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Andrew Darling

3 location data trends to pay attention to in 2017

2016 was a landmark year for mobile and programmatic advertising. It saw the rise of ‘moment marketing’ with brands reaching out to consumers in real time, fuelling an increase in programmatic ad spend across the globe. Mobile ad spend overtook desktop globally, and this resulted in the rapid expansion of mobile ad exchanges.

Looking ahead, here are the trends we predict for 2017:

More location data and added dimensions

As Pokémon Go showed us this summer, mobile location data is here to stay, and it is only going to get more powerful and prevalent. Mobile users are sharing their real-time location with a growing number of apps, which is causing the volume of location data available to brands to explode.

This “place” data revolution will continue throughout 2017 and, if brands access accurate and verified location data, it will offer marketers incredible insights into consumer behaviour and provide opportunities to optimise campaigns, and deliver increased ROI.

As a part of this explosion of data, the industry will start to analyse new types of information that can be passed from devices as part of location services. With accelerometers and barometers now standard features within new smartphones, speed and altitude will increasingly become additional dimensions of location data.

Adding speed and altitude into the mix of location and behavioural data will deliver more context and audience understanding for advertisers, helping to create even more accurate ways to map location behaviour.

Knowledge of these variables opens the door to further creative ways to target consumers, especially within high-rise, high density cities around the world.

With mobiles, tablets and now wearable devices, the currency of context is growing exponentially. This means gathering and analysing more kinds of data as we become increasingly connected to the environments we move within.

In turn, this creates more ways for advertisers to understand the context of device movement and behaviour and cutting edge companies will find smart ways of using this insight .

Artificial intelligence

Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.

Advertising is a natural test bed for AI driven technologies. The vast amounts of data available, including location data, to train the algorithms naturally allows for fast results when these technologies are applied. Location data is especially suitable as humans tend to follow specific patterns of location behaviour which gives us insight into

As consumer adoption of AI powered assistants such as Alexa and Google Home increase so will the importance of AI driven advertising to ensure marketers are reaching their potential customers.

With the rapid rate of research and development of this area scientists expect human-like decision processing to be possible. However, exactly when this will happen is debatable.

Machine-learning

Machine learning (ML) or deep learning is a type of AI that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

Today, AI and machine learning is seen in deep learning and predictive analytics. It does not attempt to perform the full range of human cognitive abilities. Humans still have to impose certain rules before the machines can learn.

Machine learning is helping to make sense of huge amounts of data and harnessing this power is becoming more common across different social realms. Now marketers are starting to use it as a way of enhancing programmatic campaigns to provide better, more relevant, experiences for consumers.

Knowing where your customer is going to turn up in the future and making the ad more contextually relevant for them when it’s served.

Leveraging 2017’s trends

Use a range of deep learning technologies such as: behaviour profiling, time series analysis, anomaly detection, unsupervised classification and matching to build custom audiences with higher expected conversion rates.

In depth insights of consumers’ real-world locations and behaviours coupled with robust location data set can create innovative new products, such as predictive analytics solutions.

AI and machine learning helps make sense of the location data deluge. Advertisers can understand customers more accurately and tech partners can optimise campaigns and streamline operations.

The writer is Andrew Darling, director of communications, Blis.

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