name for impressions into variable 1. Use the page view metric as the denominator. Pass the widget name into variable 1. Use the page view metric as the numerator.
16. CTR of a widget per domainPass a variation of the widget name for impressions and the domain for the third party page into variable 2. Use the page view metric as the denominator. Pass the widget name and the domain for the third party page into variable 2. Use the page view metric as the numerator.
17. Site activity driven by a given widget For each link that needs to be tracked to its destination, pass a tracking code in the destination URL query string. This is recorded in the Campaign variable.


The Omniture set up is straight forward. As in any other tag based tracking you will need a beacon. You will need to set up some variables to record the clicks, impressions, domains, and page names. You will need to configure the s-campaign variable to look for your tracking code.


To pass this information the widget will need to call a beacon that can pass the information to Omniture. For html based widgets the beacon call is part of the code. For mobile widgets you will likely use the Omniture Action Script and it will be part of the flash file. For compiled applications, access to the beacon or the beacon itself will be built into the code.

Clicks, Impressions, Domains, and Page Names

You will need five variables to contain the following:

  1. Widget Name
  2. Domain + Widget Name
  3. Widget Name + Link ID
  4. Domain + Widget Name + Link ID
  5. Host page name (url)

Using an OnClick event, you will pass your beacon the name of the widget (or a widget ID) and an identifier for the link that was clicked. You will need to have your developers write some additional code to grab the host page name and then from that derive the host site’s domain.

  • [domain]_[widgetID]_[linkID]

Some more code will parse the values into the 5 variables. These will be sent in a single Custom Link call (not as a Page View). Alternatively, you can create an Omniture Vista Rule to parse the values into the 5 variables. You will need to use a consistent separator for the values in order for the parsing to work. In the example above, I have used an underscore. You could use any character so long as the character will only appear in the value as a separator.

For impressions, every time your widget is displayed, pass a value for the impression. The value should be the Widget name with an added modifier to identify it as an impression. For example, [widgetID]-imp. In this case there will be no linkID or you could pass a linkID of “imp” in addition to the “imp” passed as part of the Widget Name.

For the [widgetID]_[linkID] values, every link in the widget will pass the same WidgetID but different link identifiers. For example, if the widget is called “ticker” and there are three links in the widget, the values for the OnClick event would be:

  1. ticker_1
  2. ticker_2
  3. ticker_3

Because Omniture can only accept a 100 character string, you may need to pay attention to the number of characters you allow for the Widget Name and Link ID. It depends on how verbose the naming tends to be at your company.

For the Host page, pass the URL of the page into a variable. The temptation will be to pass this into the Page Name variable. However, in most cases, you will want to use a different variable. I will discuss this more when we consider report suites set up.

You will also need to set up a correlation between the widget name and the host page name.

Tracking Codes

Tracking codes will use a 6th variable. This will likely be the Campaign variable that is pre-defined in Omniture.

Omniture has a beacon plug-in for just this purpose. You list the value of your tracking parameter in the plug in and Omniture will grab the value of that parameter and put it into the variable. For example, if you have chosen to use “cid” as the parameter and “wgt_ticker” to identify your widget, the link might look like:


You will use the query string for all your Widget’s links that drive to your site or another destination you want to track. Use the same tracking code for all the links in the widget that need to be tracked to the destination.

This chart outlines the implementation above by variable:

Omniture Field Name Business Name Implementation
s_accountAccount IDThis is the name of your Omniture report suite
Prop1Widget NamePass the [widget-name]
Prop2Domain + Widget NamePass the [domain name of the host page]_[widget-name]
Prop3Widget Name + Link IDPass the [widget-name]_[link id]
Prop4Domain + Widget Name + Link IDPass the [domain name of the host page]_[widget-name]_[link id]
Prop5Widget Host Page NamePass the [URL of the host page]
Prop6Report SuiteIf you use multi-suite tagging, you will need to pass the value for the destination Omniture report suite. If you don’t, this is not needed.
s_campaignTracking CodeSet up Omniture to look for your tracking code parameter. For example “cid”. Add the value for that parameter into the link href in the widget.

One thing to consider is whether to put this tracking in your regular report suite or put it in its own report suite. While these can be custom link calls and do not have to be counted as a page view, they will count as visits and visitors. If you are wildly successful, this will affect your site calculations for Page Consumption and Repeat Use. While widgets are your content, one could argue that the activity does not take place on your site and should not be counted as if it did.

Extra Credit

The above implementation uses custom link calls and various “prop” variables. For reporting, you will need to do a simple calculation to get the CTR metrics discussed above.

However, it has been suggested that if the same approach were done using Omniture “evars” instead of props, this CTR calculation can also be automated. Note that I have not tried this myself yet. With that disclaimer, I pass the thought along.

In addition to passing the values as outlined above into evars instead of props, pass a success event along with each impression. Pass a second success event along with each click. You can then create a calculated metric using the two custom event counters and view the calculation for each widget and each domain the widget is on.


One thing to keep in mind is that these are custom link calls and for most contracts (so far as I know), there is a cost for each call. Be mindful that you are tracking impressions and clicks in a viral situation that can rapidly expand the number of those calls and therefore your costs.

The same general approach used for Omniture can be translated for use in other analytics tools such as Unica or Web Trends. Of course this is not the only approach I have seen nor is it the only approach one can use in Omniture. What is best for you will depend on the metrics you need.

In part three of this article, I will go through how these metrics are pulled from Omniture and what those reports might look like.

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Friday, October 30, 2009

Segmenting Compound Metrics

I recently posted an article about segmenting metrics, including compound metrics. In this post we will see how looking at the components of compound metrics can lead to greater business insights.

A typical compound metric is Page Consumption (page views divided by visits). This is a proxy for user interest. Another compound metrics is Repeat Use (visits divided by unique users). This is a proxy of interest over time. There are many compound metrics, some common and some exotic. They are expressed as rates, percents, ratios, rankings, indicators, yields, averages and all sorts of things in hopes of inferring user behavior. Web Analysts delight in creating calculated measures.

For our example we will use Page Consumption. The approach can be applied to any compound metric, however. We will look at the action items that may follow from looking at the calculated PV/Visit and then the Pages and Visits separately.

Typically, if you request a Page Consumption report from your Analyst, you will get some numbers and a chart that may look like the one below. This example compares the PV/Visit for six subject areas on a media site that makes its money from the volume of page views.

A PV/Visit Report

“Home Repair” and “Decorating – Men” are your clear content winners. They drive a lot of pages for a visit relative to Music and Gardening. One action might be to buy some more keywords for the lagging areas or put more links to them from other areas of the site. You can spend fewer resources on “Home Repair” and “Decorating – Men”. Those topics are doing great.

However, there is more you need to know before you act. Also request the information above broken out by its components. The second chart presents the same data with the metric components side by side. It provides a more complete picture and suggests a very different set of to-dos.

A segmented PV/Visit Report

For example, it’s apparent the men’s decorating section has low volume but a really high level of consumption (the first chart shows it has almost the same consumption as “Home Repair”). Try to drive more visits there. A few more visits will go a long way. “Decorating – Men” is not doing ok after all.

The music section has a relatively low volume of consumption but the highest visit level. There could be many reasons for this. Is there a usability problem? Does the content suck? Is the site built in such a way that one can spend hours listening to content but all on one page? Whatever the reason, if you just drive more traffic to Music, your reward in increased page views will be limited.

Home Repair looks great in the first graph, but the second graph shows an issue with the drivers. It has the second lowest visit level. You might consider spending resources to drive more visits here. New visits are likely drive your volume at a higher rate than the other site sections.

As you can see, the second graph can lead to very different action items; actions not so easily understood from the top graph alone. This is a really simple example to illustrate the power of segmentation. In the real world, even this segmentation would not be the end of the investigative road. For example, one might look to see if a larger audience of men in need of decorating even exits.

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Sunday, October 11, 2009

Comparative Metrics for Media Sites

If you have been in marketing for any length of time, you know a given number presented in isolation means nothing. For example the answer could be “42”. What does that mean? Is it good or bad? It has meaning only when compared to something. Comparing it to something is the first step in answering the question “What should I do?” For media web sites there are some standard comparisons that can be made. This article discusses various metric “baselines”.

In this article we will look at “internal” comparisons, comparing your site’s performance to itself. This is distinct from an “external” comparison, comparing your site’s performance to your competitor’s sites or industry “norms”.

Comparing Site Performance to Itself

An internal comparison looks at the performance of an element on your site where the numbers create their own baseline. These are “relative” comparisons used to identify an increase or decrease and by how much. It answers the basic question “Is it better now?” There are basically three ways to compare the metrics: trended, segmented, and contribution.

A trended metric is a time based measure; it looks at the number over time. In January it was 42, in February it is 43. This is a typical marketing measure for Media sites. It shows you how things are performing over some useful duration. In effect, the history of the measure becomes the relative benchmark for the current value.

Some forms of testing are an example of simple trend tracking. In this case one compares the value before and after the change is made. It is a comparison using a specific point in time around which to create a comparison.

Trending is often used in the context of segmented and contribution comparisons (described below). In fact, trending is one of the most useful measures of success you have.

This is an example of a trended report from Omniture showing performance for a subject area:

Web Analytics Trended Report

This type of comparison allows you to monitor overall performance then drill down to see what is affecting higher level numbers. It is a vertical segmentation. Changes in higher level tracking are investigated by segmenting the contributing metrics at a more granular level to see what changed. Typically, if a trend changes, you investigate the reasons for the change by segmenting to find the “contributors”.

The basic segmentation levels are:

  1. Site
  2. Page Group
  3. Page
  4. Link Group (Module)
  5. Link

For example, if you are looking at PV/Visit for the site overall you would investigate any changes by looking at the same metric for various page groups. These are sub-sets (segmentations) of the site. Examples of page groups are subject, page type (form, article, news, etc.), site area, tool, and application. You can then go further and look at the PV/Visit of individual pages within the page group and then the links on any given page of interest.

Note that these are all page segmentations rather than audience segmentations. In addition to tracking what pages changed, you can also segment by audience to determine if the audience or its behavior has changed.

These types of segmentation will tell you what components are driving changes on your site. In general, the more granular the level of tracking, the more the information tends to be tactical and actionable. Looking at the trends of your segments can provide insight into the near future of your site.

There is another type of segmentation that applies to compound metrics. These are rates and percentages etc such as Page Consumption (PV/Visit), Repeat Use (Visits/User) or Visit Duration (Time Spent / Visit). In this case, when the metric changes, look at the component of the metric itself to see what changed. For example, for Page Consumption did the Page Views or the Visits change, or both? Again this can be looked at for various levels of the site to understand what is driving the change.

Contribution compares a part of your site to another similar thing on your site. It is a horizontal comparison which allows you to compare pages, page sets, and applications at the same level. It provides insight into relative value. Is your News section providing more business value that your Sports area?

You need to make sure you are comparing similar levels and similar things. For example, you can compare different subject areas but not the contribution of a subject such as “Flu” to your site search application. In other words, make sure you are comparing apples to apples or fruit to fruit, not apples to plywood.

Contribution can be an extremely helpful way to understand performance. Did one segment improve at the expense of another or was there a net gain? For example, you launch a new application on your site. You see that it quickly contributes 10% to your site’s overall page views. Great! But wait. You see that your site did not grow overall; it stayed the same. This is actually quite common. You have shifted some of your existing audience to your new application from somewhere else on your site. By looking at the contribution of the other applications you can see where the audience for your new application came from and then determine whether you have shifted your users to higher or lower value pages.

As with the segmentations noted in the previous section, you can trend contribution over time. Comparing the trends for similar things you can very quickly see the contribution and interdependence of your site’s elements.

Contribution can also help you know where to invest resources and attention. For example, you may have a topic such as “Gardening” that generates very high page consumption. However, it contributes only a very small portion of your sites page views compared to other topics such as “Decorating” or “Recipes”. Once identified, you can then look at the growth potential of Gardening and decide whether or not to spend dollars promoting it or whether the current Gardening dollars are better spent improving your high value Recipe section.

This is an example of a trended comparison report using Omniture:

Web Analytics Contribution Report

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Wednesday, September 16, 2009

About PVC: A Key Metric for Media Sites

This article provides an overview of a key conversion metric for a media sites: PVC. It provides a measure of the pages most involved in driving page consumption, whether that is overall consumption or the consumption of specific high value pages. If you are a CMO or report to one, you will care about PVC.

  • Here is what we will cover:
  • What is PVC?
  • How is it related to other metrics?
  • How is it used?
  • Extending the metric.

What is PVC?

PVC stands for Page Velocity Consumption.

Page indicates that this is a page based measure. Velocity is a term taken from Economics and is a measure of how much money is circulating in the monetary system. In this case it’s a user, not money, which is circulating1. Consumption is about how much that user is circulating. As we will see, for media sites the metric is correlated to revenue.

When a page, page set, or link is seen or clicked by a user we begin counting all subsequent pages in the visit. It is the average count of pages consumed in a visit after touching a particular asset (page, page set, or link). So the PVC calculation is “pages seen after / visits”.

A simple PVC example:

A simple PVC example

In this case the PVC for page 1 is 4. Note that we count from the first “view” until the end of the visit. Even though Page 1 is seen twice, we only count from the first view. The PVC for the second page is 3.

Here is an example of counting from a link click:

A link click PVC example

In this case, the PVC for the link on page 1 is 3. We don’t count the page the link was on because that view was before the click action to be tracked.

PVC can be thought of in a number of ways

For many media sites driving additional PV is the purpose of every page or application. Over many visits, PVC is a measure of the likelihood to drive additional page views. PVC can be a measure of “Persuasion Architecture”2 (from Brian and Jeffry Eisenberg) when applied to media sites.

PVC measures the downstream impact of changes made to the site. On most media sites the paths people take through the site are just too numerous to be useful. However, PVC lets you see the impact of changes to a given page or page set over many paths. If the goal is to drive additional page views, does your new page lead to a more or less involved experience?

PVC is an engagement metric. It is different than regular Page Consumption (PV/Visit) in that it measures consumption over the whole visit, not just for the page or within the page set.

PVC is a conversion metric for media sites that sell ad or sponsor impressions. It is thus correlated to Ad revenue or Sponsor revenue. It is a conversion metric similar to Average Order Value (AOV) on commerce sites. (We will discuss Sponsor PVC later in this article).

Because it reflects revenue, PVC is used to measure business optimization. That makes it a key metric concerned with the economic success of the company and its strategic goals. Any CMO should care about that!

How is PVC related to other metrics?

  • PVC is a metric used to understand the ability to convert. Examples of other standard media metrics are:
  • Repeat use (Visits/UU).
  • Page Set Contribution. (PV/Site Visits).
  • Page Consumption (PV/Visit).
  • Click Through Rate (Clicks/Impressions).

These metrics and PVC are used together to understand site activity and contribution to the site. Sometimes these metrics are used to understand usability, sometimes product viability, sometimes campaign success. Usually PVC is used to understand business optimization.

  • Like the other metrics in the list, PVC is also available to track site performance at various levels of granularity. This makes it excellent for inclusion in monitoring reports. Levels of tracking can be:
  • Site
  • Page Set (Page Type, Tool, Product, Subject, etc.)
  • Page
  • Link
  • Tracking PVC at various levels allows you to monitor the larger trends and then drill down to identify what component of the site is driving that trend.

PVC is most closely related to the Page Consumption metric (PV/Visit). In fact, at the site level they are the same number. PVC can be used as a segmentation of site wide Page Consumption.
The difference between PVC and Page Consumption for a given asset measures the ability to drive additional page views (PVC - Page Consumption) for that page or page set.

PVC, like most of the other metrics in this list, is based on a visit. This provides some consistency between these metrics. However, if your technology supports it, PVC can also be based on visitors and longer time periods such as pages per month or per quarter.

How is it used?

Page Velocity Consumption can be used to identify trends in consumption over time. For a given asset (page set, page, link), is the trend showing improvement? By segmenting at various levels of the site, you can identify what components are driving that change. For example, look at individual pages for a given page set or at individual links on a given page.

It can provide a measure of the overall business outcome for changes you intentionally made to an asset. For example, the CTR of a page may have doubled, but the overall engagement may have gone down. In this case, you may have lost money overall. Compare the PVC values before and after the change to determine the level of impact.

PVC can provide a measure of relative value for different assets. Do some assets provide a better monetized visit than others? Can a high performing asset be better utilized by driving more traffic to it? For example, if you track tools or applications, compare the PVC values for the various tools. If you track content by subject, compare the PVC value for the different subjects. You can go further and compare the values of the pages within a given subject.

Here is an example of a PVC report in Omniture (the real values have been changed):

An example of a PVC report in Omniture

Extending the metric

A variant of PVC is Sponsored PVC. Some media sites allow the sponsorship of specific pages. In this case, the Sponsor PVC metric is constructed by counting only the sponsor pages seen after touching a given asset. It is analyzed in the same manner as PVC. One can then go further and look at which sponsorships the asset drove to.

Ads and sponsored pages are not likely to all have the same value. So the monetized value of pages can be different. PVC can be extended by incrementing the value of the page instead of simply incrementing the count of the page view. Pass the summed value of the ads on the page or its sponsorship value. This will provide a measure of the monetary contribution of the asset to the business. As Avanash Kaushik might say, “How cool is that?”

The PVC concept can be used to track value by referring sources or campaigns. In this case, you are tracking the PVC value from a source rather than an asset on the site. (The number is the same as if you calculated Page Consumption where the entry source was X). The source could be a search engine, direct traffic, newsletters, etc. Simply pass the value for the referrer on the landing page and start counting. You could then answer questions like which keywords are driving the most consumption and should I buy more?

1This explanation of “velocity” was provided by the Jared Cook at Omniture.
2Brian Eisneberg & Jeffry Einenberg, Call to Action: Secret Formulas to Improve Online Success (Wizard Academy Press, 2005).

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Sunday, August 9, 2009

Monitoring Reports and their Attributes

Reports can be generalized into different types. For example there are testing reports, ad-hoc reports, predictive reporting, dashboards, etc. They have different purposes. The most common type of reporting is a monitoring report.

Monitoring reports are intended to do just that: monitor. They contain the metrics for your site or product that you look at all the time. This differentiates them from reports that are meant to answer specific short term questions such as testing reports or ad-hoc reporting.

These reports are not necessarily actionable based on the report alone. Changes in your reports are often investigated further to determine the cause of the change and the appropriate action needed.

The monitoring report contains the metric drivers for the business and detail about the components of those drivers. For example, if a key metric is repeat use, the report would also provide the visit and visitor components of the repeat use metric. It would also provide the tracking level below to see what contributed to the higher level figure. For repeat use for the site overall, this lower level of detail might be the repeat use by site section, application, or other functional area.

The report is often presented in a trended view and/or in comparison to other similar site elements. In the first case, you are comparing the thing being tracked to itself over time. In the latter case, you are looking at relative value. These become your benchmarks by which you evaluate your tracking.

The report, by nature of its purpose, is provided on a regular schedule. This can be monthly, weekly, daily or whatever the business needs. If you don’t need the information on a regular basis, then it does not fall into this category of reporting.

In order to do apples-to-apples comparisons the report needs to be consistent over time. It should track the same metrics in August as it does in January. Because the reports are consistent they are often be automated. In fact, the site itself should be built to specifically track these metrics consistently.

Note that this is a report and not a dashboard. A dashboard is meant to contain only the top level KPI and would not include the level of detail one would expect in a monitoring report.

When asking your analyst for reporting, keep in mind what kind of report you are requesting. This will help both you and your report provider to better understand the goals of the request.

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Wednesday, May 13, 2009

5 Types of Success

Not all success is the same. In deciding on the success measures for a project, one must be aware of what type of success is being considered. There can be a success by one measure while at the same time producing an absolute failure in another dimension.

One can have a product of significant interest to users but has poor usability. One can have a product that generates a great deal of activity that contributes little value to the business. Your application’s interface could be perfect where no user ever makes an error. However, the application only calculates yesterday's date and no one will ever care.

There are generally 5 purposes for which various metrics are used. These are concerned with different types of success:

  • Business optimization
  • Feature optimization
  • Campaign optimization
  • Process optimization (usability)
  • Quality assurance

The different types of success measures answer different kinds of questions and the same metrics are often used to measure the different types of success. (Unique Users, Page Consumption, Repeat Use, etc.) Those metrics will have a different meaning depending on which success question is asked. This can lead to confusion both for you and others in your organization so care must be taken to carefully identify what type of success you are discussing.

The type of success measure also tends to map to the responsibility of the “metric consumer”. A Producer responsible for links might tend to look at process and campaign metrics while a product Vice President might tend to look more at business and feature metrics.

  • Business optimization
  • Business Optimization is concerned with the economic success of the company and the metrics are highly correlated to business revenue or strategic goals. In other words, it’s about the money. If you have a media site, this may be ad revenue or Page Velocity. If you have a commerce site, it’s about sales. This type of success measure gets reported to senior management.

  • Feature optimization
  • Feature optimization is concerned with the success of a specific product or application. Metrics are focused on customer interest in the product and its parts. Is the product something your customers want? Is the feature set complete or needlessly complex? A Product Manager will follow these metrics closely.

  • Campaign optimization
  • Campaign optimization is concerned with maximizing the effectiveness of a discrete effort over some period of time. These are the marketing measures used to track reach and acquisition. SEO fits into this category. This success is, of course, what your Marketing Department lives for.

  • Process optimization (usability)
  • Process optimization focuses on user interaction with the product. The metrics are used to improve the ability to use the tool and the effectiveness of the user interface. Can your users operate the tool? Your usability engineers and designers may tend to use this one more than others.

  • Quality assurance
  • Used to monitor the operational conditions of the site. For example, was there a sudden drop in traffic due to server failure or an increase in 404 errors? This type of metric is usually of concern to Operations and Development managers.

When you plan a project and define how you will measure the success of that project keep in mind what type of success you are considering. Often you will be looking at more than one measure of success. Often you will get different outcomes for the different types of success. You may have to decide as a business to prioritize them.

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