Maximising advertising spend through qualified targeted marketing


We live in a world full of information and insight into how we behave. There is information available on what we buy, where we spend our money, where we travel to and from at which times of day and even what we think about things.

This data generated is tracked through closed schemes run by networks and retailers and some is publically available as we tweet, post or message freely from our numerous devices. We are no longer tied to a location which makes it more difficult to ensure we get the intended advertising messages.

All of this available information is of great value to our marketing partners who are looking for better ways to profile social and geo data so they can device better ways to get their message to the right person first time.

Traditionally there are categories of data that will be purchased to look at the behavioural movements of a target audience. These are things like Footfall Flow and Demographics. Depending on who you purchase data from you tend to get one or the other or in some cases both. Although interesting in terms of what this data represents it is not without its limits and usefulness. There is still very much an element of scatter gun marketing required when using this data.

Locomizer consumes multiple data types across footfall, and social media sources then categorises the single combined data set into lifestyle category keywords refined by our patent-pending algorithm that filters by location, interests, sentiment and other unique filters allowing our marketing partners to identify the best physical location and time of day to advertise.

Using the Locomizer platform we are able to increase the value of existing campaign data by unlocking insights that were previously unavailable in the data. Advertisers look to demonstrate a good level of CTR (Click Through Rate) and engagement rate. When you run the data through Locomizer this are our core delivery metrics and we have seen increases of more than 100% on traditional general audience data.

As such we have been recognised by the 2016 Huddle Trends report as one of four Proximity 2.0 companies to watch in 2016.

Using the Locomizer platform we are able to increase the value of existing campaign data by unlocking insights that were previously unavailable in the data.

What about if you could filter by lifestyle interest categories and by times of day, across any age group, add affinity scoring, filter by location and be able to see the best place to put an advert all mapped out in a heat map?

Now there is. Here is a demonstration and a small example of this profiling in action.

Locomizer geo-behavioural heatmap demo

Our market is a global one with the ability to consume and cross reference multiple data sets from any geographic audience location we ensure our marketing partners differentiate themselves on marketing campaigns.

We welcome the opportunity to discuss specific campaigns and to demonstrate this in real world terms. I can also provide a copy of the Huddle Trends report 2016 if anyone would like it please email me

Author: Craig Marston

MWC – visit our booth and learn about geo-behavioral profiling



Locomizer is exhibiting at Mobile World Congress in Barcelona from February 22nd – 25th this year. If you are attending, our team would love to chat with you! Send us a meeting request to or visit our booth. You will find us in at Hall 5 Congress Square, Catalonia Pavilion Booth CS50.


If you are on a demand side, learn how you can differentiate by adding GEO-BEHAVIORAL profiling to otherwise plain vanilla ad inventory. We will show how you can stand out and attract brands by offering the brand-centric audiences.


If you are on a supply side, become our data partner to turn raw geo data into premium audience profiles, which are in a strong demand with advertisers. Learn how you can monetize your geo-data in a privacy ensured way.


But there is more. If you have a need of understanding consumer behavior for business-to-consumer marketing, business intelligence and analytics, including but not limited to out-of-home, ecommerce, mobile payments, insurance, retail, security and surveillance, crowd management and urban planning markets, drop by our booth to learn how we are enabling a GEO-BEHAVIORAL INTEREST GRAPH – a dynamic, rich, contextual knowledge about footfall for any given place by day part.

Locomizer is driving the paradigm shift from location-based to location-behavioural targeting with our proprietary Biological Intelligence know-how. After all, location is not just about where someone is. Ask us what does it mean at MWC.

We look forward to meeting you at MWC 2016!

Platform vs. Product: Why Data and Inventory-Agnostic Design Limits Programmatic


‘Programmatic’ thrives on the notion that the historically inefficient advertising industry is democratizing through the real-time exchange of ad budget for the instantaneous intersection of inventory and data.

As with any efficient market, pricing in programmatic would thrive on perfect information. However, with unreliable data and publishers of all measures of quality, the market for real-time advertising is far from efficient.

Even as the industry matures, programmatic remains less automated than many suppose – many transactions executed programmatically are still preceded by a very human negotiation.

One thing is for sure: ‘programmatic’ is too promising to languish at a standstill. But is the widely popular ‘platform’ approach to progress, wherein largely generic DSP execute programmatic exchange differentiating only in vague terms, the right one? One could argue that indeed gravitation to commoditization is truly the only way to achieve an efficient programmatic market. But in an industry yearning for profits, and slowing progress towards programmatic perfection despite the platform approach, one must consider the direct opposite as a viable strategy for industry players at large – a move towards the productization of programmatic, trading openness for closed and generic for proprietary.

If the attributes that define the value of programmatic inventory plus data were more clearly valued, we could enjoy an efficient commodity market. In that case, we would arrive at a few simple exchanges, much like NASDAQ and NYSE dominate equity trading. But with disparate data and attributes which hold different value to different buyers, such a future is unlikely to be delivered profitably. So why is our industry trying to achieve it?

Perhaps it’s a vision for a winner-takes-all outcome, where the leaders in neutrality and transparency gobble up the industry. But even in this scenario, profitability and long-term sustainability would be dubious for any commercial provider.

Product Creates Profit

The opposite of the status quo via ‘platform’ is ‘product’ and it is the only route towards progress and profit. With a product, providers shun pure openness and compatibility for uniqueness and proprietary. With a product-driven approach, differentiation takes priority over commoditization and unique proprietary design fuels unique deliverables which advertisers will be willing to pay a premium for. In an industry that is constantly benchmarking, it is possible that an alternative approach – pure differentiation could be the way to break away from the pack and claim market share.

What Defines ‘Product’?

Many of the platform concepts alluded to in this post have product-like features, such as unique algorithms, user-interfaces and more, but they can’t get past the openness and interoperability that could lead to their demise. They seek to remain transparent and agnostic, yet simultaneously claim uniqueness. This is the programmatic paradox.

True product, and true uniqueness is just that – it’s truly different. As Peter Thiel said in his best selling book ‘Zero to One’, competition is not always good- that a business should be so uniquely good at what it does that there is no substitute. This type of approach is a major contrast to the look-alike approach in the programmatic business, and we’re not talking about audience modeling.

How Can Programmatic Productize?

Programmatic can only really differentiate on a limited number of fronts, namely ‘process’, data and inventory. And whereas process can always be improved, and inventory will often go to the highest bidder, better data is the king of all deliverables – the most exclusive of any proprietary offering and the answer for platforms making the shift to product. Data is finite, data holds intrinsic value and without data, programmatic lacks the third dimension, transacting only on the availability of inventory, without knowledge of its audience. And yet too many programmatic providers pseudo-differentiate towards better data with attempts at a better process – more efficient, more highly targeted or both.

Meaningful differentiation with proprietary data requires innovation that is often beyond the core competency of the traditional platform. Artificial Intelligence is no longer enough. Vast networks of data capture, or DMP integrations are now table-stakes. For real productization, platforms must seek data solutions that move past vague segments, and towards behavior-driven insights beyond the website visit and even beyond the digital experience. The real world presents increasingly sophisticated opportunities for non-PII data capture that given modeling such as Biological Intelligence over the traditionally binary artificial, add new dimensions to predictive advertising that far outshine the obsolescence creeping into programmatic. Programmatic’s legacy of website retargeting, still a dominant technique, will pale in comparison to the potential that mobilization, Internet of Things and other methodologies existing far away from the desktop will enable in not just advertiser performance, but provider profits.

Final Thoughts

If the product direction turns out to be the winning approach for programmatic, it is unlikely that many providers will successfully make the leap. The unique product design and truly scarce data assets will be inherently in short supply. Therefore first-mover advantage comes at a premium. Which platforms will deploy truly unique approaches to data, and in doing so deliver exclusive, proprietary value? The very notion of premium pricing and higher margins requires exclusive differentiators buyers are willing to pay for – which platforms will strive for true uniqueness, delivering data and data methodologies in a closed system that outshines the competition? Only time will tell. But in a stagnating market, on the wrong side of the capital equation, the players in ‘product’ and not platform will deliver profitable growth and fuel the Product Era for programmatic, because an agnostic approach to a revolution rarely finds a following.

photo credit: constant motion via photopin (license)

The Sense of Place: Brain Science Breakthrough and Marketing Revolution


Among the amazing scientific breakthroughs Nobel Prize winners have brought us in the last few years, few hold such promise in the movement towards Biological Intelligence away from all things Artificial.

The 2014 Nobel Prize for Physiology or Medicine was awarded to John O’Keefe, Edvard Moser and May-Britt Moser for the discovery on how the brain considers location and functions as a natural GPS.

O’Keefe first described what he called ‘Place Cells’ back in 1971, but their characteristics and function seemed, well, too good to be true. But with today’s science and forward thinking, and the help of the husband and wife Moser team’s 2005 grid-cell discoveries, he was able to gain worldwide acclaim for his discovery of the truly remarkable capacity of the brain to physically identify ‘place’ with an explanation for the neural mechanisms driving spatial memory.

In their press release about the award, the Nobel Foundation described the discovery as solving for one of humanity’s most complex challenges:

“How does the brain create a map of the space surrounding us and how can we navigate our way through a complex environment?”

While some of the more ‘directionally-challenged’ of us must have this area of the hippocampus less prominently developed, we can all agree that this is a fantastic discovery from the perspective of science, with potentially far-reaching impact to include Alzheimer’s and dementia research – diseases that affect the same area of the brain.

And with discovery of the brain’s remarkable capabilities comes new opportunity to leverage our learning’s for the greater good. Perhaps nowhere greater than in efficient messaging, where today we inefficiently receive 360 interruptive marketing messages per day. With Biological Intelligence defining consumer behavior by ‘place’, and our now improved understanding of its neuro-structure, we can combine the science to further move away from 1’s and 0’s in our pursuit of commerce and communication. It’s the nuanced, biological behavior that predicts human patterns – increasingly driven by time and place. Shailendra Rathore, studying the Subiculum (part of the Hippocampus) in the lab of Dr. Francesca Cacucci, a former PHD student of O’Keefe says “The way people behave in different environments does indeed seem to have influence on spatial representation, I believe that studying what people do in particularly novel and rewarding settings and machine learning these situations from behavioral parameters may reveal interesting information for targeting purposes.“

Biological Intelligence in marketing is built on the idea that collectively, crowds of people move through their days with a singular intelligence – the sum of all intent. With millions of individuals making up the collective intelligence as a ‘system’, we can isolate sub-systems and better understand human behavior by location.

BI first-mover, Locomizer is the only audience platform that is leveraging the identity of place and it’s meaning for the characteristics of frequenting individuals to help brands improve their predictive marketing. With the marketing industry’s dearth of accurate demographic and psychographic data, Locomizer enables a third dimension for brands to better understand their customers. Academia is striving for some of the same learnings. Rathore continues, “Fundamentally we are trying to understand how the brain represents space and memory. Perhaps our conscious experience is explicitly structured spatially. We reconstruct a spatial scene and then move within it when we perform autobiographical recall. Trying to ascertain what is memory and how it is structured in the brain is a key stepping stone towards understanding conscious experience and also building the next generation of Artificial Intelligence.” In the case of Locomizer, AI takes on a ‘BI’ scientific approach.

Biological Intelligence is not only defined by human behavior, but it can be the collective intelligence of systems as seemingly chaotic as, say, ‘cells’. And until now, one could better argue that the daily movement by humans through time and place had equal elements of chaos, driven less by collective intelligence and more by unpredictable causation, aka the randomness of our daily lives.

But with the brilliance of O’Keefe, Moser and Moser, and their discovery that our ability to navigate complex environments is a lot more sophisticated – and therefore intentional – than previously assumed, we can leverage Biological Intelligence for predictive marketing with even more confidence. Whether it’s our navigation through shopping malls – indoor or outdoor – city streets, neighborhoods and suburbs, the chaos driving our patterns and therefore our commerce, is quickly rationalizing. And Biological Intelligence has the formula.

photo credit: Brain diagram via photopin (license)

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.

photo credit: Sunny Pubs in Google Earth via photopin (license)