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Data Marketing & Advertising

Using Location Audience Segmentation Directly In Your DMP

Why location is important

Advertisers use audience segmentation so that they can eliminate any unnecessary spend from their marketing efforts, become more efficient and effective and boost key KPIs.

Using data, advertisers can create much smarter audience segments. They can prioritise the right consumer, with the right ad using the right message.

That’s why we’ve made sure that our precise, first-party data sets are available for marketers and advertisers to use directly in their DMP. Advertisers should be able to do this directly in their media buying solution.

 

About our data

At Tamoco we think that data accuracy is the most important thing for advertisers using data in their targeting, segmentation or attribution. That’s why we built a network that is leading the way in the drive towards more accurate data.

 

Proprietary location data focused on precision and accuracy

Tamoco’s data is industry leading. We use our proprietary SDK for data collection along with our extensive network of sensors to understand consumer location with higher levels of precision and accuracy.

 

Detailed visit behaviour

Tamoco’s data methodology is designed to reduce the number of incorrect data points. We understand visits with granular accuracy. False visits are filtered out, and our data methodology is transparent. This methodology means that advertisers can be confident that Tamoco visits data is more accurate than other visits-based targeting solution.

 

Benefits of using location directly in your DMP audience segmentation

Fine-tune audiences and incremental ROI gains

You have the first-party data which contains, for example, age, gender, brand loyalty and products owned, amongst others. Location allows you to segment these audiences even further.

To drive incremental gains to ROI location can signify which of these users are relevant to your campaigns. Location data is real-time and behavioural based. These attributes mean that you can exclude irrelevant audiences, and save valuable marketing dollars in the process.

Advertisers can then tailor their campaigns to users that have physically exhibited certain behaviours, such as visited a specific store or frequented a series of physical locations.

This targeting helps to build relevant messaging and ensures that your segments are squeaky clean in terms of precise targeting. No more wasted budget on consumers that aren’t relevant to your brand campaigns.

 

Location is a good indicator of intent

Add intent to the segmentation process. Retargeting campaigns are more effective if you can reach consumers when they are in the right frame of mind. Retargeting works well in the online world, but this is often limited to your current inventory.

Consumers show purchase intent in the offline world as well. Visiting your store is a good example. However, by mapping the offline world, advertisers can use location to identify consumer intent in different ways.

Retargeting to consumers who have visited (or are currently visiting) a competitor or a store in a similar category is a powerful way to reach the right audiences.

 

Using location data to fuel analytics

Using location signals directly in your data management platform enables smarter cross-selling. If you have built up a database of descriptive and behavioural audiences, adding location can provide a better way to upsell new products or promote return purchases.

Using your analytics solution, location data can directly increase how you understand consumer trends, patterns and intent. Location data is a tool for building up a more detailed view of your customers.

These insights can be used to inform future segmentation and predict which audiences are more likely to convert at a specific stage in the buyer journey.

 

Using location to build lookalike audiences

Reaching new customers that are currently outside of your customer data set can be challenging. It’s something hard to know if the process of building lookalike audiences is reliable.

Using location data, it’s possible to build real-world behavioural based audience segments. For example, by taking an audience that converts highly, we can understand similar consumers based on how they move and behave in the real world.

This generates lookalike segments that are based on current real-world behaviour rather than vague similar interest data. Ultimately it will build segments that are more likely to convert.

 

Example segments and audience segmentation strategies

Some of our location-based segments are available already. Here we will look at some familiar audiences segmentation use cases using this data.

 

Women’s clothing stores

Brands looking at segmenting their audiences based on consumer interests can use location to refine their audiences. Let’s look at how this would work with an audience based segment.

You already have a pre-built audience that is relevant to your women’s clothing brand.

Using location-based filters directly in your DMP you can further filter this audience to reach the most relevant users.

You can filter based on the number of visits to women’s clothing stores. You can set the time period for these visits.

This will segment your audience based on those that have physically visited a clothing store in your defined time period.

 

Drinking places (alcoholic)

Using location, you can build retargeting audiences in your DMP to maximise your ad budgets.

If you are looking to retarget consumers based on their behaviour, then location can help to define the right audience.

Set your audience to include those that have visited an alcoholic drinking place.

You can filter these visits based on past visits, or on specific dates or days of the week.

This can help you to build incredibly specific audiences – such as Friday night venue attendees.

 

Speciality food stores

As previously mentioned, location data can be helpful to build new lookalike audiences based on consumer behaviour. This method can help you to create unique lookalikes based on actual measured real-world behaviour.

We can build an audience based on visits to speciality food stores. Here we have our seed audiences that consists of consumers that we know have visited a health store at some point in our defined time period.

We can do one of two things here:

  • Move the identifiers into our current lookalike modelling solution. This will create a new audience based on a unique seed audience.
  • Use a location-based lookalike solution. This will use the audience to match with devices that have exhibited similar real-world behaviour.

All of the above segments are readily available in leading DMPs and other media buying solutions.

 

How to activate Tamoco’s precise location data

Our data is currently available through DoubleClick, AppNexus, AdForm. Here you can begin segmentation immediately using Tamoco’s location data.

We can activate these segments instantly in The Trade Desk, Adobe Marketing Cloud, Facebook Advertising, Sizmek, Beeswax, Widespace and BrightRoll. Please contact us to enable this.

 

Want something more custom?

We can build custom segments on demand with our team of data scientists. These can be fed into the above solutions. Here are some examples of what our team can provide for your campaigns.

Brand affinity – we can create segments that are based upon brand affinity to your brand, a competitor or another relevant brand.

Detailed visits – Our team can help segment audiences based on verified visits to any physical POI, venue or location.

All of our data solutions can be fed into your current data or targeting platform. Our team of data scientists are ready to support your integration and take your marketing to the next level.

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Get in touch to learn more

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Data

Story Of Data Report – Consumer Trends In The 2018 World Cup

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Understanding consumer trends and behaviour during the 2018 World Cup

Download our free report to understand:

  • How footfall traffic changes to different venues during the World Cup
  • Which venues performed well during the tournament
  • On-trade and off-trade patterns
  • Busiest games, times and days of different categories
  • Trends in the behaviour of different demographics such as millennials.
  • How to use these insights to inform marketing and other business decisions

Read the introduction below.

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Introduction

We wait four years for a world cup to come around. The final is the most watched event in the world. That’s a whole lot of consumer interest – it’s one that businesses need to get right in order to maximise the effect of this once-every-four-year event.

The World Cup consists of 32 nations. IN the UK every game is broadcast on terrestrial TV. Globally around 3.4 billion people watch some part of the tournament. It’s a huge event for a number of industries, but none more than perhaps the beverage industry.

That’s why it’s important to understand consumer behaviour during these monumental global events. Understand how customers move and behave can help business perform well across a number of different functions, from advertising and marketing to insights and planning.

To identify alcohol and consumer trends during the World Cup we analysed visits to over 5000 venues across London during the 3-week tournament.

Our accurate first party data-set combined with our network of precise location sensors provides detailed insights into how consumers behaved during the World Cup. Our visit methodology provides a powerful way to reveal trends and behavioural information around consumers.

To keep up to date with the latest data trends, sign up to our monthly newsletter. 

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Data

How Big Data Is Changing The Finance Industry

The finance industry is a highly competitive space. It faces a new generation of disrupting banks and regulations. It’s an industry that needs to utilise big data to drive personalisation, boost customer loyalty, security and fuel everyday investment decisions.

The financial services industry has always been at the forefront of technical innovation. The availability of new datasets has provided a powerful way to understand behaviour and offers new directions for the financial industry to be predictive.

Big data application in financial services goes beyond predicting share prices. New data types are revolutionising the space.

We’re going to break down some examples of how big data is being used in financial services.

 

Personalisation

Personalisation has long been a priority when dealing with consumers. This is true both inside the finance industry and across other verticals with a strong consumer presence.

Disruptor banks have begun to establish themselves in the finance sector. One thing that these banks have done well is personalisation.

By understanding users spending habits and behaviour, they can offer more personal spending products and recommendations.

For example, in the world of digital banking, if a bank had the right data sets around its customers, it could provide services that truly bring value to the end customer. If a bank understands what their customers spend, where they go and where they work they can, for example, suggest that a travel card could save them a lot of money each month.

It’s in these areas that financial services can learn from disruptor banks. With the right dataset, the banking experience can be personalised for customers. This will allow financial services to boost customer loyalty and drive cross-selling of their products much more effectively.

 

Security

There are few industries where security and fraud are more of a threat than in the financial services.

As technology has advanced those in the space must be smarter and better adapted to the fast-changing tactics of those looking for weak points in sometimes outdated systems.

Adding location intelligence to the equation adds an extra layer of security for customers and allows financial institutions to instantly provide checks based on where a customer uses its products.

Location offers a new way for security teams to identify fraud. Location data can help to educate security systems on customer behaviour and can form a strong base from which to detect irregularities in financial behaviour.

Financial services continually process an incredible number of transactions. A location overlay which includes operational rules can help to determine when to flag records as fraudulent.

These processes enable financial services to provide better safeguards to their consumers and clients.

 

Investment

The rise of big data carries enormous potential for investors. Many have implemented predictive systems that are designed to understand data sets, digest vast amounts of data and then inform investment decisions.

These data sets have proved successful, but accurate location data is rarely used to optimal effect in this area. Location intelligence provides a more detailed understanding of trends and ingesting these precise datasets can help investors to stay ahead of the competition.

Understanding how populations move and behave en mass is readily available in the online world. Location offers something slightly different. It helps to understand how consumers move in the offline world.

Using location allows minimal lag between an aggregated understanding of consumer trends and investors being able to forecast the performance of portfolios and financial markets.

This allows investors to act quickly and decisively. Location is a fantastic indicator of market, brand or individual behaviour. With the progress made in accuracy by some in the space, this will prove to be one of the next steps in predictive analytics for the financial sector.

 

Insurance

For insurance location underlies everything. Many insurers can immediately benefit from quality location data to better model risk and dramatically improve their underwriting and pricing.

In insurance, nearly every data point has a relation to location.

Crime data – understanding crime risk in specific locations including historical data and predictive solutions can provide significant advantages to insurers.

Catastrophe – using data analytics insurers can mitigate the risks involved with catastrophes based on a customer’s location.

Behavioural data – using new kinds of behavioural data, such as location, can allow insurers to understand consumer behaviour better. This helps to predict risk and assist with pricing.

These datasets are now available in large quantities. Precise location allows for more accurate insights and the ability to generate this data, as well as store and process it, has changed the game for the insurance industry.

 

New revenue

For financial businesses, consumer data also holds the key to new revenue. It allows companies to maximise revenue from existing channels.

For example, using precision data marketing financial companies can identify products and services that are a much closer match for specific customers. This provides more value for both customers and branded partners.

Understanding customers behaviour allows companies to understand which type of person is more valuable for their business.

Using this data, it’s possible to build lookalikes and use this to drive more targeted marketing activity to customers that represent a greater potential for higher revenue.

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Data

The Challenges Facing Location Data & Location Intelligence In 2020

What is location data and location intelligence

In 2019 the number of connected devices will continue to grow to its highest ever. With more devices and increased sensors, the amount of data generated will explode.

It will be more accessible than ever before to use data to inform everything from business intelligence to advertising. Location-based data will be more accurate than ever before. These factors will mean it be used more commonly in areas where big data can have a profound effect.

Across a wide range of industries location data and location intelligence is helping to maintain a competitive edge. It is being used to deliver insights that have previously been inaccessible.

Location intelligence is the practice of using location data to achieve business outcomes. The process uses mobile devices and sensors to visualise and enrich understanding of how devices move in the real world.

The growing amount of precise data available will provide some challenges for those in the industry.

Privacy concerns will remain front and centre as they have done for most of 2018. Data quality is still an issue that many providers need to address. Businesses will need to find an effective and seamless method of consuming and getting the most value from location data.

Here’s what we think will be the biggest challenges facing the location data and location intelligence industry.

 

Challenges in location data

With this in mind, what are the biggest challenges that location faces in 2019?

 

Consent and privacy concerns

2018 saw the introduction of GDPR in Europe.  In the US the upcoming CCPA act data privacy will still be front and centre in the data community. We are quickly moving towards a world where each individual will have control over their data.

Businesses using location data will need to take a similar approach. It’s pivotal to allow the individual to take control of their data. Companies must inform users of how their data is used. They must provide clear opt-in and opt-out solutions so that transparency can be placed at the centre of the big data revolution.

Businesses that utilise location data will need to be clear on how they collect and use consumer data. Location data providers need to have a clear opt-in process that allows consumers to understand how their data is used.

Data providers should provide solutions at the point of collection which allow them to manage consent preferences all the way through to the point of data use.

 

Data quality and standardisation

Many businesses look at a lack of accuracy in the location data that fuels location intelligence as a big challenge for the industry.

With the growth of geodata, many new providers have offered sub-par datasets with limited accuracy. These providers often have little transparency in how their data is collected and how accurate it is. For the proper application of location data, businesses need to be able to verify the data collection methodology.

The most accurate providers will be able to verify their first-party data sets. They can provide a detailed explanation around data collection. Accuracy in location data can be useless when it is just a metre out.

To avoid these poor data sets, location intelligence solutions should actively verify and remove inaccuracies in the data. The space requires a clear and transparent process for data users to see the entirety of the process, from collection to use.

For example, Tamoco is providing an extra data set which provides a clear and transparent rating for every data point collected. Our visits dataset demonstrated the accuracy of each datapoint. It can be used by business to filter out inaccuracies. It also provides transparency, allowing the end data user to understand data collection and data methodology.

 

Being ready to consume the data

More industries are looking to benefit from location intelligence. It will be more critical than ever for these businesses to be ready to consume the dataset.

For some, the use of location data to understand movement patterns will be a new data source. Providing a dataset that is ready for instant consumption will be a crucial challenge for many location intelligence providers.

Another challenge will be creating a solution that allows business to combine and manipulate location data alongside current datasets. This will maximise the effect that location intelligence can have on business functions.

There will be a move towards integrated solutions that will work as a service for location intelligence. With standardisation, it will become easier for companies to ingest large amounts of location data.

 

Effectively cleaning and normalising the data.

Many businesses that are interested in location data have concerns when processing data. Location data can be a challenge to clean and normalise for a business’ analytics or other functions.

These challenges usually involve transforming the data into a workable format. Verifying the data is up to date, and accurate is another issue for companies using location data.

A related but slightly different challenge is being able to understand when the data is appropriate for specific analytics.

At Tamoco we are working closely with our partners to create a data source that is thoroughly verified and cleaned. Our sensor driven approach makes us industry leading concerning accuracy and provides a more precise base to clean our data for customer use.

In 2019 location data providers will need to work closely with their customers to understand the data cleansing process. They must provide standardised documentation and solutions that help partners to get the most from the data.

The potential for location data remains enormous. In 2019 challenges in data collection and processing will need to be addressed. Fine-tuning a robust process that addresses security concerns will be the highest priority. Developing and managing the standardisation of location data will be another.

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Data

Location Data And Location Intelligence In 2021 – What To Expect

What is location data and location intelligence

In 2019 the number of connected devices will produce more data than ever before. As this increases the data will become fundamental to many industries and businesses.

Data will fuel everything from city planning to advertising and marketing. Location-based data will be more accurate than ever before, and it will be used more in areas where big data has already had an initial impact.

Across many industries, location data and location intelligence is proving to be a powerful tool for companies looking to maintain a competitive edge. It is being used to deliver insights that have previously been inaccessible. In 2019 accurate and precise location data will be leveraged to generate new insights and power better understandings of behaviour and movement.

Location intelligence is the practice of using location data to achieve business outcomes. This uses mobile devices and sensors to visualise and enrich understanding of how devices move in the real world.

These interactive data sets are used alongside a business’ current solutions to create a powerful competitive edge and optimise business functions.

With strong roots in advertising location data and location intelligence are increasingly being utilised in new verticals from finance to planning and construction. In 2019 location data will be more commonplace, and location intelligence solutions will be fundamental to the success of many different businesses.

 

The future of location intelligence in 2019

With the rise of location data and location intelligence businesses will see new use cases and more powerful datasets.

Changes that we expect to see in the space will include the following:

 

Combining datasets

The next step for location data sets is combining with extra information to enhance the value for data users.

In 2019 data providers will provide more detail around datasets as standard. Demographics and other metadata will add value out of the box. As well as this there will be an increased number of businesses that will combine their datasets with identifiers in the location data to improve functionality and create new ways of understanding existing datasets.

This also means that it should be clear and transparent which data sets are used. Providers should have a standardised way that business can filter and understand which data points should be ingested and used alongside the correct datasets.

In 2019 location data will be more transparent and more accessible to use alongside existing customer datasets to maximise business functions. By linking location data businesses will instantly have a more detailed view of their customers or relevant segments.

 

More uses

Location intelligence is beginning to gain prevalence outside of the marketing industry.

A considerable majority of marketers (82%) are now planning on upscaling their use of location data over the next two years.

As location data use in marketing reaches a critical point, we will begin to see it adopted more readily in other industries and feature in new use cases.

As more organisations document and formalise their use of location intelligence the value of these datasets will become more apparent and are adopted outside of the traditional verticals, such as marketing and advertising.

While marketing and advertising are still the most common use case for location data and location intelligence; there is a dramatic increase in both the number of industries that say they will invest more in the technology and those that already have.

Expect to see location data involved as a fundamental part of any effective BI strategy. Understanding device movement with precision will fuel predictive capabilities. Location intelligence will be widespread in everything from City planning through to logistics, automation and investment.

New use cases will emerge. Location data will be used to understand global trends for journalistic purposes to understanding environmental changes and populations.

 

Data accuracy and quality

New industries will require better accuracy and a better way of consuming the insights that location data can provide.

In 2019 we will move away from talking about postcodes and large scale geofencing. Instead expect to see granularity, three-dimensional location and precise location intelligent solutions.

Businesses must take advantage of a focus on accuracy in the industry and start visualising and analysing datasets at a deeper geographic level.

Many businesses are currently using geographic boundaries such as postcodes or large geofences around more extensive areas.

The technology is now much more granular than this and businesses should be aware of the new and custom functionalities that the leading location data solutions can provide.

Expect to see more detailed datasets that include variables to filter out inaccuracies and other data points that are not useful for the end data user. These processes will become automatic in 2019, and the best providers will be able to boast systems that can automatically detect these outliers in the data.

 

Analysing the data

As big data becomes fundamental to many organisations, more datasets will be available.

More data types mean better unification is needed for customers because of this 2019 will see the emergence of LaaS (Location as a service).

Ready to use location platforms will empower businesses to leverage the unique insights that location data offers. It will allow them to visualise and digest insights easily. It will support manipulation of data and provide functionality to integrate these into current business functions and solutions.

These platforms will be closer to real-time than before and provide more granular insights that will allow businesses to act immediately on these insights.

The experts in precise location

Get in touch to get ahead with location intelligence or to see how you can use location data to benefit your business.

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Data

Big Data And Big Brands – Why Data Is The New Brand

Big data brands

The modern brand faces many challenges. In today’s world consumers exist in multiple locations, across many devices and they have different expectations of what a brand should be.

The modern consumer has set the bar pretty high. Brands need to keep up with consumer’s fast-changing needs and desires.

From personalization to the user interface, the consumer has demanded that brands can adapt and communicate with each consumer on an individual level.

Information should be the heart and soul of a brand. Big data is helping to achieve these goals and create a new kind of brand.

In this way, data is becoming the new brand.

Why is big data important?

Most brands realise that data is important to the success of their business. But the brands that are really at the forefront of the digital revolution are the ones that have learned how to make the most of this data.

Placing data at the centre of business strategy is more important than ever. As the competition gets smarter, those that can find a way to utilize the data that is generated from the modern world will be the ones that succeed.

 

Why is data the new brand – big data applications

Big data and personalization

Data is the new personalization. The best brands know exactly what their customers want. They know when they want it. They know how they want it.

The thing is this isn’t guesswork. These brands are delivering personalization with information. Big data and personalized messaging form part of any successful brand strategy.

These brands have a method of generating data from their customers. They are effective in managing this data. And finally, they are efficient in using this data to understand customer success.

 

How does data inform personalization?

Big data personalization is being utilized by more and more brands. It involves using consumer-generated data to understand behaviour. The brands that are doing this best utilise multiple data sets in order to deliver engaging and personal experiences to customers.

 

Big data and product development

Developing products is hard. Data helps to generate product ideas by helping to illustrate how the customer actually use a brand’s product.

Brands that get product development right are using information to inform their process. They are using data in product rollouts and how new features are used to inform future product development.

These metrics can be more than just simple usage data. The explosion of different data types has provided much better insights into how customer use a product.

Powerful data mapping techniques are being used to map new datasets over existing data sources to provide even more insight to product designers. It’s not just about when and how much the customer interacts with the brand.

It’s now about looking at where and why customers use a product or interact with your brand. The top brands are using this data to inform their decision making. Extra datasets, such as location can give far more insights into how your product is being used.

 

Big data and business intelligence

Implementing a data strategy has had a wide-ranging positive impact outside of the marketing and product departments.

Data is helping brands to become predictive instead of reactive to consumer trends. The leading brands don’t just understand what their customers want, they can identify these earlier than ever before. All thanks to data.

In verticals such as retail, analytics data can be crucial. Becoming a digital-first brand involves using actionable data to gain a competitive advantage.

New data sets are helping brands to fill in the gaps. Especially in industries that have traditionally been slow to gather consumer data.

Business intelligence is now fuelled by data sets such as purchase data, location data and IoT data, amongst others.

The financial sector is using data sets such as location to predict the earnings and ultimately the financial success of brands. So why can’t brands use the same data to inform their strategy?

These data sets can be instrumental in helping brand management plan intelligently. Big data can help predict where to open new stores. They can help understand what consumers want and help find where these customers will be in the future. As well as this big data can be used across a brands marketing arsenal, from targeting to SEO tactics. You could even get yourself a UK SEO consultant to implement these. Even when using the big tools (such as BuzzSumo) there are many great free BuzzSumo alternatives to help with these tactics. 

Using data as a competitive advantage should be a fundamental part of any brand’s strategy.

 

Brands and big data examples

Many brands come to mind when you think of big data. What do all of these brands have in common? They understand the value of different datasets. They use the data effectively

 

Netflix

Netflix has understood the benefits of data from the very beginning. A user’s viewing history is used to suggest new content in real time. It’s used to recommend new shows and it’s used to do this at the right moment.

Netflix uses data and analytics to understand what it’s users want to watch. This data is also used to predict trends and it’s used to commission new original content. This is an example of using data sets to become a predictive brand.

 

Hedge funds

The investment space is quick to realise the impact that data can have. This is an industry that lives and dies by its ability to predict the trends of huge amounts of people and the businesses that they interact with.

The sheer quantity of data is not all of the story. Getting the right data is more important. Hedge funds have seen some success by utilising data sets that aren’t commonly used. These are useful as they can provide insights that have previously been unattainable.

A great example of this is location data. Accurately mapping footfall and visits with location data can give a new insight into the offline world – a place where large-scale macro trends have been difficult to pin down.

Using these insights to predict financial trends can give financial companies the edge.

 

Conclusion – data is the new brand

As a brand you need to ask yourself – Is there a better kind of data that can help to inform your needs. You need to be sure that you can use data quickly and effectively.

Data is instrumental in the process of personalization, building great products and gaining business intelligence.

If you want to be a smart brand that can understand and predicts what the consumer wants you need data. If you want to deliver this in a personal and engaging way you need data. If this is what you aspire to then you will eventually arrive at big data. That’s why data is the new brand.

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Data

Location Data Accuracy – What Makes Good Location Data

Location is a powerful tool to understand how audiences move in the offline world. It is being used across a number of industries, fuelling everything from innovative mobile apps to providing detailed intelligence and analytics around device movement.

For these applications to prosper the data that underpins them needs to be accurate. This is not always the case. We’d like to talk about some of the attributes which make location data reliable and actionable.

 

Accuracy and precision

These two things sound similar. But in the world of location, there is a subtle distinction. Accuracy refers to how close the measured location is to the actual location of the device. Precision refers to how close together a number of separate measurements are.

The precision provides the granular insights that overlay the accurate data sets. At Tamoco we add this precision through sensors and our proprietary SDK.

 

How we determine device location

Tamoco uses sensors that exist on our network to help with issues in accuracy.

The Tamoco SDK identifies these sensors which are in ‘known locations’. This allows the SDK to determine where the device is in relation to wither Bluetooth or wifi signal strength.

In some locations, GPS location can be inaccurate and imprecise. For example, inside a shopping centre where the GPS signal is not as strong. This is where Tamoco’s sensor-driven approach provides extra precision.

 

What are the sensors and what are the benefits?

Beacons

Beacons provide very accurate and precise data. These sensors can be used to place devices with an accuracy of 1m. Beacons are the most precise location sensor that is widely deployed.

WiFi

Very useful in identifying precise location in densely populated areas. These sensors help identify device location in areas such as shopping malls and large buildings.

GPS

Under the right conditions, GPS can be extremely accurate. The signal quality deteriorates quickly when the device is indoors and in areas where the device does not have an unobstructed view of GPS satellites.

The Tamoco SDK uses multiple sensors simultaneously to identify the location of the device. Often the device location is constantly updated as more sensors are identified. The SDK uses this information to determine context and then discount outliers in the data.

This process of identifying location can be visualized as a linear process. In this process, the Tamoco SDK uses a number of sensors to validate and adjust an initial location signal to a finalized and verified location, in the form of latitude and longitude.

 

Other factors that help to identify location

We use other device information to identify the accuracy of each location signal. We use vertical and horizontal accuracy to show the reliability of the lat-long derived from the device.

These fields are intended to provide transparency around location signals. Tamoco wants to include these so that partners, developers and clients can understand the level of accuracy in every data point that they process.

Standardizing this into an industry-wide and agreed measurement is the next step. Only location data providers with inaccurate data sets will have a reason not to adopt these standards.

 

Benefits

Why is this relevant for publishers?

These levels of accuracy mean that app partners can maximize their CPMs from the monetization of location signals. Many monetization partners pay higher amounts for accurate data sets. They are, unsurprisingly, unwilling to pay for data that is either inaccurate or imprecise.

If you use location in your app experience adding extra accuracy and precision will boost the experience for users. Delivering location-based communication and contextual app experiences are more likely to be a success with greater location accuracy.

Why is this important for data buyers?

Of course, the most important thing for a data buyer is the accuracy of the data. This is true regardless of the desired use.

Accuracy in location data means that insights are more reliable. Targeting is more effective. Using location data for complex tasks like visit attribution is nigh on impossible if the data is unreliable.

All data buyers should be sure that the location data they use is verified. Location data providers should be able to explain their methodology in detail to demonstrate that data sets are precise.

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Data

How Big Data & Location Intelligence Is Changing The World

There’s no doubt that the explosive rise in the number of smartphones has changed the world as we know it. The increased number of sensors and connected devices has produced mountains of data. This is being used to transform the way that we live our lives.

IoT, location data, location intelligence, big data. Whatever your name for it, it’s hard to dispute the potential across a variety of industries

It’s now apparent that granular location data can provide unprecedented insight into the offline world. More businesses are realising the value of mobile location data and the impact it is having across the globe.

As we move away from unreliable data sets, sensor-driven accurate data sets are taking centre stage. This kind of accurate data has many applications. But I’d like to look at some that are having the greatest disruptive impact.

Business intelligence

The ability to notice trends by using data isn’t new. The ability to do this based on people’s activity in the offline world, in a close to real-time manner is.

Location intelligence reveals relationships between big data sets that often would be missed. It turns these insights into actionable business intelligence. Helping inform decisions, from the boardroom to the storefront.

From the small bar that is competing with huge chains of venues through to the small retailer competing with online mega corporations. These businesses are gaining valuable insights from using this kind of big data to inform their business strategy.

The truth is that mobile location data has now matured enough to solve many problems that both small business and enterprise face. Let’s look at a few:

Financial services – understanding footfall through big data sets is valuable for the financial sector. Mobile device data can help to forecast earnings and other KPIs before they are formally reported. This helps inform investment decisions.

Retail – big data can help both small and large retailers. Understanding store visits, as well as customer behaviour through mobile device data, is having many positive effects on the retail sector. These insights can help inform business decisions such as store layout, opening times, staffing and more.

 

Infrastructure and planning

We’ve all heard of the term smart city. We like to think that there’s more to it than just adding a few data points and putting the word smart in front of it. It is, in fact, more than that. We’re moving towards urban centres with huge populations and aspiring towards self-driving vehicles. Big data is the key to unlocking this truly smart future.

The rise in mobile device data has provided better opportunities to understand how cities work.  It’s helping to create systems and infrastructure that reflects this.

Combined with the increasing number of connected devices in cities, central planning authorities now have a set of tools that can inform decision making in many different areas.

Mobile location data is contributing to a better understanding of where demand for public infrastructure is greatest. For example, examining mobile device location data to understand the most cycled roads within a city. This information is precise and invaluable when planning where to implement new routes.

The same is true of traffic and congestion. In increasingly crowded and polluted megacities, it’s important to understand how traffic issues can be alleviated. Understanding traffic flow and where to build new road structures, or introduce new low emission zones is crucial to building the kind of smart city that can sustain current levels of population growth.

Big data is having a huge positive effect on this kind of planning. Thanks to the accuracy and uniqueness of mobile device data and location intelligence, it is changing how decisions are made in cities and towns around the world.

Marketing and advertising

Big data and marketing have always complimented each other. Marketers have always looked to use data sets to improve the efficiency and effect of ads. Using big data to create tailored and relevant audiences is not a new practice.

But mobile location data allows marketers and advertisers to connect digital advertising to how consumers behave when they are offline. Understanding how consumers move in the offline world is helping marketers to become more effective. It’s assisting marketers in providing more personal advertising to consumers.

Location intelligence is disrupting many stages of the consumer lifecycle. It’s bringing the analytical capabilities that have been available for the web to the real world.

 

Segmentation

Mobile device data is helping to build up complex pictures of how people move and behave. This helps advertisers to build complex customer profiles. Brands are finally understanding the places that their customers go and how they interact with the physical world around them.

This is far more effective than other methods of audience segmentation. This is because a person’s location is often a much greater sign of intent than when they are searching for something on a computer, or browsing on their phone whilst sat on the couch.

This allows marketers to identify exactly where consumers are on the buyer journey. Moreover, it allows them to do this with a greater level of detail.

 

Personalisation

One big breakthrough that big data has had on marketing and advertising is by increasing the ability personalise at scale

Location data is allowing brands to be helpful and human by understanding the situation of the customer. The concept isn’t new, but the accuracy and increasing size of data sets in the space have allowed commutation to really get personal.

Location help provide promotions at the moment when the customer can actually redeem it. It allows the ‘customers also bought’ experience to reach the real world retail store. In this way, big data is providing digital solutions to offline problems. Location intelligence is tailoring brand communications to a person’s unique experience of the real world.

 

Customer experience

Big data has changed customer experience for the better. Location intelligence can help to automate way-finding, ordering, assistance and queue management. Understanding the physical location of a person has helped improve the guest experience across many sectors.

Stadiums, resorts, airports, transport hubs all stand to improve the experience of the people who spend time and money in these places. It might be location based ticketing – you buy your ticket by walking onto a train. Or it may be ordering food and drink to your location.

There’s still huge scope for big data and location intelligence to be applied to improve the customer experience.

 

Attribution

Mobile device data as we have seen has connected many digital walks of life to offline consumer behaviour. Another way that this technology is revolutionising the marketing and advertising space is through attribution.

Traditionally many advertisers have been blind when it came to measuring the impact of offline ads on offline KPIs.

But the mobile device location data is filling in the blanks. Location intelligence can understand when a person is in front of, for example, OOH advertising. It can then measure how many of these people are then seen inside a store or in front of a specific physical product.

Connecting the two provides an accurate way for marketers to measure the impact and ROI of offline advertising inventory. it also allows them to measure the effect of digital advertising on an offline goal. These things have just not been possible with certainty before. But big data has changed the way that advertising can be measured.

 

AR

If AR is really going to live up to its promises, it will have to rely on complex data sets and accurate location intelligence.

As AR gains prominence it’s application will move beyond fun to play games to useful productivity applications (you can even combine it with some powerful notion templates to really level up). As AR develops, it’s used as a way of reaching audiences with content and advertising will grow. Like previous marketing activity, it will be improved by the use of big data and location intelligence.

AR will require huge amounts of accurate and real-time location data to function properly as a user moves around the real world.

Optimising the supply chain

Big data and location intelligence is impacting organisations that want to optimise the supply chain.

The obvious application of location intelligence in the supply chain lies in the ability to understand and track deliveries and supplies. It already being used to generate data sets which can optimise and improve these services.

But location intelligence isn’t just helping business to optimise the process. It’s helping to understand the demand for products. There’s a lot of history of people building something in the hope that people want it to then find out that actually, they don’t.

Another way that big data is helping manufacturing industry to optimise is by helping it to adjust the type of transportation, pickup location or place of sale.

With the rise of location data, these insights are now fuelled by information from the offline world. Insights that have previously been unattainable or have lagged are now available in real-time. This lies at the heart of what is disrupting how the supply chain operates.

 

Privacy and transparency

As usual with new disrupting activity, the focus in on the responsibility of these new technologies. And rightly so. Indeed those in the big data space will need to be more transparent in how data sets are sourced. 

It won’t be enough to simply check a box and start collecting and aggregating personal data. More needs to be done in order to clean up the data supply chain. More control needs to be handed to the user. 

In this way, it’s our responsibility to communicate the value of big data and location intelligence to the user. It’s having a huge positive impact across the globe, and that’s just more reason to get the privacy part right. 

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Learn more about big data

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Categories
Data

Location Data And Location Intelligence In 2018

The big data analytics space is growing at an alarming rate. We’re all increasingly connected through our mobile devices. This growth has provided huge amounts of data around audiences and how they behave. What kind of competitive advantage can we expect to see from this location data going forward?

Location data is rising in importance. But there are challenges that companies face as location data becomes commonplace. Businesses will need to understand the best application for this data. We’ll look at this as well as how location intelligence will be put to use across many verticals.

How will location data change the way that businesses approach marketing?

Attribution

Location data will become invaluable to marketers as they look to close the online to offline attribution loop. Traditionally this has been a problem for marketers. It’s difficult to attribute real-world visits and purchases to online marketing. 

Location intelligence will help to illuminate what happens in the offline worldwith unprecedented accuracy. Big data will provide insights well beyond the capabilities of loyalty scenes.

As programming becomes more prevalent, location intelligence will be used to ensure that budgets are optimised. With the improvements in attribution, marketers will be able to accurately understand where ad fraud occurs.

Personalization

Big data breeds personalization. Many a case has been made for the importance of big data in personalization. Location intelligence will help marketers to automate personalization. This, in turn, will deliver accurate, personalized content to the right user in the right place.

Big data insights from location data

Location data will provide a competitive advantage, and not just in marketing and advertising. More industries are realising the benefits of real-time mobile location data.

Location data is being collected and stored by over 90% of companies with over 500 employees. The applications of this data are wide-reaching. Understanding consumer behaviour accurately, and in real-time will signal the adoption of location intelligence outside of the marketing and advertising industries. Location data will allow cities to become smarter. It will allow transportation to become more efficient. 

 

Accuracy and reliability of big data

The accuracy problem

The problem has previously been the accuracy of the data. For companies to use big data to inform business decisions, the data has to be reliable beyond doubt. Location intelligence and precise sensor-driven data now provide this certainty. 

Accuracy and precision will become of paramount importance as technology gets better.  It’s important that businesses that benefit from location data and location intelligence can safely say that they are working from accurate sources. 

First-party location data

Challenges in sourcing accurate first-party data were found with scalability. With a lack of first-party data, unreliable third-party data was used. Often out of date and imprecise, the insights were not reliable. Especially for businesses serious about using location intelligence for commercial success.

Today Tamoco adopts a network approach to location intelligence. This means that through our mobile SDK we can understand mobile device location directly. Thanks to our network approach, we can understand these signals across multiple sensors types. 

This improves accuracy and will allow businesses to act based on a more precise understanding of audience behaviour. 

Real-time data

Location intelligent decisions must be informed by real-time data. Location data must be instantaneous. Data that is out of date is simply not useful for businesses. This means that data must be communicated in real-time to optimise and achieve the desired goals. 

This is another reason why first-party data is important. It allows for location intelligence to occur quickly.

The location of things

The IoT will soon be in everything. For example, by 2020 IoT technology will be in 95% of new product designs for electronics. Now that’s great but it’s only the beginning.

This huge growth will produce large amounts of data. However, it’s may be difficult to interpret data sets without location. Location adds context and a better understanding of what is happening in a specific location. This allows us to better understand the offline world. 

The location of things – the IoT is actually only useful if you know where the thing is. Tamoco’s sensory agnostic approach allows us to gain location intelligence across a wide range of sensors. These range from beacons, Wi-Fi and Geofences to connected IoT devices. This approach allows us to understand location across many different sensors. This creates a deeper understanding of the offline world. Businesses using location data will have to take a similar approach to understand the connected world we live in.

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Discover location data

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