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5 ways geolocation will change marketing in 2018

Advertisers have had the option to target search and social media ads based on geographic locations for some years now. But, when Pokémon Go came out of nowhere in 2016 and became an instant global phenomenon, it represented a watershed moment for location-based marketing opportunities. Brands discovered that geolocation technology could not only help them find out where their customers are, but also the context of their visit to that place.

Not surprisingly, last year, we saw Facebook announcing it will show you ads based on the stores you have visited in person, and Snapchat’s parent company Snap acquired location analytics firm Placed to show advertisers how online ads lead to offline store visits.

Javier de la Torre, the CEO of location intelligence platform Carto, tells Geoawesomeness he’s positive marketing campaigns based on consumer proximity and daily habits will become standard practice in 2018. “Data about where consumers eat, shop and socialize is increasing, and marketers will continue to lean on these insights to tailor data-driven advertisements based on real-time location.”

So, what are some ways in which we can expect geolocation to dominate the world of advertising and marketing? Let’s find out…

1) Augmented marketing and events

Augmented reality (AR) relies on a person’s actual location in the real world to overlay additional computer-generated information on top of it, to create a virtual world of sorts. Several brands like L’Oreal, Tesco, and Sephora are using AR for their advertising and marketing initiatives. At the recent North American International Auto Show in Detroit, Ford used AR to show attendees the technology under the hood of its vehicles in an X-ray vision of sorts.

2) Turbocharged predictive analysis

Give predictive algorithms a dose of location and you can take behavioral insights to another level. If advertisers are aware of the route a user normally takes or if they can see a certain area which the customer frequents (like an ice rink or a nature park), they can send targeted adverts at a time when they would be most impactful.

3) Ads kindled by indoor mapping

With map providers, major retail locations, airports, and other large avenues pushing for comprehensive indoor maps, expect to see more deals or discounts from advertisers pop up while you are passing by their stores. Apart from allowing advertisers to push out timely communication, geolocation allows gives them invaluable access to the customer behavior.

4) Weather-based geo advertising

Geolocation-based weather triggers give marketers an unprecedented opportunity to send out personalized messages to their target base. For example, on a particularly hot day, a beverage company can invite customers in for a refreshing iced tea. Or if the forecast predicts heavy showers next week, an outerwear business can offer deals on raincoats. Expect to see more such targeted advertising this year.

5) Digital influencers for local retailers

Finding digital influencers from around the globe is not a difficult task. It only takes a quick Google search. But, a national-level influencer program will not provide the desired return on investment to a local or regional retailer who wants to target the nearby community. Geolocation is now helping retailers to find relevant influencers based on geographic context as precise as their own street level.

Have you seen a brand using location-based marketing in a unique manner or is your business leveraging geolocation to bring in customers? Share your story in the comments section below!

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What’s the difference between Artificial Intelligence, Machine Learning and Deep Learning?

Artificial intelligence has held a place in our imagination from the beginning of the XX century. Already in the 1930s and 1940s, the pioneers of computing such as Alan Turing began formulating the basic techniques like neural networks that make today’s AI possible.

Today, AI is already all around us. Google uses Machine Learning to filter out spam messages from Gmail. Facebook trained computers to identify specific human faces nearly as accurately as humans do. Deep Learning is used by Netflix and Amazon to decide what you want to watch or buy next.

Ok. So you hear these buzzwords almost every day but what is the actual meaning of Artificial Intelligence, Machine Learning, and Deep Learning… Are these terms related and overlapping? What’s the technology behind it? And what are their applications in the geospatial industry?

Deep Learning is a domain of Machine Learning and they are state-of-the-art approaches of AI (source: NVIDIA Blog)

Artificial Intelligence is the broad umbrella term for attempting to make computers think the way humans think, be able to simulate the kinds of things that humans do and ultimately to solve problems in a better and faster way than we do. The AI itself is a rather generic term for solving tasks that are easy for humans, but hard for computers. It includes all kinds of tasks, such as doing creative work, planning, moving around, speaking, recognizing objects and sounds, performing social or business transactions, etc.

Researchers tried many different approaches to creating AI, but today the only area that brings promising and relevant results is called Machine Learning. The idea behind it is fairly simple. Rather than programming computers to be smart by hand-coding software routines with a specific set of instructions to accomplish a particular task, you give machines access to a large number of sample data and code them to find patterns and learn on their own how to perform the task.

In 2007, researchers at Stanford’s Artificial Intelligence Lab decided to give up on trying to program computers to recognize objects and took a different approach. They’ve created a project called ImageNet that aimed to label millions of raw images from the Internet in with a level of detail similar to what a child can recognize at the age of three. Then they’ve started to feed them to so-called convolutional neural networks set up on powerful processing machines. By being shown thousands and thousands of labeled images with instances of e.g. a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.

Machine Learning is built based on algorithmic approaches that over the years included decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks among others. But only the developments in the area of neural networks, which are designed to work by classifying information in the same way a human brain does, allowed for such a breakthrough.

Although Artificial Neural Networks have been around for a long time, only in the last few years the computing power and the ability to use vector processing from GPUs enabled building networks with much larger and deeper layers than it was previously possible and it brought amazing results. Although there is no clear border between the terms, that area of Machine Learning is often described as Deep Learning.

In the most basic terms, Deep Learning can be explained as a system of probability. Based on a large dataset you feed to it, it is able to make statements, decisions or predictions with a degree of certainty. So the system might be 78% confident that there is a cat on the image, 91% confident that it’s an animal and 8% confident it’s a toy. Then you can add on the top of it a feedback loop, telling the machine whether it decisions were correct. That enables learning and possibility to modify decisions it takes in the future.

So what are the applications of AI, ML and DL in the geospatial industry?

One of the most useful applications of Machine Learning for the geospatial industry is image recognition. The systems, trained with thousands on images to detect particular object, learns which pattern of pixels is associated with expected result. This technology can be applied on many different levels and has huge impact on the industry.

A startup called Orbital Insight uses Machine Learning to make sense of satellite imagery:

PwC is applying Neural Networks to drone-based areal imagery for construction monitoring:

TomTom uses AI to classify detected traffic signs for map making process:

Machine Learning is a key enabler of autonomous driving technologies:

But image recognition is just a part of the game. Machine learning and other AI techniques have been transforming many areas in the geospatial industry. In particular, areas that require analyzing location-based Big Data for pattern recognition, forecasting, and data modeling.

The more data (and geospatial data) we generate the more help with understanding and interpreting it we need. The potential of AI techniques for our industry is huge and we should not be afraid of it. Within the next two-three decades, the majority of simple, manual tasks related to surveying, map making, GIS, and remote sensing will be done automatically or by robots, making our life easier but the majority of the conceptual work will still have to be done by humans. I believe that in our industry, the loss of jobs caused by automation will be balanced by the new services enabled by the innovations.

Similar to the industrial revolution or the digital revolution, the AI revolution is sure to pave the way for some significant changes in our lives. Machines will gradually improve, slowly replacing jobs that require repetitious behavior. But what happens when one day the machines become smarter than us? There is no good answer to that question…

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