Business Data Marketing & Advertising

Why Using A Customer Data Platform Will Take Your Customer Experience to the Next Level

Why Using A Customer Data Platform Will Take Your Customer Experience to the Next Level

There’s no shortage of marketing tools that capture and analyze customer data. The problem? When businesses analyze customer data, each data set is usually treated as a standalone. But siphoning through data set after data set can be costly and inefficient. 

So, how do marketers combine different data sets into a single customer view? 

They use customer data platforms (CDPs). 

What are CDPs?

CDPs are data platforms that capture data from various sources and display it in a single unified customer database. In other words, they consolidate and integrate customer data into one central platform. This way, businesses can pull insights on a specific customer or prospect during various points of the customer journey. 

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Like all marketing tools, businesses use CDPs to understand their customers’ unique preferences and behaviors. 

But what sets CDPs apart from other tools is their ability to help businesses create customer-centric experiences.

CDPs give businesses the data they need to create relevant messaging — all in one place, in real-time. This helps them create messages that are custom-tailored to their customers’ preferences. When customers feel like a company knows them, they’re more likely to stick around. 

Let’s take a closer look at how you can use CDPs to take your customer experience to the next level.

Building a sales process 

Using a customer data platform can be invaluable to building a sales process that entices your customers to say “yes” without resorting to slimy tactics. 

Below, we’ve listed a number of ways businesses can use CDPs. 

1. Map out the buyer’s journey

Buyer journeys aren’t simple straight lines that lead to a sale. They zig, zag, turnaround, and zig again. 

Today, a buyer’s journey could start on one channel and toggle between several steps. Knowing how that journey ebbs and flows gives your organization a chance to ensure no customer slips through the cracks.

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That’s why it’s essential to map out your company’s buyer’s journey. 

Every step and interaction a customer goes through during the buyer’s journey produces customer data. 

Businesses that study these interactions and look for patterns can predict future buying habits. Understanding this behavior shows you what customers align with and what turns them away. 

To sum up: Understanding what actions customers complete before or after purchasing helps you nurture leads and create more enjoyable customer experiences.

2. Craft customer-centric sales funnels

Mapping out the buyer’s journey and analyzing customer behavior gives you a competitive advantage. 

When you can predict customer behavior, you’ll be able to:

  • Understand buying habits
  • Share relevant offers
  • Personalize content 
  • Build long-term customer relationships 
  • Speak to customers in their preferred communication style 

You’ll also be able to craft customer-centric funnels. 

Customer-centric funnels use the data you mapped in your buyer’s journey to take customers through a personalized sales experience. 

Each stage of the funnel is personalized for your customers. Some people will need more nurturing before making a purchase, while others won’t need much coaxing at all.

For instance, some customers will need a whole series of offers while others will buy after just two. 

Funnels also take some of the pressure off your sales team and create passive business revenue. 

If done well, they also help you build long-term customer relationships and repeat sales. 

3. Analyze detailed customer profiles

The most valuable part of a CDP is the personalized customer profiles. These in-depth, single customer view profiles are what set CDPs apart from other systems. 

Profiles detail each person based on data pulled from an array of channels. This means wherever your customer is — you are. Whether they’re in person or online, you have a compilation of their behaviors and preferences. 

This helps you craft unique experiences you know a customer will love. 

In the end, customers want to be seen as individuals, not as lead prospects. It can get cloudy when you’re analyzing data. Sooner or later, prospects look like sales targets on a spreadsheet. But CDPs have the distinct ability to create comprehensive profiles that feel human.

Every data point serves a purpose, is cleaned, and deduplicated. Next, the datasets are grouped together by theme. Finally, the data generates a unified customer profile. 

In a CDP customer profile, you’ll see details such as:

  • Their behavior 
  • Their engagement 
  • How they feel about your business
  • If they’re a frequent user
  • If they’re likely to re-engage 
  • Their likes and dislikes

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Personalizing marketing content and promotions 

Understanding customer behavior and preferences is essential to personalizing the content that enhances the customer experience. 

CDPs help businesses such as SaaS content marketing agencies personalize content and marketing promotions in a number of ways. 

1. Relevant messaging 

Customers don’t engage with campaigns that aren’t personally relevant. The data you collect from a CDP helps create relevant messaging your customers will connect with. Relevant messaging through target campaigns increases customer experience and loyalty.

For instance, Millennials prefer different messaging than Baby Boomers. Baby Boomers prefer different messaging than Gen Zers.

2. Content optimization

Understanding your customers also helps you optimize content to match a customer’s search intent. This helps you produce the right content at the right time.

Picture this:

You own a simple skincare brand, and you’re looking to increase your product base. Before you start getting creative in a lab, you analyze customer data in your CDP.

After noticing a series of patterns, you realize that your customers are searching for:

  • Sun protection sets
  • Bath bombs
  • Jade rollers
  • Beard oil

Not only does this show you what products to create, but it also shows you which target phrases to optimize your content around.  

You use those target phrases to plan out content briefs, research secondary keywords, and prepare SEO plans. 

By the end of your preparation, you’ve planned out three months worth of:

  • Topics to target
  • Secondary keywords
  • Frequently asked questions to answer
  • Content scores to target
  • Sales copy
  • Blog article outlines
  • Social media content 

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Along the way, you continue to keep an eye on your customer data. If you see a change in pattern, simply adjust your content as needed. 

This commitment to custom tailoring content to customers’ preferences enhances their experience with your company.

Segmenting customers 

While every customer has their own preferences and behaviors, it’s common for many to exhibit common patterns. 

CDPs give you tools to define your audience by segments based on these shared attributes and behaviors. Segments are based on rules, or they’re built using machine learning and AI. With these tools, you can enrich customer profiles with data you wouldn’t be able to gather on your own.

With the segmenting features, you can:

  • Predict customer churn 
  • Deliver relevant recommendations based on buying history
  • Identify customer advocates and frequent buyers
  • Identify similar patterns
  • Identify upsell and cross-sell opportunities 
  • Segment your customers using common attributes 
  • Tailor messages to those segments

Businesses can use CDP segmentation tools to optimize the entire customer journey from discovery to advocacy. 

To analyze and segment profile data, look for a CDP that has:

  • Prebuilt code
  • Visualizations that feel intuitive 
  • Out of the box features
  • 24/7 customer support 

An example of audience segmentation 

Let’s imagine that your business sells digital courses on personal and career development. You just set up a CDP, and you’re looking forward to trying the audience segmentation features. 

After identifying customer patterns and behaviors, you notice you have three main types of customer patterns:

  1. Frequent buyers that mainly buy career development courses
  2. Infrequent buyers that mainly buy personal development courses 
  3. Moderate buyers that buy a mix of both


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This insight helps you segment your customers into the following customer avatars:

  1. Career development customers 
  2. Personal development customers
  3. Career and personal development customers 

Later, you improve your marketing strategy to cater to these three customer avatars. You have three different kinds of social media campaigns, email newsletters, landing pages, websites, and paid ads. 

You can also repurpose content over time by launching a podcast or producing videos, for example. After this: This helps you produce the right content at the right time.

For your career development customers, you:

Focus your content on career advancement, job skills, and networking tips.

For your personal development customers, you:

Focus your content on personal growth, coping skills, and building self-confidence.

For your career and personal development customers, you:

Focus your content on how personal growth contributes to career growth.  

At this point, your content strategy is laser-focused and serves your three most common types of customers. 

Over time you keep an eye on behaviors and make adjustments when needed. You also keep an eye out for new customer avatars and buying habits.

Isn’t a CDP just a CRM?

CDPs and CRMs both work with customer data, but the two are pretty different. 

You may have wondered, “Why do I need a CDP? Isn’t a customer relationship management tool (CRM) the same thing?”

It’s actually not. 

While CRMs contain data you already know (i.e., name, email, and zip code), CDPs collect data you wouldn’t know over a specific time. 

But there are more differences between the two tools. 

Here are some major differences between a CDP and a CRM.

1. Data capacity 

CRMs were intended to keep track of customer and prospect interactions to automate the process for sales teams. They’re great for sales and marketing teams that need to pull customer information quickly. 

CDPs are great at handling large amounts of data from various channels. 

2. Known data 

CRMs only contain known data — they won’t be able to pull anything on potential customers you’ve never met before. 

CDPs work with both known and unknown data making them more valuable than most martech tools. 

3. Storage information 

CRM data stores simple information into a few fields — almost like a flashcard. It includes basic customer information such as name and contact information. 

CDPs have detailed information about a customer’s buying patterns, online and offline activity, and behaviors.

4. Data format

CRMs can’t handle data in a free-flowing manner. The system can only recognize data if it’s formatted in a specific way (i.e., a CSV file). 

CDPs take information from several sources and act as a central hub for customer data. They can handle both simple and robust information while also making sense of complex data. This includes online and offline data and behaviors. 

5. Monitoring and engaging 

CRMs are helpful for monitoring and engaging with customers and prospects throughout all phases of the buyer’s journey. They work well at managing contact information and also have automated workflows and reports. Solopreneurs and small teams often use CRMs. 

CDPs are helpful for tracking all aspects of customer behavior on and offline. They pull information from various sources for customers you know and don’t know alike. They help you segment audiences and refine your messaging. Startups, mid-size, and large companies use CDPs.  

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Which tool is better for customer experience — a CRM or a CDP?

While CRMs are helpful for engaging with customers at various parts of the customer journey, they’re still limited on information. 

The more detailed information a company has about their customers, the more insight they have as to what those customers crave.

First, you have to know what their pain points are. Then, you have to craft messages that align with customer needs. But a CRM can’t help you with that. It can help you manually look up customers and engage with them directly, but it doesn’t use machine learning or artificial intelligence to scour customer behaviors.  

That’s why, in the end, CDPs are better tools for enhancing the customer experience. 

Ready to transform your customer experience to the next level? 

Customer experience affects every aspect of success, making CDPs invaluable to a business. 

With a CDP, you’ll understand how your customer thinks, what they’re looking for, and what they like. You won’t need three tools, and multiple data set extraction tools. Instead, your CDP will pull information from various channels for you. 

Not only does this save you time, but it also helps you understand how to reach your customers in a profound way.

From knowing how to craft messages to building long-term relationships, a CDP is every business’s trusted tool for up-leveling the customer experience. 



The Role of Location Data in Google Searches

Although its parent company, Alphabet, might tell its mission is “to make the world around you universally accessible and useful”, Google is very much the information arm of that goal. 

Google needs information to achieve its goal, and so it hoovers up as much as it can from wherever it can, whether that’s through implicitly permitted activities like scraping websites, review sites and social media sites for information it can pass on to its users, or through more explicitly permitted activities like requesting and using your GPS location on your device.

It’s the latter that I’d like to talk about today: specifically how this location data impacts and informs what you see when you perform a Google search. As we’ll discover, very little of the search experience is untouched by location data, and in the case of local SEO, it’s an integral part of the search algorithm. Other methods to make your SEO better include SEO split testing, quality content, proper keyword research, etc.


Why does Google need to use location data?

To understand why Google uses location data and how Google scraper works, we first need to look at the history of local search. It’s sometimes hard to believe, but there once was a time when local search functioned in the same way as organic search. Namely, you’d need to type the search term and location to see businesses near you, e.g. “pizzas brooklyn new york”.

Then, with the introduction of location data gathered via GPS, it was easier to find local businesses by searching “pizzas near me”. Fast-forward a bit further, and Google is now so in tune with the intent of your search that it can tell merely from a search term whether the searcher is looking for something local. In fact, 46% of all Google searches are estimated to have a ‘local intent’.


The Local Pack and Google My Business

These days, you can simply type “pizza” with location data enabled, and you’ll be shown pizza places near you. These are presented in what’s called the ‘local pack’, a selection of three businesses that Google believes fit with the search intent, are local enough, and have a good enough standing to rank.

There are many individual local ranking factors that play a part in how well a business is likely to rank here, and these change slightly every year, but it shouldn’t come as a surprise that the #1 factor comes down to the use of a Google product. It’s called Google My Business, and it’s the single most important tool in any local SEO’s armoury.

The information visible in every listing you see in the local pack all comes from these business’ Google My Business profile, which is made up of a mixture of objective information submitted by the business (e.g. opening times, category, description), subjective information submitted by consumers (e.g. Google Reviews, Google Q&A) and information scraped from the business website by Google (e.g. Services, Menu – though these can be overwritten manually). One can also submit the URL to Google console manually to have their website indexed.

Here’s an example of a Google My Business profile as it appears in the Knowledge Panel in a branded search for this business:

You’ll see that one of the fields here is ‘Address’. This is one of the pieces of information that you submit when setting your business address. It’s not automatically gathered by Google based on information from directory listings or the Post Office. However, it’s worth noting that consistency across all these is key to building trust with Google and consumers.

Google uses your business address location data not just to pin you on Google Maps and give you the privilege of a Google My Business listing but to ensure you enjoy relevant placement in local search results for local searches within a reasonable proximity of your business.

What that proximity is very much depends on a few factors, but the saturation of your business type and the population density in your local area naturally have an impact.

For example, in the example above, a search for a popular term like “pizza” resulted in an incredibly tight area being presented on the map. That’s how far Google needed to go to find three relevant businesses. However, plug in a niche business type like “taxidermist” and Google has to go as wide as the whole of the South-east of England to present the results.

This is a very basic overview of how location data is used to personalise search results for business types, but what happens when the search isn’t clearly for a business but could still have local intent?


Localised organic search

“Localised organic” search results are those in which, as the name suggests, organic search results have been put through a local filter based on the search location.

For example, if I search “best pizzas”, one could infer from the unspecific nature of the search that I’m simply looking for a list of the most popular pizza types, or perhaps best pizza places in the world. Not Google, though.

Based on its collection of millions of search terms, results, and data on what links clicked after which searches, Google thinks that what I really mean is “best pizza near me”.

It’s worth noting that the above appears below a local pack similar to the one shared earlier. This tells you just how confident Google is that what I want to see is a list of the best pizzas near me, based on my search location.


Google Ads

Another key area in which Google uses location data to inform what it presents in search results is Google Ads.

When you set up a Google Ads campaign, you can determine whether you want your ads to be seen by an audience searching from a specific location. This might be useful in the example above, if we wanted to appear in search for people in Brighton looking for pizzas.

Obviously, because Google Ads is a paid-for service (and one of Google’s key revenue streams, to boot), there are extensive, and evolving, options for advertisers to use search location to increase targeting of their ads. Search Engine Land has a great rundown of what’s changed with Google Ads and location recently.


How does Google collect location data?

The sharing and usage of location data have long been entwined in arguments and controversy around privacy on the web. There have been countless suits, countersuits, and pieces of legislation that can impact what private information can be requested and used (most notably the GDPR back in 2018 and, more recently, the California Consumer Privacy Act), but as far as I’m aware the use of location data alone hasn’t been central to any.

As it’s not a piece of personally-identifiable information alone, your location data tends to be up for grabs, but critically, only when you agree to share it. This can be done at a device level (for example, in iPhone settings) or app level (in your Google Account), but there’s one instance, as we’ll see below, in which you cannot prevent Google from knowing a little about where you are.

Let’s look at some of the ways Google determines your search location.


Device location and GPS

Provided you’ve set up access to this information on your device, Google can see where it is (and if it’s a mobile device, exactly where you are, at least if your mobile-carrying habits are like mine). For local searches, this is particularly relevant to smartphones, as we’re much more likely to perform local searches when we’re out and about, looking for a cafe in a town we’re visiting, or looking up an address for directions.

Most smartphones are equipped with a GPS chip that communicates with satellites to allow you (and others) to pinpoint your location to a remarkably accurate degree. If your device location was used to help get your Google search results, the location information at the bottom of the search results page will say “From your device”.


Location History

Log in to your Google Account, head to ‘Location History’ in ‘Activity Controls’, and you can see a timeline of exactly where you’ve been telling Google you are and when you were doing so. It even goes as far as telling you how Google uses this information.

I actually have to applaud Google’s brazen way of turning what could be considered by some as creepy tracking into a fun feature. (For what it’s worth, I would happily share an example of my own location timeline if I weren’t writing this in a pandemic and looking at a fairly non-existent variety of location data points.)

You can also schedule posts on your Google My Business profile, much like a Facebook post but for local offers and updates.


Your ‘labeled places’ in Google

If you’re a big Google Maps user, you may have set your Home and Work addresses in your Google Account. This makes it a lot faster to determine directions and route times in Google Maps.

However, what you might not know is that, even without knowing your precise location via GPS, Google can use this information to make assumptions about where you’re likely to be searching from. For example, if Google knows, through past location data, that you work 9-5 in an office in town and generally spend the evenings at home, it can tailor search results to those places and timelines, allowing you to search for a lunch spot near you at lunchtime without needing to explicitly tell Google where you are.

If the location of your labeled places was used to help get your search results, the location information at the bottom of the search results page will say “From your places (Home) or (Work)”.


Your IP address

Here’s the one I mentioned before that you cannot control. As Google points out in its own help article about location data,

“You can’t prevent apps or websites that you visit, including Google, from getting the IP address of your device because the Internet does not work without it. This means all apps and websites that you visit can usually infer some information about your location.” – Google.

You might think that an IP address is only applied when using a wi-fi connection rather than just on 3G, 4G, or 5G, but the messy truth of it is that IP relates to these services, too.

The location data that systems like Google can glean from your IP is much, much less accurate than GPS, covering a broad area rather than your exact location in the world. Still, it’s interesting to note that the very act of using the internet comes with a signed contract to give up some of your location data.

If your IP address was used to estimate your current general area for your Google search, the location information at the bottom of the search results page will say “From your internet address”.


Business location data

Completely different to dynamic location tracking is the act of storing a business’ address in Google My Business. As mentioned above, this is critical to allow Google searchers to know how to visit your business location. Still, it’s worth noting that even service businesses without a physical location can get Google My Business listings and appear in local search results based on the submitted service area.

Google collects business information through a submission and verification process, but when it comes to local landmarks, much of this is user-created or a matter of public record. For example, no-one ever needed to tell Google where Buckingham Palace is.


Google location data types

There are a few different codes and URLs that local search tracking tools use to define search location and review content. These are mostly defined by Google and are of particular importance to a business location.



The Ludocid, sometimes referred to as the ‘CID’, is a unique ID that Google assigns to a specific business location to identify it within its systems. The Ludocid can be used within Google search URLs to return the Knowledge Panel for that specific business. It can also be used within Google Maps to view a specific business.


Place ID

The Place ID is another unique ID that Google uses to identify a specific business within its systems. There are two common uses of the Place ID. The first is in the Google Places API; passing this ID to Google can return extensive information about a business, including its name, address, website URL, opening hours, etc. For example, if your website uses WordPress as a CMS, you can add your Google account to WordPress. Additionally, make sure to use the best-managed WordPress hosting so that your website gets found more easily.

The second use is in constructing a Google search URL to trigger a page that displays all the reviews for a business, or the page where users can write a new review for a business. When Google sees these IDs in a URL, it automatically converts them to a different URL that combines the Ludocid with the FID.



The FID is a unique ID that relates to reviews that Google holds about a specific business. The FID has fewer practical uses than the Ludocid and the Place ID, as it’s primarily used in combination with the Place ID to generate a ‘write reviews’ or ‘read reviews’ URL.

If you know the Place ID, then Google converts this to the FID for you, so you’re unlikely to use the FID directly. It’s easier to create URLs to read or write reviews using the Place ID, but we thought we’d add it here in case it’s of use.



As you’ve seen above, location data is integral to the evolving search experience. We might not be out and about right now as much as we’d like to be, but when lockdowns are lifted, and we roam the streets once more, where we are and what we search for will once again combine to deliver a streamlined, almost invisible personalised search experience that we barely even think about.

Google has already used location data in a creative way to show how little the world is moving due to Covid-19 in its Mobility Report. What Google will do with an explosion of movement, post-pandemic, remains to be seen.


Author Bio

Jamie Pitman has worked in digital marketing for over a decade and is currently Head of Content at local SEO tool provider BrightLocal. He specializes in local marketing and the many factors that affect local search performance, from Google My Business and consumer reviews to branding, content marketing, and beyond.



How Top Businesses are Using Geospatial Data in 2021

Uber, Google, Wendy’s, CDM Smith Inc, and Amazon – you couldn’t get a much more diverse set of organizations. A search engine, a fast-food chain, to an online retail store – these businesses might be diverse in terms of their operations, but they’re all linked by certain business practices. 

That is, these top business players all utilize geospatial data to optimize their operations for a healthier bottom line. In this article, you’ll discover what geospatial data is and how it’s used by these top 5 businesses to gain a competitive edge.


Let’s jump straight to it.


What is geospatial data?

Geospatial data is also known as place-based or location-based data, such as longitudes, latitudes, stress addresses, and postal codes. Geospatial data analysis collects, displays, and manipulates GIS – Geographic Information System – data like imagery, satellite photographs, and historical data. The aim is to collect, store, retrieve, and display vast amounts of information in a spatial context.

For instance, can you imagine life without GPS?

In this vein, you’ll likely remember buying a paper map to plan your routes from one place to another. This system was slower, hard work, and vulnerable to human error. Today, geospatial data has been a game-changer when it comes to providing location/place-based information. Whether it’s MapQuest, Pokémon Go, Google Maps, or the in-dash car navigation system, everyday citizens use geospatial data more than they know. 


How do top businesses use geospatial data?

Just as we rely on geospatial information every day, leaders worldwide use geospatial data to guide them towards making the right decisions at the right time. With that said, let’s take a closer look at how Uber and other top businesses are using geospatial data to optimize their strategic decisions for efficient operations and sustained business growth.



In 2019, Uber brought in $14.1 billion in revenue, showing exponential growth from 2013 (where revenue equals $0.1 billion). Founded in 2009 by Garrett Camp, the organization has since grown into a disruptive tycoon that’s ripped up the cab industry by storm. 

Central to its success comes geospatial technology. 

With the Uber app, the user can request a cab. This user’s location is then taken and matched with the closest driver. The driver accepts this match and is guided by applying it to the user’s location to transport the user to their chosen destination. This entire process draws from the application’s geospatial data. 

This isn’t the only way Uber uses geospatial data — there are countless others. For instance, the application identifies areas with the highest need for drivers and advises active drivers to be near those hotspots during high demand times. 

Without geospatial data, Uber would not be the business disruptor it is today.



Google is the goliath of the business world. In the third quarter of 2020, Google’s revenue amounted to $46.03 billion, up from $38 billion in the preceding quarter. Taking a good chunk out of this revenue comes from Google’s map application, which brings $4.3 billion a year.

Google maps has 154.4 million monthly unique users. And behind every map, there’s a much more complex system, the key to your queries but hidden from your view.

This more in-depth system contains the logic of places, all the left and right turns, freeway on-ramps, speed limits, traffic conditions, you name it. And to produce such a system, Google uses geospatial data provided by a third party to deliver digital maps and other dynamic content for navigation and location-based services.



Square hamburgers, sea salt fries, and the addictive Frosty, this fast-food giant brought home $1.687 billion in revenue in 2020. 

What’s the secret to Wendy’s success?

I say geospatial data

Wendy’s carefully researches locations, leveraging mapping software and census data (population information). The fast-food chain searches for sites with a high population and potential customers and looks at household demographics, average income, and nearby businesses. 

But this analysis doesn’t stop when the right site has been found. Wendy’s continues to examine this geospatial data after construction at the given location. Before construction, prior construction results can then be compared to continuously tweak and improve their GIS analytics model and processes.


CDM Smith Inc

CDM Smith Inc is a global engineering and construction firm providing solutions in water, environment, transportation, energy, and other facilities. In 2015 the organization’s revenues totaled over $500 million, and one of the reasons for the success has been the organization’s use of geospatial data. 

That is, for CDM Smith Inc, geospatial data provides design and engineering capabilities to create plans, layouts, and maps. GIS applications for design and engineering make use of both imaging and planning functions. Such functions mean geospatial data is commonly used in industries such as landscape engineering, environmental restoration, commercial and residential construction, and development. CDM uses geospatial data for environmental engineering and remediation projects. 



In 2019, the online retail platform, Amazon, reported a net income of $11.59 billion, up from a $10 billion U.S. net income in the previous year. To stay ahead of the curve, Amazon is always coming up with new and innovative ways of doing business. And one example is Amazon’s Prime Air drone project, expected to officially launch on August 31st, 2020.

By integrating GIS with Artificial Intelligence, it’s possible to fly drones over much larger distances than other previous attempts. Amazon has jumped on this bandwagon, delivering packages by drones. The aim is to deliver packages to customers in 30 minutes or less using unmanned aerial vehicles – drones – operational thanks to geospatial data.


Gain a competitive edge by using geospatial data

As technology advances, geospatial data is becoming more complex, with widening business potential. At the end of this article, we saw how Amazon is combining GIS software with Artificial Intelligence (drones) to expand the use of both. Does this represent the future of things to come regarding geospatial data?

Geospatial data analysis has the potential to: 

  • Match consumer demographic data with spatial information about the places they live 
  • Validate existing GIS data sets 
  • Monitor and report weather 
  • Survey habitats 
  • Model landscapes 
  • Assess disaster damage 

While these are just a few examples, by combining geospatial data with AI, the possibilities of its use are expanding. To grasp a competitive edge, and be a top player in the business arena, dig deep and see how you can leverage geospatial data technology for your business operations.

Data Uncategorized

How Big Data is Poised to Disrupt Personal Investment

Last year, in a piece about big data’s impact on finance, we touched on the notion that location data sets can simplify the practice of investing. The idea is that accurate data regarding consumer movement can provide insights on consumer trends — and, thus, potentially, corresponding market movements. In this article, we’re going to expand on that general idea with a closer examination of big data’s potential influence on personal finance.


Big Data & Investment Today

First and foremost, it’s necessary to briefly discuss the current state of big data in the investment world. In the piece, as mentioned above, we mainly covered an idea of how data, and specifically location data, can be applied to market management. However, the truth of the matter is that some significant investment funds and financial firms are already using massive troves of data of all kinds to inform their investing decisions.

At this level, AI and big data are transforming the investment process in several ways (and through companies as big as JP Morgan, BlackRock, SoFi and others). In some cases, AI labs are being used to analyze investor performance and recommend changes that yield a quick, significant result. In others, advanced AI applications are using a deep-learning approach to sift through astonishing amounts of data with the straightforward goal of predicting near-future stock prices. For example, looking at realtime car purchases to predict Rolls Royce shares. There are mixed results with approaches like this one, but the potential for genuinely predictive analytics in large-scale investing is significant.

There aren’t full AI operations of this nature explicitly focused on making use of location data. However, deep-learning approaches are reasonably comprehensive in theory. They can certainly make use of this specific type of data and share prices, company data, macro-economic indicators, asset histories, and so forth. Location data can primarily be used as a piece of a sprawling analytical effort.


Automated Investing Tools

While significant investment firms may be well situated to take advantage of big data and act on it quickly, most ordinary people can’t do the same. A true day trader with access to adequate data (on locations and otherwise) may be able to make quick decisions to react to fresh information. But for most people who are simply managing stock portfolios, it’s more realistic to attempt to leverage data relating to broader movements. That’s where some of today’s automated investing tools come into play, especially in some online depot tools.

When you hear about automated tools, you might first think of mobile apps like Acorns and Stash that can, to some extent, manage your investments for you. Generally, these apps allow you to create a portfolio — or at least a type of collection — based on your risk aversion and/or category preferences. They then conduct trades according to your preferences, such that your money goes to work for you in an automated fashion.

Automation is also involved in more traditional trading methods, though, and it introduces an interesting way of potentially leveraging big data. On some platforms, when trading contracts for difference or stock futures, for instance, investors can place profit and loss limits on their positions. This means that while investors still determine entry points on individual stocks, they can set up automated safeguards that will pull money when a certain profit has been attained, or a certain loss has occurred. Through this sort of feature, investors can still set up their investments and contracts, but trust automation to manage them after that.

This all relates to big data because it can be more comfortable for an investor to make a long-term play based on big data than a short-term trade. To give an easy contrast, consider consumer location data based on a product release versus that based on a more significant trend. A surprise hit film might lead to data suggesting a short-term surge of moviegoers, such that a day trader or larger fund could stand to profit by investing in major cinemas. But the average investor likely can’t recognize and act on that data quickly enough. On the other hand, if consumer location data indicated a widespread, gradual return to shopping malls in 2021 (when they’ve been largely abandoned in 2020), an investor could make a play to buy stock in department stores, with automated limits set in place. In short, automated options allow for more responsible long-term positions, such that investors can feel more comfortable attempting to leverage certain types of data.


Location & Digital Payment

As you’ve likely noted in the sections above, one of the problems with big data in personal investment is that the most information and the best analysis tend to belong to the giant funds and finance companies. While there are ways for individuals to access more advanced analysis of investment-related data, it isn’t easy to compete on a day-to-day level. Sometimes, the significant funds will interpret data and move on a trend quickly that there’s not much opportunity left for everyone else.

This is why location data may prove to be particularly interesting for individual investors, however. When we talk about comprehensive data analysis done by leading financial firms, we’re referring to all sorts of in-depth material that is difficult for an individual to manage, or in some cases, even get a hold of. By contrast, location data may be getting more convenient in the near future.

This assertion is based on the simple fact that electronic payments are up; by 2019, some 2.1 billion consumers were estimated to have used a digital wallet of one kind or another, and moving forward, even more people are likely to be embracing digital payments. These types of payments are meant to be more secure and convenient for consumers and businesses alike, but they’re also generally more traceable. In theory, it may soon be the case that information about where consumers are spending digitally will be reasonably easy to access (whether through public ledgers, payment processing company reports, etc.). That would turn consumer location data into something individual investors could access and use with relative ease compared to some other kinds of relevant data.


In Conclusion

The use of big data in investing is still an evolving concept with less to do with individual investors than larger funds. However, certain methods of investing and certain types of data can be of use to independent traders, and will likely only become more useful in moving forward.


Is business intelligence the same as data science?

Data science is growing immensely in today’s modern data-driven world. Business intelligence and data science are two recurring terms in the digital era. These involve the use of data that are totally different from each other. Data science is a bigger pool that contains huge information; business intelligence can be considered as a part of the bigger picture. These are both data-focused processes, but there is some difference between the two. Business intelligence focuses on analyzing things, whereas data science aims to predict future trends. Data science requires an effective technical skill set as compared to business intelligence. 

Power BI certification allows interested candidates to explore Power BI concepts such as Microsoft Power BI desktop layout, BI reports, dashboards, power BI DAX commands, and functions. Microsoft Power BI is a widely used business intelligence platform, and this follows a hands-on applied learning approach. 


Business Intelligence:

This is a means of performing descriptive analysis of data with the help of technology, skills for allowing one to make informed business decisions. The tools which are used for business intelligence collect, govern, and transform data. This allows decision-making by enabling data sharing between internal and external stakeholders. The main aim of BI is to derive actionable intelligence from data. BI enables acton such as gaining a better understanding of the market, uncovering new revenue opportunities, improving business processes, and staying ahead of competitors. This has shown its impact on cloud computing. Cloud has made it possible to collect data from resources and use this efficiently. This deals with the analysis of structured and unstructured data, which paves the way for new and profitable business opportunities. Business intelligence tools enhance the chances of enterprises entering a new market as this helps in studying the impact of marketing efforts. 

Importance of business intelligence:

As the data volume is increasing, business intelligence is more essential than ever in providing a comprehensive snapshot of business information. This provides guidance towards informed decision-making and even identifies the area of improvement, which leads to greater organizational efficiency and even increases the bottom line. 


Data science:

Data science mainly involves extracting information from datasets and creating a forecast. This involves the use of machine learning, descriptive analytics, and other sophisticated analytics tool. This is a process of collecting and maintaining data. Further, this involves the process of data via data mining, modeling, and summarization. After this, data analysis is conducted, etc. After analyzing the data, the patterns behind the raw data can be discovered to forecast future trends. Data science is used in different industries. Companies can use a devised approach to develop new products, study customer preferences and predict market trends. Here high volume of data can be collected from electronic medical records and individual fitness trackers. 

Importance of data science:

Data science in different companies is able to predict, prepare and optimize their operations. Data science plays an important role in the user experience; for many companies, data science is what allows them to offer personalized and tailored services. 


Business intelligence vs. Data Science: Is it the same or different?

Business intelligence and data science play a key role in producing companies’ actionable insights. Let us check on some common attributes between the two:

  • Perspective: business intelligence focuses on the present, while data science looks toward the future and further predicts what will happen next. Business intelligence works with past data in order to determine the responsible course of action, while data science creates predictive models which recognize future possibilities. 
  • Data types: business intelligence works with structured data, which is typically data warehoused or stored in data silos. Data science works with structured data and further results in greater time, which is dedicated to cleaning and improving the data quality. 
  • Deliverable: reports are used when it comes to business intelligence. Different deliverables for business intelligence include creating dashboards and performing ad-hoc requests. Data science deliverables have similar end goals and focus on long-term projects. These projects include creating models in production instead of working from enterprise visualization tools. 
  • Process: the difference between the processes of both comes back to the time, same as how this influences the nature of deliverables. Business intelligence mainly revolves around descriptive analytics. This is the first step of analysis and sets the stage for what happened in the past. Here non-technical business users can understand and interpret data via visualization. Data science would take the exploratory approach and means investigating the data via its attributes, hypothesis testing and exploring different trends, and answering questions on a performance basis. 
  • Decision making: business intelligence and data science are used for driving decisions, and this is central to determining the nature of decision-making. The forward-looking nature of data science is used at the forefront of strategic planning and determines the future course. These decisions are preemptive instead of responsive. Business intelligence aids in decision-making based on previous performances which have occurred. These fall under the umbrella of providing insights, and this supports business decisions.



Both business intelligence and data science have differences, but the end goal of these are ultimately aligned. It is important to note the complementary perspective of both. From the company perspective, both data science and business intelligence play similar roles in business processes that provide fact-based insights and support business decisions. Data science and business intelligence are facilitators of each other, and it is said that data science is best performed together with BI. These are required to have an efficient understanding of company trends which are hidden in the large amount. 

In order to summarize simply, data science and business intelligence are not the same things, but this represents the evolution of business intelligence; thus placing data into introspective plays a central role in the business. Data science and business intelligence are equally vital roles on the same team. The individual roles are different, and when together, they serve the broader business analytic world. Though there is a difference in the way data science and BI handle objective tools, the end game is the same. 


How Real Estate Agencies Can Make Data Driven Decisions With Geospatial Data

No matter what type of business you are in, there is no denying the importance of geospatial data as it relates to literally every area of your company from marketing to planning and everything in between. In terms of real estate, in the coming years any real estate agency that doesn’t make use of and rely heavily on geospatial data will almost certainly be left behind. In order to understand that rather marked and definitive statement, it is first important to understand exactly what geospatial data is, how it is collected, and why it is especially relevant in real estate.


A Brief Definition of Geospatial Data

In its simplest definition, geospatial data is that which is descriptive of any event, object or feature located on or very near the earth’s surface. It is typically a combination of:

  • Location (coordinates)
  • Characteristics (relating to objects, phenomena, or events)
  • Temporal Information (point in time or lifespan)

All of which play a significant role in reading data with the intent of forecasting future events or movement.

For example, let’s look at how geospatial data helped to track and forecast the movement and spread of the SARS-CoV-2, Covid-19 pandemic. Temporal data gave us a short-term location of what was to be the pandemic in late 2019. We know that the location was Wuhan, China and thought to have originated at one specific market which then became ground zero on the geospatial chart. From there a long-term progression of the pandemic showed its movement outward which are temporal and location data. Along with characteristics such as how it was spreading, scientists became better able to forecast its movement around the globe and as early asMarch of 2020 a global pandemic was announced. 

Even then, it was too little too late because some of the much-needed data was not forthcoming soon enough to predict an accurate geographic spread and rate of spread. Had geospatial data been shared better in the early days, many virologists and epidemiologists believe the pandemic may have had better outcomes earlier on. With that, you can see just how important it is in forecasting business dynamics going forward.


How Real Estate Can Benefit

In the real estate market properties for sale have always been valued primarily on location and what we knew about that that particular property in terms of the condition it was in and what was going on around it in the general vicinity. Were there plans for future development and if so, how would that affect a particular property that an owner wanted to list for sale. Realtors and assessors would look at other properties in the area to see what they had sold for in order to relate that price to the property in question. This is how comparables were calculated and how an actual list price and marketability were determined.

With advances in technology, geospatial data can actually have a profound effect on the profitability of a piece of commercial property. Instead of using historical data to predict a given market going forward, temporal data gathered and analysed in real time can indicate what that property is worth today in the here and now. To be specific, comparables calculated even a week previous to a major break in a pipeline may not be relevant today. That property would be greatly devalued if the repairs would be weeks or months in coming. Real time data can affect the price today and that’s why the real estate market will, at some point in time, need to rely on what is happening on the ground at a very precise location.


A Key Selling Point

Conversely, if new schools are being built, for example, and an influx of families are moving into a neighbourhood, a commercial venture for a theme park might want to jump on a parcel of land zoned commercial. Satellite imagery would show that kids are out playing in fields and on side streets with few parks and nothing in the way of entertainment. It would take weeks, if not longer, to collect that kind of data without the benefit of a literal bird’s eye view from above and a poor data set can have a terrible effect on the real estate market.

AI could possibly collect data on the types of commercial or public properties families might frequent already in existence, but even that isn’t quite as all-encompassing as actually seeing movement on the ground. Just because a property exists doesn’t mean it is being frequented by the locals. Satellite imagery would document that and indicate whether or not there is a need for family entertainment at this time.

Imagine what a real estate agent could do in the Greek Islands with information like that? As a popular tourist destination, geospatial data could indicate what kinds of attractions are being frequented, which are ignored, and what types of venues would do well in areas with currently high levels of traffic. It’s interesting to imagine just how this type of data can, and will, affect the real estate market going forward. One thing is for sure. Geospatial data will almost certainly replace the archaic system of buying and selling real property based on comparables. That’s a given.


How To Take A Data-Driven Approach To Demand Gen Campaigns

When it comes to delivering a successful demand generation campaign, data and measurement are critical. While this should go without saying, it’s amazing how many companies still conduct their marketing without any clear visibility on their performance. Without access to the right metrics, marketing activity can’t be justified, evaluated or improved.

Just consider that for a second:

You want to provide accountability for your marketing spend

You can’t.

You want to identify what went well…

You can’t.

You want to evolve to deliver better results next time…

You can’t.

I think you probably get the picture by now – measurement is a key component and it can’t be treated as an afterthought.

With that said, collecting, analyzing and acting upon performance data isn’t always simple. With so many pieces in play, understanding what is and isn’t working is critical to making reporting results actionable. Remember, collecting information is pointless unless it’s going to be used!

To embrace a data-driven approach in your demand generation campaigns, you need to take a methodical approach. The simplest way is to break down your approach into 4 stages; Discovery, Design, Deploy and Optimize.


The Discovery Stage

At the Discovery Stage, you should be focusing on what it is you’re trying to achieve and then identifying the metrics that influence that objective. So for example, take the likes of prospect conversions, cost per click (CPC) and return on investment (ROI), these are all different metrics that offer highly valuable insight for various objectives within a demand generation campaign. Each will have varying importance based on the tactics and objectives within the said campaign and will require relevant prioritization as a result. In some campaigns, ROI may be irrelevant (unusual, but does happen), in others, prospect conversion rates won’t matter. It really all comes down to the objective.

Unfortunately, many companies focus on the wrong metrics when it comes to measuring performance. They have the right mindset, but get lost in vanity metrics that don’t actually make all that much difference to the results that impact their objective.

For example, instead of looking at conversion rates on an asset landing page, they’ll look at the number of people arriving to the page. This creates a disconnect between the figures and the desired results. These vanity metrics look great on paper but aren’t indicative of success or failure, so don’t really offer a lot in the way of insight.

The Discovery Stage should be where you identify what metrics matter, the role they play in achieving your objective and why.


The Design Stage

Once the key metrics have been identified, you need to determine how they can be monitored and measured, and this is where the Design Stage kicks in.

Just because you know what data you want to focus on, it doesn’t mean you can effectively access that information. You need to think about what systems you have and whether they integrate well to provide accurate data?

In addition, you need to know if there are silos that prevent you from seeing the full picture. These are all factors that have to be considered carefully as you build your data-driven approach.

You can’t afford to be working from only a partial view on key data, particularly if that information is going to be used to guide future decision making.

In order to get the big picture, technology not only needs to be compatible but also effectively interlink together to ensure there are no anomalies that impact the validity of the data. Designing a system that gives you a full view of critical information is essential to taking a data-driven approach to your demand generation campaigns.


The Deploy Stage

With objectives identified and a clear understanding of how data management will work, you need to move onto the Deploy Stage. This is all about the practicalities of building the system that’s been designed.

You already have an idea of the information you want, why and how it will deliver value and now you need the technology and systems in place to ensure that information is easy to access.

Many companies underestimate how challenging this can actually be. Few consider the limitations of their legacy tech stack and many often struggle to take the right steps to overcome challenges.

Often it will require custom APIs, enabling systems to play nicely together. This is where the interconnected nature of demand generation campaigns can make measurement challenging.

Beyond achieving data accuracy, the Deploy Stage is also where you need to think about how data will be collected and visualized. Will it be gathered automatically in a centralized location or will each piece need to be manually collected? These are considerations that require further thought based on the needs and complexity of the project.


The Optimize Stage

At this final stage, your objectives are clear, your system is designed and built with the relevant data collection capabilities and integrations. Now, it’s about analyzing and using that data, and this is what the Optimize Stage is all about. In this stage, you will be reviewing your key performance metrics and developing actionable conclusions.

This may involve trend analysis, or focus more on recognizing anomalies, it really depends on the metric and the objective. Whatever the case, the outcomes of any evaluation should be actionable and designed specifically to improve future campaign performance.

Some demand generation campaigns fall down due to the failure of a single component and so optimization really is key. If one metric in particular is undermining the rest of a campaign, this stage can turn a struggling project into a powerhouse.

With the ability to A/B test most changes, it’s easy for iterations to be made and trialed with a small test audience before deploying them across the entire campaign. This not only helps to maximize the impact of changes, but encourages experimentation with alternative approaches.


Driving your demand generation campaigns with data

Running a successful demand generation campaign can be tricky, but with a data-driven approach, companies can quickly identify what works and why. This helps to build fully justified campaigns that can be iterated upon to drive performance and ROI.

With the right data points to hand, at the right time and in the right format, campaign performance can be reviewed in real-time, unlocking the opportunity for regular changes and improvements.

Data can turn your demand generation campaigns into ‘live’ assets that adapt as and when required to achieve their objectives. This ensures every component is working to its full potential and delivering the necessary results to drive the metrics that really matter. Using the ‘Discover, Design, Optimize, Deploy’ model you can take your first steps to achieve a data-driven approach in your demand generation campaigns.


How Smart Cities Can Help Defend Against Pandemics

As the world remains gripped by the threat of COVID-19, questions over community spread, and prevention tactics loom large. Did cities and representatives do enough to stop the spread of the virus, or could they have done more?

An estimated 68% of the world’s population will live in cities by 2050, which makes urban development’s role in disaster response essential for protecting the population.

Cities and other population-dense areas have been flagged as one of the major issues impacting the virus’ community spread. Yet, smart cities may have also helped slow the spread of the virus in Chinese communities such as Wuhan.

China has 500 smart cities currently underway, and while the question of whether this smart technology has helped slow the spread of the virus in Chinese communities remains up for debate, here are some ways the urban planning of smart cities could help defend against pandemics.


Disease tracking

Disease tracking can be one of the biggest defenses against virus spread as it allows scientists, researchers, and city officials to analyze real-time data to make informed recommendations. So how does it work? This data-tracking system uses artificial intelligence to track the spread of infectious diseases.

Big data and natural language processing make it possible by allowing companies to track the spread of information from hundreds of thousands of sources.

A disease tracking company based out of Canada known as BlueDot was the first to sound the alarm about the novel coronavirus, even before any world health organization informed the public. On December 30, it noticed a cluster of “unusual pneumonia” cases in Wuhan and alerted its customers of the outbreak.


Autonomous delivery

One lesson the world is learning from COVID-19 is the importance of social distancing. Because COVID-19 can live on some surfaces for up to 3 days, restricting access to public areas has been deemed essential in cities across the globe.

Autonomous delivery systems would eliminate the need for drivers to deliver supplies and goods, such as food. Technological advancements such as delivery drones and driverless trucks will make this process more seamless in years to come. Incorporating these technologies into smart city management will give local governments more control over delivery systems to prioritize needs, for example delivering medicines before goods.



Many of our phones are already tracking our location-data, but what if our local governments used this information to make predictions during pandemics? Data companies such as Tamoco make tracking and predicting behaviors easier than ever for marketing and intelligence purposes.

One example of how geolocation data can be useful during a pandemic is by analyzing popular shopping times. City officials could use data around when people are most likely to shop for groceries and goods to help make decisions about store hours and restrictions, and when to establish senior hours.


Drone surveillance

While the application of robot surveillance is controversial, it has helped countries like China monitor citizens during the coronavirus outbreak. Rather than sending police officers out to monitor cities and streets where shelter-in-place orders are in effect, cities can send out drones to survey the areas and make sure citizens are abiding by city ordinances. Insect drone technology is fast becoming a powerful tool for cities to utilize. 


Thermal cameras

Another technology the city of Wuhan used in its fight against the coronavirus is thermal cameras. While the technology hasn’t been perfected yet, these cameras, which also feature facial recognition software, can detect body temperatures in citizens passing by.

Since a fever spike is one of the most common symptoms of the coronavirus, these thermal cameras could, in theory, alert city officials when infected members of the population were out walking the streets, potentially spreading the virus.


Internet connectivity

In the age of information technology, the best way to stay dialed in is by staying online. Cities such as New York City have already activated smart technology that gives all citizens internet access in public places. This allows citizens to stay informed during times of crisis, no matter how much they make or whether they pay for WiFi at home.

Giving everyone access to the Internet allows for local city leaders to communicate with their citizens and get public information out faster. Many smart cities also have information kiosks that can be updated instantly to spread awareness as situations develop.

One of the scariest parts about the novel coronavirus is the lack of data researchers, scientists, and leaders have. Smart cities that collect data in real-time can revolutionize pandemic responses by making data available faster, and we’ve seen examples of this in action as China utilized their city technology to help fight the spread.

Smart cities can also help cities conserve energy, manage traffic congestion, optimize waste removal, and improve water and energy management. To learn more about how smart cities work, head to The Zebra. 


This post is a contribution by Karlyn McKell

Karlyn is a writer who specializes in the technology and insurance spaces. She believes the best ingredients for success are passion and purpose. 


How Big Data Is Changing The Event Planning & The Events Industry

Big data plays a crucial role in many industries, and event management is just one of them. People all across the world are relying more and more on their smart devices, and it has become increasingly easier for event organizers to gather and use data to cater more directly to event attendee’s experiences.

Event planners are leveraging big data to deliver highly personalized events and boost attendee engagement. In this article, we’ll look at some of the ways you can use big data to provide better event experiences and learn more about your audiences’ needs and expectations.

Let’s put everything into context before we begin.


What Is Big Data?

Generally speaking, big data refers to the enormous volume of data that is collected daily. The data itself doesn’t necessarily have to be useful on its own. It’s the insights, trends, and patterns the data reveals that event planners are most interested in.

For event organizers, big data analysis involves collecting vast amounts of data from their attendees, sponsors, and target audience. This might be in the form of emails, surveys, tweets, photos, or location data.

Event planners can use big data to organize attendee-centric events, increase attendee engagement in real-time, and deliver enhanced event experiences.

The good news is that implementing data analytics in your event planning doesn’t have to be complicated. You can work with the data sets that are already available to you and extract valuable insights from them.

If you’re not collecting any data from your target audience or attendees, the first step is to identify the key data points you’re most interested in. For example, if you’re hosting a business conference, you will need to know where attendees are traveling from so you can provide each attendee the best advice for lodging and commuting to the event venue.

Now that you know what big data is and why it’s important for event organizers, let’s look at how you can implement big data in your event management business.


4 Ways Big Data Is Driving the Events Industry

Below we discuss some ways big data can help event organizers plan for successful and engaging events.

#1: Improving Targeted Promotions

Promoting your event takes up a lot of monetary resources, which is why you need to make sure you can cost-effectively maximize your audience outreach. By gathering and analyzing data on your attendees, you’ll be able to find ways to connect with them.

For example, attendee surveys might indicate that most of the respondents first learnt about your event from your social media pages. So, instead of focusing on company spending on offline marketing strategies, you might consider spending more on your social media campaigns.

Location data {information about geographic positions of devices such as smartphones or tablets} allows event marketers to connect their digital marketing efforts to how prospective attendees behave in the real-world. As a result, event marketers can provide even more personal advertising to their target audience.

To attract more attendees, you need to ensure that you’re advertising to the right audience; otherwise, you might experience a drop in event attendance and audience engagement. Analytics can help you discover when your target audience is actively using social media, what is the best way to connect with them, and how you should craft your message to get their attention.

Big data can help you identify what your prospective attendees respond to, which you can then use to improve your event planning. Having access to this information will not only help you customize your ads to attract more attendees but will also be useful in delivering personalized event experiences to your guests.


#2: Gaining Insight from Analytics

Analytics can help you figure out the specific topics and themes that most interest your audience, the guest speakers they want to listen to, and any tools or presentation technology they’d like you to implement in your events.

By gaining access to the right information, you’ll be able to discover their pain points and what your guests expect from your events. Start by collecting online search data or using pre-event survey forms to gather this information directly from your attendees. Similarly, you can search through social media sites, community sites, as well as this you can make use of powerful location intelligence and analytics to reveal your attendees’ motivations.

Big data can help you identify the different factors that affect audience behavior and leverage them to your advantage. You’ll be able to predict trends months (or even years!) into the future. Predictive analytics lets you make near-accurate guesses and allow you to see which existing topics have growth potential so you can jump on them before your competition.

Google Trends is also an excellent tool for discovering what your prospective attendees search for. By comparing search terms with historical data, you can predict what will be popular in the future, and when you should host an event around it.


#3: Personalizing Attendee Experiences

The data you collect from your attendees gives you insights into how you can enhance attendee satisfaction. You can use technologies such as location data, RFID, VR, and beacons to deliver unique event experiences.

For example, you can send information to nearby devices using beacons, allowing your attendees to only focus on the booths they’re most likely to be interested in. This is also a great way to offer location-based experiences to your guests.

Pepsi organized a dance party at SXSW, where dancers wore wristbands that would gauge their reaction to a stimulus. The wristbands measured body temperature, the volume of music, body movement, as well as the physiological arousal through body sweat. This information was used by the event’s DJ to figure out what music people loved the most. In addition to this, it enabled the crowd to control lighting, bubble machines, and smoke machines.


#4: Crowdshaping

Another way big data can help you improve your event planning efforts is by letting you better manage crowd densities at the venue, also known as crowd shaping. It’s an effective way to make sure attendees can enjoy your events without openly influencing their behaviors.

For example, if you see a crowd of people around the book signing tables, you may decide to extend the time allocated for that particular activity in real-time. If a guest speaker is about to take the stage, you can send out notifications to let attendees know that they can get their books signed after the lunch break.

In addition to this, you can use data from past events to improve upcoming events. For example, if data from one event indicates that attendees like open spaces to walk around during breaks, you can use this information to book future venues with plenty of extra space.

In its simplest form, geolocation is capable of giving you information about the location of a person. Beyond that, it can be used by event organizers to gather crowd density data about events. This allows them to manipulate crowd flow more effectively.

Crowdshaping and big data come together to help you identify problems in your crowd flow and enable you to solve them by making proper adjustments quickly. In the long run, this information will allow you to make better event planning decisions (such as opening more check-in lines) and go for venues that meet your audiences’ specific needs.

Event organizer at C2 Montreal gave RFID badges to their attendees that collected their location data. This allowed organizers to see where the majority of the attendees gathered and which event places received less traffic. They found more crowds near food tables, which led them to send more food service staff to those places.



The data you gather on your attendees is a valuable resource that can help you improve your event experiences. You can always start small and use whatever data you have to learn about your audiences’ needs and expectations. With time, you’ll feel more comfortable collecting and working with big data.

Event Espresso lets you collect, control, and own all the data you collect for free. You can export your event attendees’ data into Excel or CSV format and use it however you want.


Best Guide To Location Data 2022 – All You Need To Know

This guide will tell you everything that you need to know about locaiton data:


The global adoption of smartphones has grown at incredible speed in the last decade.

Mobile devices are a powerful tool for understanding the aggregated behavior of consumers.

Understanding device location opens doors to a wide range of use cases that are unique in many different ways.

Mobile location data provides a granular solution for consumer understanding. Combining this understanding with other datasets are helping to solve business problems and achieve goals across many different industries.

For these reasons, location data has quickly become the holy grail of mobile. It’s applications are broad and run across a number of different industries and verticals.

But before we get onto that, what exactly is location data?

What is location data?

The smartphone

The mobile device or smartphone has been revolutionary. Its growth has been incredible – many predict that there are now more of these devices in the world than there are people.

Smartphones have transformed everything about our everyday lives -we rarely leave home without it, and it’s always on our person, ready to provide us with instant information or guidance.

These devices have enabled the location data industry to understand how audiences move and behave in the real-world. This information is location data. It comes in many different forms and from various sources.


What is location data?

Location data is geographical information about a specific device’s whereabouts associated to a time identifier.

This device data is assumed to correlate to a person – a device identifier then acts as a pseudonym to separate the person’s identify from the insights generated from the data.

Location data is often aggregated to provide significant scale insights into audience movement.


How is location data generated?

Companies are collecting location data in many different ways. There are several different techniques to collect location data. These techniques differ in reliability (but more on that later).

For now, the primary process of collecting location data requires the following ingredients.

A location source/signal

The first ingredient is a location signal. This signal is not a product of the device itself – it comes from another piece of technology that produces signals. The device listens to these external signals and uses it for positioning. These signals are as follows:



GPS is shorthand for the global positioning system and was first developed in the 1970s. The system is made up of over 30 satellites which are in orbit around the earth. This technology works in your device by receiving signals from the satellites.

It can calculate where it is by measuring the time it takes for the signal to arrive.

GPS location data can be very accurate and precise under certain conditions, mostly in outdoor locations. In the best instances, the signal can be reliable down to within a 4.9 metre radius under open sky (source) .



Wi-fi networks are another source of location signals that are great at providing accuracy and precision indoors. Devices can use this infrastructure for more accurate placement when GPS and cell towers aren’t available, or when these signals are obstructed.



Beacons are small devices that are usually found in a single, static location. Beacons transmit low energy signals which smartphones can pick up.

Similarly to Wifi, the device uses the strength of the signal to understand how far away from the beacon it is.

These devices are incredibly accurate and can be used to place a location within half a meter with optimal signal strength.


Carrier data/cell towers

Mobile devices are usually connected to cell towers so that they can send and receive phone calls and messages. A device can often identify multiple cell towers and by triangulation, based on signal strength, can be used to place a device location.


An identifier

Each smartphone needs to be associated with an identifier to understand movement over time. This identifier is called a device ID. For iOS, this is called an Identifier for Advertising (IDFA), and for Android, it’s called an Android Advertising ID (AAID).


Meta data or additional dataset (optional)

A location signal combined with an identifier will allow you to see the movement of a device over time. However, for more detailed insights and to get more value from location data, you’ll need some metadata or an addition dataset.

The most common dataset to do this is a POI dataset. This dataset includes points of interest that are important when comparing how audiences move and behave in the context of the real world..

For example, a series of latitudes and longitudes showing how Londoners move between 7-10am could be useful. Tying this to a dataset that included tube stations and key travel routes would allow you to do much more with the initial data.

Location data sources – where does location data come from?

So, we have already looked at the ingredients that combine to make location data, including the different types of location signals. However, what are the sources of location data? If you are looking to use location data in your organization, then you need to know the differences between every potential source. It’s also important to have a data governance strategy to manage the data effectively.

The source can have a significant effect on accuracy, scale and the precision of devices. So, from where does location data come? There are three primary sources:


The bidstream

A sizeable proportion of location data comes from something called the bidstream (also referred to as the exchange). The bidstream is a part of the advertising ecosystem. Don’t worry if you’ve never heard of this – we’ll explain everything.

Explainer: The ad buying ecosystem

The ad buying ecosystem

Before we talk about bidstream data, it’s helpful to understand how ads are bought and sold.

  • Direct deals with publishers such as an app, site, or network.
  • Ad networks which group ad inventory to sell it to advertisers
  • Ad exchanges provide a solution for publishers to offer up their inventory programmatically, allowing advertisers to buy it in real-time. Purchasing advertising inventory in this way produces a bid request.


Why is this relevant for location data I hear you ask? In every bid request information is passed on – this data contains several attributes used to determine whether to serve the ad on the device.

Included in this dataset is a form of device location. A company will package up this location data, and the result is the bidstream location data that is available today.

Bidstream location data is appealing because of the sheer amount of it – it can very quickly provide a large amount of scale. However, bidstream data also comes with specific issues – it can be inaccurate, inconsistent, and even fraudulent. Because it’s captured programmatically ,then bidstream location data also has the benefit of being immediately actionable.

“Up to 60% of ad requests contain some form of location data. Of these requests, less than a third are accurate within 50-100 meters of the stated location”



Remember, in the last section, when we identified location signals? Cell tower location is one of these and is the process of triangulating the strength of mobile cell tower signals to place the device in a specific location.

This kind of location comes directly from a telecommunications company (telco). Usually, they have some demographic data associated with the location data.

Similarly to bidstream data, the scale that telcos can offer (they have an extensive reach as in many countries few companies serve the entire population) is appealing.

However, in the same way, this scale is masking many issues with the accuracy of the data. Some studies have found that as little as 15% of data sampled was incorrect.


Location SDKs

A software development kit (SDK) is a toolkit that app publishers can add to their app to provide third party functionality. Developers add location-based SDKs to their apps to access the most precise and accurate location data signals from the user’s device.

Location SDKs come in many shapes and forms – some make use of the core location functionality present in the OS, others do a degree of data processing on top, to boost accuracy.

Some SDKs only operate in the integrated app when the app is open. Others can run in the background to gain broader insights into the movement and behaviors of the device.

Location-based SDKs collect data with the user’s consent – the apps native permissions often collect this consent, but some SDK providers offer consent tools to ensure that the location based app is collecting data in accordance with relevant regulations.

The difference between SDK generated data, and other sources of data can be seen in the accuracy and precision of datasets. Data collected by location SDKs are more accurate because they can listen for multiple location signals.

For example, SDKs can use the device’s built-in GPS to place the device and then, using Bluetooth signal strength from beacons, verify and fine-tune the location of the device down to within a meter of accuracy.

Location SDKs usually have a more sophisticated way of understanding how the device is behaving. For example, the Tamoco SDK uses motion behavior and other entry/exit events to know when a device visits a venue or location.


Why isn’t all data collected using SDKs?

If location SDKs are the most accurate and highly precise, then why don’t we use them to collect all location data?

The issue with many location SDKs is that they require integration into a publisher’s app. This app then needs to cover an adequate number of devices before the data is representative enough to gain any valuable insight or relevant patterns.

However, some SDKs have been built with functionality that benefits the publisher and limits battery usage to a minimal level. These SDKs are the ones that have achieved significant scale.

For example, the Tamoco SDK is optimised to send data in batches to minimise the number of requests. We also modify how data is collected depending on the current battery level.

All of these factors are a direct result of a close working relationship with our developer partners and allows the Tamoco SDK to scale along with our partners.


Publisher datasets

It’s possible to obtain location data directly from app publishers. Some publishers have developed methods of obtaining location by using the devices inbuilt location services.

These will usually coincide with a location-based process within the app – such as looking up a nearby restaurant.

These are often not as accurate as the location SDKs that have been carefully built to collect verified location signals. However, they can be a good source of location data as long as you can validate and understand the process of data collection put in place by the publisher.

We’ve already said that good location data is accurate and precise. However, let’s take a step back and ask ourselves a question – what do we actually mean by accurate and precise location data?

Location data collected by smart devices usually come in the form of a latitude and a longitude coordinate, or a lat/long. This reading refers to the perceived location of the device at the time.

However, how can we make sense of this number and understand if it’s accurate?


Location accuracy v location precision

You might think that accuracy and precision can be used interchangeably. However, in the world of location data, they have different meanings


Accuracy is a measurement that helps us to understand how close the device’s geographical reading is to the actual location of the device.

So how do we measure accuracy? The location accuracy of the device changes depending on the type of signal and the device. Accuracy is measured by looking at the signal type (GPS, wifi, cell tower). The device provides us with a reading of the location and then an accuracy rating. This unit is usually a measure of distance and is the margin of error associated with the measurement.



Precision is the level of detail associated with the location measurement. The more this is is similar to the other measurements in the dataset, the more precise the data is.

In location terms, we use lat/long to measure this. Firstly we check to see if the data points are realistically within the same area.

The number of decimal points in the lat/long is essential in measuring the precision of location data. The more digits there are after the point, the more precise the data is.

The following table helps to explain precision when looking at lat/long:

Decimal Places Decimal Degrees DMS Qualitative Scale
0 1.0 1° 00′ 0″ Country or large region
1 0.1 0° 06′ 0″ Large city or district
2 0.01 0° 00′ 36″ Town or village
3 0.001 0° 00′ 3.6″ Neighborhood, street
4 0.0001 0° 00′ 0.36″ Individual street, land parcel
5 0.00001 0° 00′ 0.036″ Individual trees, door entrance
6 0.000001 0° 00′ 0.0036″ Individual humans


Not all mobile location data is equal

As many in the industry have stated: the type of location data and methodology is of significant importance. The relevancy of different kinds in different scenarios is often contented.

Mobile location data requires some fundamentals to provide granular insights that we discussed earlier.

So what’s the best way to accurately and precisely collect location data and what happens when signals such as GPS aren’t working?

We think this is another argument for SDK generated data. For example, the Tamoco location SDK can listen for multiple signal types simultaneously. Processing these signals allow the SDK to measure accuracy and then determine which signal to use.

Our SDK, therefore, uses Bluetooth and Wifi to help position the device in areas where GPS signals are weak. This sensor agnostic approach means that the SDK can place the device with better accuracy and more precision by using multiple signals.

Remember, when we talked about the three main ingredients that combine to produce location data. We’ve covered the device and its identifier. We’ve also covered the signals that the device used to position itself.

However, we are yet to cover the additional data that is needed to make use of the dataset. As we have discussed location data is usually a lat/long associated with a device and a timestamp.

We need to understand what this location is to make any use of the data. Knowing a device location is half of the challenge. To do this, we use database that allow us to connect this online data to the offline world. We call this a POI dataset.


What is POI

A point of interest (POI) dataset is a data representation of the physical world. A single POI is a geographic boundary and is usually associated with a physical location (think a store or building).

As with location data POI datasets come with a series of challenges including accuracy. Business regularly move, and as changes happen in the real world, the datasets evolve accordingly.

At Tamoco, we set up our own Place database to explore-in-depth how devices move and behave in the offline world. This database is slightly different from a POI dataset.


Explainer: Tamoco places

  • Contains metadata associated with the place – opening hours, floor level, polygon footprint and other essential information that can help to verify if a device entered the POI.
  • Combines with an associated geographical boundary (geofence) that can be used to understand the device activity inside and how long it stays inside.
  • Combines with any known sensors (beacons, Wifi, or other signal based tech) to help understand when a device is visiting the POI and not in fact staying in a place nearby.


What’s the importance of POI?

Perhaps the best way to understand the importance of a useful POI dataset is by using a real-world example.



In the above example, we don’t have a POI dataset. We have multiple lat/long, which might be accurate and precise, but we get no value from this as we have no connection to the real-world.



Here we have a POI dataset which connects the lat/long to a physical location. However, the POI is slightly in the wrong place, which means we think the device has visited the coffee shop, but they are waiting outside, or elsewhere. The implications of this will become more evident in the next section.


Places database

Here we have a place with opening hours and altitude. We have a geofence which allows us to see when the device enters and exits. We also have a Wifi and beacon sensor that we know is inside the coffee shop. Using this, we can verify with accuracy that the device was inside the place.


Connecting location to POI

At Tamoco, we do this through a process called visits. This methodology is a powerful data science technique that allows us to validate whether the device is inside a place and to say with a level of accuracy how long a device was inside.

Where other data providers will claim a device is inside a store if a single lat/long shows up inside a POI, we go much further.

What happens if this single data point is an outlier from a car driving past. What if the POI is in the wrong place?

Tamoco uses essential device information (yes, this is possible only by using a location SDK) such as motion type to verify visits to a place and filter out any false visits.


Location data use cases – how to use location data

Hopefully, by this point, you will have understood more about how location data is collected and how device location is used to understand the connection between online and offline.

However, what are the uses for accurate and precise datasets? How can your business benefit from adding location data to your business? How do you integrate this data effectively?


Segmentation and targeting

Marketers are always looking for ways to identify relevant audiences for their advertising campaigns. They want to segment their audiences as much as possible to maximize campaign relevancy and convert more users into paying customers.

Location data is an effective and unique method to achieve those goals. The reason for this is that location is a significant indicator of behavior, interests, and intent.

For marketers, the patterns that you exhibit can be used to create a very detailed image of what you look like as a consumer. Location data helps to create an accurate representation of your interests, and this can be used to bring more targeted and relevant ads to potential customers.

When using location data to target audiences, there are a few things to consider. Depending on the business and the campaign marketers may use a different combination of each of these in a single campaign.


Real-time v historical

Marketers might want to run a different campaign depending on the kind of data available to them. One way that they do this is based on time.



Realtime location-based targeting involves identifying when a device is in the desired location and usually involves a mobile targeting. The process is simple – when the user is in the desired location, deliver an advert instantly on that users device through programmatic advertising.


Historical location targeting

This form of targeting is usually called retargeting, and it is similar to real-time that we discussed above. The difference is that over time, the devices that appear in a predefined location are used to build an audience. The advertiser will then retarget this audience at a later date.


Visits vs interests


Targeting based on visits is a clear way of building an audience that has visited real-world locations such as a specific coffee shop.

Depending on the value of the POI database this can be extended to include devices that have visited all of the stores across a brand (for example every Starbucks) or every visit to a type of venue (example – visits to coffeeshops in Austin).



Using location to target people based on interests is another way of reaching a highly specific audience. This method is similar to visits but usually consists of several repeat visits to a location or combined visits that fit particular criteria.

For example, an interest-based target audience, such as big coffee drinkers could contain devices that have visited any coffee shop at least three times in a weekly period.

Another example could be active consumers – these could visit both a gym and a health shop within a month.

Interest-based location targeting is interesting because you can create very specific segments. However, as with other aspects of location-based targeting, the more specific you get, the less scale you can achieve with your campaigns.


Channels for location-based targeting + examples

By combining these, you can create highly targeted audiences using location data. But how do you then reach them?



Using device identifiers marketers can feed relevant devices into their programmatic stack to automatically buy ad impressions and target the desired devices in near real-time.

The same data can be used to retarget at a later date in a social feed or via another programmatic channel.

The benefits of this strategy are that you can automate a lot of the marketing process. By using location-based audiences, you can ensure that you are reaching the right audience with the right message.

Tamoco offers these as pre-built segments (both visit and interest based) that can be activated directly in your DSP for targeting, or in your Data Management Platform (DMP) for combining with other data sources.. This process can be used to reach consumers across several programmatic channels and on different devices.


Some examples

Drinks brand targeting consumers in real-time when they visit a venue.

In this situation, we would identify several venues that stocked the relevant products. By feeding visit data into the programmatic stack, it is possible to deliver mobile ads to the device while that visit is going on, or after the visit has occurred. This ad could appear in-app inventory or while browsing the web on the device.


Retargeting through social visitors to gyms with a health drink

Here visits to the category of gyms would be used to build an audience. Next, we would feed the audience into the social targeting platform (facebook ads or similar). The campaign would deliver the retargeting ads to the consumer in their social feed.


Targeting a competitor’s bank customers with a better offer

In this example, devices seen inside a competitor bank are targeted with advertising intended to initiate a switch to a new bank. The data would be historical and might include multiple visits to verify the person is a customer. This data could be used as part of a campaign across several different channels, depending on the marketing stack.


What about location-based segmentation?

The examples we have given include building a new location-based audience to feed into targeting solutions. However, the same principles can be applied to an existing audience.

For example, you can use location data to segment your audience into more specific segments and tailor each targeted ad to be more relevant to each segment.


Personlization & engagement

Today’s consumers demand a high level of personalized communication. Location data can help to bridge the gap between communication and personalization.

Location data can help to personalize ads and messaging to new customers. It can also help to personalize the customer experience.

Consumers want personalization, and everyone from marketers to product designers wants to deliver it.


Location-based marketing personalization

In marketing, location data can help to personalize ads, changing the creative for segments of the audience. This personalization is done by segmenting the ad audience based on location data behavior. These segments are then used to deliver creatives that are relevant to their behaviour – think ‘enjoyed your coffee today’?

Tailoring the ad message boosts personalization and boost the key metrics that marketers are always looking to improve


Location based engagement

Location technology can also be useful for personalizing the customer experience. Integrating a location SDK into your consumer-facing app can support location-based personalization, boosting engagement and retention in the long term.

For example, you can deliver contextual notifications when a user is in a relevant location. Remind users of items left in their app basket when they are nearby to a physical store, for example.


Using location to predict what your customers want

The data that marketers now have at their disposal has enabled them to do more than just personalize based on past consumer behavior.

Location datasets can take personalization to the next level. B2B content marketing personalization is becoming predictive. Brands and advertisers can now combine multiple data sources to understand how consumers behave on both a micro and macro level.

Using this information, it’s possible for marketers to become predictive with their personalization.

Marketers can continuously update their perceived customer profiles with data that explains a consumers profile clearly. This process helps the business to personalize the consumer journey and remove potential barriers to purchase.


Measurement and attribution

As we have seen, the world of marketing and advertising can benefit from using location data in their targeting, segmentation, and personalization strategies. However, location data is valuable in another area where marketers have struggled – attribution.

Advertising is usually quite easy to measure in the online world. If a consumer clicks an ad and makes a purchase, this can be measured and attributed pretty accurately to the ad.

However, what happens if the goal is a store visit instead? Marketers have been scratching their heads for years trying to solve this conundrum. Location data is the missing link that can connect the two.

Location data can act as the link between the online and offline, linking a digital programmatic ad to a store or venue visit.

This link allows marketers the ability to measure and quantify the return on investment from their campaigns. The same capability is useful for out of home (OOH) providers who are looking for a way to link their real-world ads to digital or physical conversions.


It always goes back to accuracy and precision

Location-based measurement and attribution are useful, but it requires data that accurately represents a consumer’s real-world behavior. This data needs to be more than just a single data point – marketers need to know with certainty that a store visit is attributed to an ad to measure ROI effectively.

This requirement is another argument for a place visits methodology that we have already discussed. Device characteristics such as motion and dwell time are essential in providing an online-offline attribution solution that accurately links digital ads to store conversions.



Digital campaign attribution

An agency is running a campaign for a clothing brand. The campaign is delivered to audiences programmatically. The campaign aims to drive footfall to stores stocking a new range.

The impressions and clicks can be measured by the agency, but the brands want to know if the campaign is driving customers to their stores.

Using location data and matching against the IDFA/AAID’s targeted during the campaign, an exposed audience is created. A control audience is also built to compare the exposed group against users who weren’t targeted during the campaign. By having an exposed and control group who were equally likely to visit the clothing brands stores before the campaign, it is possible to isolate the impact the advertising had on store visits by seeing how store visits between the groups move during, and for a period after, the advertising period.



A brand runs an OOH campaign across multiple OOH sites and wants to understand which of these was the most effective in driving online purchases, or whether the OOH advertising was driving online purchases in the first place.

Through an accurate understanding of where the OOH sites are located, and by accurately and precisely understanding how a device moves in relation to the site (an accurate view of this needs to factor in how much time a device spends close the site, how fast they move past the site and a number of other factors the Tamoco SDK factors in) it is possible to build a group of devices that were likely to have been exposed to the OOH advertising.

These devices can be compared to similar devices that weren’t exposed to the advertising, and their device identifiers can be matched to customers in the companies CRM or DMP to measure the impact the OOH advertising had on store purchases as well as which of the OOH locations was the most effective in driving purchases.


Analytics and insights

Location data is a useful tool to analyze how large numbers of people move and behave to identify large scale trends and patterns.

These kind of insights are usually difficult to attain at scale in the offline world. Location data works as an indicator of where people go and how they behave – and how these change over time.

In the realm of advertising and marketing, location-based analysis can deliver valuable insights, such as:

  • Comparisons between brand, category, or another group of physical locations over time. Such models will look at the footfall changes over time.
  • A brand can use location data to understand more about its customer demographics – where they live and work, where else they shop
  • Insights into their store performance – average unique visits per month, number of repeat visits, average visit length.

This analysis can be used for a variety of adjustments. Including changing campaigns to suit the real-world behaviour better, to fundamentally changing market strategies to match the data of how a customer is behaving in the real world.


Beyond advertising

These same insights can be applied outside of the marketing and advertising vertical. Using footfall can be useful across a range of industries including retail, finance, real estate, healthcare, and government.



Location data can be useful for both smaller and large retailers. Understanding store visits, as well as customer behavior 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.



Location data is an essential tool for finance analysis. Device location can help to identify fraudulent activities and protect users with an added layer of security.

Understanding footfall through big data sets is valuable for the financial sector. Mobile device data can help to forecast earnings, number of customers and other KPIs before they are formally reported. These insights help to inform investment decisions.


Real estate

Anyone looking to invest in real estate, or open up a new store branch can use location data to understand how busy certain areas are, what type of people you’ll see in certain areas and how well similar businesses in that area perform.



The rise in mobile location 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 most significant. For example, we could examine 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 cycling routes.

The same is true of traffic and congestion. In increasingly crowded and polluted megacities, it’s crucial 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 vital to making the kind of smart city that can sustain current levels of population growth.

Location data can have a substantial 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.



Transparency – why do we need it

As the amount of location data available to businesses increases, there is likely to be more bad data. Poor third-party data sets are becoming more frequent, with providers unable to validate the accuracy and precision of the data.

We’ve already discussed the need to for accuracy and precision in location data – the difference can mean a falsely attributed visit, irrelevant targeting or a negative impact on customer engagement.

The most accurate providers will be able to verify their first-party data sets. They can provide a detailed methodology around how they collect data. This is one of the main benefits of working with a provider that controls data collection – their data is first-party and therefore reliable and transparent.


Explainer: 1st, 2nd and 3rd party data

Third party data is data that is purchased from outside sources where the provider you are working with is not the direct collector of the 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.

First party data is your data that is collected directly from your audience or customers.

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Of course many businesses don’t collect first-party location data so they work with a location data company to source the data for their campaigns, or other business needs.

In this scenario, second party data is much more reliable than third-party data. You can understand how the data is collected as the methodology is transparent, and the data accuracy can be verified. Of course, this doesn’t confirm that the data is accurate – but at least you can check yourself if this is true.

The best providers can explain how they collect data, how they filter out inaccurate data and can usually provide a reliability score with data to allow the end-user to understand the data they are working with.



2018 saw the introduction of GDPR in Europe. In the US, the upcoming CCPA act data privacy will still be front and center 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. Businesses 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 center of the big data revolution.

Businesses that utilize location data will need to be clear about 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 through to the point of data use.

As with the verification of accuracy, understanding data privacy is more accessible if your provider is working with first-party data.

For example, at Tamoco, we have built consent functionality into our SDK. This allows the publisher to collect user consent at the point of data collection in accordance with the IAB framework.

For the data user, this means that they can understand how consent was given, and for which purposes.

Companies will now need a robust framework of data management and governance to move forward.

When choosing a location data provider to work with, there are many things to consider. With several different sources, signals, and methodologies available, it’s essential to understand exactly what each provider is offering.

We have put together the following list of questions that are useful when selecting a location data company.


Questions to ask a location data company


What is the source of this data?

How much of your POI data is 1st party vs. 3rd party?

How do you organize the geographical area around a POI or place?

Can you share how precise your POI/place data is?

How many POI locations do you have?

What metadata is associated with these places?

How do you verify your place database?



How do you collect location data? Is this process first-party, or is the data 3rd party?

What type of device data do you use (GPS, wifi, beacon, etc.)?

Is your data sourced from an SDK?

Do you have a method in place to filter out data that isn’t relevant for my campaign or merely inaccurate?

what is the scale of your dataset?


Red flags

The number of Businesses in the location data space can make it hard to differentiate between them. Below are a few red flags that you should keep an eye on the next time you’re speaking to one of these companies.

All of our data is accurate to 5m

Some data providers will make big claims regarding how accurate their GPS derived data is. As mentioned earlier, GPS can be accurate within a 4.9 meter radius, and this can be further improved when combining with WiFi and Bluetooth signalling.

The truth of the matter though is that GPS accuracy will vary massively, possible reasons for this are:

  • Mobile devices lose and regain mobile reception as they move around
  • Buildings, bridges, trees and roofs can block and reflect GPS signals

The better data providers don’t just look at the accuracy of GPS signals. They will take additional data fields into account, such as looking at the motion type, speed, altitude etc of the device to determine the likelihood of a device visiting a store at a given point in time.

Accurately measuring how a device moves is a complex issue, and you should be wary of data companies giving simple answers with blanket statements.

Our visit data is correct because of our precise polygon geofences

Accurately mapping POI is important to try to understand whether a device actually spent time there. However, a lot of data providers out there will claim that the reason they’re able to attribute POI visits is because of the precise polygons they’ve been able to draw around POI.

As mentioned above, GPS accuracy has a high degree of variability. You can have a precise polygon geofence around a 20 square meter retail unit, however if all the signals you place inside the geofences have +/- 50 meter accuracy, you’re not doing a good job at understanding who spends time in that POI.


Want to learn more?

At Tamoco, we are always innovating in how we collect and use device location. We’ve spent years fine-tuning our methodology to correctly verify how a device moves and behaves in the real world.

What is location data?

Location data is geographical information about a specific device’s whereabouts associated to a time identifier. This device data is assumed to correlate to a person – a device identifier then acts as a pseudonym to separate the person’s identify from the insights generated from the data.

How accurate is location data?

Location data is only as accurate as the source. GPS is usually the most reliable but only outdoors. Usually a combination of Bluetooth, GPS and other signals will provide a more accurate reading of device location.

Is location data compatible with GDPR?

Yes. Businesses that utilize location data will need to be clear about 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 through to the point of data use.

What is location data used for?

Location data can be used to target, build audiences, measure and gain insights and understand the offline world.