[Build Backlinks Online] What I Learned from Scraping SEOmoz's Active User Base

Build Backlinks Online has posted a new item, 'What I Learned from Scraping
SEOmoz's Active User Base'

Posted by iPullRank
Many moons ago, when Moz was SEOmoz, I had the idea to scrape all its publicly
available profile data on active users just to see what I could learn about the
community. Quantitative market research is an incredibly powerful method to
quickly grab insights on a brand's users. Using those insights, we can develop
strong content strategies and link-building campaigns, as well as develop
competitive insights.


What easier way than scraping the data from a brand's user profiles?




In Soviet iAcquire, the web crawl you.


Oh, you may have heard of Gary and Cogswell, the Russian-coded robots that
escaped the Ministry of Education and Science and sought asylum in iRank (our
homegrown targeting and reporting technology for scaled content marketing). They
were originally assigned some very menial tasks, but I've since reprogrammed
them to aid us in better marketing. They are here to lend a hand to their idol,
Roger Mozbot, in the hunt for Red October. As the Russian saying goes, "Many
hands make light work."


Special thanks to our creative director Robb Dorr for capturing them in the
act.


So we built (and by "we" I mean I had our Manager of Research and Development
Joshua Giardino build) a multi-threaded crawler in Python, and we fired it at
all of the profiles of Moz users who had logged in during the previous 60
daysâthose people whom I'll call "active users." For those that have
forgotten what their Moz profile looks like, they contain a lot of great info
ripe for the plucking. I personally don't know what Moz uses them for, but with
this post I hope to touch on some potential use cases. Your profile looks (or at
least looked) like this, and has all of the following data points in it if you
provide them.




How SEOMoz profiles once looked



Full Name
User Name
Email
Title
Company
Type of Job
Location
Favorite Thing About SEO
Bio


Favorite Topics
Instant Messenger Handles
MozPoints
Level
Membership type
Rank
# of Comments & Responses
Length of Membership
Links to other sites
Social Media profiles



So, now that we got this treasure trove of data on SEOs in a highly engaged
community, let's see exactly what we have.

Crawl stats
Crawl date 2/15/13 â Yep, Casey, that was us.
14,036 out of 14,872 profiles were successfully crawled â It wasn't a
polite crawl at all.
Average crawl rate of 4 URLs/sec â I'm surprised we didn't get throttled
more.
Total URLs Crawled 14,872 + 299 directory pages to extract profile URLs
(=15,171 URLs)
Methodology
Scrape as many users as we can
Cross-tab everything until we find useful insights
Run linear regressions to test the validity of correlations
Limitations of the data



According to the About page Moz had over 15,000 subscribers in February of
2013, but you can be a user without being a subscriber. I've asked Mozzers in
passing how many users the site has, and have gotten much bigger numbers than
that. After I originally submitted this post, it was revealed to me that Moz has
over 250K+ user accounts. So the issue with this data is that it is just a
sample. However, sampling is inherently a part of market research; after all,
you can't survey everybody. The more important point, however, is that the users
we scraped were all active users within the previous 60 days, and therefore were
likely more reflective of the needs of those who are highly engaged in the
product.


Also, many users have not completely filled out their profiles, so when
performing cross-tabulations we are often dealing with samples of slightly
different sizes. Therefore, all of the insights presented only account for
respondents. That is to say, we don't mention the number of people that have not
filled out a given data point. Again, for those who want to know, the base
number of total respondents for this study is 14,036, which makes for an
approximate 5.6% sample of all users (but presumably a much larger percent of
active users). Feel free to check our work.




I've talked a lot about market research and how SEO as an industry doesn't
value it. Many SEOs I've encountered prefer taking shots in the dark or the
guess-and-check method. This line of thinking is why the erosion of keyword data
in analytics matters so much to SEOs. Market research is why it doesn't matter
to channels like social media or (ugh) display.


In fact, for enterprise clients it is only about "are we capturing the right
people," and "how many are we getting through each channel?" This way of
thinking allows marketers to think bigger and be involved in conversations
beyond meta tags and links. For those that are leery of the application to
small-business marketers, you can easily leverage canned market segmentation
provided by Nielsen, Experian, and others, or you can leverage segmentation in
other ways.


So first, let's go over some high-level insights. Our Inbound Marketing
Analyst, Jiafeng Li, ultimately cross-tabbed the data a ton of different ways,
and the entire analysis that we've performed is available for download at the
bottom of this post in the "Parting gifts" section.

Membership type

The Membership Type field in the Moz Profile refers to the type of Moz
subscription that a user has. For the purposes of this study we basically care
whether the user is "basic" or not. Basic means they are a Moz user without a
paying account, while any other membership is a paying customer of six user
types.




As the histogram indicates, the majority of active users are Pro members.
Roughly 60% of this group has an active subscription. While interesting, this
data doesn't tell us much until we bring it into context of other data points
that we will examine shortly. It should be noted that this field is set
programmatically, so all "respondents" have this field filled out in their
profiles.


Most active users are either basic (unsubscribed) or Pro (standard
subscription) usersâ42% basic and 49% Pro. Therefore, a large segment of
these users are active subscribers paying at least the regular rate of
$99/month. This also means most users are genuinely affected when the product
has issues. However, it's notable that Moz does a great job of being transparent
when this happens.


Moz Insight: There's no real actionable insight here without looking at data in
context of other data points that we will examine later in the post.


Competitive Insight: Nearly half of Moz's active user base doesn't subscribe to
the product. It would be worthwhile to segment further and reach out to these
people to understand why.

Years of membership

The profile also tells us when the member signed up for her account. This is
interesting to get a picture of the retention of the Moz active user base. The
actual data point is the number of years since signup which shows that year over
year Moz has retained more active users.




Note: Remember this data was collected in February of 2013 so that explains
the small negative delta between years one and zero.


Congrats to Moz for their sustained user retention. Based on the sample
they've retained more active users every year (not including year 0 which had
just started).


From the outside looking in this is a clear indicator of a growing and
thriving community. When researching viable opportunities this is far more
important to me than any link metric. To be clear, though, this data is limited
in that we don't know exactly how many users signed up and ultimately canceled
altogether. Nor do we know how many users have switched user types over time.
Therefore the data is a jigsaw puzzle with a couple of middle pieces missing.


This is also how we realized this is just a sample of the user base because
Moz reports its subscriber growth on the About page as:

2009 â 5K
2011 â 10K+
2012 â 15K+

However due to the fact there is an account base of over 250k+ this is clearly
not indicative of all user accounts. Also, in a recent conversation with Rand I
learned that the subscriber base has continued to grow well beyond the number
displayed on the About page at the time of this writing.

Time spent on SEO

One of the more interesting data points requested in the user profile is the
amount of time a given user spends on SEO per week. This is particularly
interesting because we can use this as an indicator of savvy or engagement in
the spaceâespecially in context with job titles.




The biggest segment (20% of respondents) spend more than 50 hrs/week on SEO,
and as you might imagine, the active user base is mostly made up of people that
spend a ton of time on SEO. However, there are also very large segments that
spend smaller amounts of time on SEO.


Insight: As a content creator, there is space for really advanced content, but
there's likely an even more lucrative opportunity for basic content built for
people with a shorter attention span for SEO.


Moz Insight: Moz should consider some cartoon-based shorts starring Roger
explaining SEO basics and quick-hit tactics for less advanced users.

Level/MozPoints

Moz has a rudimentary system of gamification that comes into play based on how
active a user is on the blog or in Q&A. Points are awarded forâyou guessed
itâfilling out your profile, publishing blog posts on YouMoz or being
promoted to the main blog, commenting, and acquiring thumbs up.




This value is set by the system and the data indicates that 90% of active
users are lurkers. There's only a handful of Gianlucas out there. Based on how
MozPoints are awarded, this histogram helps me understand how many users are
engaged enough to be "thought leaders" as defined by the Journeyman, Authority,
Guru and Oracle levels. These are the influencers I would reach out to if I
wanted to place links or I wanted to get buy-in before I posted on YouMoz and
wanted to ensure I got traction.


Moz Insight: Moz's gamification needs work, and actually isn't very TAGFEE.
There are more actions that are beneficial to Moz that should also award points
to users. For example, sharing a post on Twitter should result in a point for
the sharer and the author. The rewards are also not that compelling. With all
the Mozperks and free swag Moz gives away they would be well served to build a
marketplace where users can redeem their points for fun stuff.




Note the change in the level names since the change to Moz. Guru has become
Expert, and Journeyman has become Specialist.


Competitive Insight: 90.16% of Moz's active users are not that engaged in the
blog, Q&A or comments. While the community thrives in different ways on
different channels there is an opportunity for another site to spring up that
rewards user engagement in a more in-depth and (dare I say it?) transparent way.

Type of work

Users self-identify the classifications of their work, and with this data
point Moz better understands how well they are capturing their targets.




Moz speaks to all segments of the audience with its offering and content, but
as Rand mentioned enthusiastically at MozCon, they are focused on helping small
business owners do better marketing. However, the active user base is 25.7%
agency or independents that are likely floating across many clients.


The remaining big segments are:

16.69% Business Owners
15.65% In-House

Moz Insight : Moz's active user base is not primarily made up of their core
target. The real question that needs answering is, why is that? I believe
cross-tabbing a little further gives us some more clues later in this analysis.


Competitive Insight: Moz's user base is full of people that make great targets
for agencies and enterprise products. Product brands that serve the enterprise
like Conductor or Brightedge; and agencies like Distilled, SEER, Portent, and
(ahem) iAcquire are obviously well served by being featured here or at Moz
events.

Years of membership vs. membership type

Since we don't have any indication of how user account types change over time,
the best we can do is look at account types in context of account age to try and
understand if there are any trends.




For users with membership less than a year, a higher percentage are basic
users; while at more than 1 year, a higher percentage of users are pro users,
indicating possible conversion to pro users after 1 year. The data indicates
that the longer people are engaged with Moz, the more likely they are to
subscribe to Pro.


Competitive Insight: The best time to convince users to try another product is
in their first year of using Moz. The data indicates that Year 0 members aren't
quite convinced this is the product for them. A competitor would be well-served
to offer a longer free trial than Moz does, and actively engage the user with
how-to content via email to keep them actively engaged throughout their free
trial so they can understand the value of the product.


Moz Insight: The data indicates that Moz does a good job of keeping these
active members happyâif they can keep them around. Users are likely kept
due to Moz's investment in upgrades and remarkable content. The real question is
which types of content lead to those initial conversions and which types reduce
the churn? Don't worry, I've got some ways to figure that out as well.


Naturally, Moz would also be well-served to develop ways to keep users highly
engaged during their free trial process with "Did You Know" weekly emails based
on app usage and non-usage.

Type of work vs. membership type

We wanted to understand how the type of work correlates to membership type.
What types of users own what type of membership?




Pro usage is dominated by in-house professionals, and independents are the
only segment that is mostly basic users.


Moz Insight: The hypothesis I've drawn here based on the data about these
active users is that independents either don't see the value in subscribing to
Moz or they can't afford it. Moz should consider a certification program similar
to that of HubSpot, which would allow independents to generate leads. Once
certified, these independents can enjoy a cheaper subscription rate. After all,
independents are even smaller-business owners.


Competitive Insight: There is an independent market worth tapping with a tool
suite that costs less than $1,188 per year. It would be worth performing
exploratory research to understand what type of tools independents believe are
worth investing in.

Time spent on SEO (heavy users) vs. membership type

We wanted to know what types of memberships the most engaged SEO practitioners
have as these people are likely the hardest to please and may have the most
influence of the bunch.




For heavy SEO users in the active user base, those who spend more than 50
hours/week on SEO, agency users and in-house users have higher percentage of Pro
subscribers while business owners and other types of users comprise higher
percentages of basic users.


Moz Insight: The data about these active users indicates that a large portion
of business owners that are heavy SEO users are basic users of Moz. Moz may be
too expensive for the people it wants to serve most, or even worse, these people
may not truly see the value of Moz. This may be the most useful insight to Moz,
and is definitely worth exploring further through interviews of this segment.


Competitive Insight: The independent and small-business owner is the
battleground for those competing with Moz. Agencies and in-house professionals
typically have access to bigger budgets and a variety of tools, whereas
independents and small business owners often have to choose. Therefore, this may
be where all-in-one products like RavenTools and HubSpot outperform Moz. It's
worth following up with exploratory research and examining any publicly
available data on their users.

Level/MozPoints vs. years of membership

We wanted to see if there was any correlation between the number of years of
membership and the amount of contribution to the community, wondering if it
would be possible to predict when the next John Doherty or Tom Critchlow would
pop up.




Among the "aspirant" users, who are less active, most of them are
comparatively newer members; while among "contributor," "journeyman," and
"authority," most of them are comparatively older members.


The data indicates that the insight is obvious: The longer you're with the Moz
community, the more likely you are to become more engaged. The biggest group of
contributors lies at the two-year mark. It would appear that Moz is already
proactively cherry picking the best-of-breed posters to add to the Associate
program. Competitors looking to quickly identify people for potential guest
posting could look here, but again this is obvious, because if someone is good
their posts tend to get tons of visibility anyway.

Regressions on membership type

There have been many discussions as of late on the value of correlation in
SEO. Rand has already gone in-depth as to why correlations studies are
worthwhile, but I will briefly say while correlation != causation it does bring
up some interesting insights. That said, we ran linear regressions on the data
that we cross-tabbed in the last few charts as follows:

X = "years of membership"
Y = "membership type value"
Y = 4.74x + 55.75
Adjusted R-Square = 0.0017 (It is extremely low, meaning the regression can't
really explain the data).
When X = "time spent on SEO," "type of work," and "level," the adjusted
R-square is low.

The results of our regression indicate how strongly membership type correlated
with time spent on SEO, type of work, and level. We found that membership is not
strongly correlated with any one of those given metrics, which means that while
there are a lot of happy "coincidences" here, they doesn't necessarily mean any
given factor is a driving force behind that correlation.

Job titles

Users have the ability to enter their job titles in their Moz profile.
However, free-form text fields are difficult to analyze, since everyone's answer
is very different. Enter: the word cloud.




Perhaps I am innumerate, but I've never really been a fan of Word Clouds.
Bigger words, bigger value. Big whoop. That said, this one would be pretty
useful if I didn't already know a lot about the Moz community. If I'm looking to
create content it's probably not best to go with code-heavy stuff. This word
cloud tells me that I'm mostly speaking to people that are pretty far in their
SEO careers, such as marketing directors and managers. As the marketing lead for
an SEO and social media agency, I could quickly verify that my exact audience is
here.


Moz Insight: There is a large opportunity for higher-level or big-picture
content such as what Rand delivers on his personal blog. Since the majority of
the active audience appears to be pretty far in their careers, this content may
prove more valuable to them.


Competitive Insight: This data further indicates that Moz is a great place to
get in front of enterprise professionals, especially in a less "sales-y"
capacity. Two words: Case. Studies.

Users' favorite things about SEO

Users also have the ability to share what is they love about SEO in an
in-depth free form text area within their profiles. Again we leverage a word
cloud due to the difficulty of segmenting responses otherwise.




This word cloud is also pretty helpful in understanding what content will
resonate with the audience. One of the highest occurring ideas is that users
love to get results or see their work on the first page of the SERPs. That in
context with users loving the constant challenge and, to a lesser extent, the
creativity required to get there leads me to believe this is an audience that
will be very receptive to new approaches with proven results.


Insight: The active Moz audience is far more interested in results (and
therefore case studies) rather than just ideas. This is an insight for both Moz
and other marketers looking to appeal to this audience. Bring data or go home.

Users' favorite topics

This section of the user profile is somewhat of a more succinct version of the
last field. Users are given options to choose from which makes it a lot easier
to analyze. Even so we've leveraged the word cloud here to see what really
stands out for the Moz community.




Optimization, content, analytics, research, link building appear to be the
hits with the active users in the community. It looks like I've covered them all
in this post, but how the post performs will be truly indicative of how well
these types of content reaches those people. And that's a good point worth
raising right now. How people say they act is not necessarily how they actually
act. It will always be up to analytics to prove these insights right or wrong,
but the point is to start out with an educated guess backed by data.


Moz Insight: As Moz is expanding its offering to be more about inbound
marketing rather than just SEO this will be a good data point to measure to
determine whether they are capturing more of that broad audience. However the
choices are still reflective of Moz's historical SEO focus as seen in the
screenshot below.




Now would be a good time to update this to reflect more of the granular facets
of Inbound.


Competitive Insight: This data really drills in the ideas of what you should
focus on if you're trying to get Moz users to come to you. Case studies and
how-tos on optimization, content, analytics, research, and link building are the
way to go, and a quick look at post analytics seems to back this up.




The real purpose of this post isn't just to show Moz how they can do better
marketing, it's to show you how you can leverage user profiles to your advantage
with your competitors for a variety of initiatives.

Lead Generation â A lot of Moz profiles show email addresses publicly, but
they're rendered with JavaScript (darn you, Casey). I could have easily fired a
headless browser at the site, pulled in email addresses and sent our sales team
at them. (Don't worry, I didn't.)
Content Strategy â As noted in the analyses, the data makes it crystal
clear what the audience wants in the form of content. A lot of content marketing
programs take shots in the dark at what users want while this type of research
allows a marketer to make a strong case for the content they would build. It's
far easier to convince a client of a creative content approach tied to an
audience with data than with just keywords and links.
Link Building â This data is basically a personalized Followerwonk. I can
slice and dice features of the dataset and grab their social URLs and sites,
then combine them with Domain Authority and Social Authority. That would give me
a highly personalized list of link-building prospects that I could segment and
target by interest. Say for example I only want links from people who've been
down with Moz since the beginning: I could just filter by the users that have
had accounts for seven years. Done.



This is quantitative research with the qualitative insights coming out of my
own experiences with the Moz community. Moz has, in the past, done a great job
of quantitative research in the form of surveys they run on their community and
user base. In fact, we could have layered that data over the data we've
collected to get a more complete picture of the user base, including
demographics with data from GlassDoor and Payscale to figure out salaries by
title. We also could have leveraged Moz's transparent analytics feature to show
how content of the different types performs by subject and use those insights to
get closer to what actually works for Moz.


We could have also performed qualitative research, much like Moz does with its
various initiatives wherein they watch users using their products and ask
questions. As a part of Moz's Customer Advisory Board (CAB), the product team
often reaches out to me to get my thoughts about using Moz Analytics and get
specific feedback. The next step would be to pull out a set of users that are
representative of the most valuable segments and similarly have question and
answer sessions.

Exploratory Research â I've mentioned it several times, but this is
process of speaking to people in small groups with open ended conversations to
understand how your audience is thinking about your product. This process is
usually performed in Focus groups or open ended surveys to help define what
needs to be answered by more data.
More Quantitative Research based on those findings â Once we collect
findings from exploratory research we could then send out survey questions based
on those findings to get a bigger sample of the segment or find those people
through other channels like LinkedIn.

In other words insights can always be understood further or fine-tuned when
used a basis to determine or answer new questions.


The mad scientists at Moz could also pull the entire 250k+ users and perform
the same analysis. However, I think the analysis of the active users proves to
be more actionable, as it limits the research to just those that are actively
engaged. Additionally, the analysis of all users may lead to insights into why
certain user segments have become completely inactive.


Moz could also layer this data with app usage data for a more complete picture
of what content keeps users using the product.







Measurement and targeting applications

This slide below from my MozCon 2012 presentation may have been forgettable at
the time, but this is the foundation for what I believe is the future of digital
marketing. This is the framework by which arbitrage and dynamic targeting become
stronger, more viable solutions.




The concept is actually called cohort analysis. Before your eyes glaze over,
this is nowhere near as complicated as the Keyword-Level Demographics
methodology I developed at the end of 2011. With cohort analysis we segment
users based on their shared features and track them accordingly. With
Keyword-Level Demographics we've done that using Facebook data to match the
relevant user data to features we've identified as relevant to our predefined
personas. With cohort analysis we're doing it from the other direction by first
collecting data and then defining segments based on actual usage rather than
just panels and surveys.


That is to say that Moz doesn't have to go as far as building personas
complete with demographics and user stories; they can stop at segments. Much
like your Google Analytics segments, Moz could develop affinity segments to see
what content resonates with which user types throughout the site. With all the
data provided in the user profile Moz can segment any number of ways and may
choose to go with membership types as the base since it is one of the lowest
common denominators between users. However for the sake of understanding let's
use the Time Spent on SEO as our defining characteristic.


Moz could define high level segments as follows:

Super Heavy Users â Time spent on SEO over 50 hours/week.
Heavy Users â Time spent on SEO 35-50 hours/week
Medium Users - Time spent on SEO 20-35 hours/week
Light Users â Time spent on SEO 5-20 hours/week

We know Moz wants to target business owners. From the high-level insights, we
have identified business owners that are super-heavy users as a segment of
opportunity, since many of them are currently basic users. Now, to drill down
into one of those segments we could target basic users that have "Link Building"
listed as one of their interests, and spend more than 50 hours a week on SEO.
Let's call this segment "Basic-50-LB." Based on the data this is indeed a valid
segment:




We now know a lot about what this segment is interested in, so we can then
test and optimize against it.


Now let's compare this to the interests of the business owners that are heavy
SEO users and have Pro accounts. It appears to be somewhat different.




The question we want to answer is, why? And how do we push those basic users
to become Pro users? There are a lot of things worth testing on the basic users
to see if we can discover what affects their perception of Moz's value.


With that segment defined, Moz could track what type of content performs and
then dynamically surface that type of content for that user when they log in.
Moz could also track how many times that user type has to see a specific type of
post before they are likely to become a Pro user. This is where geniuses like
Dr. Matt Peters and Dr. Pete Meyers come in and build predictive models and
Moz's entire digital marketing mix start to make Target's pregnancy prediction
tactic look old school.


Further, Moz could see which products a given segment likes using the most and
use that to inform their product roadmap. Did this segment become a Pro user
once Followerwonk was released? Did signups increase once the Social Authority
API rolled out? And finally, Moz could get more aggressive with these tests and
segmented emails to users that cancel in hopes of bringing them back to Pro. For
example a user very interested in link building would get emails with all of the
recent link building posts, Q&A and discussion.






But to do this we first need to set up Google Analytics for measurement of
cohorts. To do so we need to create a new custom segment that looks for the
Custom Variable that we'll be setting when a user starts their sessions.


Steps to do so are as follows:

Click the Down Arrow below your Segment name
Click Create New Segment
Click Conditions under Advanced
Select Users and Include next to Filter
Select the Custom Variable you will be setting under drop down that gives you
the dimensions to choose from.
Choose Contains and then type in the value which would be the segment name
Basic-50-LB

We'd also do this for the segment we'd like to compare it to as well as
capture the higher level segment "Basic-50" for bigger-picture insights.


This is actually something we do in the measurement planning phase with our
clients here at iAcquire. It's actually incredibly simple, when a user logs in
just pull their profile and identify which segment they are then fire off a
custom variable like so:


_gaq.push([â_setCustomVar',1,'userSegment',userSegmentName ,1]);


The steps leading up to firing the custom variable will require some custom
programming, but I promise you that it's nothing more than a bunch of if-then
statements. Tell your developer to relax.


Ultimately what you'll get in your analytics is these segments in context with
your analytics data allowing you some very precise user insights that are
completely relevant to you. In some ways this approach is actually better than
Keyword Level Demographics because it doesn't require a user to be logged into
Facebook and it leverages the data within the user profiles.




I know what you're thinking, "How does this apply to my site or my clients? It
will be impossible for my site to get users to create a profile and fill it
out." Well, can you get just a social handle or an email address? Ok, then I've
got a couple solutions to that as well: FullContact and RapLeaf.


It turns out that FullContact does more than just give Paid Paid Vacation,
they are also a contact data provider. Both RapLeaf and FullContact allow you to
pass minimal information on a user and get a ton back. Here is some high-level
information from their respective sites.



FullContact


RapLeaf











So remember when I said the email was difficult to scrape? The social handle
was not. I'd be all set for lead gen with just a few API calls.


Using one of these solutions you could pull their data when they signup use it
to determine their segment or persona, save that to your database and cookie
them. This way there's no need for them to create a profile or opt-in in anyway
aside from the initial signup. Also as long as they don't kill their cookies,
the user doesn't even have to explicitly sign in. Sometimes the Internet feels
like magic.




You guys know I can't give you a good idea without leaving you a way to use
it.



Josh's scraper code

Since SEOmoz became Moz there have been more than enough changes to the
structure of the site that this code will not work anymore, however it's a good
starting point if you'd like to build a scraper for competitor user profiles in
the future. You can find it (and some other cool things) on the iAcquire Github
repository for you to enjoy.

More market research resources from J-Li

We take market research pretty seriously here at iAcquire. Here are two posts
you shouldn't miss from our Inbound Marketing Analyst Jiafeng Li.

Quantitative Data Analysis Techniques for Data-Driven Marketing
5 Ways to Avoid Being Fooled by Statistics
Jiafeng also would like you to check out her complete analysis of the user
profile data we collected
Cohort analytics stuff
Google Analytics Visitor Segmentation: Users, Sequences, Cohorts!
Cohort Analysis for Dummies
Nudge Spot â An easy to use dynamic targeting platform based on cohorts

At iAcquire search is our craft, and this post is just another example of an
element of the new SEO process at work. This is the type of my stuff my team
incorporates into SEO on a daily basis in addition to the creative technical
ideas we come up with. The fact is, we live in the information age where big
data reigns supreme, but let's not forget smaller data like we've just examined.


So It looks like Roger, Gary, and Cogswell are ready to do better marketing.
Are you?


And yes, it feels amazing to be back on the blog.
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