Scoping Out Social Media Marketing with the Paid, Owned, Earned Media Framework

Social media has been grabbing the attention of everyone for several years now; Facebook, Twitter, and LinkedIn are popular social media platforms that have all become household names. With such enormous scale, everyone easily recognizes its potential for marketing. But what exactly consists of social media marketing, and how can we scope out the tasks for social media marketing in order to measure the ROI?

Currently, this is not really a problem for many other forms of internet marketing. Email marketing and search engine marketing are two example forms of internet marketing that have come a long way in its accountability for a company's overall integrated marketing results. With relatively mature analytical support, these forms of online marketing have enjoyed rapid growth in its adoption and optimization techniques for these marketing efforts have become a lucrative art, if not science.

Social media marketing have also enjoyed rapid growth in its adoption, and in many ways in much larger scale than other forms of internet marketing. This is because social media, by its nature, is more conducive to growth. However, our ability to measure its accountability in marketing clearly lags behind other forms of internet marketing. I believe one major reason for this is a lack of common framework in defining the scope of social media marketing. Just like in a job, you cannot be accountable if you don't know what you have committed to, and you cannot commit to something if you don't know you are responsible for it.

But why would it be confusing? Let's look at a few examples. What are some marketing activities you would consider as part of social media marketing? Promoting your brand by posting a summer sale announcement on your Facebook page? Responding to a consumer's angry outcry on Twitter regarding a poor experience? What about paying for a spot on Facebook to show an image ad of the largest and juiciest burger on earth, enticing users with some witty text urging that they must try it before they die?

All of the above have been known to be part of a company's social media marketing plan, but they are actually quite different activities. The reason the lines are blurring is because traditionally, social media is most often associated with the creation and sharing of user-generated content on the internet. (Note: While I understand the social media can often be referenced via offline activities, for the purpose of this analysis we'll scope our discussion within the area of internet marketing only). Confusion arises because both the platforms that enable the creation and exchange of these user-generated content, such as Facebook or Twitter, as well as the action of creating and exchanging the content, are often synonymous with social media. The rapid growth of social media is largely contributed to the technology that enables the social interaction at such a large scale. As a result, any activity conducted on these platforms, have been basically considered as part of social media marketing, even though they may or may not directly contribute to the creation and sharing of user-generated content.

If that is the way marketers are practically approaching social media marketing, then we must embrace it and help them optimize their efforts in an integrated manner. I believe the first step to optimization, is to define the scope of these activities. To me, the easiest way and also the most comprehensive approach to defining the scope of social media marketing is via the Paid, owned, earned, media framework, aka the P.O.E.M framework. This framework has been around at least a couple years now, and can be used not just to describe social media, as Nokia has been categorizing all their global interactive media using this framework. I feel, however, that it is particularly fitting for describing the scope of social media marketing. The framework was defined formally by Forrester Research's Sean Corcoran with the following breakdown of media types:

Paid media has been the main commodity in the traditional advertising world. To many, it is a necessary evil overemphasized by agencies and publishers because of its direct revenue generating nature. To some adopters of new media, they believe its effects have been greatly diminished in today's content-rich world where consumers are numb to one-way mass messages. Yet to others, paid media is the most justifiable media type to optimize for relevance vs. revenue in such a way that its effects will not only not diminish but amplify when presented at the right place at the right time. Regardless, paid media's role in social media platforms is definitely not diminishing; eMarketer believes that global Facebook ads spending will reach $4 billion this year.

Owned media is content produced and controlled by the company. With the growth of eCommerce owned media has extended beyond providing information about the company but is often the content a consumer sees right before the transaction, through the company's website. With social media, the companies have extended options for publishing owned media. For example, they can publish content via a blog, or post company information or products on Facebook pages or via Twitter.

Earned media seems to be the critical piece that represents the main differentiator of social media from others, in that it is content generated by consumers through interactions and awareness about the company, brand, and products. It can also be heavily dependent on the owned and paid media the company produce because it is often a response to the content that is worth sharing to prompt the act of generating earned media through feedback.

While earned media may be the most important factor of social media marketing compared to other forms of internet marketing, paid and owned media cannot be ignored as part of the overall social media marketing efforts, and an integrated plan utilizing all three types of media must be executed to maximize the effectiveness of social media marketing. Just like in search engine marketing and optimization, where the paid search results can complement the organic results and the company's landing page (owned media) can affect how Google or Bing ranks the search results (earned), integration of the different types of media pay a critical role in optimization of social media marketing.

While the exact tactics for integration and execution plans vary based on each company's marketing objectives, there are definitely strategies and heuristics to approach the integration, but that is a separate article. For now, it is important to understand that social media marketing, just like other forms of internet marketing, can be described and scoped based on the types of content generated in the channel, and how these contents are generated. For social media in particular, the platforms on which these contents are generated and shared are just as important as the act of generating the contents itself.

Deep Dive Into the Social Media Conversion Funnel

Many of you are familiar with Google Analytics' "Conversion Funnel", which shows the path an user takes from your website's landing page through the different pages leading to your converting page.  For eCommerce websites, this is usually in some forms of product page -> shopping cart -> checkout -> confirmation.  Google Analytics shows how many visitors are dropping off during each stage, and also informs you of other entrances into and exits from each page in the funnel, showing you potentially user paths that were unintended for your site design. 

GA's conversion funnel visualization is useful for two major reasons:

• The valuable information about your visitor's behaviors along the purchase path can guide you in making the proper changes as needed to increase conversion rate and ROI
• GA's funnel is modeled closely after the purchase funnel frequently referenced in marketing, first developed by Elias St. Elmo Lewis back in 1898.  The funnel described the process by which people are motivated to act to purchase based on external stimuli from sales representatives, and since then used as a framework for sales representatives to act accordingly based on the potential client's position in the funnel. 

While there have been many variations of the purchase funnels proposed since 1898, the basic layers have always been relatively unchanged: Awareness, Interest, Desire, Action, Rinse and Repeat Action.  However, in the past couple of years, we have seen some major transformations proposed for the funnel, primarily in response to the rise of social media as a new powerful marketing channel. 

One major complaint about the traditional purchase funnel has been that the funnel focuses solely on the actions of the sales representatives, where the company selling the products are in control and the customers are merely shepherded into the buying process without much say.  This is even more pervasive with an eCommerce business model; the website at a massive scale shepherds hoards of end users into the funnel automatically and efficiently.  The user has no say in the process except to participate along.

This, of course, is completely changed with social media. 

Suddenly, the megaphone is not just in the hands of the corporation selling products and services.  Each online user is now given the opportunity to leverage a megaphone to broadcast to whoever that is willing to listen.  Each user can take on a different persona and choose to be a hero or a villain in the eyes of the companies and corporations he is discussing about.  The "corporation" suddenly find itself at a disadvantage against the consumers, unsure of the next move and how to react. 

It doesn't need to be like this.  Play the game and make your move.  With the proper behaviors, you can regain the same control you used to have with the traditional purchase funnel, but this time, the two-way conversation provided by social media will make the entire experience more enjoyable and productive for both the customers and the corporations.

Just like GA's funnel visualization,  understanding the funnel layers and the users' actions can much better prepare you to make the right move at each stage. 

First thing though, we must define the actual layers of the new social media conversion funnel, which I'll sometimes refer to as the social funnel for short.  This is where there has been many interesting proposals.  Some advocates the same exact layers as before, with the social media channels adding into the variety and the additional eyeballs in the "Awareness" layer on top.   In a classic funnel, the top is always larger than the bottom, because in this case the funnel acts as a filter and only a subset of the materials pass through from the start.   Some suggests to "flip the funnel", because existing customers can be your biggest advocates with the best megaphone to bring you a flood of new customers through word of mouth.  I think most of the proposals make sense in one way or another, but for practical reasons, I propose the following model which takes all of them into account, and is, I believe, the most actionable approach in practice. 

Firstly, I think the overall shape of the "funnel" should not be restricted to the classic shape of the funnel, whether it's flipped or not. Firstly, social media is meant to be used to facilitate engagement and conversations, and engagement becomes the main unit of measurement in the funnel.  It's no longer just about the number of eyeballs that enter the funnel, but how the different visitors are engaging with you and others on the different properties.  As a result, the size of this funnel should not be restricted to where the further layers of the funnel is necessarily smaller than the top layer.  Secondly, "advocacy" is an important layer in social media that is generally not considered in the traditional funnel model, and another reason why the funnel can expand even towards the bottom of the model, where through word of mouth, the few people that converted can bring new people into the funnel. 

Here are more detailed descriptions of the proposed layers that make up the social funnel.

Awareness and Perception:  

This layer gets your brand or your products out there in order to drive people into the funnel.  Besides the traditional marketing channels, newer channels such as search engines have really made the process of building awareness very targeted.  In social media, by using your community and your influencers, the process of building awareness become more self selected.   The opportunity has really expanded as well since there are so much user generated content out there nowadays.  The blogosphere, review sites, forums, online news media, etc.,  can all drive awareness and establish perception.  While this layer is largely influenced by your advocates, they can also be influenced by your competitors and critics.   

Subscribers:  

This layer is built from all the prospects from the Awareness layer.  A certain subset of the population that are aware of your products or brand will decide to "follow" your progress and learn more about you.    Here are some of the specific behaviors of subscribers in the various social networks:

• Facebook: Like; Add as Friend; or Join Group
• Twitter: Follow
• Youtube: Subscribe to Channel
• Blog: Subscribe to RSS

Engagement and Interaction: 

Engagement is basically the big bet of social media.  Social media has made it easier than ever to engage in a 2-way conversation with your potential customers online.  Marketers strongly believe nowadays that it is critical to enter into a dialogue with your customers and letting them participate in more meaningful ways than just constantly getting fed emails or promotions.  By getting the customers actively involved, you are building trust to move prospects down the funnel.  It is important to note that with the openness of social networks today, users don't necessarily need to be a subscriber in order to engage and interact.  Here are some of the specific behaviors that constitute engagement in the various social networks:

• Facebook: Like a particular post or shared item; comments and replies to a post
• Twitter: @ Mentions; retweets; direct messages
• Youtube: Comments on a video; replies to comments

• Blog: Comments

Advocacy and Influence: 

At a deeper level of engagement, the user can essentially become your advocate and start to spread the words about your products. Depending on their status in the social media community, their actions can help influence behaviors related to you and potentially generate more prospects for your products. Generally, the behaviors of the advocates are very similar as a normal engagement, but the actions are more in the flavor of promoting rather than only participating.  Specific behaviors include sharing on Facebook and retweeting on Twitter,  as well as posting generally about your products.  You can measure the actual influence of your advocates by measuring the click-through rate of those shared items into the intended landing page, by setting special campaign  tracking codes for each of the links you share out on your social media properties.  The impact contributed by your advocates will persist throughout the bottom half of the funnel, moving prospects down to become customers and repeat customers. 

Leads: 

When analyzing the effectiveness of social media campaigns, we often assume that more engagement, advocacy with more impactful influence would drive more leads into your website, which is often the place where you are counting your conversion or satisfying your main business objective.  Your goal may be to get leads into your website in order for them to complete a purchase, or maybe just download a whitepaper or sign up for the mailing list.  Each click from the various social media properties into your website is considered a lead generated as part of your social media efforts.  In the traditional purchase funnel, this is actually towards the top of the funnel.  As we can see that in the world of social media, quite a bit has been involved to get to this point. 

Conversions: 

This is the journey on your website leading to the final conversion, which can be a true purchase conversion such as an eCommerce sale or some other micro-conversions for the website (filling out a form, download a paper, etc.)  In GA's conversion funnel, it demonstrates the path the user took to reach the final thank you page.  In social media, these are the direct conversions as a result of your social media efforts.  The representation here should be the same as with traditional conversion funnels, showing the unique visitors during each layer and the drop-off rate at each layer down to the final conversion page in question. 

Loyalty and Affinity: 

Another unique characteristic property in social media funnel analysis is the strong potential in using engagement on social media platforms for establishing loyalty for converted customers.  This, along with continued advocacy and influence, can expand the bottom of the funnel after the conversion layer, and continue to drive new users in as leads and even new eyeballs into the top of the funnel from the internet.  

Now wash, rinse, and repeat, and you have a brand new social funnel, except the next time around, it may actually have a bigger opening with greater awareness, and driving more leads, conversions, and loyal customers into the mix.  Within each layer, by adding up all the behaviors, such as adding up all the engagement and interactions that occurred within the entire layer, you can see whether the funnel is actually expanding or shrinking and at which point, and overall whether you are getting enough leads or conversions based on the level of engagement on your website. 

As we have seen, representing your social media efforts and responses by using a funnel dedicated to the social media channel allows you to act accordingly when executing your social media campaigns.   As you are listening, conversing, and building relationships with users in social media properties, you should monitor how these activities and responses are driving results through different layers of the social funnel.  Are you building a healthy fan-base that is helping to influence others to move down the funnel, and ultimately growing the funnel ?

Compound Metrics in Web Analytics: Do's and Don'ts

I used to be a big fan of compound metrics.  I basically had thought compound metrics would be the panacea to all diseases known to man-kind.   Especially back in the days when I was doing Artificial Intelligence research in graduate school, we were basically all competing to see on how complex and indecipherable we could make our function f(x) compared to our colleague's function g(x)

However, nowadays, where my primary goal is to measure the effectiveness of a marketing campaign or a web application, and no longer in academia devising complicated  equations, compound metrics, while it may still look impressive, have really lost much of its attractiveness. 

What exactly is a compound metric? And what does it mean in the context of web analytics? Compound metrics, sometimes also referred to as "calculated metrics" , "composite metrics", or  "synthetic metrics" are basically measures that combine two or more independent measures.  These kind of metrics are actually all around us; some examples are as follows:

Consumer Confidence Index: The consumer confidence index is designed to measure the degree of optimism on the state of the economy expressed by consumers based a combination of several measures of consumer attitudes.  Some of the topics used to determine the index includes business conditions, employment conditions, and upcoming family income.  Because these are all different measures by themselves, the combined index value that is formed is considered a compound metric.

Credit Scores: Your credit score is a three-digit number that creditors and lenders often use to make a decision about your credit-worthiness.  It is dependent upon many different factors, such as payment history, level of debt, length of credit history, number of credit score inquiries, etc.  Because these are all unique measures by themselves, the combined value that forms the final credit score is a compound metric.

Intelligent Quotient (IQ): The IQ Score is a very interesting compound metric.  The IQ is actually a ratio of the "Mental Age" (MA) and the "Chronological Age" (CA), which two separate metrics:  IQ = 100 ( MA/CA) . Furthermore, the MA itself actually constructed based on a variety of questions assessing attention, memory, and verbal skills, in which it is also a compound metric based on the different types of components that make up the test.

As you can see, compound metrics is quite popular with government and economic measurements, as well as several situation where a metric is standardized.  One important thing is to note is the distinction between compound metrics and various operations on a simple metric.  For example, taking the average or a percentage of a group of data points of a simple metric does not count as a compound metric.  There must be multiple unique measures combined in order to form a compound metric. 

Compound metrics have started to become very popular in web analytics, primarily because analysts practicing "Web Analytics 2.0" have started to take web analytics to the next step with trying to explain website engagement based on the combination of actions a user does on the website.  The idea is that when we are only looking at engagement of the website as individual actions such as "unique visits" or "click-throught rates", we can't tell the whole story of how the user engaged with the website.  For example, just by looking at the click-through rate doesn’t necessarily tell you if the website was worthwhile, combining the click-through rate along with the time the user stayed on the site would then give a fuller picture of whether the website engagement was strong.  If users stayed longer on the page after clicking through, the page is considered to be more engaging. And as such, compound metrics for "website engagement"  can be devised with the combination of click-through rate and time spent on the page.  The problem with compound metrics is that by blending the two metrics together into one, we are losing much of the visibility into the components that make up the metric which are the basic behaviors that we wanted to understand.  Analysts run the risk of abusing the usage of compound metrics whenever they cannot easily explain the direct actionable impact of a campaign, and need alternatives to show the ROI of a campaign, and it is easiest to do that by combining the metrics in attempt to tell a coherent story. 

Combining click-through rate and time on page can be relatively easily broken out.  However, even more complicated and vague compound metrics have arisen recently particularly in response to the challenge analysts are facing to explain impact of social media campaigns.  Because they cannot directly see the ROI of a social media campaigns in terms of conversions, or even click-through rates, analysts have worked hard to devise other metrics to explain the impact of social media for their businesses.  One particular area that has gained prominence is in sentiment analysis, where the metric aims to classify the polarity of a given brand or website, whether the expressed opinion is positive or negative.  Typically, the method to achieve this is based on several features and content around the web extracted from blogs or review sites or social networks.  The end result is a very subjective and abstract metric that is usually spit out in the forms of some seemingly random number like 3.7 or 19.3, or even worse, -1 or 1, implying negative vs. positive sentiment.  There are two major issues with this kind of metric.  With the case of 3.7 or 19.3, it is hard to understand how to take action against the number, because it is not clear how good 19.3 is or how bad 3.7 is by itself.  Furthermore, a lot has gone into the calculation of the metric and it is completely reduced to an aggregate number without any insights into the process, and it doesn't give you any clue on how to actually change for the better.

Because of the characteristics of the compound metrics, generally, these metrics tend to add more confusion than good.  This is not to say that all compound metrics used in web analytics are useless though, we just need to be really careful with using them.  The first thing to watch out for, is that compound metrics should be treated just like any simple measure.  Avinash Kaushik, a Google evangelist and a well-known blogger on web analytics, emphasized some important guidelines on the required attributes of a great metric.  A great metric should be uncomplex, relevant, timely, and instantly useful . These are fundamental attributes that should apply to all metrics, simple or compound. 

I think because of the complexity of compounded metrics, there are some additional attributes that a compound metric should possess before it can be used.   Firstly, it is very important to ensure that a compound metric is normalized, or standardized, to be within a fixed range.  As mentioned before, an arbitrary number is very hard to take action against without any context.  When a number is normalized or standardized, we know there is a range that we are working with, and if the number is closer to the extreme of the ranges, then we have a better idea of the impact of the metric. 

Along the same lines, because a compound metric is inherently more complex than a simple metric, it is always a good idea to present them in the context of other relevant metrics, or in the context of metrics that are commonly known.  This way, one can immediately relate to the data with the context and not have to think about what the arbitrary number mean in isolation.  One example is to present the metric's unit in terms of money or currency.  Everyone can understand the value of a good dollar, and if you have a metric that is presented in terms of money, more people can relate to it faster. 

A compound metric should also not contain more components than absolutely needed.  The idea of the compound metric is to answer questions that cannot be answered with a single component, but the more components you have, not only do you convolute the data and make it hard to interpret, you also run greater risk of losing the required independence of the individual component and confounding the combined factors.

Given all these criteria, it almost seem like compound metrics should be outright avoided in web analytics.  While in an ideal world, we would be able to explain all behaviors with simple measures based on direct observation, the fact of the matter is that with all the new media such as social media, compound metrics, when used properly, can help us achieve insights otherwise not possible.  Below are some of the examples of compound metrics, that in my opinion, are useful and powerful in giving new insights.

Monetization - $ Index:

I mentioned earlier that putting a compound metric in context of a familiar metric, such as in context of monetary data, is very useful because it shows direct impact to the business objective.  One excellent example is the $ Index, most commonly seen as a metric used in Google Analytics.  It is a measure to see the average value of for page that a user visited before reaching a goal page or converting on an eCommerce transaction.  While there are different calculations of $ Index depending on the tool, generally, the $ index for any page x it is based on two unique components:

Total Goal Value or eCommerce Transaction Revenue as a result of visiting Page X / Number of Unique Page Views for Page X

You can see an example of how this is calculated on Google Analytics

The $ Index is a very useful compound metric.  Firstly, it is closely related to the business objective, helping you map out a clear view of how much each of your page is actually worth directly related to your revenue.  Furthermore, the final unit is not a random, isolated unit of measure, it is kept in the form of dollars, which gives you immediate  context to evaluate the actual magnitude of the metric.  It is also "instantly useful", giving you immediate view of which pages are valuable for you and you can immediately work on improving the overall conversions of your website by targeting the poor-performing pages to improve the bottom-line.

Social Media - Twitter Metrics:

With the openess of Twitter, there are many interesting simple and compound metrics that have been formulated to help identify the effectiveness of one's influence, or clout, on Twitter.  One thing to be really careful with Twitter is the tendency to obsess over several metrics that are not useful and do not satisfy any of the fundamental characteristics needed of a good metric.  If you notice, with Twitter and social media channels, compound metrics are extremely rampant and can easily get carried away.  For example, many of you are familiar with dashboards that show your "Impact" score was at 5.7 and your influence score at 12.3, while your Amplification Score is 75.7%.  

That's GREAT (NOT?)!

Exactly, we have no idea what those scores mean, and how to act upon them or improve our campaigns.  Part of this is because Twitter analytics is so new that there isn't really any standard yet, so it's hard to put a standardized number on those metrics.   Even then, we should really focus on the meaning of each of these compound metrics, instead of obsessing over the "scores".  In my opinion, some useful  Twitter compound metrics are:

Retweets per thousand followers: This is really a ratio of two measures but since it does contain two unique measures, it is technically a compound.  This is a good, simple, metric to use to measure your amplification.  One simple extension is to also add the @ mentions as part of the measure in the numerator, since these are all amplifications tools in Twitter to acknowledge the contents you are posting.   

Churn: Measuring churn in Twitter is usually based on the changes in the number of followers vs. unfollowers over a certain period of time.  If you are actively participating in Twitter, this is a very quick metric for discovering how you are doing overall and can immediately provide useful information for you to continue doing what you have been doing with Twitter or perhaps change course. 

Engagement: Similar to amplification, engagement metrics can immediately tell you how others helping to amplify your brand on Twitter.  However, engagement metrics in terms of Inbound Messages per Outbound Messages can tell you immediately whether your efforts are getting a good return.  Understanding action and reaction is simple enough, and even with a just a ratio we can quickly determine whether our effort is being met with at least comparable results, or are we just a glorified loudspeaker without any engagement with our posts. 

Page Engagement - compounded clickthrough rate analysis:

Avinash Kaushik has for a long time criticized the general term of "engagement" as an excuse, and not a metric.  The main reason is that engagement as a term is a very vague and needs to be more specifically defined for the particular business or website in question.  Each website exists for several reasons and one or more metrics need to be defined in order to measure the engagement for the particular website.  This is where I think compound metrics can help provide a framework for defining good engagement metrics for each website.  Each website usually has an overarching goal.  For example, for eCommerce websites the main goal is usually to drive an online purchase.  The average conversion rate however usually hovers around 2%, maybe up to 5% if you are lucky, but rarely does that number change.  This means for over 95% of the time your users are doing something else on your website.  To understand the users' experience of that 95%, a few "micro" conversions should be defined. 

Engaged Clickthrough Rate: One compound metric that can be used to understand the user experience, is a compounded click-through rate analysis of the pages of your website.  One often looks at click-through rates from the variety of sources that drives traffic into the website, but if you deep dive into the traffic that comes in, a lot of the actual traffic are not really engaging with your website at all and have no intention of ever converting on anything you want them to.  They might have accidentally came to your website, or perhaps are just bots that crawl around the web.  One way to discount those visitors is to put a minimum time spent on site condition in the clicks that lead into your website, and do not count the visits or clicks that don't meet the minimum time spent on that page.  This compound metric immediately cleans up all the visits you are not interested in and focuses in on the visits that actually matter for you.  This metric can then be segmented as you need to for further analysis or comparisons. 

User Task Completion Rate: With the emergence of SaaS (Software as a service) web applications over the past decade, a website is often not just a marketing tool for selling service, but is also the interface that users engage in after the sale.  With that, users can perform multiple actions on the website and it is important to be able to measure how successful the user is while navigating through the variety of features on the website.  In this case, a compound metric for task completion can be devised to measure the user success of your web application.  The actual components though, can vary dramatically depending on your goals.  For a content heavy site, the task completion rate will contain time related metrics measuring how much time a user reads the content.  If there are multiple features in order of importance, then the metric could contain weighted completion giving partial credits to the different features on the website.  For example, if the primary purpose of your website is a dating site matching couples.  You might give higher weight to the couple that successfully scheduled a meeting on your site, vs. a couple that completed a chat session on your site.  Of course, you need to be in control of what success means to you, and the end result must be instantly obvious for you when you see the data. 

We have seen a variety of compound metrics in this post, both in the real world all around us, as well as metrics specifically for web analytics.  My take on compound metrics is that from the outside, it seems very attractive to go crazy on using compound metrics to help measure the success of your website, but it is so easy to get lost in the metric and not have an idea of how to actually use the metrics to act on improving your website, and that should be the first and only thing that should be on your mind when using any metric, whether simple or compound. 

We have also seen some cases of compound metrics that CAN be useful in help you.  Besides following the fundamental four characteristics of any metric, which are uncomplex, relevant, timely, and instantly useful, it is also good to follow the following guidelines regarding the compound metrics:
 

• Generally, metrics that relate to business outcomes, especially in terms of revenue and profit, are good.

• When the compound metric can be easily related to a common unit of measurement, such as the "dollar amount", it is generally better.  The $ Index is an example that we looked at. 

• When forming a new compound metric, it is important to ensure that the metric's scale and range are well defined and standardized whenever possible.  If you do not know how to act when the value is 9 or 454, then that is a major problem and it makes the metric totally useless.

• For new media such as social media, compound metrics can be useful in measuring the impact of the campaigns, but again it is very important to ensure that the metrics are as simple as possible with the end result easily tied to the business outcome.
• Finally, when there is a need to create more complex metrics such as undrestanding the task completion rate of your web application, it should be completely customized for your specific business and you should spend the time to understand what it means for you and your users to have a successful experience on your website. 

That concludes my post regarding what I feel are good uses of compound metrics.  Let me know your thoughts on compound metrics in general and what you think are good compound metrics. 

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