Niche Gaming Audiences using ActivationPlanner

By | Blog

Gaming has become a more accepted part of popular culture and has seen a massive expansion in the number of players. Up to 65% of American households play video or computer games every year.

The video gamer is no more a nerdy young male. A study by ESA indicates that women make up 40% of the gamer population. While 27% of game players are under 18, 26% are 50 years or older! Additionally, video gamers have genre and game preferences.  As the profile of a video gamer turns from a stereo-typical age & gender to sophisticated and unique traits, segmenting and targeting such audiences becomes a challenge.

Say you are a gaming company looking to target audiences for a new game in the MOBA genre, and you would also like to reach audiences considering Racing & Shooter games. One approach is to find data partners that have gaming purchase behaviors across these genres and run a look-alike-model.   Cost of purchase behaviors would typically be high and it would get diluted by the look-alike-model.

Affinity Answers audiences are act-alike, i.e. audiences that act similar to MOBA, Racing & Shooter game players.  Social media is a great place to find such similar audiences, and Affinity Answers brings these to the programmatic ecosystem. The recently launched ActivationPlanner provides such act-alike audiences, audience characteristics that you can pick and provision for targeting on your platform of choice.

The screenshot below shows how an audience interested in ‘Non-Violent Video Games’ can be defined using ActivationPlanner.

Once defined, you can get a reach estimation for the audience from Oracle Data Cloud and/or LiveRamp. You can even “build the audience” with just a click.

The current version of ActivationPlanner includes pre-packaged audience definitions for ‘Beauty & Cosmetics’ and ‘Gaming & E-sports’ verticals that can be combined with Lifestyles. Next available audiences via ActivationPlanner are from Automotive —stay tuned!

To schedule your demo and free trial of ActivationPlanner, please email us at

Align Beauty & Cosmetics Audiences with Seasonal Events

By | Blog

As summer comes to a close, back-to-school and holiday campaigns will be found among our favorite digital content. It is crucial for Beauty & Cosmetics brands to align with these major events. With a surge in YouTube makeup tutorials and Instagram beauty influencers, finding innovative targeting solutions is key when competing for market share.

Uncovering programmatic audiences who best align with a brand’s strategy is a challenge. As a solution, we recently released ActivationPlanner. This audience planning tool equips programmatic teams with a simple way to search, build and provision niche audience segments for their brands.

Let’s take a look at upcoming seasonal events: Back-to-School and Holiday Parties / New Years Eve. Below you will see how Affinity Answers’ prepackaged Beauty & Cosmetics and Lifestyle audience definitions can be thoughtfully combined to fulfill targeting needs.

Back-to-School Audience: Politically active students who believe reducing their environmental footprint will lead to macro changes. At the core of their beliefs is minimalism, which is reflected in their overall style.

Holiday Parties & New Years Eve Audience: Females who keep up with the latest trends in beauty and fashion. They find inspiration from social media influencers, make a statement with their makeup style and love to travel.

Once an audience is defined, the near real-time nature of the tool allows for quick RFP responses and campaign activation. This includes reach estimations from Oracle Data Cloud and/or LiveRamp and a “build audience” option—both just a click away.

The current version of ActivationPlanner includes prepackaged Beauty & Cosmetics audience definitions that can be combined with Lifestyles. Next available audiences via ActivationPlanner are from Gaming & E-sports and Automotive verticals—stay tuned!

To schedule your demo and free trial of ActivationPlanner, please email us at

ActivationPlanner Audience Definition screen

Niche Programmatic Audiences at Scale via ActivationPlanner

By | Blog

With the plethora of audience insights & segmentation tools available for audience data, there’s an overwhelming feeling of tool overload. All these tools seem to be missing one big element: context of your client’s targeting needs.

Let’s walk through an example. A typical client brief is as follows:

Request: Reach women who lead healthy lifestyles and care about the environment. They believe the beauty products they purchase should be a direct reflection of the overall lifestyle they lead. They are minimalists, who believe that when it comes to makeup – less is more. Increase category share by targeting women interested in competitive brands.

With the audience tools you have at your disposal, it’s up to you to search, discover and combine the taxonomy segments to potentially find the best suited audience for this client brief. We feel your pain!

To ameliorate this, we’ve built ActivationPlanner which puts you in control for such a brief. For a request like the above, we have pre-packaged audience characteristics which satisfy your client’s targeting needs. The screenshot below shows how this audience can be defined.

Post defining the audience, with only one click you can obtain an Estimate of Reach via Oracle Data Cloud and/or Liveramp marketplaces. The near real-time nature of the tool helps you respond to your RFP and close more deals. The tool also allows you to provision the audience to your DSP of choice.

We’re launching this within the context of one vertical: Beauty & Cosmetics. Be on the lookout for the next vertical: Gaming & E-sports.

If you’d like a demo and test-drive of ActivationPlanner, email us at

Mobile Audiences via Adsquare

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Affinity Answers has partnered with Mobile Data Exchange, Adsquare, to deliver US based mobile only audiences.  These audiences are available via Adsquare’s Audience Management Platform (AMP).  In addition, these audiences will also available on leading DSPs like Google DBM, The Trade Desk, MediaMath, Adform, to name a few.  Below are a few screen shots of our Taxonomy on AMP.

Taxonomy verticals, with coverage on Consumer Brands and Media & Entertainment.



Expanse of TV/VOD Taxonomy coverage                     ~7.7MM Device IDs are available to be Activated for the Netflix show, Stranger Things

Affinity Answers is a leading data provider with a unique approach to identifying audiences.  Our AI-based Recommender identifies audiences in mid-lower funnel of their purchase journey.  Audiences that are in consideration stage for Organic Beauty products, a Gaming console, Camping gear and many more.  For Media & Entertainment vertical, our AI-based Recommender identifies audiences that are highly likely to view a specific TV show, movie, attend a music concert/festival, sports event, listen to a specific artist, etc.

View our case studies here.  Reach us at for more information or if you’re unable to find these audiences on your programmatic platform.

How Users Have Embraced (or not) the New 280 Character Length Tweets

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In November last year, Twitter doubled the limit on tweet length from 140 characters to 280 characters.  Because Affinity Answers observes and analyzes consumer behavior across social networks including Twitter (more on how we do it here), we were prepared to observe lengthier Tweets from brands and celebrities as well as lengthier replies to those Tweets.

Sentiment analysis differs when you analyze short messages (such as SMS and Tweets of 140 characters) as opposed to lengthier ones (like Facebook comments or blog comments). What did we observe from this data?

The above chart says that there has been almost no change in the length of Tweets since the character count doubled. What does it look like when we split them across different categories?

Some of our hypothesis’ that brands and publishers would Tweet longer texts and that fans of celebrities and TV shows would respond (reply) to the Tweets with lengthier texts was proven false. However, we observed something else of interest – engagement with Twitter increased.

We were a bit bewildered with this observation, which showed that engagement of users had increased while the length of the tweets or the replies remained the same. The bewilderment was clarified during the quarterly earnings call of Twitter by Twitter’s CEO Jack Dorsey to the question “Is there less confusion for new users since you’ve expanded the character limit?

Dorsey’s response validated our observation in that “…..And one of the things we’re watching for is to see if the average Tweet size would go up as a result, and it has not. People do have — they’re even more seeing less abandonment of Tweets, but we’re also seeing a lot more engagement…..”

In effect, the addition of room for Twitter users seems to have resulted in a lower abandonment of Tweets with the doubling of the character limit the degree of brevity has remained the same. Will that make Twitter a much more dependable social signal?  We will indeed extract and analyze the metrics again in the coming quarter to determine if this the case.  

Under The Hood of the Affinity Answers Platform: Turning Online Social Interactions into Audience Recommendations at Scale

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When Affinity Answers gathers intelligence such as “Consumers interested in The Cheesecake Factory are likely to be interested in Jared Jewelry, Andes Mints and CeraVe”, how do we derive these recommendations?  Hundreds of global brands rely on Affinity Answers’ recommender system in their programmatic media buying because they trust the intelligence from which it is presented.  Powered by AI and through systematic data collection and data science, we turn publicly-expressed social engagements into audience recommendations used in strategy, planning and activation stages of media buying.

Let’s take a deeper look at what happens within the platform of Affinity Answers to power recommendations across 50,000 brands:

Stage 1: Collect the Right Data to Inform Intent

400 million unique users across 45 thousand Facebook pages, Twitter handles and Instagram accounts generate 4 billion engagements (such as likes, comments, replies) every quarter. These publicly available social engagements are at what we refer to as the pre-intent stage of engagement with a brand – when a consumer has expressed interest in a product or service but has not committed to a particular brand.  Collecting data that is a representation of the universe of social engagements with consumer and media brands in statistically significant manner is the primary objective at this stage.

Stage 2: Derive  Meaningful Engagements from Social Data

Of all the social engagements collected, filtering the noise to distill meaningful engagements is the first step. The first filter to gather reliable engagements is Active Engagement, which selects only user responses to brand-initiated actions. Responses to brand-initiated actions are observable and are less impulsive, which tend to have greater relevancy for branding at the pre-intent stage. Affinity Answers filters active engagements to gather intelligence and the course of this, we have observed that only around 10% of engagements of the total collected make the cut. What constitutes the 10% that is deemed useful?

  • Recency: Affinity towards brands change over time. The recent behaviors are weighted greater than those in the distant past.
  • Frequency: Hyper-activeness can be classified as ultra-casual users who give away a social acknowledgment such as a like or a comment without much consideration. On the other side of the spectrum are users with isolated engagement with one entity. While one is a bot, the other one is socially reluctant – both are of no use and are treated as outliers. See here on more about bot behavior that we have observed in our data.
  • Intensity: For a post about a new coffee by a Cafe, the comment “The coffee is packaged well but is absolutely useless” has the right subjectivity but a negative polarity. A post to sell one’s old car such as “I have an awesome BMW to sell; meet me at this cafe” – is a comment with positive polarity but poor subjectivity
  • Type of Engagement: Differentiating active engagement from the passive ones is like separating the wheat from the chaff. An observable, positive user response to an observable brand-initiated engagement is separated out for treatment. In the case of Facebook, comment on a content post made by the brand is considered active engagement as opposed to a mention of the brand out of context.

Stage 3: Generate Cross-Brand Audience Recommendations

At this stage, we have identified the strongest possible engagement indicators between users and brands. We have observed that on an average, each user expresses positive engagement with 9 brands (out of several thousand). That means, there are plenty of unexpressed interests. Unexpressed interests cannot be treated as dislikes. Mathematically speaking, we have a matrix like the one below, the only difference being that the number of rows runs into the hundreds of millions and the columns into the tens of thousands.

The challenge is to turn a matrix like the one above into actionable intelligence such as the matrix below:

# Score indicates the likelihood of a user with known interests in one brand to exhibit interest in the other

Social users express preferences across a huge set of brands, celebrities, sports, movies, authors and several other items. Based on the expressed interests of a particular user, we have built a recommendation system that generates what else could interest the user. How do we do that? We have built a recommender system which finds similar users and recommends what they liked, like that of or the Netflix Prize fame predicting movie ratings, which is a popular technique known as Collaborative Filtering.

What does the Affinity Answers recommender system do differently?

  • Cross-domain recommendations: Derive unprecedented cross-domain recommendations, for instance, audience consumers interested in Game of Thrones can be recommended a particular brand of bread; this can be used to expand the reach beyond the usual suspects of demographics and same-category brand audience.
  • Audience prediction: Recommendations to predict an audience interest based on known interests; this can be used to find pre-intent audiences at a scale that can be targeted across any programmatic media.
  • Scored recommendation: indicating the likelihood of a user with known interests to exhibit the recommended interest; this can be used to find the act-alike audience that best matches the campaign requirements.

Affinity Answers has mastered harnessing the power of turning the social graph into a scalable and digestible platform that brands and agencies rely on for planning programmatic media buying campaigns. We are constantly innovating the technology and our product suite to make this intelligence accessible to marketers globally and are looking for people that are as passionate about what we’re building to join our product and engineering team. Visit our career page for more information.

Marketers: Take Back Control of Digital Investments Through Buyer-Driven Branding

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There’s no doubt we are in the midst of an exciting, dynamic era in marketing – an “always on” consumer culture and new technologies to apply to the customer experience are among the opportunities at our disposal.  Yet as the saying goes, “water, water everywhere but not a drop to drink” — Marketers are data rich in direct response with search and retargeting while data-deficient when it comes to relevant branding in the digital space.  Media monopolies like Google, Facebook, Instagram and Amazon are commanding the lion’s share of digital budgets while keeping major control of targeting and relevance hidden in their “walled gardens”.

So, marketers are limited by the lack of insights and knowledge at scale of their 1P audiences that their media partners are holding onto – instead relying on lookalike audiences for scale at the detriment of performance.  These media monopolies’ “walled gardens” are hampering the ability to attract new consumer audiences higher in the purchase funnel – and causing unnecessary waste in media spend.

What’s a Marketer to do?

The Marketer’s New Swiss Army Knife: Buyer-Driven Branding

Marketers have mastered buyer-driven direct response through search and retargeting which have proven very effective.  But what about applying such buyer-driven data tactics to branding earlier in the consumer journey as well?  Buyer-Driven Branding puts existing brand buyers to work to capture new levels of knowledge about their behaviors and interests in areas such as pop culture, entertainment and music for more effective branding outcomes.  Buyer-Driven Branding identifies new target consumers who are emotionally similar to current category buyers – higher up in the purchase funnel where brand consideration is occurring.  Even more, marketers experience new levels of efficiency in their media buys – applying what is gleaned about a category buyer on Facebook for buys on Google, Instagram, NBC, etc.

Buyer-Driven Branding Put to Work

Imagine you are the category lead for Color Treated Hair Care at Aveda®.  You know your existing brand buyers and their preferences and purchases – but how do you effectively find more category consumers who would be as passionate about the brand?  Or those you could conquer from another category competitor like Aveeno®?    Working with Affinity Answers who provides unique access to social consumers, you can identify entirely new audience segments that share emotional alignment with your brand’s category buyers in what we refer to as “Feel-Alike” audiences.  Then put those Feel-Alike audience segments to work through Buyer-Driven Branding process at scale across social, search, TV, etc.

Audience Enrichment Comes in Many Flavors

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The idea of Audience Enrichment has been around for decades in the list-rental business, but it means much more in the offline/online world of data today.

It is no longer theory or even past tense; It is reality. Technology rolls on and affects all industries and in turn, how we do our jobs. In marketing & media, you can trace a path from direct marketing analytics to digital marketing analytics to the current machine learning-led predictive analytics trends we see shaping business operations today.

The progression has been led by advances in computing power, storage capacity, and advances in data science.

As prescient as Gordon Moore was when he made his 1965 prediction about the future of computing power, the announcement of the Netflix Prize in 2006 generated innovation & collaboration around applications of big data, the likes of which had not previously been seen in marketing analytics.

Even with the advances in data science, when it comes to data, more is not necessarily better. Marketers understandably want to make the best use of their own data, regardless of the quantity or quality of third-party data availability. So how can you increase the value of your data?

There are opportunities for marketers to strengthen the value of their data through various approaches, but it is important to consider the trade-offs associated with each type of enrichment.

  1. List Appends via Direct Match:

A deterministic solution. Additional variables are appended to your user-level data to create more descriptive profiles of users. These include attributes such as demographics, auto registrations, lifestyle, and category interest data. Many of the sources used are publically available and can be enrich via deterministic matches.


  • Massive scale can be achieved
  • Match is typically done against a total US adult file of 240mm records with physical addresses
  • Offline data is the primary source, which is not refreshed as frequently as online data
  • Demographic and lifestyle data is widely available and different sources do not always match each other
  • Data must be on-boarded to digital ID’s in order to make actionable for digital campaigns
  1. Digital Matching via Persistent Digital Identifier

A deterministic match using some form of a digital ID such as a login to an online account, cookie or device ID.


  • Less-scale as match rates to other digital ID’s will lower total number of users from the source file
  • Allows for greater insight into the consumer journey via cross-device graphs
  • Cross-device graphs enable digital targeting & activation
  • Sharing 1P data creates an opportunity for data leakage, data ownership rights need to be explicit
  • Enrichment is limited to known attributes from web-browsing, app usage, location data, etc.
  1. Look-A-Like Models (LAL) using Online/Offline Data

A probabilistic approach providing additional scale to find buyers that look like your target audience. Seed data, otherwise known as the truth-set is modeled. New users, fitting the profile of the truth-set are identified and appended.


  • LAL models can be quickly translated for use in digital campaigns
  • Requires common data between truth-set and larger data pool.
  • More data in common produces better models
  • Demographics are widely available across data-sets but are known to be poor predictors of behavior
  • Truth-sets often consist of loyal or high-volume buyers, which means non-buyers are excluded
  1. Social Affinity Audience Enrichment

A probabilistic approach relying on public brand engagements across social platforms.


  • Massive global scale with tens of thousands of brands eligible to be used for enrichment
  • Social affinity learning can be transferred to online & offline data
  • Uses same recommendation engine as Netflix and Amazon
  • No digital ID or sync required
  • Mapping process of taxonomy is a manual process that can take one to two weeks

The Affinity Answers approach to enrichment puts consumers first. It highlights details previously hidden from view, expanding your data’s potential. For example, Dunkin’ Donuts can leverage their fans interest in Blake Shelton and the Baby Bump app for insights and planning, but not if these insights remain hidden.

Contact us to uncover what’s hiding in your data.

audience enrichment

Audience Enrichment: What’s In Your Data?

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A POV on Audience Enrichment and Finding the Hidden Gems in Your Data

There are so many questions that marketers have about their data. A quick Google search of “too much data” yields the following results:

“Do you really have big data, or just too much?”

“Why too much data is stressing us out”

“5 reasons too much data can be risky”

What does this mean, that less is more? No, that is an oversimplification. CEO Sree Nagarajan authored an op-ed that discussed the pros and cons of probabilistic vs. deterministic data. The main takeaway is not to gather the most data. Nor is it about stripping it down to the bare essentials. The key is choosing the right dataset.

The problem is that this is not an all or nothing proposition. First-party data, while based on actual consumers, has limits in its scale. Third-party data casts a wide net, but is often not the best approximation of your consumers.

Consumers are not identified by a device ID, or a single attribute. Consumers are multifaceted people with multiple identifiers. That idea is best served by audience enrichment via social affinity. It fills in the gaps in consumer profiles, finding your best prospects – buyers & non-buyers – at scale.

Social affinity runs the table differently when it comes to audience enrichment. It does not match data records deterministically. Instead, it makes predictions based on the data that a brand already has. For example, Spotify has information on all of its 140 million users. It knows which listeners have listed songs by Seal and Post Malone as favorites. Using these attributes, among others, social affinity predicts these users are likely ideal prospects for Rolex watches. Spotify could use this information in its targeting and partnership efforts.

AffinityAnswers CEO Sree Nagarajan recently sat down with Adotas to talk about social affinity. He ended on what marketers should deem an optimistic note: “It’s about expanding your audience in the most meaningful way possible.” Meaningful is the best possible descriptor for new audiences that come from your existing customers.

Find out more about how it works here, and contact us to get started.

Brand Ambassadors in the Programmatic Era

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In this post we address how programmatic reduces the risk of picking the wrong brand ambassadors. Furthermore, audience data provide multiple ways for brand managers to make this important decision. Marketers can test a series of related third-party segments, or they can discover the insights and attributes right in their own data.

Read the full op-ed to discover how programmatic takes the guesswork out of a still very intuitive process.