Tag: Big Data

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Marketing & Advertising

1st Party Data, 2nd Party Data, 3rd Party Data – What’s The Difference?

2nd party data

Introduction

 

Data is changing everything. Data is everywhere. Over 2.5 quintillion bytes of data are created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth.

For marketers, this is an awful lot of data to keep in check. It’s easy to be overwhelmed by the amount of information available. Even more so when we hear terms like 1st party data, 2nd party data, and 3rd party data.

To help make this easier there are several ways of classifying data. This helps us as marketers and advertisers to understand the relationship between datasets and understand what each dataset can be used for.

 

Why is this important?

Data is one of the most effective tools to drive successful marketing. Marketers that can make sense of the data at their disposal and deploy it successfully have demonstrated the rewards that come with it.

Depending on your goals different kinds of data will be more relevant to you. That’s where data classifications such as 1st, 2nd, and 3rd party come in.

Let’s look at what these terms mean and how each type is relevant for your marketing.

 

1st party data

First party data is data that you have collected directly from your audiences or your customers. It includes

  • Data from your CRM
  • Behavioral data collected from interaction with your business (website, app, stores)
  • Data around subscriptions
  • Data generated from your social media accounts

This data is generated directly from your customers or your audiences. This data set is genuinely regarded as the most valuable as you are aware of the method of collection and it is generally free or costs little to attain.

First party data is easy to collect and manage in solutions like CRMs and DMPs.

As 1st party data is collected by you directly, any privacy issues are minimal (assuming you are following the correct procedures!). You own your data directly, and you know exactly from where it came.

First party data is extremely valuable as it provides valuable insights around customers and audiences for little to nothing. Companies that aren’t collecting and activating first-party data are missing a trick.

 

What you can do with first party data

For organizations taking control of first-party data collection should be an immediate priority. Because you have complete control fo the data, it is of higher quality. There are many different uses for 1st party data from monetization to engagement.

 

Engagement, personalization

Your first party data can help you to personalize your marketing and help you to engage with your customers.

1st party data can effectively segment your audience and allow you to create more specific, and personalized ads for your customers.

Insights

First party data can be invaluable in helping you to understand your customers. You can identify and map out the customer journey or see how your users behave and interact with your business or product.

 

Monetization

First party data is highly monetizable as you can demonstrate the methodology of data collection. The data carries maximum revenue as you didn’t have to purchase the data. First party data monetization can help to generate revenue to fund other areas of your business.

 

2nd party data

Second-party data is a term that is making more of an appearance these days. We hear a lot of people asking “what is second party data?”.

Second-party data is somebody else’s first-party data. This data comes from their first-party audience, the source is clear, and the provider usually demonstrates the accuracy and collection.

You can purchase 2nd party data straight from the provider – there’s no middle party in these instances. This allows you to form a relationship with the provider and understand the value in the data.

The data is usually collected from the same sources as your first-party data. However, some specialist 2nd party providers provide unique datasets that offer new insights.

These come from behavior outside of your audience, so it’s likely to include potential new customers.

 

What can you do with second party data

Second-party data is a relatively new concept, but it carries enormous potential for marketers. Because it comes directly from the partner and because you will likely have a direct relationship with the company it’s easier to verify the accuracy of these data sets.

The data is more consistent and will be more precise than a bunch of aggregated third-party data sets. 2nd party data offers more transparency for the end data user, making it easier for you to understand the value that the data can bring to your business.

 

New audiences, new business

Second party data is great for prospecting and reaching new audiences that aren’t already a part of your audience.

This allows you to expand into new regions or new demographics with new products.

 

Scale-up

Second-party data can help you to fill the gaps in your existing 1st party data sets. Your datasets might be high quality but might not be large enough to scale your business in the way that you want.

Supplementing 1st party data with 2nd party data is an effective way to make your campaigns reach more people without compromising on quality.

 

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3rd party data

Third party data is data that is purchased from outside sources where the seller is not the direct collector of the data.

These data sets are usually aggregated and could have been resold multiple times. These aggregators pay other businesses that generate the data and collect it into a single dataset.

This large dataset is then broken down based on demographics, behavior or another characteristic. This allows the data to be split into segments that are easily resold.

Third party data is often bought programmatically – this means that it happened quickly and on a large scale. This means businesses can purchase large amounts of data for their campaigns quickly. The downside to 3rd party data is that it’s much harder to verify where the data came from and how it was collected.

 

What you can do with third-party data

Third party data accuracy is hard to verify, and its availability is public, so that means that other companies (and potentially competitors) will be using the same datasets as you.

But that doesn’t mean that it carries no value for marketers. It can be useful when it’s appropriately combined with your first-party data.

 

Expand your audience

Combining third-party datasets with your first party data can help you grow your addressable audiences. Lookalike modeling can identify characteristics in your current dataset and look for similarities in third-party data to find similar prospects.

 

Tips for marketers and advertisers

Third-party data has been prevalent in many marketing campaigns in recent history. The vast amount of data that is available along with the results it could potentially generate have made it a valuable asset when combined with your first-party data.

However, two significant developments have occurred which have made second party data a much more reliable option for marketers.

 

Privacy

Unless you have had your head in a ditch for the last few years, you’ll be well aware of the GDPR in Europe. Privacy concerns in third-party datasets are always present for marketers.

Often third-party datasets are hard to verify in terms of consent. This is where a direct relationship with a second party data provider is useful. You can verify the consent process and ensure that the data is collected in accordance with the relevant privacy standards.

 

Accuracy and transparency

The truth is that your first party data is more transparent than any other kind of data set. Hover that doesn’t make it accurate. Third party data is difficult to verify in terms of accuracy and often transparency isn’t even part of the discussion as it’s an aggregated dataset.

Second-party data is as good as first-party data as long as you can identify the methodology and verify that it meets your accuracy standards. This is again why a direct relationship with your data providers is a useful situation to be in.

Many second party data providers can provide their data directly into your data management solutions. SO where your first party data sets aren’t enough, you can purchase new data from reliable and transparent providers directly in your existing infrastructure.

Data is here to stay. As a marketer, it’s your job to ensure that your data is activated and that your second and third party data is accurate and transparent.

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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. 

Categories
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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Categories
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.

Categories
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 booster 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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Categories
Retail

Big Data In Retail – How Data Analytics Can Transform Retail

The retail industry is evolving rapidly. The way that consumers shop is changing. The line between online and offline is blurring and more retailers are adopting a data first strategy, helping to understand how their customers are behaving and ensuring that they can match the right person the best product.

Retail data analytics is the new normal. The brands that have access to high-quality data, and know how to use it, are the ones that will deliver unprecedented value to their customers. Let’s look at how big data can be used in retail analytics to gain a powerful competitive advantage in a highly competitive space.

 

Complete understanding of customers

Big data analytics can help retailers understand customer trends in great detail. Behavioural analytics help retailers to predict what the next big trend will be based on these data sets. This kind of insight can have a positive impact across the retail business strategy.

This helps brands identify new preferences much quicker, helps them to avoid churn and ensures that acquisition costs are kept to a minimum. Data sets based on how consumers behave online can help retailers to predict what will be the next must-have items, for example.

Other predictive datasets include location, which provides an understanding of the demographics that visit your stores. This allows retailers to adapt to their changing customer base. For example, there might be a growing number of millennials visiting your stores. Learning this in real-time allows retailers to be proactive. This could be in terms of the in-store experience -it could make sense to open a specific area with products relevant to these demographics.

 

Customization and personlized promotions

Retail marketing means matching customers to a product like never before. Big data is making this easier than ever before. Predictive analytics can help you to find the right customer for your product with incredible accuracy.

Data sets that include location can help you understand exactly how consumers behave. This helps retailers to create a better understanding of their customers and create better targeting solutions that provide value and personalize communications.

In-feed targeting can be a hugely effective way to reach the right customer with the right message when the right data sets are used. Data sets that can tell you how customers interact with your brand online and offline allows retailers to provide highly contextual communications.

This level of personalization also helps to optimise media spend and ensure that you are reaching the right consumers with the right message.

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Get in touch to see how data can transform your retail strategy

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Physical store layout

Personalisation also includes the in-store experience. Big data provides retailers with high-level insights around how consumers move in their retail stores.

These datasets allow brands to analyse store behaviour and measure the impact of marketing spend in store.

Big data in retail stores can provide a competitive advantage in cross-selling and it can significantly boost the power of in-store promoting.

As well as this it can provide a powerful method of understanding how to layout physical shopping experiences to maximise engagement and ensure that consumers are provided with optimal smart shopping experiences.

 

E-commerce marketing strategy

E-commerce is a huge part of the modern retailer’s arsenal. It’s vital that brands can leverage retail data analytics to help make the experience seamless.

Customers want an online shopping experience that follows on naturally from their store visits. They want the two to work together without any issues. Services like click and collect as well as abandoned baskets messages are seen as a something consumers expect from top brands.

Big data can help to fill in the gaps that allow retailers to link the Ecommerce world to the physical retail store and provide a seamless shopping experience for consumers. Linking Ecommerce to the physical store and ensuring that consumers can find solutions easily is a major benefit of big data in retail.

 

Order management

Behind the scenes, big data can help to inform the supply chain and optimize production. This means that customers aren’t disappointed when a trend takes off and the product is out of stock.

Using big data in retail supply chains is instrumental in predicting trends and ensuring stock is in the right place. When big retail events hit, such as Black Friday, data set such as location insights can be instrumental to monitor areas where there is increased demand to allow retailers to react to changing demand and trends.

 

Big data in retail helps brands to answer questions that are crucial to growing a modern retail business. It helps you to answer these questions quicker than ever before:

  • Who are your customers?
  • What motivates them to visit?
  • What engages them?
  • How do they behave outside of your store?
  • How can you target them?
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To find out how location data and location intelligence can help your business please get in touch with our team.

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Categories
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 retail 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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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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