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Build Great Backlinks has posted a new item, 'Personas: The Art and Science of
Understanding the Person Behind the Visit'

Posted by iPullRank
Market segmentation is a basic tenet of marketing that has long been ignored
by SEOs. And that's okay, because for a long time working on the keyword-level
of abstraction was enough. In fact, you can still do SEO and marketing in any
other channel without ever having the idea of market segmentation cross your
mind despite (not provided), Hummingbird, and a whole host of changes Google is
forcing as of late.


That isâ if you enjoy 0.04% conversion rates. Right.


There have been many posts about personas in the wake of the methods I've
popularized for SEO, but nothing that truly walks through the process with data
or gives context into how measurement has matured. In this post I'll go into
detail about these approaches, giving frameworks and step-by-step instructions
on how to build and use personas.





There's something in this post for everyone from beginners to advanced
marketers. I feel that it's important to give context to the discussion to
clarify why developing and using data-driven personas is critical to the future
of search and digital marketing in general. Use this table of contents as a way
to navigate to precisely what you want to know. You'll also find "Back to table
of contents" links at the end of every section.



Waitâwhat are personas?
What is the difference between segments, cohorts, and personas?
Segments
Cohorts
Personas

Google is pushing all channels in this direction
Affinity segments/categories



Building personas
Layering data
Qualitative research
Quantitative research

Building Mozzy Smurf
User journeys
Use cases
Further applications
Frequently asked questions
Resources






First, personas are a method of market segmentation wherein we collect a
combination of qualitative and quantitative data to build archetypes of the
members of our target audience. In other words we take data to tell a predictive
story about our users based on past behaviors and attributes.


I mentioned keywords as a level of "abstraction;" Google has obscured that
type of abstraction with (not provided), taking an otherwise perfect
direct-response dataset and turning up the opacity. Nonetheless, it was always a
representation of a person taking an action to fulfill a need. However, that
abstraction removed us completely from those people and placed our focus clearly
on the keyword and the Boolean idea of whether or not their visit on that
keyword led to the completion of a task.




If the keyword-level of abstraction is a stick figure a persona is an action
figure.




Over the past few years I've built methodologies in a land of marketing make
believe to develop personas and apply them to the intersection of Search and
Social Media helping us understand the person behind the search. Much like the
cartoons that action figures are modeled after personas have a set of attributes
that they are to (ahem) personify. Dictated by the business goals and the data
that can be collected and analyzed these attributes are typically a picture,
demographics, psychographics, user needs and a user story, but can be as
in-depth or as vague as you want - as long it's actionable. For example, some
people like to give each persona a "quote" that sums them up. Personas also come
with a user journey which is a collection of steps a user takes in fulfilling
those needs.


Ultimately, though, you're trying to tell the most actionable story with your
data. Think of it as another layer to your analytics. The most important layer.
The people layer.


People often ask me why they need to use personas. In my previous role of
selling SEO services to people that talk about SEO and marketing as separate
things I've experienced a lot of pushback. Fortunately there were far more
instances where a CMO or VP was in the room and speaking in terms of segments
and market opportunity rather than just keywords, meta tags and guest posts
helped us win the business.


But I digress.


One of the main reasons for using personas is that when you target everyone
you actually target no one. The art of segmentation is about narrowing your
focus in on people in the market more likely to become your users/customers so
you can better serve them. This applies not only to your product and/or service,
but your content as well.


Donald A. Norman of the Nielsen Norman Group explained it best when he
declared "A major virtue of personas is the establishment of empathy and
understanding the individual who uses the product."


In the Content Strategy world one of the major concepts they push is
"empathy." How can we understand and then fight for the user to create the best
possible content experience to fulfill their needs? Not just the right words,
but the right structure, the right metadata, the right presentation.


User Experience professionals use that idea of empathy with personas to plan
and build things that work for the target audience. For example, if our audience
is people over 50 then it may make sense to design a site with the larger text.


In the world of marketing this is all a means to specific end of course, but
ultimately we just want to know who we're talking to so we can improve our rate
of persuasion - or conversion.


Organic Search as a marketing channel is about just that - persuading people
who have a specific intent to believe you can fulfill their needs. Building
personas allows you to speak directly to their needs from as early as the page
title and meta description. This applies to not only your product and/or
service, but your content as well.


Back to table of contents





The terms are often used interchangeably, but they can mean slightly different
things. All of these concepts are abstractions of people, but the basic
difference between the three lies in their specificity. A segment is the
broadest concept of a person while a persona is the most specific snapshot of a
user archetype.




For the purposes of this discussion the Smurfs will act as a way to make these
ideas a little more real (whoa, meta). I tried to get G.I. Joe, but, they were
busy fighting wars and stuffâ yeah, anywayâ

Segments

Segments are groupings of similar entities. You can (and should) quite
literally segment by any set of rules in your data as I've discussed in my last
Moz post. On the cartoon the Smurfs you had humans, animals and Smurfs. Each of
those could be a segment. You could segment just the Smurfs themselves by color
of their mushroom homes. You can segment them based on things that happened on
the show. Two segments could be "Those that Gargamel Has Captured" and "Those
that Gargamel Has Not Captured." You could segment by where they live in the
Village. North Smurfs, West Smurfs, Southeast Smurfs. You could sub-segment any
of these groups with any granularity that you see fit or combine criteria just
like you would with standard clickstream data in Google Analytics.


The point is, although you can segment by anything you can track, will it be
actionable? Popular actionable segments that are used every day are geographic,
behavioral, seasonal, and benefit segments.


Nielsen PRIZM is a popular market segmentation system that is based on zip
codes where people are chunked into subsets regarding their location, income and
behavior. Nielsen builds this system on top of US Census data and sends out
surveys to a large sample of people to create 66 segments throughout the United
States. Experian Simmons is similar, and maybe more interesting to inbound
marketers with its connection to Hitwise, but Google has recently brought
segmentation purely online and has the potential to supplant them all. More on
that later.

Cohorts

Cohorts are groupings based on similar experience. Common vernacular for
cohorts would be generations. In the Smurf Village you had three generations of
Smurfs. The baby Smurfs (which for whatever reason had the only other female
Smurf). Let's call them Generation Next. You had the adult Smurfs like Jokey,
Vanity, Brainy, and Smurfette's cohort. Let's call them Generation Now. A cohort
that walked around believing shirts were optional.


And you had Papa Smurf and a few of his buddies. Let's call them the Elder
Smurfs.




Obviously, each individual in any of these groups is different from the next,
but they are grouped by their shared temporally attitudes, cultural interests
(ex. fashion sense, music), and life experiences (Gargamel captures, first
appearance of Smurfette).


In the real world we have Baby Boomers, Generation X, and the ever elusive
Millenials. Baby Boomers were a generation defined in the post-World War II era
of increasing affluence, Civil Rights movements and the death of JFK. Generation
X was a people defined by rebellion, MTV, baggy pants, the dot Com Bubble, the
rise of Grunge, Microsoft, and the death of Kurt Cobain. Millenials are defined
by 9/11, job-hopping, Apple, Google, Facebook, free music, nerd glasses, tight
jeans, everybody having a startup and the death of Michael Jackson.


Right now every big product-driven company is asking how do we get Millenials
to care about us?

Personas

Personas are specific archetypes of people in the target audience. The
attributes identified across the group are collected to give birth to a single
entity that represents these users. A persona has a descriptive name and is
meant to be thought of like someone that actually exists. They are generally a
composite of people that do exist.


In this case we will use individual Smurfs themselves as our personas. While
some people in the 80s viewed the cartoon as communist it can also be seen as an
exercise in behavioral segmentation. Each character was clearly differentiated
by what they specifically did or how they acted within the Smurf Village.


You had Brainy Smurf, the original hipster. He's a bit of an introvert and
likely to be found at a Barnes & Noble sipping a macchiatto latte and discussing
Sartre, injecting barbs of sardonic wit. He spends a lot of time updating his
blog, and he's a freelance copywriter for a multinational ad agency, but he only
shops at the mall. Brainy prefers Facebook over Twitter as he would rather have
a long-form discussion where he can definitively disprove what you believe. He
listens to NPR and of course is a Mac rather than a PC.


You had Smurfette. Well, you had two Smurfettes, each of which could be a
persona.


The first Smurfette was a tom boy who just wanted to hang with the homies.
After all she was created by Gargamel as a way to distract and trap the Smurfs.
She shopped at second hand stores before it was in style. No, really.


Old Smurfette goes to open mics and loves to be around music. She enjoys
vintage vinyl records and playing with her rescue cat. The Old Smurfette is a
bit of a couch surfer who frequents SmurfBNB and eats at Baker Smurf's
restaurant rather than the big chains. You guessed it; Old Smurfette is a
persona based on the female hipster Millenial cohort.


Later, after Papa Smurf turned her into a real Smurf she got all high-end
fashion on us, dying her hair blonde, wearing Diane von Smurfstenburg dresses
and Christian Smurfboutin shoes. She's more likely to be found at high-end
establishments, but only goes out when invited. Smurfette would rather be
shopping than go to a music night spot. She's all about convenience over
supporting her local community. Smurfette likes to see and be seen.


Then you had Jokey Smurf. His persona name would probably be Terrorist Tom
because he loves to hand people presents that explode. In the context of
marketing Jokey is the type of user who loves extreme sports, sites like
Break.com, and the type of content that Red Bull creates. He's highly likely to
buy Ed Hardy clothing. Only the jeans, though, because males in his cohort don't
wear shirts. Jokey loves craft beer, Xbox One and action movies.


In the above cases I've taken what I know about the millennial cohort and
layered it into a story about the different Smurf characters based on things
that could be observed on the show. As marketers building personas we do this
with regard to the context of our marketing programs. That is to say we focus on
elements of the story that is relevant to our goals rather than including every
data point we can find.


A key distinction to be made in the context of inbound marketing programs is
that between the buyer person and the audience persona. The audience persona is
typically someone looking to consume content for education or entertainment.
These people are not actively looking to purchase a good or service and are
better measured via KPIs having to do with the spread or the building of
authority for that content or the building of community.


Conversely, buyer personas may also be looking to consume content, but only as
a means to make the specific transaction to support their needs. There is
frequently overlap between the two types of personas and a given user can also
transition between the two types. Keep this in mind as you develop your
personas.


Once this in-depth profile of the audience is created smart marketers ask
questions and take actions with regard to how these personas would best be
served to meet the business objective.


At Amazon, Jeff Bezos leaves a chair empty at meetings to signify the customer
persona is in the room listening to the decisions they are making. At Experian
they have developed the character and placed her on banners throughout the
office and in the company newsletter to keep the customer top of mind. When I
worked on LG they sent a poster of their home appliances persona Wendy and she
came up often in our strategy meetings. At AirBNB they have a section of the
office with the personas in storyboards on the wall along with illustrations of
those of personas going through the user journey.


No matter what method you use, it is important to keep the consumer, customer,
user at the center of the marketing initiative. Don't just build personas and
forget they exist.


Back to table of contents





"Why should I care," you say? Well for some time I have touted this idea of
the intersection of search and social media to take intent to match it up with
the person. This and some of Google's actions towards the end of 2011 (remember
the consolidation of the privacy policies ?) led me into the idea that they
are using G+ to model users to apply a sliding scale of authority based on
topical relevance for better search quality and to provide the Holy Grail of
advertising opportunities. In fact I believed the whole purpose was modeling
beyond the keyword to make every dollar worth a lot more by marrying multiple
data sets. It turns out this is exactly where Google wants to go with their
marketing products and I'm basically just ahead of my time. ;]


Ian Lurie has also been talking about this extensively for the past few years
as well through a concept he calls "random affinities" which is similar to
something I was (perhaps mistakenly) calling "co-relevance" when I built a tool
for getting ahead of search demand with social media.




Forgive the quality of these screenshots, but in a recent video from Google
featuring Forrester Research's VP/Principal Analyst Nate Elliot they discussed
the concept of affinity and market segmentation. What he describes as Smart
Affinity is what a methodology like Keyword-Level Demographics is looking to
harness. This is a capability that marketers in general have yet to embrace
because it's simply too complicated for most. Google is taking us there kicking
and screaming.


Diya Jolly from Google gives some of the insight into why Google is obviously
the best suited for the job in her discussion of the data signals available
across the Google ecosystem. The amount of data combined with the sample size
allows Google to have probably the most robust and accurate model of user
behavior which potentially render other modes of advertising and market research
nearly obsolete or at least less effective.




I dug a little deeper into the process and found the "Inferring User
Interests" patent where they discuss more in-depth how they figure out user
interests. For example:


"In the situation where a first user lacks information in his profile,
profiles of other users that are related to the first user can be used to
generate online ads for display with the first user's profile. For example, a
first user Isaac may not have any information in his profile except that he has
two friends-Jacob and Esau. The server system 104 can use information from Jacob
and Esau's profiles to infer information for Isaac's profile. The inferred
information can be used to generate online ads that are displayed when Isaac's
profile is viewed."


How's the saying go? When it's free, you're the product.

Affinity segments/categories

All the data we give Google for free has allowed them to roll out this new
Affinity Segments product which is Google's own new segmentation system.


In their own words :


"We use the main topics and themes from the page as well as data from
third-party companies to associate interests with a visitor's anonymous cookie
ID, taking into account how often people visit sites of those categories, among
other factors.


Google may use information that people provide to these partner websites about
their gender, age, and other demographic or interest information. We may also
use the websites people visit and third-party data to infer this information.
For example, if the sites a person visits have a majority of female visitors
(based on aggregated survey data on site visitation), we may associate the
person's cookie with the female demographic category."


In typical Google fashion, aside from the video and a few articles in the
Adwords Support site, the detailed information about these segments is pretty
sparse. Luckily, I was able to get my hands on a deck with short user stories
and targeting ideas for each segment. I'm sure your Adwords account manager
would be able to furnish something like the below if you asked them nicely.




Affinity Segments is the broad name for these targeting types, but in practice
Google offers "Affinity Categories," "In-market Buyers," and "Other Categories"
as targeting types in AdWords. Affinity Segments are users in a broad sense,
In-market segments are people that are actively looking to purchase and other
categories are a variety of things. You're likely to see other categories the
most if you're not in the US.


I appreciate that Google makes the distinction between "Affinity Categories"
and "In-market Buyers" as this directly mirrors the approach I take in creating
both "Audience Personas" and "Buyer Personas." More on that later.


As an end user you can see which demographics and interests Google has
associated with you in your Ad Settings. You can also opt-out or change your
features as seen below.




However, the most important point for this persona discussion is that you can
now measure everything in Google Analytics based on these segments.




Let that sink in for a second. Google has Google+ as an "identity platform"
which is pretty much a front end for data collection and modeling of people.
They have Google Consumer Surveys so marketers can poll the audience and I
imagine at some point you'll be able to ask questions by affinity segment. And
now you have Google Analytics showing website actions in context of those
affinity segments. Google has just set itself up to disrupt the entire market
research industry with end to end people modeling. If that doesn't sell you at
least on the power of segmentation nothing will. This is completely
unprecedented.


Back to table of contents





Ok, enough with the background; let's get you building personas. There are
many methods for developing personas and I will discuss several of them, but you
should choose your approach based on the data and resources at your disposal.
Again, what we will be doing is collecting data, segmenting it and telling a
story about that segment. First I'll outline different processes then we'll walk
through the creation of a persona for Moz leveraging data from the scraping
post, Twtrland, Followerwonk,the community Q&A forum, and feature requests.


In my experience a combination of approaches yields the best personas.
Otherwise you'll end up relying too much on your own assumptions. Also I
typically build four personas with Googlebot, which AJ Kohn has aptly named the
Blind Five Year Old, acting as the fifth, but you can build as many as you see
fit.

Layering data



If you've seen me speak in the past year or so you've probably seen this
image. When I was at my previous agency my market research lead Norris Rowley
and I developed a methodology wherein we layered data from Nielsen Prizm and
Experian Simmons to collect data on segments at scale.


When I say layering I mean that we look for commonality between datasets and
if there is enough commonality or overlap we consider all features potentially
valid for sub-segments. That is to say if enough attributes of a Prizm Code and
a MOSAIC Type are shared we consider any data in one to be potentially valid for
the other and we applied this approach across all the available datasets.
Whether or not that is scientifically sound can be debated, but remember that
personas are hypotheses that will ultimately be validated or invalidated through
measurement.









Since the Prizm and Simmons surveys deal mostly in offline behaviors we'd plug
those data points into Social PPC inventories (Facebook Ads, Twitter Ads,
LinkedIn Ads) to ensure that those segments were valid online. If they proved to
be valid then we'd take that segment and build a persona.


I still believe this to be a solid approach especially if you can leverage
this data in context with some of Simmons' other products measure online usage
behavior as well as Google's Affinity Segments.


No matter which method you use you should start by determining the business
objectives which will then help to determine the goals of your research. Then
define how these personas will be used. Are you just looking to focus on your
buyer personas or will you be thinking about audience personas as well?

Qualitative research

With Qualitative Research you're asking open-ended questions to small sample
sizes to get a sense of the "how"s and the "why"s behind a specific problem.
You're typically looking at unstructured data to inform commonality amongst your
user group and any insights are then validated for scale throughout quantitative
research processes. Qualitative research within our context is often user
interviews, focus groups, content analysis, text-mining, ethnography and
affinity mapping.


(image source)

Affinity mapping / affinity diagramming

When most people think of a persona-building exercise they think of this.
Affinity mapping or affinity diagramming is the process of collecting everyone's
thoughts and segmenting them into meaningful groups. In the context of personas
this is typically done in a several hour session of everyone writing their ideas
of their customers on post-it notes with Sharpies, discussing them as a team and
then grouping them.


This process is great for putting the consumer back in focus for the team and
also for getting executive buy-in. However it's mostly based on assumptions so I
would not suggest doing only this when building personas as your research may be
attacked and biased by HIPPOs.


When you do this you want to get all the key stakeholders involved, especially
the upper management team but most importantly the people that deal with your
customers or users on a regular basis like your Sales or Help teams. The former
helps with internal adoption. The latter helps get closest to the right answer.
Many people building personas stop here to save time and resources, but when you
do these profiles are typically known as "proto-personas."


Affinity Mapping is typically done in the following 90 minute rounds:

Assumption round one (Needs) - Each person spends 5-20 minutes jotting down a
goal, activity, need, or problem for any user. This is to be exclusive of any
attributes of the user, rather it's about what the user is trying to accomplish.
An example assumption could be "User needing to make a confident decision on
which LSAT prep course to enroll in."
Once everyone has comfortably collected their ideas you go around the room and
each person introduces one of their post-its. The entire team weighs in on how
valid they believe the assumption to be. Those that are valid are placed on the
board. Those that are not are discarded. You continue until all post-its are on
the boards or in the trash. Throughout the process groups start to emerge as
assumptions begin to fit together. You can give the groups names if you'd like,
but at this point it's not that important as names can be given later in the
analysis phase.
Assumption Round Two (Attributes) - Each person again spends 5-20 minutes
jotting down information about the target audience, but this time they present
attributes of the user groupings from the first round. Again, you'll go around
the room and everyone will share and discuss their assumptions. The ones that
the group agrees are valid will then get added to the wall.
To continue the example from above an assumption could be "College graduate
25-34 who is unhappy with their career." Starting with the needs in the first
round helps to really zero on the demographics and psychographics of the people
in this round. If you go the other way around the parameters of the people may
be too broad.
Factoid Round - The final round of the exercise involves everyone in the group
spending 15-30 minutes finding data points to back up the groups of assumptions.
This data can come from any number of other relevant sources including
analytics, sales data, internal and external research. Again, the team discusses
the data points and decides their validity and adds it them to the groups.
An example fact could be "20% of all signups for our LSAT prep course
graduated college 4-11 years ago and reported in their registration that they
want to make more money."
The factoid round helps perfect the user story based on quantifiable realities
instead of just assumptions. It also allows you to potentially dump segments if
there's no data to back them up.ProTip: Although it sounds like a daunting
procedure that requires in-person interaction it can be done very effectively
remote by using Mural.ly and Google+ Hangouts.


The screenshot above is from a recent session I did with a startup called
Trip.Me in Berlin. We got members of the marketing team, the CEO and the
Operations team together on G+. We color-coded each round of assumptions and
factoids with the virtual post-it notes and then the tool makes it easy to bring
in links and content that supports any assumptions anyone on the team had. The
Google+ effects made it a fun time for all.

Build Personas - At this point you have all the data to build out the skeletal
personas. Your goal should be to whittle all of these insights into 3-5
actionable personas. While you can make as many as you'd like, it's difficult
for teams to stay mindful of too many. We'll go into more of how to formulate
stories based on the data when I actually walk through the process, but at this
point that is what you'll do.
These are often referred to as skeletons or proto-personas because they don't
have direct user research or a large wealth of quantitative data behind them.
However for many people this is just fine because the team may be most invested
in this type of persona, and that will help with adoption.
Focus groups

Focus groups are formal meetings with people of the target segment wherein a
moderator asks research questions to understand users and their needs. I've
personally never run one of these, but the ones that clients have conducted and
shared with us have made useful inputs in the creation of personas. They help
with determining questions and need states of users. However I often find that
moderators lead the group on some of the questions thereby invalidating their
responses to draw bias conclusions.





The quality of the output from a focus group has entirely to do with the
experience and biases of the moderator, the quality of the questions, and most
importantly the selection and attentiveness of the panelists. Another thing to
be leery of is the dominance of one opinion in group settings as people are
often swayed by the loudest participant. Furthermore the incentive the people
set for being involved may be their only reason to participate and they won't
give thoughtful answers.


We're about halfway through the post so I encourage you to take a break and
watch Conan O'Brien go undercover and moderate a focus group about himself:








User/customer interviews

These are similar to focus groups except they involve a one-on-one or small
one-on-two group environment where you directly speak to a user or customer. For
design, products or CRO this can be usability testing and eye-tracking or it can
just be direct Q&A as in the case of personas. All of the insights on how
customers user the product can be valuable to both the personas and the
determining the user journey.

Ethnographic research

Ad-Hoc data collection is what I've been calling the method of using social
listening, forum searches and keyword research to build personas , but I've
come to learn that research such as this when you watch users act in their
natural habitats is called "ethnography" or when it's on the web "netnography."




This is a great way to build personas when you have few resources because you
can easily identify online communities or watch hashtags and specific
representatives of your users on Twitter. Great tools for this include Topsy,
Sysomos, Radian6, Google Discussion Search, Keyword Planner, and the Display
Planner, Twtrland and Followerwonk.





The Display Planner, Quantcast, Compete, Twtrland and Followerwonk will all
give you demographic data that helps you frame your personas. Where Twtrland
bests Followerwonk is in its ability to infer interests from tweets and not just
user bios. The Keyword Planner gives you the keywords associated with the site
for use as the vocabulary to find your users in discussion search and eavesdrop
on their conversations with Social Listening tools like R6, Topsy and Sysomos.


Naturally, you'll need to do several iterations of looking at keywords and
conversations to identify trends across your users. You can also uses sites like
Quora and Reddit by going as far as to pose questions to kickstart the
conversation.


While the screenshot above is a good framework to work within there's no
defined structure to ethnographic research. You'll have to judge for yourself
when you feel your research questions have been answered. However you should
generally expect to do the following:

Collect examples of what you see users doing in their natural environments
called "field notes"
Analyze notes to discover new questions and reiterate
Look for shared patterns of belief, language and behavior
Write the ethnography which in this case is the persona

Ethnographic research is both the easiest and hardest of approaches because it
just requires observation, but the approach is completely subjective so it's
hard to convince people that the insights should stick in and of themselves
without quantitative research to back it up.

Quantitative research

If you're reading Moz you're probably a data-driven marketer so this end of
the research spectrum will appeal to your sensibilities. Quantitative research
is about using numbers and statistics to understand behaviors of users
empirically. The sample sizes are often quite large so that the insights can be
applied to broad populations of people.

Multiple-choice surveys

Polling your target audience allows you to ask precise questions. There are
many options for this, but I prefer SurveyMonkey Audience for this type of work
simply due to the fact that they collect of demographic data explicitly from
users while Google has is inferring it from user behavior. Survio is also a good
choice for surveying non-US markets. Survey Design is a science in itself and
SurveyMonkey has great resources on it , but the key thing to note here is that
at this point you want your surveys to not be exploratory or open-ended in
nature. You want your surveys to give users well-defined choices that you've
defined based on your qualitative research. The results will need to be
cross-tabbed until insights are wrangled out and personas begin to appear.

Market segmentation tools

As I mentioned before Experian Simmons, Nielsen as well as tools like MRI and
ComScore provide market segmentation based on surveyed panels and usage data.
These tools are incredibly helpful with scaling the persona building process by
providing prebuilt segments as well as a wealth of data in context of those
segments.





These tools fail when there if a specific question has not been included.
These providers are eager to take feedback and insights to add to their
quarterly surveys, but even when they do you are at least 3 months away from
seeing your questions answered and input into the system.

Analytics

Even without demographic tracking your analytics can have a wealth of
knowledge especially internal search, paid search and historical organic search
keywords in context of site actions. Also looking at location demographic data
as well as the times your users are visiting can be helpful determinations of
their attributes. Really what you can pull from analytics is completely
dependent upon your setup.

User profiles

If your site has user profiles, especially those that have collected data from
Social logins or other identity data providers there is a wealth of data that
users have explicitly set.




Internal data

Data on sales, calls, returns, reviews, users and transactions of all types
can be leveraged to give parameters and color during the persona development
process.



Publicly available studies

Every industry has public research and data that can be leveraged when
building personas. For example Google has the Consumer Barometer where you can
pull various data points.


I tend to use a combination of these approaches in my persona building
depending on what resources are available. In my client work experience I've
found it best to start with an affinity mapping session and then to prove or
disprove those assumptions and gain additional insights with data from the other
sources.


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For this exercise we will be using data I've scraped from Moz in context with
some social analysis and listening tools to build Mozzy Smurf. I'm calling this
persona Mozzy Smurf just to keep with the theme of the post, but I generally
like to give personas an alliterated name in the form of [adjective] [name]. For
example, this persona might normally be called "Busy Bob."


Naming is incredibly important because the adjective helps all the people that
will use the personas to recall their attributes much easier and the name
portion helps us imagine them as a real person.

State our goal

One of Moz's key business goals is to increase the number of users that signup
for free services that become monthly subscribers. Therefore the goal of this
persona exercise will be to discover a key segment of Moz's audience that is
very likely to share and link to content, but hasn't purchased a Moz Analytics
pro membership yet. Let's get to the bottom line of how we can show Moz is
valuable enough to pay for. The ultimate output will be the user story, user
needs, psychographics, demographics and engagement insights.


Additionally, we'll have all the values required to set up a segment to
measure this persona in Google Analytics including which Affinity Segment best
represents the persona in the data that we've collected. We'll be using data
from the Google Display Planner, Twtrland, Followerwonk, Moz Q&A, and data I
scraped from Moz user profiles almost a year ago.

Demographic data

First, I'll start by pulling demographic data from the Google Display Planner.
If you remember the DoubleClick Adplanner this has replaced it. Starting from
the demographic data allows me to determine what parameters of features are
valid for the user segments that I'm looking to discover. While the Display
Planner will be the most relevant we could have also pulled this data from sites
like Compete and Quantcast. If there's no data for your site pull data on a
high-performing competitor site.




Based on this data most of the people that visit Moz are between 25 and 34,
Male, and use Mobile devices. They are interested in SEO, Marketing,
Advertising, and Loyalty Programs. By the same token based on this data it's
also valid to build a segment that is 65+, female, is a heavy tablet user and is
interested in Loyalty Programs, but not SEO. While this segment is valid it's
not actionable to Moz so we wouldn't create a persona based on that combination.
As we collect more data the attributes we'll zero in on who are persona is.


There's one big caveat to this data, I've noticed that when comparing this to
client analytics that the devices data is typically way off. You must keep in
mind that every analytics program measures differently and ultimately your
analytics is the proving ground for any assumptions.


Another caveat is that since I'm so close to the Moz brand and the 25-34,
Male, mobile devices segment is me it's easy for me to lean on my assumptions.
This is the very reason that I'll need to pull data from a variety of sources in
order to validate any hypotheses and get the most value out of this exercise.

User needs

Normally user needs are best surfaced qualitatively through user interviews,
but as digital marketers we can discover the user needs that we aren't currently
serving through internal search analytics and social listening. Before (not
provided) we could also look at Organic keywords, but now only PPC will work for
that data.





Once needs are determined we'll be able to identify "need states" which are
the specific goal the user is looking to fulfill with their search and/or visit.
An example need state could be "How do I found out the best software for
rankings?" and this could be mapped to the awareness phase of the consumer
decision journey. We'll speak more about this when we get to the user journey.


In this case we already have a quantified user needs data set from the user
profile data that Jiafeng Li already analyzed. While this information was pulled
in early 2013, it'll still work to illustrate the process. From the screenshot
we see that the biggest segment of users with Basic accounts is the Business
Owner which we can assume means Small Business Owner in the case of Moz.




Some more key data points from the report are:

The largest single group of Basic users has been using Moz for less than a year
though there are many that have been users for 2-7 years.
There is a large group of Business Owners that spend more than 50 hours a week
on SEO and are Basic users.
Super Heavy Basic Users that are Business Owners are mostly interested in
on-page optimization, link building, content & blogging, intermediate & advanced
SEO, analytics, SEO technical issues, social media, keyword research, and
entrepreneurship and web design - in that order.
Business Owners make up 22% of the entire sample of users.

Next, I'll switch to netnographic research. I'll take a random sampling of
Moz Q&A threads looking at popular questions in each of the categories that fits
my audience to identify what their needs are. I'll also look at the feature
requests section of the site and finally do some social identification and
listening.


In Moz Q&A there are filters that help with this process allowing me to pull
the questions with the most responses of each of the topics. Unfortunately this
is a relatively time-consuming process because I'll need to double check the
profiles of the contributors to ensure they fit within my basic user / small
business segment. In interest of time I'll only review the first page of results
for each topic looking at only the past 30 days because I'm not sure whether or
not the old private Q&A was merged into public Q&A when Moz made the change.


Next we'll look at the explicitly requested user needs with regard to the Moz
product. The issues and features request section of the site provides just that.
I'm sorting by the most popular feature requests and looking at the top 10.
Again, this may not be completely scientifically sound because I'm looking at
different windows of time for each dataset. Unfortunately, this is a hazard of
netnography, but it's worth keeping track of the dates of posts when you collect
your data so you can decide the range you'll be looking at after data collection
is complete. A lot of this data will be captured in the form of screenshots and
if you're using a tool like SnagIt it will keep track of the URL so you can
refer back.




Then I review the people asking and contributing to the questions to see what
they are specifically talking about.




Since the feature request app is on Zendesk I have to search for people's Moz
profiles for verification.




After this process I've found that the small business owner segment is largely
underrepresented in the feature requests section of the site. Those that do give
feedback are mostly agency, followed by in-house, and followed by independent
consultants or agency owners. Naturally, Moz does proactively reach out to users
for feedback, but the mom and pops that the getListed.org acquisition was likely
to be target are definitely underrepresented in the online conversation I was
able to find.


Roughly, in the order of pain points that had the most business owners, we
have:

Multi-seat accounts - Users have been incredibly vocal for the last couple of
years about wanting to be able to associate multiple email addresses with an
account so multiple users can login. The conversation has gotten a bit heated
because the team hasn't been able to deliver on the timelines due to other more
pressing features, updates and the rollout of Moz Analytics. This was the
biggest issue across all account types, but it was definitely dominated by
agencies. This makes sense because business owners typically will not require
multiple parties to login to their account.
The Value of Moz - Based on the insights I got from the segmentation I went
into this exercise I assumed the biggest pain point would be in a small business
owner not understanding the value of Moz.
These users seem to understand that there is some value in the Moz toolset,
but they can't quite justify the expense when they are a small fry.



Moz iPhone App - Some People want at least top line metrics from Moz
Analytics and Whiteboard Friday in a native phone app.



Cloning / Altering Campaigns - Users need to be able to make changes to the
domain name in accounts and not lose their historical data






Analysis of More Competitors - Users need to compare more than 5 competitors.
Some are asking for as many as 15






Moz Link Manager - Some users appear to be big fans of the toolset, but wish
it had features of other tools so they could just use Moz for everything





From this I've found some specific user needs and validated that there are
indeed users within the demographic that the Display Planner reported.


The next step is social listening. I'll be leveraging free tools with keywords
identified in the user needs collection phase for this, namely Twtrland and
Twitter Search. Normally, I would have used Discussion Search, but it seems like
Google killed it recently. Luckily Twitter Search allows us to search by
sentiment and return tweets that have questions. The negative sentiment filter
is a bit of a joke though because it just looks for a frown smiley face rather
than performing sentiment analysis.


I'll keep it simple and search for tweets with questions.




Immediately I find a user within our target group is asking for feature. It's
good old Justin Briggs asking for improvements to the workflow. Justin is no
longer a small business owner, but was until recently so I'd consider his
feedback valid. However this reveals my bias and context so I will dump it.




Further searches through the tweet with question marks reveal more ephemeral
questions regarding the status and uptime of Moz. However that's an insight in
and of itself, Moz should do a better job of making the Application Status
experience more visible. It took me 10 minutes to remember where it was and I
couldn't find it by searching.


My next step is to review the users that fit my demographic data to look for
commonalities. In this case I can use Twtrland to look at that specific subset
of Followers. Twtrland has filters that allow me to set the gender, the age
range and whether or not the user is an entrepreneur.




I'll also take a quick peek at Graph Search on Facebook to see what type of
people it returns when I look for Men who are not my friends and li

You may view the latest post at
http://feedproxy.google.com/~r/seomoz/~3/KoYbrPtkO4Y/personas-understanding-the-person-behind-the-visit

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