Lance Wills' Blog – "Internet Lifestyle Trends" (subscribe)!

Every day I communicate with business owners who have a website and are trying to drive traffic, increase conversions and improve their visibility on the web. Yet most business owner’s have little specialized knowledge in this area and throw money into poor solutions such as costly website redesigns, contracting self-proclaimed Internet Marketing gurus (or worse, Social Media Marketing gurus), or oversee poorly run paid search (PPC) campaigns.

So What Should A Website Owner Do?

First, and foremost you must educate yourself on the basics of SEO, Paid Search, Analytics and Social Media Marketing. You can’t simply leave this to the “experts” because you will soon find that they are rarely experts at all; they may be showing you results, but not the results that you SHOULD be achieving.

SEO In 30 Minutes

Obtaining this elementary knowledge that I speak of doesn’t have to be painful, and can be accomplished in about 30 minutes by watching a simple FREE presentation. Moreover, every person that I introduce this to becomes passionately interested in learning more. It’s VERY addictive!

I have tracked down a great slide show that will give you the basic amount of information to be dangerous. When you are finished with this slide show, you will be able to ask better questions and demand number driven data from your team members who are responsible for driving traffic to your site.

Click on “Menu” to enlarge this presentation to FULL SCREEN. Use your left and right arrow keys to advance or rewind.

Compiling Your Target Cohort

The first step is to aggregate our target cohort candidate, either through keyword searches within user profiles, or through keyword searches in user-generated content.  Not all valid target users indicate their interests in their profiles and some that do are not necessarily appropriate, so a dual pronged approach should be implemented to obtain the best quality pool of candidates.

A great way to build a library of associated target keywords would be to scrub specialty (say “video game”) sites or aggregate them internally through flagging potential keywords due to above average keyword frequency in user-generated content items.

Initial KPIs

The first KPIs that I would implement are:

1. Content Generation KPI (broken down by content type: video, pictures, links,…etc.)
2. Comment Thread Length KPI
3. Cohort Fb Friends KPI
4. Cohort Group/Pages/etc. KPI

1. Content Generation KPI

A simple count of content items generated over a stated period of time, say 1-month, which contain keywords relevant to the cohort is an excellent starting point for measuring user engagement.

2. Comment Thread Length KPI

Users who generate long comment threads should be recognized as inspiring engagement.  The best way to do this is by taking a rolling monthly average of the number of comments posted to content originated by the target user.

3. Cohort Fb Friends KPI

The number of Fb friends the user has that is significant to the cohort should also be measured.  Obviously, if your friends are talking about video games and that is relevant to you, then it inspires engagement and should be accounted for.

4. Cohort Group/Pages/etc. KPI

The number of Groups/Pages/etc. that the target user is associated with that share the target topic are extremely important to user engagement and should be measured.

Levers For Improving KPI

The best thing and the worst thing about social networking sites is that the bulk of content is generated by the user, and the sites, like Fb, sometimes keep an arm’s length approach to steering users.  However, I feel that it is important to the future success of Fb to become more than a platform, and begin crafting a more enriched user experience to facilitate fellow cohort user discovery and interaction.

Here are the levers that I would implement to increase the aforementioned KPIs:

1. Inspire Users To Create Topic Content Through Compelling Seed Content

I have previously visited numerous (Product) Pages where user comments have barely a discernible theme and eventually degrade down into bathroom wall graffiti.  I don’t believe that this is what fulfills the user, nor is it what the advertiser wants.  What inspires engagement?  Compelling topics and content.  By creating a more focused Pages framework and interjecting targeted content, such as “Polls” or questions on new products, users will be inspired to participate and share their opinion.  Users want to be heard by creating content, but they don’t always know how to start it.  All they need are some conversation starters on topics that they are passionate about.

2. Make It Easier For Users To Agglomerate Via Applications

When developers create applications targeted to specific cohorts, everyone wins.  They serve as magnets for target users and improve targeted advertising effectiveness.  The most difficult part is spreading awareness; however, cross-advertising (covered in #4 below) is a great way to spread the word.

3. Allow Pages/Groups To Have Forums

This will create a framework for structured conversation and dialog that will inspire users to generate content, increasing the KPI for 1-4 above.  Users will want to join groups because they will be able to get their questions answered and talk about their passion (without having to remember yet another specialized website login/pwd).  They will also be able to establish trusted relationships with other Fb members through genuine conversation on topics that speak to them, in some cases, resulting in new “Friend” adds, even better facilitated by the creation of alternative networks outside of family and friends (see #6 below).

4. Cross-Advertise Groups

There may be many Pages/Groups for a particular topic but not all users may know about them.  Some ad units could be used to market other Pages/Groups, perhaps something similar to AdMob’s iPhone Download Exchange.  The “free market economy” nature of Facebook will ensure that the Pages with the best content (& best Forum) wins, so this will inspire admins to improve the quality of their content or watch their users slip away.

5. Improved Video Tutorials

Show users how to make the most out of areas of the site that they are not already using.  Fb does a great job of providing a white canvas, now let’s guide user’s hands and help them to create their masterpiece.  They will want to spread the word about their creation, which will increase user engagement.

6. Allow Users To Build Non-Personal Networks

It is clear in our capitalist society that just about everyone operates their own business, participates in an MLM, belongs to an organization or conducts networking for business.  So why not allow users to build networks outside of their personal networks within Fb.  Most users would already prefer to keep their networks of friends and family separate on Fb. Allowing users to create multiple networks, if done correctly, would greatly improve user engagement within and across all arenas and further delineate cohort groups.

Closing Remarks

User’s engage when they are provided compelling content or an accommodating avenue.  Implementing effective KPIs is important for reaching engagement targets for cohorts of Facebook users.  Maximizing user engagement with cohorts is only limited by the flexibility of the social media machine.  The best way to increase user engagement is to increase the ease with which users interact, create, network and share within Facebook, and to educate users on the tools available to make Facebook the soap box that fulfills all of their needs.

*Note: It is important to clarify that we are looking at the value of a Fb user from the perspective of Fb, not from the perspective of a Fb advertiser, marketer or user.  However, the methodologies used herein may also produce extremely valuable insights for the marketer using Facebook, and provide an invaluable starting point for your own user lifetime value calculations.

Strategy And Value

Prior to building a model for determining value, an Analyst must know the short and long-term strategy, and correspondingly what exactly carries value for Facebook.  For the sake of continuing our analysis here, I will assume Fb is pursuing a mixed strategy of long-term content and user expansion along with current advertising revenue growth.

1-Year Value Of A Facebook User

To build a model to determine 1-year and lifetime value per user, we will take a granular approach.

First, we will define the model used to determine the value for each individual Fb user, and then adjust this model to account for user engagement archetypes, demographic groups, and finally index this model to user country.  The baseline model will be for users located in the US.

Furthermore, we will use KPIs (Key Performance Indicators) that Fb is most likely already measuring.

The 1-year value per Fb user, can be modeled by first making a few assumptions about what value exists for Fb for each user, and then applying empirical user data to create the actual model.

First, value is hypothesized to be the SUM of several distinct components:

1. Value of the user’s personal network
2. Value of the user’s total time on the site (AKA content interaction)
3. Value of advertising

Each of these can be broken down into further sub-categories:

1. Value of the user’s personal network is a function of:

• User demographic info (income, location, education, age, privacy settings, etc.)
• Total number of Fb Friends
• Number of friends that this user invited to Fb via email (i.e. who were not Fb members prior)
• Number of in-bound friend requests within Fb for this user
• Total number of Groups, Applications, (Product, etc.) Pages
• Application invitations sent out
• Devices configured for regular Fb access (mobile, PC, netbook, etc.)

2. Value of the user’s total time on the site is a function of:

• Time spent CREATING original content (Broken down by media type: pictures, video, products, etc…)
• Time spent CONTRIBUTING to existing content, such as comments (where user may or may not have originated content)
• Time spent VIEWING content (Broken down by media: pictures, video, products, etc…)
• Time spent PARTICIPATING in apps (Games such as Farmville, etc.)

3. Value of advertising is a function of:

• Ad units shown
• Ads served (impressions)
• Ads clicked (ad revenue per user KPI)

Next, we will acknowledge that user engagement varies between every user on Fb, and we will segment all users into three user engagement archetypes. Using empirical data, we could easily define a percentage to each segment corresponding to the percentage of content that each group produces.  For the sake of brevity (since we don’t have that data), we will use a 90%/9%/1% distribution and assign it appropriately, namely:

• Spectators – (90%) those who use the site, but do not contribute, or very rarely contribute
• Contributors – (9%) users who contribute on occasion
• Advocates – (1%) users who contribute the majority of all content on Fb

Each user should then be assigned a value multiplier, or index, based upon their user engagement archetype (e.g. user’s who contribute more content are indexed at a higher value).

Thirdly, we may also elect to dial down value to the user demographic group if empirical data revealed consistent statistical significance:

• Gender : Male, Female
• Age : 0-17, 18-24, 25-34, 35-54, 55+, unknown

Each demographic group could then be assigned value multiplier, or index.  Gender and age would not be handled separately, but would be grouped, such as: (Males, 0-17), (Females, 0-17), (Males, 18-24), (Females, 18-24), (Males, 25-34), (Females, 25-34), etc…

For example, the (Female, 18-24) demographic might have an index value of 1.20.

Lastly, we will index each user’s value based upon their country.  Since our baseline model is for users located within the US, our index for the US is equal to one.  We could quickly derive this index for other countries from empirical data based upon revenue generated from that country divided by number of total users.  However, a more accurate model for each country could be obtained using the same granular approach detailed above; segmenting by user engagement archetype, demographic, etc.

The resulting model for determining 1-year value of a Facebook user is:

User Value = (Country Index) * (Demographic Index) * (User Engagement Archetype) * SUM (Value of the user’s personal network + Value of the user’s total time on the site + Value of advertising)

Lifetime Value Of A Facebook User

Using empirical data over the lifetime of a Fb user we could easily build a model to determine lifetime value.  However, because Fb is a young company and life cycle data may not exist over the entire life cycle of a user, we must make some assumptions in building our model.

The lifetime value of a user will be calculated based on the following criteria:

1. The (first) 1-year value of a Fb user
2. User Engagement Archetype (Spectator, Contributor, Advocate)
3. Life cycle curve fitting

First, we would use empirical data and existing external life cycle data from comparable industries to define curves that best represent user engagement life cycle. Parallels between social networking user engagement life cycle curves and other, similar phenomena would be used.

We would segment and assign each user to one of three user engagement life cycle curves.  Correspondingly, we would tie each one of these curves to our pre-defined User Engagement Archetype categories (this may or may not hold to be valid and may require the creation of a new “User Engagement Life Cycle” category unrelated to the “User Engagement Archetype”).  For example, we are assuming the “Advocates” user engagement archetype would have the longest user engagement life cycle, since they are the group that most frequently generates content, we are also assuming that they will be engaged with the site the longest.

This curve would then produce a polynomial equation that would be used along with the 1-year value (independent variable) to calculate the lifetime value of a Facebook user (dependent variable).

This model is a simplified approach to a very complicated and nebulous challenge; we are committing all users to belong to one of three user engagement archetypes based upon their activity within their first year.  Then we are calculating the lifetime value of this user by plugging the value of the user’s first year of activity in to the projected engagement life cycle curve equation.

However, due to the simplicity of this model we are able to determine the lifetime value of a user on Facebook very quickly, and with only empirical data from the first year (or first several months).  We could easily modify it to include advanced parameters and increase accuracy in the future.  Furthermore, as we accumulate more empirical data and recognize life cycle trends in user engagement, we could very easily modify our approach and look at other KPIs to improve statistical relevance.

Closing Remarks

In closing, there are some factors that I would like to address.

First, I initially wanted to include some of the revenue generated downstream from a user by his/her social network that he/she originated (e.g. ad clicks by a user that our target user invited onto the Fb site).  However, at this stage, I found it to overly complicate the model, as it would involve fractional revenue added to the value of our target user, with the balance of this revenue added to the value of the “friend’s” value.  Furthermore, what about the value generated by the “friend’s” of the “friend?”  As you can see, this MLM approach could become quite complicated very quickly, but could be integrated if deemed appropriate.

Value on social networks is still amorphous.  Revenue generated from ad clicks is obvious, but how about the value of a heavily influential user?  If everyone wants to be a user’s friend then some value exists there, and should be integrated, which I have done.

In conclusion, determining 1-year and lifetime value of a Facebook user is a new and complicated challenge.  I believe that I have created an approach that is both a credible starting point and a powerful calculus capable of satisfying advanced needs, especially given the limited availability of data.