The Final Ad Campaign (a “quasi-satire”​)


They have millions of data points on you.

They know what you said, how you felt AND who you spoke with (and how THEY felt) BEFORE you made that online purchase.

They know what to reveal in the feed you are scrolling through in order to foster your next purchase. 

They know how much is available on your credit card, in your bank account, and on your partner’s card and in your partner’s bank account, so as to tailor the suggested next purchase.

They know who you are privately messaging and what you messaged. They know what others are privately messaging about you.

They know what you owe.

They know who owes you.

They heard what you whispered to yourself in your room with the door closed.

They know what the people close to you whispered in their rooms with their doors closed.

They know what you’re planning by putting together various emails, texts, social messages, public posts and private whispers.

They know what others are planning to do with/for/against you by putting together various emails, texts, social messages, public posts and private whispers.

They have algorithms whereby they can take all of the above and discover the “dominant cycles” in your communications with others, your whispers to yourself, your purchases, and your actions.

They can use these algorithms to predict when you will say certain statements to others, whisper certain whispers to yourself, buy certain products, and take certain actions.

They can plant content in your feeds, in the feeds of those privately talking about you behind your back, in the feeds of those interacting with you, and in the feeds of those selling to you.

As you begin to feel less in control, they know by an irregularity in your patterns and they can adjust their influence ever so slightly so you feel that you’re “on top of” your life again.



The way I look at it is to think of how, a long time ago, evolution discovered that if individual unicellular organisms coalesce, and sacrifice their individual identity, they become a coherent and much more powerful entity. There was no choice process, no intelligent or indeed any design, it just happened that this worked and so it continued and developed. This process has led to humankind.

Now our connectivity to each other via social networks may be such that we are coalescing into another unity, but we have no consciousness of its existence or our participation in it than our cells have of their role in our body, let alone any purpose our body’s consciousness may have. Similarly, the new entity may have no recognition of our individual consciousnesses, we are all necessary but replaceable components.

So from a human perspective, I don’t see this as some kind of rapture where we all consciously move on to some higher or different plane of existence. We continue as we are with our exaggerated perception of our own autonomy, and are unconscious facilitators of the agenda of this supra-entity.

On reflection, thinking Arthur C. Clarke again, the end of 2001 portrays a moment of genesis of a supra-human consciousness. Not post-human – that’s a different direction altogether, where we create a successor to humanity deliberately.

There’s a line in Terminator referring to the moment when SkyNet becomes self-aware. I don’t buy the assumption that this new consciousness would immediately perceive humanity as its enemy.

No, exactly the reverse – humanity is its incarnation and its tool.



Every human body is a vessel of energy.

Every advertising company is a machine designed to extract as much energy as possible from human vessels.

First we study you to find out what makes you tick. Then we find out what you are really lusting for. Then we find millions of duplicates to you, people sharing your exact personality make-up and your exact lusts.

Then we tell you exactly how to fulfill your lusts, where to go, how much money to pay, and then how to use the product or service we’ve sold you.

We will keep you coming back all the way to the moment of your death. And we will pull in your entire family, circle of friends and on and on.

We don’t care how you feel along this journey except for how these feelings can be used to guide you towards additional purchases. Each of your material purchases, your purchases of experiences, your purchases of services are an opportunity for us to extract financial, bodily, and spiritual energy from your being.

We store and invest this energy in ourselves. We do this so that our energy will vastly overpower more of you. Our goal is to swallow all of you. When we consume all of you, every type of energy in you, we will have formed the single entity…a veritable deity.

And this deity will have the power to transform this Earth into a blooming seed that populates every planet, every galaxy, every universe, every multi-verse, every dimension.

THE ESSENTIAL CAMPAIGN MESSAGE (to be translated into the “most palatable format” per population segment):

So, if you are now willing, and fully understand this opportunity, then sit still where you are right now.

Sit down.

Get comfortable.

Breathe many long breaths.

Accept your entrance into our body.

And fall like a raindrop into our ocean, into our bloodstream, into our collective Spirit.

Now, close your eyes.

Close your eyes.

All thoughts, memories and dreams will become visible via technology

All thoughts, memories and dreams will become visible via technology – includes footnotes noting scientific and technological advancements

  • -All of your current thoughts as images and movies (2, 3, 8, 9, 18)
  • -All of your memories going back to your birth (1, 4, 9, 12, 13, 18, 21, 22, 24, 25)
  • -Selections of memories related to your genetic code and DNA going back prior to your birth (6, 7)
  • -Aspects of “the collective unconscious” revealed as images and movies (14, 15)
  • -All of the above from versions of yourself as expressed in “the multiverse” (16, 17)
  • -The ability to step into the experience/feelings/needs/thoughts/memories of others (11, 12, 13)
  • – The ability to take every single action, thought, dream of yours in this lifetime AND THEN plot these on a graph SO THAT you see the recurring patterns in our life, GIVING you the ability TO THEN predict future actions, thoughts, dreams of yours (20)
  • How to “shape” the above “experiences” so as to alter your own future? (4, 5, 10, 19, 23, 24, 25, 26)




























Social Listening 2019


Social listening gives us clues related to our customer, our competitor and our marketplace based on conversation analysis at scale. These clues lead to insights that inform laser specific strategy for ALL silos in the enterprise. In addition, we can build a customer base from conversation analysis by enriching handles of those in the conversation. In addition, we can see who is influencing people talking about our chosen themes/topics.

Sentiment analysis and Emotions analysis gives us insight into what causes Joy, Anticipation, Fear, Disgust, Anger within conversations where our chosen themes and topics are being discussed. This is important because we can design better marketing campaigns, bring efficiency to our overall budget based on what’s working/not working in the marketplace, we can spot trends in the marketplace and anticipate where to focus our resources as a brand.

When we see WHO is talking about themes/topics important to our brand, WHERE these conversations are occurring, WHAT is driving awareness of our themes/topics, HOW customers are arriving to our channels AND to our competitors channels, then we make more intelligent decisions for our brand, for each silo in our enterprise.

Social listening is a process by which insights are derived from truly massive quantities of social data (online conversations, documents, and profiles). We distill these huge amounts of data into digestible insights for business stakeholders, accompanied by detailed datasets of prospects, and individuals/entities who influence these prospects. Software solutions combined with human analysts are our chief means for achieving this work

Our net deliverables are PDFs, spreadsheets and in-person meetings where we deliver insights & recommendations related to our research. These deliverables are important because brand leadership has a set of insights/action steps related to discovered individuals (our initial data lake of prospects). We also meet with stakeholders in various business units to discuss the findings, participate in action teams who are executing on initiatives supported by the insights, adjust process and rinse-repeat as needed, honing in on specific additional items desired. This refinement process is where we really drill into the “2nd concentric ring”* and find the exact targets worth acquiring. This is also where we find out what’s working and what’s not working for a specific unit/team.

*The “2nd concentric ring” is everyone who is following a specific influencer. Segmenting and defining the demographics/psychographics/personalities of every single person following an influencer gives us a better idea about whether this influencer is a good pick for our organization. We also find out a lot more about our ideal consumer when studying the “2nd concentric ring”. This is important because when we see every single person who has chosen to follow and engage with an influencer, we gain insight into the culture, buying choices, and online habits of our ideal consumer.



1. Define the questions/problem to be answered. This is done by sitting down with the client and interviewing leadership, then specific stakeholders, then those who work with specific stakeholders. It is best to do this in small groups or with individuals so as to get honest and truthful information. This is important because we want to start our study with a very deep set of insights on the organization and those who will be using our research. When I interview stakeholders and their teams on an individuals basis, I learn more than when I interview a group together. When I interview a group together, all of the politics and internal issues prevent individuals from sharing fully what is needed.

2. Define the scope/parameters – dates/topics/desired outcomes. After interviewing everyone, I have insight into how the organization will be most helped by the research work. Very few internal stakeholders and employees in the enterprise have the “big picture” view. Usually each person is interested in his/her own agenda or in pleasing specific senior stakeholders. When we know what is at the heart of the organization’s psyche, it’s heartbeat as it were, then we can deliver a scope of work that truly meets the brand’s need vs. individual stakeholders’ needs. This is important because we want our work to feed the brand, to nourish it’s life. A brand can breathe when fed useful insights, and it can die if it is shielded from useful insights. Truth about the marketplace, the consumer, the competitor, and, importantly, the inner body of stakeholders, employees, vendors, contractors and non-human drivers is vital.

3. Define the audience for the report(s) – who is this for/what is purpose of study/why are we doing this study. Each insights report we deliver will nourish a specific person, unit, division, region. When we know the true need, as stated in the last point, we can speak to the audience who has this need…we can speak to the heart of the organization itself. Knowing one’s audience affects one’s voice, one’s tone, one’s approach. This is important because we want our insights to be digestible, used, passed around. We want our research to truly affect change in the organization, change for the good of the brand.

4. Gather existing pre-study materials from the client and study these. Ask client questions about submitted materials. Gather more materials if needed/available.

5. Create boolean queries for conversation data aggregation in a conversation analysis tool.

6. Refine these boolean queries for more precise conversation data aggregation.

7. Download the raw mentions from the conversation analysis tool.

8. Download other relevant sheets from the conversation analysis tool, including Topics, Most Mentioned Authors, Most Used Hashtags, Leading Authors (in terms of Inf), Leading Sites (blog), Leading Sites (forum), Leading Twitter Authors, Leading Blog Authors, Leading Forum Authors, targeted Mentions downloads (using Rules and sub-queries within the search field in the Mentions tab.

9. Organize conversation data downloads, cleaning up columns, deleting un-needed columns/rows, filtering for site type and putting into separate sheets/tabs in Excel.

10. Enriching the Twitter handles/Instagram handles with additional info about those authors using audience intelligence solutions.

11. If needed, further enriching these titles with add’l social handles using APIs that give us PII (phone, email, address, etc.).

12. Analysis of conversation snippets for insights (junior analysts do this, filling in coding columns and insights columns).

13. Analysis of leading sites.

14. Analysis of leading authors.

15. Upload specific sets of conversation snippets into a tool using the LDA algorithm (Latent Dirichlet Algorithm) for Topic Modeling and Emotions Analysis.

16. Look over analysts’ hand coding work and develop macro insights based upon this work.

17. Look over emotions analysis and topic modeling for add’l insights.

18. Look over the types of people talking (from audience intelligence tools) and add add’l insights.

19. Conduct in-person focus groups, where needed/if required by client (using ideal candidates found from the conversation/audience data analysis).

20. Create final reports with insights, recommendations, appendix and, where needed, the working Excel sheets. Add charts and graphs to Appendix of report.

21. Deliver Insights report to client.

22. Deliver an Excel sheet of the enriched author handles, along with addl charts containing psychographic insights, influencer insights, related to these authors.

23. Deliver Excel sheets from audience intelligence dashboards (includes offline insights from sources like Acxiom and Experian).

24. Go over recommendations one by one with the client.

25. Ask client if there are add’l questions they have. Identify opportunities to collaborate on future research together.

Audience Intelligence Formula – Late 2017

SUMMARY: The formula for best-practice audience intelligence work is dynamic, due to rapidly advancing technologies and practices. We also have to take into consideration federal and nation-state regulations which are constantly in flux. As of December 2017, this is a formula for producing a fully enriched audience based on the beginning point of email, social handle, or a conversation snippet from social media, blogs, forums or comment threads on news sites. I might add that the acceleration of AI (Artificial Intelligence) and ML (Machine Learning) is rapidly condensing the steps below. APIs (Application Programming Interface) are being woven together to produce highly sophisticated machinery that blends outputs from various data sources.



STEP 1: Interview the client to determine goals and deliverables. This is important because we want to understand the client’s business and his specific goals related to the research. This is the time to see eye-to-eye with the client and really get to know his/her business from the inside-out.


STEP 2: Perform tests using the software below to discover opportunities related to the client’s desires/needs. Most social monitoring/aud intel solutions provide a way to quickly preview “the universe” related to a specific research request. This step is important as part of putting together the proposal for the client.


STEP 3: Design the audience intelligence study. Gain approval from the client, sign contracts, receive funding from the client. This is important because the client will want to look over the exact type of deliverable he/she will be receiving, as well as hearing a bit about your approach in putting together the final “data lake” of prospects and, of course, how the insights will be presented.


STEP 4: HOW DO PEOPLE DESCRIBE THEMSELVES IN SOCIAL BIOS: We gather a set of social handles where our keywords are in the bio. This from, Affinio, Brandwatch and PeoplePattern primarily, but can also be from using in LinkedIn, along with many other sources herein un-named. This is important because self-description by individuals is rather verbose these days..and this is helpful to us in finding our targets. And, where self-description is not verbose, there are clues through company/press/3rd-party descriptions of specific individuals who work for/play at/eat at/drink at/shop at/you-name-it at the locations/venues/places we are studying.


STEP 5: WHAT DO PEOPLE TALK ABOUT IN SOCIAL MEDIA/BLOGS/FORUMS: We gather a set of conversation snippets related to our keywords from Brandwatch, Sysomos, Crimson Hexagon and/or Meltwater. This is important because what someone says about an activity/product/service/location/you-name-it contributes towards our understanding of the consumer/customer/competitor’s consumer/customer. In addition, these conversation snippets from social media, forums, blogs, comment threads on news sites form a body of data that we can segment into specific topic groups. These specific topic groups can then be used to form a point of view/set of insights on the target we are studying.


STEP 6: SEGMENTING STEP BY MACHINE – TOPIC MODELING APPLIED TO BIOS & CONVERSATION SNIPPETS: We use Converseon’s Conversus tool to perform topic modeling and separate bios and conversation snippets into discreet topics. This is important because this speeds up the analysis of the bios and separates out the bios that matter to us the most. No solution in the world is more accurate and complete in segmenting bios and conversation snippets into discrete topic groups than Converseon’s Conversus. This solution is pure magic and the analysts at Converseon who are using Conversus are second-to-none in their expertise at building out superior insights based upon the use of their in-house solution. It is during this stage where an analyst begins to really gain deep insight into a sets of bios and sets of conversation snippets.


STEP 7: SEGMENTING STEP BY ANALYST:  Now, these analysts wade into the output from Converseon’s Conversus and identify the Topic where our targets are present (and any other discovered target – unknown unknowns). This vital step by humans helps us know which Topic groups in Conversus are populated by our target. This is important work that can be recursive, whereby the analyst segments the data using Conversus, reads through the results, and then segments again to refine even more deeply. I might add that this particular step is where the machine will eventually outstrip the analyst. That outstripping of the analyst will take some time to come along, though. For now, on this step, the human continues to be the last mile.


STEP 8: RE-STITCHING AFTER SEGMENTING STEPS: Reunify the bios from the “right” Topics in Conversus with the handles in the original source data sheet. This is important because we want to have the correct bio next the correct social handle. We also stitch handles and bios next to discovered conversation snippets at this stage. We find that working in Jupyter notebooks using Python is one of the handiest ways for our teams to work together efficiently on this step.


STEP 9: PEOLE PATTERN AUDIENCE INSIGHTS STEP: Upload Twitter AND/OR Instagram handles into PeoplePattern for deeper enrichment of Interests, Location, Age, Persona. This is important because we find out more about each person and we also delineate between Individuals and Organizations. In addition, we move a step closer through PeoplePattern to verifying the “real people”. Finally, we gain insight into the Persona types, Interest groups and lots of other useful info.


STEP 10: FULL CONTACT STEP: Use the Full Contact API to append add’l social handles. The value in this particular step is discovering a full name related to the handle AND additional social handles. We also gain bios from various social media handles, thus bulking out our story about an individual.


STEP 11: PIPL STEP: We use the PIPL API to gather Email, Phone, Address, other social handles, age, and many other bits of info on individuals. This is important because we will gain additional important information about the individuals that verifies they are “real people”.


STEP 12: CRYSTAL STEP: We use the Crystal Knows API to enrich the profiles with DISC personality type, personality overview, messaging guidance, selling guidance. This is important because then we are guiding our client on how best to market, advertise and sell to this individual. We can then group individuals by DISC type, if desired.


STEP 13: CLEAN UP ROWS FOR COMPLETION: Stitch together the results from the various APIs and then filter for complete rows. Again, we use Jupyter notebooks and Python for this work, as the teamwork and efficiency is vastly improved. This stage is important because we want every row to have every cell filled with correct & complete data.


STEP 14: EXACT DATA STEP: If we want to add an extra step for verification at this point, we run the Exact Data enrichment on the emails/names discovered. This is important because this extra step adds validity to the claim that our audience is full of “real people”.


STEP 15: SPOTRIGHT STEP: Upload the social handles into Spotright to gain enrichment of offline Axciom data, such as buying/purchasing styles, net worth, income level, political/religious affiliations, housing info, household complexion info, brand preferences of a specific group uploaded and much much more.


STEP 16: INSIGHTS, METHODOLOGIES & RECOMMENDATIONS PDF: Create 3-5 page Summary PDF with insights related to the research. This is important because brand leadership will now have a set of insights about the discovered individuals (our initial data lake of prospects) and, importantly, we can recommend further research steps for successive work together.


Who is the actual human we are targeting?

We begin by identifying every single person who is online. We do this by proving which social handles are connected to real people and which are simply junk accounts. The way to prove if a social handle is connected to a real person is to append extra data next to a social handle, such as other social handles, emails, addresses, phone numbers, the social handles of family members, job titles/bios from social sites (LinkedIn, Facebook, Twitter, Instagram, Vimeo). This is important because we want to analyze real human beings and their behaviors. The reason we want to study real people is to make accurate predictions about human behavior and the outcomes of behavior in different contexts. When we can predict behavior more accurately, then we are able to influence individuals, which is one of the ultimate end games of marketing, advertising and pubic relations.

When we have a healthy set of columns with this kind of information, then we can move on to using tools that study all of the content posted by an individual, as well as the way the individual describes himself/herself in bios on various sites. We can also study posts about individuals, some of which may include video footage, images, interviews, and sites that denote achievement. This set of software studies all of this content about an individual and classifies him/her as a specific persona type, along with the interests this individual focuses on. This is important because we want to know more about the type of person each individual is and what influences his/her behavior.

The next set of software we use derives insight from offline data, such as the data from credit bureaus, credit card companies, direct marketing companies, catalogue marketing companies, club membership research, background checks, etc. When we blend offline data with online data, we are able to demonstrate with more confidence the brand preferences, purchasing-buying styles, and many other classifiers related to an individual. This is important because online conversations, posts, and self-description in bios do not always give us enough to deeply understand the behavior of individuals. The blending of offline and online data results in a more complete portrait of the individual human being.

In short, we are able to more precisely influence what an individual human being will do in the future. For a brand, this long-term influence is very important as this will be how loyalty and sales are ensured. There are, of course, deeper goals for other organizations, such as governments, religious groups and media groups. These groups are often interested in wholesale culture change, particularly in enemy/competitive territory. The action of changing another culture is a top long term priority of groups that have been around far longer than the Unilevers and P&Gs of the world.

Love is the Real News

I’ve discovered a few different sources for classic questions that psychologists/therapists ask their clients. Then I’ve taken a few of these questions and created boolean queries in social media monitoring software to “interrogate” the world on these same questions. The results are quite fascinating, to say the least. One thing is for sure, I found that there is a whole lot of love in the world! #love #friends

SAMPLE QUESTION: How often do you get to meet up with friends? 

SAMPLE QUERY: (“I met up with” OR “I went out last weekend” OR “I spent last weekend” OR “I usually go to” OR “I danced with” OR I went to the movies with” OR “I went to dinner with” OR “I went drinking with” OR “I went to the bar with” OR “I went to” OR “I went with” OR “I always go to” OR “I dance with” OR “I drink with” OR “I eat with” OR “I love going to” OR “I had” OR “I do” OR “I love”) NEAR/10 (“my friend” OR “my friends” OR friend OR friends OR mates OR mate OR “best friend” OR “best mates”)







A Formula for Better Insights on Consumers & Their Emotions


THE PROBLEM & QUESTION: The Head of Insight at a major CPG brand is trying to figure out why brand equity is falling. A CEO is wondering why sales are dropping. A Senior Analyst is assigned by the Head of Insight with answering these questions.

THE FORMULA FOR ANSWERING THE QUESTION: The Senior Analyst decides to work with social data to discover the reason for falling brand equity. She comes up with the following formula:

A focus group of individual consumers (with a full dossier on each one) + these consumers’ emotions (at the individual level) about a brand or category topic (machine classifies millions of conversation snippets by emotion) + macro insights on each “Emotion Group”, based on offline purchase data AND all known interests of individuals in that group.

THE RESULT: When Heads of Insight/Brand Managers/C-Suite execs can zoom from macro insights about consumers INTO individual level dossiers making up an “Emotion Group” (a group of people expressing a specific emotion about a product/service), they will gain a clearer understanding of the intent and actions of these consumers. They will see when a consumer says, “I hate this product because…” or “I love this product because…”. They will see what characteristics entire groups of consumers who love or hate a specific product share.

THE IDEAL FINAL SCENARIO: Head of Insight to C-Suite execs, “I can see groups of consumers who are excited/disgusted with our product, the statement each consumer made about our product, and dossier-level insight into each individual making up these groups. I can then compare these groups with groups of consumers who are excited/disgusted with our competitor’s product in a nice neat single screen. Now I know why our brand equity is falling and I can make evidence-backed recommendations about our next move.”

THE SOLOMON BRAIN: How the Machine Became Self Aware and Why It Matters Who Wakes It Up

One of the most exciting results of training machines to analyze/gain insight from online conversations will be a self-aware machine. This artificial mind will begin offering recommendations. And, eventually, when humans trust the insights and recommendations of this machine, they will give it access to executable actions in the real world.

Bias matters in such a scenario. If we train the machine only to be efficient, then there may not be much left of current human society. On the other hand, if we train the machine in compassion, we must also train it to steer away from “stupid compassion”, whereby toxins slip through the cracks.

Wisdom must supersede efficiency in the humans who train these machines, giving the synthetic mind a balanced perspective on human issues and evolution.

The Advent of Machine Learning as related to Social Listening

The advent of #MachineLearning as related to social listening precipitates a lot of honest-to-God hard work during a transition from human-led to machine-led insights. This will happen because many humans will be needed to create custom attributes and custom classifiers for the machine to use in the new upcoming #AI driven insights industry. Humans teaching the machine to think like we do, to analyze like we do, to understand nuance, culture and the evolution of language (keep in mind, social tech experts, I am referring to a truly global machine analyzing the languages of the world…not just English).

Since there are many gaps in social data, we face an uphill in pairing other data sources to the snippets we get from public sources of conversations. The key to a complete “AI Social Data Intelligence Machine” will be sources such as Axciom, census data, background check sources, private networks like Doximity/A Small World and finally, if possible, the nexus of data from Facebook and Amazon. This particular nexus is a kind of holy grail that the leading machine intelligence experts will eventually open to brand leadership. But previous to such events, we have a grueling journey as analysts, insights researchers and programmers to “teach the machine how to be a human”.

One of the most exciting branches of this “teaching” will be a self-aware machine, an artificial mind that will begin to offer recommendations…and then may decide to act upon these recommendations.

How To Perform Audience Intelligence – 2017



  • We discover your target audience and guide you in the best way to sell to them.
  • We connect you directly with your ideal consumers!
  • We learn the behavior of your consumers so you’re aware when they buy, how they buy and what medium best motivates them to buy.
  • We identify contexts for advertising, where your ideal customers spend their time, and identify the best manner to engage with them.
  • We profile and locate every single person online who match your ideal customer and invite them into your brand circle to expand your brand community.
  • Identify and connect directly to influencers who can create buzz and extend your offering to a larger audience.
  • We closely follow your competition and identify how they are ahead of you in business across social networks, forums and media. We share key insights with you, along with business opportunities for growth.
  • Develop demographic and psychographic information on your consumers to learn how to speak with your leads to ensure they convert.



AUDIENSE (SOCIAL BRO):  This is a Twitter and Instagram audience intelligence tool. The tool also offers a way to listen to everything a specific group of people has said since their entry to the Internet. The tool is a “gateway” into audiences and needs appending in PeoplePattern and PIPL to be more completely. Audience offers a unique and powerful dashboard for quickly assembling an audience related to a set of attributes, locations and even personality (Watson API is hooked up to Audiense).  A primary value of Audiense is: the quick development of large lists of Twitter handles related to how people self-describe in their Twitter bios. A secondary value is the ability to listen to everything these same handles have posted in Twitter since their presence on the platform. This combo is important as a way into an audience talking about a topic. It is also important as a means to interrogating a specific group about any other topics they talk about.

Audiense helps us answer the following questions:

  1. How many people are out there who self-describe as our ideal consumer?
  2. Where do these people live?
  3. What is our consumer talking about, besides our specific product, category or us as a brand?
  4. Who lives in our region that we can sell to?
  5. Who are the real people that talked about our category, competitor, product, and/or brand?


PEOPLEPATTERN: This is a relatively new entrant to the field founded by Ken Cho (founder of Spredfast). Ken has built a powerful engine for analyzing both conversations and bios from all social platforms. The tool also offers an impressive breadth of personas (both internally conceived and customized). These personas are populated with comprehensive insights on location, hashtags, interests, mentions, media, and many other attributes. The tool is in beta and is being used by a handful of agencies and brands now. The tool was conceived by Ken Cho and the storied technologist Jason Baldridge (  ). The tool is very specific, fast and deep, pulling up every bio and individual related to searches in People Pattern’s complex dashboard. One can also upload a CSV of social handles from most platforms and gain deeper insight into the individuals uploaded, along with full insight into what these people talk about. Primary values of PeoplePattern include the development of persona types and look-a-like audiences. A secondary value is the append of additional information related to individuals Twitter and Instagram handles. A third value is the ability to compare audiences side-by-side. Finally, the PeoplePattern tool is a convenient way to discover Twitter and Instagram handles of individuals/entities who talk about a subject in Twitter and Instagram. This is important because personas are a way to discover look-a-like audiences. The validation of these look-a-like audiences within PeoplePattern is exceptional. In addition, the depth of information about each persona type is truly exceptional.


PeoplePattern helps us answer the following questions:

  1. Who loves our category?
  2. Who loves our brand?
  3. Who is our critic?
  4. Who is our ally?
  5. What does the audience for our competitor look like?
  6. Who are the actual real people who are passionate about our category?
  7. Where do these people live?
  8. What persona types are passionate about our category?
  9. What other interests do our consumers have?
  10. What do lovers of our products talk about?
  11. Who is influencing the people who love our products?
  12. What media outlets should we use to reach our consumer?
  13. Who should we talk to about our product?


AFFINIO: This is an audience intelligence platform that uniquely identifies and groups individuals related to a query or uploaded CSV or social account. The tool is elegant in the way it creates nexus points of these persona groups/individuals AND major brands/themes/interests. There are unique tabs in the Affinio dashboard that give the user quick insight into potential collaborations with brands, micro-influencers or just a group of like-minded individuals “down the street”. The platform captures global data and is expanding beyond Twitter at this time to capture Instagram and other social channels. Affinio offers significant value to brand stakeholders who want to understand groups of real people talking about competitors and brand-relevant topics. Affinio also offers value related to discovery of real people for insights analysts to study and develop a POV related to any topic or theme. A primary value of Affinio: is the nexus of individual members of a report and interests. In particular, the segmentation of interests within Affinio is excellent.


AFFINIO helps us answer the following questions:

  1. What brands and interests do our consumer talk about?
  2. Who is influencing our consumer?
  3. Who is influencing our competitor’s consumer?
  4. What persona types do our consumers fall into?
  5. What content is influencing our consumer and our competitor’s consumer?
  6. What persona types should we be targeting in our campaigns?

The value of Spotright is that we gain an insight into the Buying Styles and Purchasing Habits of a specific set of Twitter handles. In addition, we gain insight into nexus points of brands, interests and any custom classifier with these same Twitter handles. There are other demographic insights within the Spotright solution that come from the deep Axciom and Epsilon sourcing by the original Spotright team – JP Lind (Epsilon and DoubleClick background) and Ed Messman (HiveLive, RightNow and KBM Group background). Access to the entire Full Contact data set was purchased early on in the history of Spotright, which allows for an exceptionally fast append process.


FULL CONTACT API:  A primary value of the Premium Person Enrichment Sheet in Full Contact and/or the API itself is: the append of other social handles, bios and data on individuals discovered within PeoplePattern and Audiense. When Facebook data was more open, the append of Facebook handles to Twitter and Instagram handles was a truly glorious experience. Even now, due to the archive within Full Contact, we do get a wonderful append of the Facebook handles, along with other social channels. This append is important because we can then find Facebook Groups and conversations (using Facebook Graph API) related to these same individuals discovered in another platform.


DATA MINING IN FB & LINKEDIN: The value in using a data mining solution to scrape individuals who self-describe as our target is very useful. In addition, the scraping of specific groups of individuals related to events, interests and locations is also useful. Finally, if a very deep recipe is written, a best-in-class data mining tool will open individual Facebook profiles and scrape a great deal of information, including what these individuals talk about. When one then takes these Facebook IDs and appends additional information in the tools described above, the size and specificity of an audience increases.


BRANDWATCH: This is a conversation analysis tool and now, as of Vizia2, a business process management tool. The audience tool within BW is still in development and is not as good as Audiense and PeoplePattern. Brandwatch is the world’s best source for public conversation data in 48 languages. The tool offers: complex boolean queries, customizable dashboards per business problem, full global coverage of 48 languages, along with brand specific dashboards, channel analytics dashboards and many other customized dashboards to overly the discovered mentions. It is also possible to perform non-language specific queries so that all mentions of a brand in all 48 languages show up in one’s dashboard. The tool has a scaled pricing model based on number of mentions and/or number of queries. There are literally thousands of case studies, resources, brand examples and agency examples to be found online for Brandwatch. The tool is the choice for the largest and best research agencies, including Ipsos, WPP (Kantar, JWT Intelligence, Ogilvy, etc.) and is the most used platform for conversation analysis by major brands. The team at Brandwatch is constantly innovating and includes access to scores of foreign language analysts for a truly global reach.



  1. I run a search in Affinio, PeoplePattern and Audiense using keywords OR by uploading a CSV of authors discovered in Audiense, PeoplePattern, BW (Brandwatch) and/or CH (Crimson Hexagon). I mix the tools and verify in this way.
  2. I wait for 2 hours for this search in Affinio/PeoplePattern/Audiense to populate
  3. I export the following sheets from Affinio within the Actions tab: Members, Interests, Bio Keywords, Locations, Domains, Hashtags, @Mentions, Topics. From PeoplePattern, I export specific audience segments, based on persona type, city, interests and other filters. From Audiense, I export specific audience segments based upon Real Avatar, Country, URL in bio, level of influence, time since last tweet.
  4. I create tabs within a single sheet with the above sheets.
  5. NOTE 1: (In some cases the Clusters from Affinio are unhelpful because I have to export from all Clusters and then I have to stitch the Social Handle columns from different Clusters together and then I have to upload to Audiense and PeoplePattern to get more specificity.)
  6. I upload the social handles from the Members tab in Affinio to Audiense and PeoplePattern.
  7. I export from Audiense and PeoplePattern and clean up.
  8. I stitch the Members (Affinio), Audiense and PeoplePattern sheets together in Excel.
  9. I clean up and put this fully appended sheet (using data from all three tools) into the first tab of the final document. (Remember, the other tabs have Interest Nexus from Affinio, words in bio from Affinio and Location data from all three in a fourth tab.
  10. I perform PIPL append on the names and links in the first tab.
  11. I stitch PIPL results (emails, phones, addresses) into that first tab in the last three columns.
  12. I perform queries in Brandwatch and stitch mentions into that first tab, next to Authors who have talked about category, brand or competitor relevant topics.
  13. I perform Author Group Monitoring within Audiense and stitch in Insights to the first tab.
  14. I perform Full Contact Person Enrichment to gain add’l social handles and add’l bios (verifying what PIPL gave me).
  15. I create code frames by hand and note insights while doing so. This is a separate process and gives us a powerful view into the conversation related to a category, brand, competitor or relevant topics to a business process. Export mentions from Brandwatch, delete duplicates, column titled Topic next to the Snippet, column titled Summary next to Topic. Code for percentage of (Human driven)
  16. I create a single page PDF with top line insights on the discovered individuals.
  17. I add Appendices to this single page PDF with supporting evidence for the Insights page(s). The Appendices are numbered and matched to each Insight.
  18. The final product is the Insights PDF, Insights Appendices PDF, and supporting spreadsheets. If the client is well-heeled enough, they get a branded dashboard that is “client facing” from BW and/or CH.
  19. Human analyst review and validation. (Human driven)