It’s the Data, Stupid!


As the 2016 American election season starts to heat up, and primary candidates fight for position in their party, it isn’t unusual to hear the oft-remarked James Carville quote “It’s the economy, stupid.”  It came during Carville’s strategizing for Bill Clinton’s successful 1992 campaign against George H. W. Bush.  It may not be the most eloquent quote, but it’s one of the most memorable.  In today’s programmatic ecosystem, vendors are lining up for agencies, brands and, well, other vendors.  They’re lobbying for position just like a political candidate.  Some add this, others add that, but far too many forget what could also be defined so aptly:  “It’s the Data, Stupid!”

We’re now up to 2000+ marketing technology vendors in the industry.  There are E-Mail platforms, Display Platforms, Marketing Automation Platforms – platforms for just about everything.  And while each facet of the landscape has a unique pitch, they all require targeted data to execute.  Content without an audience calls to mind another famous quip:  “If a tree falls in the woods and nobody is there to hear it, does it make a sound?”  With poor data, that’s the question many campaigns have to answer.

Programmatic currently solves the data challenge with complexity.  Where there is poor data, there is optimization.  Where there is no data, there is prediction.  Where there is great data, there is personalization.  This is a good effort.  However, with mixed results, and a lack of demonstrable success in programmatic, many brands are still steering clear of pouring budget into the space.  And many marketers are yet to understand it.  If the complexity, and blurred ROI comes from compensating for poor data, better data should surely solve everything.

So what constitutes better data?  Programmatic essentially has two categories of data.  First, segment data.  This is data typically associated with a demographic.  It’s largely B2C.  Next, is behavioral data.  This is data associated with an action, such as a search query or abandoned shopping cart.  It’s much better but too scarce on which to build a multi-billion dollar industry.  Individual brands can pay a premium for behavioral data and find success, but the industry at large must find another answer.

Quietly building steam is another category of data.  In fact, it’s so smoothly entered out vocabulary that we hardly realize it’s the first data source that’s actually native to programmatic – behavioral and segment data have existed since the beginning of digital marketing.  The new category is geo-data, or even geo-behavioral data, the amassed non-PII information generated through mobile, social, beacons and other local pings. Geo-behavioral data gives programmatic it’s best opportunity yet to solve its challenges.

Most readers will say “geo data is simple.”  “I can use geo.”  But that’s exactly the problem.  Early usage of geo-data has been just as it sounds – data about where someone is or was.  It might try to push a coupon if you’re in the mall, or some similar gimmick.  In a worst use case, it might utilize segment data to determine user demographics by where they are.  Because geo-data is native to programmatic, it needs another dimension to be activated.  It’s not just about where someone is.  It’s about what that place represents, when they are there, who else is there and how they are moving through that space which ultimately solves for ‘why they are there’.  In assessing geo-data, programmatic should realize two concepts:

  1. Each place has it’s own identity, which can change at different times of day
  2. A consumer’s geo-behavioral characteristics are defined not just by the place identity, but by other consumers there at the time. We are a product of who we hang out with.

If these facets are important to the simple, successful processing of geo-data, to augment the poor segment data and scarce behavioral data for programmatic, then why aren’t they more widely used?  Because analyzing this data contradicts a lot of the traditional AI/Machine Learning algorithms on which programmatic was built.  Traditional binary computing struggles to identify such human nuances as passage through time and space with a crowd.  So how can programmatic improve its process to activate this data and make profitable use of it?

Biological Intelligence is the simplest solution to enabling marketing tech to digest, make sense of and scale geo-behavioral data, the missing component in programmatic.  BI, considers human behavior and the power of association and affinity as a native computation.  Biological Intelligence has a major advantage at it’s core:  It understands that systems have intelligence and a collective behavior, even without a brain (no group has one brain).  Therefore, instead of trying too hard to predict one consumer’s behavior in isolation, BI can effectively crowd source behavior, and save a big computational headache.

With programmatic iterating into the future, the complexity and dubious results many marketers are experiencing can be traced back to one thing:  poor data.  However, trying to solve the data conundrum with the two standard data types will prove difficult – especially as programmatic scales to demand even more scarce data.  The only solution is to improve the use of geo-behavioral data, and in doing so leverage new ways of analyzing that data.   In an industry that uses “Intelligence” all too often, it’s an interesting contrast to see the word ‘stupid’ in a post about data.  But until we realize the potential of new data sources, our technology will be just that.  Because of it’s superior methodology for unlocking our richest data set yet, Biological Intelligence, and the easy interpretation of geo-data should get everyone’s vote as programmatic seeks new ways to drive and prove ROI for a simpler, more marketable future.

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Geo Data is the new Yelp


At the end of his early 2014 article in ‘StreetFight’, which focuses on the business of ‘Hyperlocal’,  Steven Jacobs made this point:

Social recommendation products from Facebook and Twitter could threaten Yelp’s position in the market, but the longer-term threat to Yelp comes from a more data-driven approach. As mobile positioning and battery-life improves, and devices can persistently track consumer movements, there’s an opportunity over the next decade for companies to use those data to facilitate discovery similar to Amazon recommendations. Content isn’t going anywhere soon; but data will have its day.

The larger premise was a discussion of ‘Perfect Information’, which in economics is ideal for the educated decision-making of consumers.  In business, it can lead to commodization, as brands struggle to differentiate and often mimick the services of competitors.  Take, for example, the airline industry.  Near-perfect information about the features customers cared about, led to lots of price-comparing platforms, and the eventual commodiziation of the travel offering.  But in the local retail market, better information will drive more commerce and enable upstarts to thrive, which is a step in the right direction.

Jacobs’ point really speaks to an evolution in information the mobile web will enable, as non-social data begins to match social data in terms of relevancy, accuracy and supply.  Social and publisher content has been essential in filling the gaps that business and location data leave because without updated data points in local such as store hours, offerings, sentiment, location and more, social content is a useful substitute.

Before the internet, and throughout the early internet, the Yellow Pages were the resource for such data.  Whether in the book, on their website, or via phone, this old-fashioned approach sought to provide consumers with the information they needed to make decisions.  It was updated once or twice a year, and unless they bought nice advertisement, did little to drive demand.  As Jacobs pointed out, this culture of information led consumers to trust primarily large brands, such as “Hilton” for their known quality – the information to advocate for smaller brands just didn’t exist.

Enter social.  Social content and sites such as Yelp, Trip Advisor and many more empowered people to curate the information, data and content of businesses in their locale and those they visit.  With a social sense of community, and hopefully enough iterations, these platforms could produce a reliable proxy for verified data, on all things important to retail.  And not only does this content illuminate where the business is, when they are open and what’s on the menu, but it goes a step further, to depict the kind of traffic a business or commercial center was seeing.  With high review volume a reader can be more confident that the business in question is worthwhile.

Non-social, Geo-Behavioral data has the ability to do the same thing, at scale, with less reliance on the nuances of social authorship.  By tracking non-PII consumer movements locally, including time spent, and affinity with other known data points like sporting events, concerts and more, geo-profiling at scale will fill our local information sources with automatically curated data – which in many cases will prove to be more reliable.

The reliability challenge with social content is that it often requires a polarizing subject to receive volume.  Therefore, people must be passionate about it – whether or not it’s role in the community is truly superior.  For example, a fun Frozen Yogurt shop will receive more content than a reliable breakfast joint that serves an older demographic and certainly more than a local oil-change garage which has served the community for decades.  With improved data automation through geo-locational aggregation, non-polarizing yet high quality businesses will regain equal footing with the trendy places.  Social content does a great job of communicating what we love and what we hate, but a poor job of communicating what we need.

With more iterations, driven by the world-wide proliferation of mobile devices, more instances of mobile ‘check-in’ and interaction and better data capture from both structured and unstructured sources, real-life ‘data’ will start to communicate as much about local businesses as real-life sentiment through social.  And through automation, local behavioral data will paint a very accurate picture of both essential business information and the more nuanced as algorithmic modeling depicts interesting facts about a place such as ‘Best for Sporting Events’ driven by unbiased, non-polarized real-life data.   Many small businesses post the sticker “Think Global, Act Local” on their windows.  The data ecosystem is enabling just that as local actions will drive more data and insights globally for a new level of near-perfect information for retail.

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An Artificial Intelligence Alternative for Replicating Emotional Intelligence

Biological Intelligence as Artificial Intelligence AlternativePaul Frampton’s argument for improved Emotional Intelligence should be taken seriously as marketing requires an increasingly creative approach in increasingly real-time social channels. Yet in our industry’s pursuit of all things scalable, we try to recreate instinctive human ‘EI’ with the non-human, automated and hopefully scalable AI. Unfortunately, despite sophisticated algorithms and plenty of investment, these AI based algorithmic approaches cannot yet command a fraction of the emotion garnered by creative marketing.   Many will argue that it will only be a matter of time before we see a breakthrough and we should continue to iterate forward. However, we are actually solving this challenge incorrectly. Artificial Intelligence will never catch up because while binary computing is effective at crunching numbers, it is fundamentally poor at predicting human behavior. It’s time to look elsewhere for emotional intuition and luckily an alternative approach – Biological Intelligence – offers a solution that is starting to gain major traction.


The biological approach to solving such challenges has been overlooked for years because many presumed that Biology is mainly a ‘descriptive’ discipline rather than ‘predictive’. However, the evolutionary, genetic, neural networks and swarm algorithms are just a few examples where the workings of life were perfectly formalized in the form of a theory and equations. Now we start to witness how quantitative and theoretical Biology could be used to solve current and future problems in all aspects of our lives.


In understanding how this can be applied in marketing to define the behavior of consumers, Biological Intelligence begins with an important learning from nature. Systems, from groups of cells to crowds of people need not have a single, collective (or any) brain to be intelligent. The collective decision making of the group is not conducted in the way we make decisions as individuals but rather adaptively and collectively, driven by internal and external stimuli to which systems react for survival. The first step to recognizing the superiority of BI is to stop trying to replicate the human brain and observe intelligent systems.


And this is a much sought after pursuit in traditional marketing – with or without an algorithmic approach. Joseph Vita DeLuca, VP Marketing and Communications at Yieldr, aptly described the challenges and opportunities of better understanding separate biological systems in the marketing sense as ‘hypersegmentation’.   The basic principle is that brands can communicate best to audiences who are properly segmented, therefore having enough commonalities for relevant messaging. That is harder than ever today however as segments must adapt for rapid change. DeLuca states Part of incorporating a hypersegmentation strategy is creating an automated process that habitually incorporates new data points while refreshing older ones, in order to optimize messaging through every point of the customer journey.” It wasn’t mentioned in the article, but one way to achieve that automated process is with Biological Intelligence.


Such an approach is increasingly important given Artificial Intelligence’s limitations in predicting ‘irrational’ human behavior. Nick Seneca Jankel, creator of ‘Breakthrough Biodynamics’ defines the issue quite simply. Artificial Intelligence assumes that people will behave in predictable ways, whereas true ‘breakthroughs’ are unpredictable. He points out that Stuart Kaufmann defines this as ‘Partial Lawlessness’ which we can take to mean ‘unpredictable’ or ‘irrational’ decision making within otherwise logical patterns. Consumer behavior is ultimately very unpredictable and trying to solve it with AI based on rationality will deliver the same frustration as trying to accurately predict a football team’s outcome based on common sense – actual outcomes don’t play out that way.


While effective marketing through intelligent decision-making is a difficult task, pursuing it with Biological Intelligence is a move to the simple from the complex. The simple and natural behavior of systems is a refreshing panacea for the quirks of Artificial Intelligence. In this case, a natural approach is better and consumer behavior patterns can ultimately be defined by simple and universal rules. Switching to Biological Intelligence from AI represents a simple opportunity for a paradigm shift in the predictive and analytical results of marketing technology.


About Alexei:

Locomizer Founder Alexei A. Poliakov holds PHD, Biology from Moscow State University. He has dedicated more than ten years of scientific research on spatial behavior in live systems.

photo credit: Terminal via photopin (license)

What Does Better Look-Alike Look Like?

look-alike powered by Biological Intelligence (BI)

The scarcest resource in the marketing technology ecosystem is quality data.  Data is truly the ‘oil’ or ‘gold’ of the programmatic revolution.  Brands have small amounts of first party data, which is rarely scalable, ample amounts of brokered third party data which is too scalable, and almost no ‘second party data’, the grassroots data version of a book exchange – take a book, leave a book.  However, despite a scarcity in data, ad tech shows no signs of tempering the programmatic sales strategy.   As each subsequent prediction ‘ups’ the growth of programmatic to full steam, the pressure only mounts for providers to fulfill big promises of big data.  Look-alike modeling is often the solution.  It promises to analyze, mimic and replicate the characteristics a true data set presents.  As look-alike modeling is required to fulfill more and more data requirements, the process will need further optimization and alternative data sources to provide long-term ROI.  Location-based strategies offer a possible solution and should be strongly considered as an addition to look-alike modeling.

We are beginning to see more location-derived modeling strategies developed for the ‘probabilistic’ world. With this approach (often in device matching), technology providers seek to identify a cell phone for example, to match a laptop. As the cell phone ‘checks in’ more often than not with the same laptop, the technology deems the devices ‘matched’ and enables advertising deployment cross device. A good percentage of mobile ad targeting is beginning to take place in this fashion.

Look-alike modeling requires more sophistication than ‘cross-device’, but can take a page from the simple success cross-device matching has seen with geo data.  Look-alike modeling requires ‘matching’ across multiple dynamic characteristics of randomized audiences, requiring algorithms to sift through immeasurable amounts of data to find hopeful patterns.  The general imperative is to build scalable models from finite amounts of data.  As you can imagine, the success in this approach is mixed, but Locomizer offers a solution which changes the odds for successful ‘look-alike’ application.  Locomizer can identify GPS coordinates in real-time and work with historic GPS coordinates to better understand an audience’s relationship to time and place.  With Biological Intelligence, Locomizer can then better define the characteristics of the users being matched for look-alike purposes, by the users around them and the attributes of the places they frequent.  BI presents a natural model to provide incredible insights based simply on the movement of audiences through space and time.  This provides an opportunity to significantly optimize traditional look-alike modeling by adding a geo-spatial dimension to the archaic, linear approaches most common today.

If all data were first party data, marketing tech wouldn’t need much of an algorithmic approach.  However, demands for data will always outpace the supply.  In essence, it’s no different than oil – as in our analogy, data is the ‘oil’ of our industry.  While some oil is easy to come by, the only way to obtain it cost effectively as demand increases is to improve the technology.  And breakthroughs such as horizontal drilling have been game-changers for the energy industry.  As demand continues to outpace the cheap supply of data for programmatic use, look-alike practices will become increasingly important to mine actionable data from large quantities of the unusable.  Locomizer is that game-changing technology that will help take the process to the next level and make better data more available for all.  By adding the locational dimension augmented by Biological Intelligence, Locomizer vastly improves the old model.  We are all a product of where we are and who we are with.  And with this component increasing the probability of accurate targeting, geo-profiling data will help ensure look-alike data looks great across all programmatic channels, as its demands continue to scale.

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You don’t eat artificial anymore, why should your marketing budget?

Artificial Intelligence is like artificial food - yack! Biological Intelligence is like organic food - yum!

In marketing technology, Machine Learning and Artificial Intelligence are to the layman seemingly interchangeable, overused concepts and jargon designed to say a few things:

  1. Our technology is proprietary
  2. Our process is complicated
  3. Our results are superior

And most of all, Artificial Intelligence says a marketing technology company might just have a high-tech solution to marketing’s all-time challenge – how to reach the right person at the right time, with the right message.

Artificial Intelligence has been intriguing us, scaring us, or both on the big screen for decades but it hasn’t been until the last few years that it’s made its way into our businesses as marketers.  For most of us, AI has become synonymous with Ad Tech, accepted as ‘table-stakes’ for being more than just the next platform – especially on the buy-side of advertising.

However, in many recent Ad Tech missteps, Artificial Intelligence has been front and center, delivering anything but the results marketers had hoped for.  While the promise seems great, the reality is not.  How is this so?  It seems simple enough.  Computers love crunching numbers even without artificial intelligence.  Give them AI, lots of ‘Big Data’, many iterations and success should be a given.  That’s the sales pitch at least.

The challenge with Artificial Intelligence in marketing applications is that real business is anything but artificial.  The engine that drives commerce is not a machine, it’s the human heart, forming behavior patterns only nature can explain – not 1’s and 0’s.  In fact it’s those very binary digits that holds AI back from truly connecting with human behavior the way it should.  Those values, along with ‘Truth Values’ (True/False) are major limitations in Artificial Intelligence.  No matter how hard it tries, AI is only as strong as 1 and 0 – with no room in between.  With no intermediary values, traditional AI is archaic when it comes to replicating, or in the pursuit of Ad Tech, ‘predicting’ human behavior.

Enter Biological Intelligence.  ‘BI’ is flexible.  It can adapt to change in the universe in a way ‘Artificial’ never could.  BI sees the world not in 1’s and 0’s but in a fundamentally flexible and adaptive way found in nature – the right way, if you prefer natural over artificial.  With Biological Intelligence, for example, algorithms can solve for the behaviors of people in time and space, relative to who they are, where they are and why they are there.  BI is superior at deciphering such behavior, because like the human brain, it has a continuous range of feelings about things, not just binary values.  Marketers must connect with consumers from all walks of life with varying sentiment about their products; there is no room for a one size fits all approach like Artificial Intelligence.

So, given its flaws, and dubious results, why has AI proliferated throughout Marketing Technology when what we need to better understand humans is BI? Because AI is simpler.  It’s more widely understood in computer science.  It’s easier to program.  And in an ecosystem requiring compatibility, portability and open standards, it’s simply the cheaper way to market.   The scientists programming Artificial Intelligence models best understand computers.  And that’s useful in matters of bits and bytes.  But in matters of consumer preferences, like marketing, marketers who want results, must seek product design built based on more advanced science – human behavior.  It is indeed rare to find a computer scientist who is also a people scientist, but that’s what it’s going to take to understand people as data.

For a really low-tech and ‘common sense’ understanding of the issue at hand, consider turning to another domain – something we all consider on a daily basis – our diet.  While we all enjoy occasional sweets, and most of us occasionally indulge in the fattiest (and tastiest) of hamburgers and the like, we are quickly losing our tolerance for artificial ingredients in our food.  Brands such as Whole Foods have made an industry out of this preference, as we evolve to loathe the cheap, synthetic and toxic ingredients of yesteryear’s food products.  Natural food is grown, prepared and consumed the way nature intended.  It tastes better and it’s better for you – a simple win-win.  In your pursuit of Marketing Technology, it’s time to take the same leap.  It’s time to leave the synthetic, Artificial Intelligence behind and move on to a more natural solution.  Biological Intelligence enables just that, with a natural approach based on real behavior too complex to be calculated by artificial means.  Because in an Ad Tech community that has as many PHDs as marketers, real, human approaches are just what the doctor ordered.