Marketing & Advertising

What Is Behavioral Targeting? – All You Need To Know in 2021

Advertising can be a challenging endeavor. Carefully crafted campaigns can often fall short of desired goals, with no apparent reason. Marketers can easily reach the wrong audience or fail to deliver the correct message that can covert or engage consumers.

Today, random targeting is a thing of the past. Marketers have a variety of methods to ensure that the right message reaches the right person at the right time. Advances in behavioral tracking and the increase of powerful datasets have enabled advertisers to boost conversion rates across both online and offline campaigns.

Campaigns that use behavior tracking and utilize behavioral targeting are yielding incredible results.


What is behavioral targeting

Behavioral targeting is a marketing strategy that uses historical behavior to personalize the types of ads consumers see.

Historical behavior is sourced through powerful datasets that illustrate how audiences behave. Marketers can then use this to create ads and campaigns that match each consumer’s actual behavior.

Behavioral targeting involves building up a detailed user profile and using this to deliver better messaging and better timing. It limits the possibility of advertisers delivering irrelevant ads and helps to boost advertising campaign KPIs.


What are the benefits of behavioral targeting

Behavioral targeting is a powerful marketing tool that is rooted in the modern, data-centric world that we live in. But it isn’t all about using numbers and tech. Behavioral targeting provides value to both advertisers and consumers.


Advertiser benefits

Improved engagement for advertisers

Understanding consumer habits helps advertisers to identify audiences that have engaged with specific products or touchpoints. It also helps to identify audiences that are in the right moment or behavior for a particular campaign. Targeting users with no behavioral intent or brand awareness will limit engagement. Using behavioral targeting will increase a number of critical metrics, such as clicks or conversions.


Matching consumer needs with creatives and messaging

Personalized messaging converts more users and ultimately reduces the amount of wasted ad spend. Relevant ads are much more likely to move consumers along the purchase funnel than generic ads that are not personalized. Ads that align with a consumer’s previous behavior are much more likely to convert than ones that don’t.


Improving the bottom line

Ultimately advertisers want to get the best possible return on investment on their campaigns. Delivering ads that match with audiences previous behavior is more likely to drive conversions than ones that are generic. With behavioral targeting, companies can see a rise in new business, repeat customers, engagement, and other key metrics.


Consumer benefits

An improved ad experience

Consumers aren’t always keen on giving up their personal data. But they also dislike ads that aren’t relevant or ads where the experience is unengaging. That’s why, when surveyed, more consumers prefer personalized advertising. This personalization ultimately improves their experience.


Better efficiency

Ads can be a quick route to purchase, providing a fast way of identifying the best product for their needs without a long searching process. This increases efficiency for consumers, allowing them to get to storefronts quickly and finding the most relevant products, rapidly.


Awareness of new products

By seeing ads that are personalized to them, consumers can keep up to date with new products that interest them. Retargeting based on behavior can also help to complete purchases that a user was distracted from.



As well as behavioral targeting benefits the advertiser and the consumer, it also helps the publisher. Where these publishers use ad monetization as a revenue stream, the ads mustn’t be irrelevant to the user as it might reduce engagement with their product, app, or publication.


How does behavioral targeting work in 2021

The process of behavior-based targeting on the highest level consists of collecting information about a user or a person and then using this information to deliver ads that match this information.

Collecting information can be done in many ways, and it can come from many different sources. Often a data management platform (DMP) is used to aggregate this information for advertisers.

Here are some common data sources that are used for behavioral targeting:

These sources provide a huge variety of data that includes:


Website cookie data

Data on how users behave and interact with websites is a valuable method of behavioral segmentation. Users spend a lot of time browsing the web, so the information is rich – pages visited, for how long, in which regions. Therefore these insights can provide a lot of information that is useful to boost engagement and conversions.


Mobile device data

Cookies also work on mobile devices. Understand the behavior of the potential customer on a mobile device can help to understand which format and which message could work best in an advertising campaign.

These web-based insights can be combined with social signals, check-ins, and mobile purchases to understand the best way to target audiences.


Geographic location

Anonymized location can be extremely valuable for advertisers. Especially when accurate and precise. Since the early days of bidstream datasets, device behavior can be accurately tracked to build up detailed profiles of behavior than can form powerful, behavioral-based segments for advertising.


Subscription data

Businesses that have some log-in system require the users to enter details and information about themselves. These fields can be used to understand the users, with address, interests, and contact details help with behavioral targeting. Let’s say you are looking to purchase a Notion template, it makes sense to pre-fill any forms with relevant subscription data, if you have it.



DMPs and other marketing software can collect large amounts of demographic segmentation information, such as age ranges, interests, and gender, to create a detailed profile of audiences. This process usually works without using personal information but these ranges are used to create campaigns that can communicate more personally with audiences.


The process of behavioral targeting

The data collection process

User data can come from several different sources. Depending on the source, there are many different ways to collect data. For website behavior, a pixel is used. This process creates and updates cookies that understand how the user interacts with the site. Apps have a similar process. SDKs can collect other behavioral information, such as location data.

This data is usually stored in a DMP, but there are other adtech solutions for storing this information.


Organization and segmentation

Once this behavioral information exists in a central location, the next step is to sort individual users into groups that share the same behaviors.

This segmentation varies significantly depending on the company, product, or goals. For example:

  • Potential customers that go to the gym
  • Visits gym location 2 times a month
  • Current customers who like meat
  • In CRM and visited meat weekly delivery page
  • Users who are interested in SEO (maybe even more specific, like a London SEO consultant, Bristol SEO agency, or even a industry specific one like B2B SaaS SEO consultant)
  • Existing customer who have read at least one blog post related to SEO trends 2020


Delivery and application of behavioral targeting in advertising campaigns

Specific ad campaigns are delivered to match each segment. This process makes the advertising more relevant for each segment and increasing the chance of engagement and boosting conversions.


Activating behavioral targeting

All of this behavioral data can be used across multiple campaigns and in different advertising channels. That’s the benefit of having a centralized place to store the data.

There are multiple ways to activate this data to create behavioral-based ad campaigns that deliver. Here are some examples of how to enable behavioral targeting to drive engagement and increase conversions.


Examples of behavioral targeting

Cross-selling and upselling

Knowing what your customers like and understanding how they interact with your business is a powerful way of knowing which additional products to promote to them. If you can link product A and B, then your audience that has shown interest in product A that are likely to engage with a campaign promoting product B.


Behavioral targeting in targeted email campaigns

That’s right, and behavioral targeted email campaigns doesn’t just sit in the world of programmatic media advertising. Creating personalized email campaigns based on how your audience is using your site or app is a great way to start.

Examples include targeting cart abandonment sessions, including viewed products in routine updates or directly linking content related to what your audiences have already read rather than generic content. Behavioral targeted email campaigns is a powerful way to increase email productivity and boost your targeting options.


Remarketing with behavioral targeting

An advantageous and accessible way of using behavioral targeting is to retarget. By identifying users that visit your site, you can reach them on other websites to encourage them to visit again and complete goals.

The most common solutions for this are facebook and google as they have simple to install tracking pixels that can understand users that visit specific pages on your site. You can then activate these segments directly in their platforms.


Location-based targeting

Location-based targeting is an excellent way of reaching audiences based on their real-world behavior. You can retarget audiences that have visited your physical stores, or a competitive store. This can also be applied to e-commerce and other online stores.

These targeting campaigns can be useful because the insights are related to how consumers behave in the real-world. This allows you to create compelling segments based on how people behave over time.

What is behavioral targeting?

Behavioral targeting is a marketing strategy that uses historical behavior to personalize the types of ads consumers see.

What are the benefits of behavioral targeting?

Behavioral targeting can deliver better engagement, better messaging, and better marketing results.

How does behavioral targeting work?

The process of behavior-based targeting consists of collecting information about a user or a person and then using this information to deliver ads that match this information.

What are some examples of behavoral targeting?

Cross-selling, targeted email campaigns, remarketing and retargeting, location-based targeting


Marketing & Advertising

What Is Ad Fraud? How Location Data Can Detect Ad Fraud

Online programmatic advertising is a huge, multi-billion dollar per year industry. Ad spend in this area is expected to reach over $300 billion next year. As well as this, other forms of online advertising are expecting similar growth.

The ease of programmatic ad buying and the vast growth of the market has lead to a rise in ad fraud that has infected the advertising ecosystem. Some estimates state that up to $42 billion will be wasted in 2019 due to fraudulent ads.

Some measures have been taken to protect against some basic types of ad fraud. But to counter more sophisticated fraud, advertisers need a more robust solution to counter the huge amount of ad fraud that exists in the industry.

Location could be this solution. Understanding device location and historical behavior can help to identify fraud better than other methods. In this post, we’ll look at the ways that advertisers can use location data to reduce ad fraud and limit the damage from malicious actors in the advertising ecosystem.

What is ad fraud?

Ad fraud is the practice of fraudulently impersonating online advertisement impressions, clicks, conversions or other KPIs in order to generate revenue.

Eliminating ad fraud with location

Does the device exist?

The first step is to identify if the device is a real device and not an emulator. Emulating a device is a common way of faking ad impressions and clicks. In some cases, emulators can even generate IP addresses to pass as a real device.

In this case, location needs to be more precise; the ad inventory needs to combine with sensors to verify that the device exists.


Are the ads landing where intended?

Let’s say that you have a campaign running in the US, and you are only targeting devices in the US. You can use location data to map where the ads are being delivered to the device. If there is a considerable disparity between targeting and delivery, then it’s highly likely that some of your campaign inventory is fraudulent.

Identifying fraudulent inventory is essential as often devices can change or move, and the targeting solution will not update these. But other times, audiences can contain bad inventory, deliberately including devices that don’t meet the criteria. This is why you should always carefully vet your data and audience providers.


Countering smarter ad fraud

What about eliminating the more intelligent fraud? Some ad fraudsters are generating fake IP addresses to spoof IP location monitoring.

Using a powerful location SDK can eliminate this. An SDK uses many signals to identify device location with greater accuracy correctly. Subsequently to trick a location SDK into registering a click in a fake location is much harder to do than a simple IP trick.


What about click farming?

In some cases, fraudsters will pay a real person to click and interact with specific ads in several different locations. Sometimes these devices are kept in one place; other times, they are the person’s personal device.

Location data associated with a device can be used to see if the device moves and behaves like a regular device. Understanding if a device stays in one place and combining this with other fraud detection methods, such as time to install, can dramatically increase the identification of ad fraud.


Towards a version of location ad fraud detection.

Integrating precise location into your ad stack can have a substantial positive impact on your ability to detect ad fraud. But using location data in a more traditional way can also help to understand if clicks and conversions are being bought.

For example, location-based attribution is the process of measuring if an exposed device eventually visits a physical location. This can also act as a way of vetting audiences and inventory.

Location data has many powerful applications across the advertising ecosystem. Detecting ad fraud is another application that allows advertisers and marketers to use location to reduce wasted budgets and identify partners that may be supplying them fraudulent inventory.

Marketing & Advertising

5 Ways In Which Big Data Is Changing The World Of Marketing

If there is a ‘silver bullet’ to marketing, it would be data. 

Data is the raw material marketers need to make sense of things so that they can do their job with precision and purpose. There’s a million marketing tools and templates available on platforms such as Notion, but this understanding is what empowers marketers to understand their customers, based on the actions they take, revealing their most authentic intentions.

In a recent survey, 40% of brands plan to expand their data-driven marketing budgets, while 88% of marketers used data to enhance their understanding of the customer. Quite clearly, data is slowly becoming to marketers; what oxygen is to humans.

In this article, we’ll look into how data is revolutionizing the world of marketing. If you’re a marketer, you’ll get some robust insights into how data-driven marketing is shaping the future of your profession. So let’s get into it!


Personalization Of Marketing Campaigns 

Imagine being greeted by your first name the second you visit a website. Or receiving an email addressed to your name with a list of your favorite products. Better yet, being notified about ordering your groceries every week with personalized recipe recommendations. Are you impressed yet?

That’s precisely what data-driven marketing enables you to do. By collecting critical pieces of data about customers at various marketing touchpoints, like their name, contact details, what they’re interested in, what they’d like to hear about and who they want to hear from, marketers are empowered to be strategic in all their campaigns.

Amazon, for instance, uses customer data to analyze their historical purchases to show them product recommendations based on their shopping habits. They’re able to showcase customized product pages to appeal to customer interests, so they’re more likely to buy. These customized product pages could include customized product recommendations, and even personalized pricing to induce customers to buy.

Capitalizing on customer data is excellent for both customers who receive personalized communications, and marketers, who make a positive impact on the bottom line!


Predictive Analytics

HIstorical customer behavior is one of the best ways to predict how customers are likely to behave in the future. For instance, if you’re a grocery store, and a customer has always been health conscious his whole life, ordering sugar free granola bars for the last three years, the chances are that he is going to continue ordering them from you in the near future.

When customer purchases become predictable, businesses can capitalize on this information by ‘automating’ their sales. Essentially, they’d offer the customer a chance to make ‘repeat purchases’ based on the predictability of their shopping behavior.

Building on the Amazon example, they’ve recently obtained a patent on a system called “predictive dispatch” to send products to customers even before they buy it! This is a real game-changer for Amazon, and it’s customers. Amazon can stabilize their revenue, and customers purchase products without having to put any effort into actually making the purchase.

And with the rise of tools that can help access public data, it’s easier to say “what is web scraping, and how can this provide data that will boost my marketing”.


Customer Segmentation

Customer segmentation is the act of ‘clustering’ customers into groups to identify unusual patterns. For instance, your data may reveal that customers from a particular city are more likely to buy your products than others. Or you may discover that customers belonging to a specific age demographic purchase different products from you when compared to other age groups.

Tools like Tamoco can be extremely useful to gather location data to help you segment your target audience. For example, Tamoco collects information based on location to further segment your audience based on specific interests, how many times they’ve visited a location, common preferences, and demographics including age, gender, and even home and work location.

These insights can be golden opportunities for marketers to create bold campaigns that optimize their return on investment. By clustering customers into specific groups, they can create separate marketing campaigns for each segment, making their content highly relevant.

While there are several techniques marketers use to segment data, the overarching goal is to identify anything of interest that helps them boost revenue and meet their goals. With effective customer segmentation, marketers can:

  • Identify who their most profitable and least profitable customers are.
  • Predict future customer patterns.
  • Improve their marketing focus by creating relevant content.
  • Build loyal relationships.
  • Price products differently.
  • Develop better products based on customer interests.

Inevitably, high-quality data is the precursor to doing segmentation well. That’s why businesses must continue to invest in collecting high-quality data so that all segmentation efforts can be fruitful.


Optimized Paid Campaigns

By collecting data about customers, social media platforms like Facebook and Linkedin make it possible for marketers to customize paid ad campaigns, at scale. What this means is that they can create separate ad campaigns for specific groups of prospects, for paid campaign success.

Additionally, big data makes it possible for marketers to conduct “remarketing” campaigns. Remarketing campaigns are ads that literally ‘follow’ your customers online, wherever they browse, once they’ve visited your website.

Chasing customers online with your paid ads can be an effective strategy when executed well. As per the ‘rule of 7’ in marketing, a prospect needs to be exposed to your ad for a minimum of 7 times before they decide to take action on it.

That’s why some marketers are looking to focus on specific platforms, such as LinkedIn marketing.

While big data makes it possible to chase customers with paid ads, it’s important not to make your customers feel stalked when creating remarketing campaigns. You should endeavor to expose your ads to customers by spacing out the time of exposure, so your ads appear more natural, and don’t come across as being pushed onto prospects!


Your Business Size Doesn’t Matter Anymore

Whether you’re a big business with over 500 employees, or a small business with under five employees, software tools make big data easily accessible to all. That’s the beauty of marketing on the internet!

You can be just as effective at marketing no matter what size business you are, because success in this game is dependent on how well you utilize the data to your advantage. While the size of your marketing budgets may certainly have a role to play in the extent of your success, you can still achieve a high degree of precision in your marketing campaigns and acquire customers rapidly, with big data being your marketing backbone.

Free online tools like Google Analytics have made it possible to collect and analyze big data in real-time. Small businesses need not spend money on buying expensive tools to get a deeper understanding of who their customers are since google makes it easily affordable. Before the ‘big data era,’ this was impossible to do.


Wrapping It Up

Big data makes the future of marketing very bright! With high-quality data being collected in real-time, and the availability of technologies like machine learning and AI, the world of marketing is up for massive changes. It’s no surprise that only the sky’s the limit to what sophisticated marketers can achieve in the near future!


Ryan Gould
Vice President of Strategy and Marketing Services

Elevation Marketing

From legacy Fortune 100 institutions to inventive start-ups, Ryan brings extensive experience with a wide range of B2B clients. He skillfully architects and manages the delivery of integrated marketing programs, and believes strongly in strategy, not just tactics, that effectively align sales and marketing teams within organizations

Marketing & Advertising

Customer Data, Personalization & The Customer Experience

There’s a difference between a satisfied customer and a loyal customer, says Shep Hyken, customer service expert.

These days, consumers are accustomed to being pampered and treated like royalty, which means you have to go out of your way to attract and keep them.

But what’s the X factor, the secret sauce that makes all the difference, and turns your happy customers into loyal ones?

The answer is pretty straightforward and a bit surprising – personalization.

This useful practice can make a world of difference when it comes to building brand loyalty and getting your customers to pick you over your competitors (even if they offer more for less.)

I know that sounds like an overstatement, but recent surveys corroborate my superlative; 75% of consumers are more likely to buy from a retailer who addresses them by their name, offers items based on their past purchases, or knows their purchase history – and packages this offer well.

Let’s discuss how personalization (powered by big data) can give you a competitive edge when it comes to creating unique customer experiences.


What Is Customer Experience?

Before we delve into the topic of improving your customer experiences, it’s essential first to define this concept.

In layman’s terms, customer experience is the long-term impression your brand leaves on your customers. It results from all the interactions they have with your brand and products or services. So, every time a customer or a prospect interacts and engages with you, they asses and analyze whether their expectations have been met or surpassed, thus adding different nuances to their overall perception of your company.

These customer touchpoints can be your logo, social media posts, website content, graphic design, product offers, newsletters, purchases, or chats with your sales or customer service reps. Content marketing for smaller businesses is even more relevant than the others.

It’s more than evident that you have to deliver at all times if you want to keep a perfect score and win your customers over.


Go Big or Go Home

To personalize your customer experiences, you need to know as much as possible about your prospects, their issues, needs, and interests, and implement this knowledge into your marketing and sales strategies.

In other words, what you need is big data, that is, vast and diverse sets of data which you then also have to process, analyze, structure, and make sense of. This data can refer to information from Twitter feeds, explainer videos, webpages, video interviews, video editors, mobile apps or audio recordings, among many other sources and formats.

A couple of years ago, it was virtually impossible to store and process such huge volumes of data, but today, the latest analytics tools allow you to handle these vast amounts of unstructured data and use them to get to know your customers better and tailor your offer to best fit their needs.


How to Collect Customer Data?

Although this seems like a difficult undertaking, in reality, approximately 70% of people are more than willing to share their personal information if this will improve their customer experience.

Although GDPR has introduced limitations when it comes to collecting and using personal and sensitive information, there are numerous effective methods to obtain these valuable insights and still comply with regulations.

  • Incentivize your customers. It’s a good idea to offer your prospects a discount or additional features for free, in exchange for their first name, birthday, preferred communication channel, phone number, and list of products they’re interested in.
  • Gate your valuable content. Show only a sneak peek of your most relevant content, such as e-books, reports or how-to guides, and allow your customers to download it after they sign up for your newsletter. This is a great tactic, and the fact that a prospect decides to leave their email address suggests that they’re genuinely interested in your content, which means they might be a good fit for your company. From there you just need a good email marketing solution to deliver the content, as well as a robust content distribution strategy. If you don’t want to use the most popular tool sout there then check out some of the best Mailchimp alternatives to find the one that works best for your business.
  • Implement chatbots. According to Gartner, by 2020, 85% of all customer service interactions will be powered by these smart algorithms without any human interference. Chatbots can provide 24/7 support, collect customer information, and obtain valuable feedback in a conversational manner. Such always available customer service will engage your prospects and improve their experiences with your brand – they won’t have to wait for their turn to talk to a human agent or your regular working hours to get an answer to their product-related questions. Chatbots never sleep, and they can serve several customers at the same time. Find out how to build a Facebook chatbot and take advantage today.
  • Create surveys. Customer surveys are another easy and legal way of getting the information you need directly from your customers. Make them easy to fill and do take into consideration what you learn. People love to see some of their ideas implemented.
  • Remember that consumer data can exist on your other channels. For example, let’s say that you have started a podcast. This is valuable customer data for your business as it can help you to learn more about your customer’s needs. Of course, you’ll need a podcast hosting service that supports the collection of listening data.


How to Make the Most of Customer Data?

Now that you’ve managed to gather relevant information on your prospects, it’s essential to put it to good use.

Successful personalization goes well beyond first name basis, so here’s what you can do with all the information you’ve got.

  • Segment your email lists. If you are using email marketing (and you should because this somewhat traditional strategy boasts a 4,400% ROI), you need to segment your contact list based on different parameters and personalize your outreach to the fullest. Email analytics and marketing software like Moosend which is a great MailChimp alternative allows you to do just that. While it’s true that all your prospects are interested in your brand, they might not be interested in the same products or services. For example, if you’re offering a 50% discount for womenswear, your male recipients will consider an email with such a deal to be spam. The same goes for your Facebook and other ads. Targeting will be successful only if you hyper-personalize your offer. You can visit LiveAgent to learn more.
  • Use product recommendations. Amazon generates 35% of its revenue through personalized customer recommendations. By offering your prospects items similar to those they have already bought or viewed, you’re increasing the odds that they’ll make a purchase. This tactic removes friction from the buyer’s journey and streamlines their search for the products they’re interested in.
  • Speed up your checkout process. A complicated checkout process is one of the main culprits behind shopping cart abandonment. Namely, if your potential customers always have to provide their personal information when making a purchase and paying for the items they want to buy, they’ll be more likely to churn and leave without buying. But, if you ask for the information necessary for making a purchase only once and store it for speeding up the checkout process in future, you’ll improve their user experience and get them to make their purchasing decision faster.
  • Tap into geotargeting. By using your customers’ location information, you can additionally personalize their experience. For example, offers different currencies on their website, so their users can see how much a particular accommodation option costs in their local currency, thus adding to pricing transparency and preventing hidden expenses.
  • Create a customer loyalty program. Your aim shouldn’t be to generate one-time sales alone. Loyal customers spend more money and take less time to make a purchasing decision. A customer loyalty program will result in recurring purchases, which will make it easier for you to predict your revenue. It’s a good idea to offer a reward, a discount, or a freebie after a predefined number of purchases. You can personalize the entire process additionally and introduce spending-based tiers so that, for example, customers who are your biggest spenders get the most valuable rewards.

Personalization is an indispensable sales and marketing tactic and a vital differentiator which helps you gain a competitive advantage over your competitors. Given that most markets are highly saturated and that your customers have a wide range of options to choose from, you have to reach out to them on a personal and meaningful level if you want to attract and keep them. That’s where personalization comes in to build loyalty and make your customers happy.


Michael Deane is one of the editors of Qeedle, a small business magazine. When not blogging (or working), he can usually be spotted on the track, doing his laps, or with his nose deep in the latest John Grisham.

Marketing & Advertising

Programmatic In-housing – What About Programmatic Data?

Programmatic advertising spending now accounts for more than 80% of digital ad spending. This spend is increasingly being moved in-house. When surveyed, 35% of brands had, to some extent, reduced the role of agencies.

Over the last few years, marketers have developed this in-house programmatic media buying so that it makes use of first, second, and third-party data sets.

The role of data in the in-housing process shouldn’t be overlooked. It offers many benefits to brands that can successfully create a centralized data environment, such as maximizing ROI, increasing, and enabling real-time behavioral based triggers to create more personal and engaging programmatic campaigns.


Things to consider with data and programmatic in-housing

All of your data in one place

For in-housing to succeed, it’s essential to develop a data-centric environment.

This means that different data platforms, people, partners, and processes are brought together to create a single data solution.

It depends on the business as to whether the data sources are first-party only, or if the organization wants to integrate third-party data sources to improve its programmatic efforts.

Whatever the decision, the key to making a success of in-housing requires a single centralized data strategy.

Another requirement to succeed when in-housing programmatic media buying and programmatic data is to focus on the dialogue between DMP managers and media users. Developing a consistent and productive conversation between these two is one of the main benefits of moving programmatic in-house.

Making sure that you own the data you’re bringing in-house. In-housing allows you to take control of these data sets and use them across different kinds of marketing activities and touchpoints.

External partners are often unable to provide the same, and this means that you are unable to see the effect of marketing efforts on driving business growth.


Improved performance and ROI

Combining 1st, 2nd, and 3rd party data sets into a centralized in-house data solution maximize positive impact on advertising success.

The benefits of integrating 1st, 2nd, and 3rd party data into a DMP that is managed internally will provide a significant boost to marketing and advertising performance.

In-housing data allows organizations to understand customer data better and use it to implement better targeting throughout the funnel.

This drives ROI by facilitating smarter and more personalized cross-sell, up-sell, and optimization.

A combined in-house dataset allows marketers to improve further up the funnel as well. Internal datasets allow for better programmatic lookalike modeling. Anonymized IDs can be sent to DSPs to identify audiences across devices and model new audiences based on real-world behavior.

In-housing doesn’t always guarantee better programmatic marketing performance. But combining it with a data-centric approach can help to improve ROI for marketers in both in terms of short term results and longer-term programmatic performance.


Real-time capabilities

In-housing programmatic media buying allows organizations to be more adaptive and support real-time targeting capabilities. Combine this with data-centricity, and very quickly, marketers can optimize their campaigns to drive even more performance.

Data in-housing has a significant impact on effectiveness and provides a real-time boost to programmatic advertising. It reduces the time for tweaking and improves the speed in which advertisers can react to behavior signals.

Granular real-time optimization requires data in-housing. But the benefits for brands are clear as more look to align their programmatic spend and their datasets in-house.


Cost efficiency and transparency – data transparency and cost benefits

Some brands see in-housing as a potential problem in terms of data privacy. Taking control of customer data and managing it in-house alongside programmatic media buying can seem daunting, but it is instead an opportunity for brands to take control of the role that data plays in their organization development.

Robust data-centric organizations are looking at in-housing data because it puts them in control of the data and how it is used. This naturally requires an organization to look at its data sets and understand the consent and collection process. In a GDPR world, this is an essential requirement for programmatic buying companies.

Data and programmatic in-housing have also led to an increase in transparency. On the programmatic side, false impressions and the ability to understand exactly what costs are associated with each campaign have led to the rise in-housing.

For data, it is a similar story – transparency allows them to understand and perform their tests on data accuracy.

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Marketing & Advertising

What Is Bidstream Location Data – Why Is It Inaccurate & Imprecise

What is the bidstream?

The bidstream is a network of advertising requests that deliver ads to mobile devices.

A bid request refers to the moment when a publisher auctions off an ad slot to an advertiser. This request delivers an ad to the device.

When the ad is delivered, some information is passed back the other way. This information contains ad related information, but ofter it comes with additional details. These sometimes include a form of location.

This location data is then packaged and used for a wide range of applications.

But sadly this isn’t always a great proposition. Here’s the thing with your bidstream data…


The problem

Geodata is no longer just an experimental solution. Location data is fueling some of the most advanced marketing efforts.

Because of this, marketers are rightfully demanding greater transparency around this data, where it comes from and how it is created.

The problem with bidstream data is that it is often inconclusive, inaccurate, or even fraudulent.

The thing with bid stream is that it can very quickly provide a large amount of scale. Due to the sheer number of devices that display ads, the number of location points can be quite appealing.

However, too many marketers are blinded by this scale and refuse to focus on data quality.

This quality is what provides lasting ROI for marketers and allows for effective targeting, attribution, and insights.


The common pitfalls with bidstream data

General precision issues

Not all bidstream data is inaccurate but the data is often imprecise. What’s the difference? Well, it comes down the detail of the device location.

Some bidstream data is based on the IP address of the device. Sometimes this can cross over an area as large as 1km. In a city, this is not precise enough to understand the context of the device.

bidstream data that is collected in this way doesn’t go far enough to understand the context around device moment. SDK based data, for example, can understand the difference between a device walking past a store and a device visiting a store for a coffee.


Cached IP address

A common issue with bidstream data is that the device often passes back cached location signals. If a device has connected to a network before it can sometimes deliver this cached address, even when the device has moved to a new location.



Bidstream data is often confusing if you sit down and analyze it down to a device level. For example, we’ve seen devices move across the world in a matter of minutes!

This disparity demonstrates the issues that bidstream can present for marketers. The use of a VPN can cause these discrepancies.

These factors mean that bidstream data is unreliable. Some reports have places accuracy levels of bidstream data at less than 10%.

Marketers may be able to get their hands on large quantities of data through the bidstream, but this data has to be rigorously filtered to ensure any level of accuracy. Even then, these levels of accuracy are often unsuitable to carry out the type of campaign that will produce the desired results.


How do we know this?

We know what good data looks like because we deal with it every day.

We’ve spent years building a dedicated SDK that provides anomynized location data from mobile devices.

It’s the product of years of focusing on the inaccuracies involved in device location, and we’ve built many solutions to identify location data that is both precise and accurate.

But here’s the kicker – we thought about scale as well. We released that advertisers needed a way to scale this data to satisfy their marketing goals.

That’s why we worked on deploying our SDK to compete with the scale of bidstream.



Location data comes in many forms, and each has its advantages and disadvantages. Transparency is key, and marketers should understand that the data they use in their campaigns should be rigorously tested for accuracy.

The bidstream can generate large amounts of location data instantly. This data is often inaccurate and imprecise.

SDK driven data collection offers much-needed improvements in data accuracy and allows marketers to execute better campaigns.

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Marketing & Advertising

What Is Business Intelligence & Business Analytics?

For your business to grow, you need to understand which elements are working and which will work in the future.

With the rise of big data businesses now produce a vast amount of data. Making sense of this data is where business intelligence and business analytics comes in.

But what’s the difference and what can each do for you?

We’ll break down both and help you to understand how they can become a crucial tool for your business growth.


Business intelligence vs. business analytics

Business intelligence is often used as a blanket term to describe the approaches and tools that can be used to provide useful insights that can help you to understand how your business operates.

This usually involves data and some kind of analysis to establish trends and understand why things are performing in a certain way.

Business analytics is also about exploring data that is related to your business. The goals are similar – to better understand relative performance and make better-informed decisions that facilitate more significant growth in the future.

BA is slightly different from BI in that while they both address similar problems, BA is the process of using data to predict and draw conclusions. It is also used to predict what will happen in the future.

In this sense, the difference between business intelligence and business analytics is that the first is descriptive and the second is prescriptive.

Business intelligence explains what has happened with your business or what is currently happening. Business analytics is focused more on what will happen in the future, with emphasis on prediction.



So if we look at a dataset that we are familiar with – location.

Location data is a powerful tool in understanding business performance, and it can be used to inform decision making from management to marketing.

Let’s say we were a retail store with online advertising. We wanted to see if the advertising affected store visits or how they had affected an industry such as gig economy apps.

We could use location data to see which devices then entered the store. By matching these devices to those that were exposed to our online advertising campaign we could see the number of devices that were exposed to the advertising and then also visited the store.

This is an example of business intelligence. We’re taking the number of devices that visit the store and have been exposed to our advertising to create a simple conversion rate.

Let’s use the same store to illustrate business analytics.

We created a dataset that consisted of all the devices that visited the store in a monthly period. We used metadata in the Tamoco network that is associated with these devices to get a detailed understanding of the type of consumer that is related to the device.

By association, we now have insights into the type of customer that visits this specific store. This is fueling our business intelligence.

The next step is for us to create a predictive model that helps us to tailor our online advertising to the customers that visit our store. This will allow us to optimize our budget and maximize conversions.

Using this data to predict the type of customer that will visit our store and target advertising accordingly is an example of business intelligence. We are actively using the data to predict and inform future business decisions with the view of optimizing them.


How does this fit into your business

Modern businesses need a solution that can combine both. Location intelligence is one that allows companies to analyze performance and model data to make smarter decisions in the future.

Location is one example of a dataset that can fuel business intelligence and business analytics.

To compete in today’s landscape business need to be able to understand what has happened and what will happen in the future.

This is where it’s essential to get the right data.


Location for BI and BA

Business intelligence with location

Location data can help you to measure KPIs in the offline world such as store visits, and can improve conversion copywriting in real-world locations.

It can also help you to measure behavior anywhere in the real world. Real-time business intelligence solutions can identify population movement, macro trends and other valuable metrics for your business.

Business analytics with location

Large scale device movement data can help to inform a robust business analytics solution. By combining location data sets with other existing data or solutions, it’s possible to predict how your customers will behave.

The use cases for this are incredibly large. Everyday use cases could be city planning, connecting smart vehicles, optimizing advertising and marketing or optimizing the supply chain.

Marketing & Advertising

Lookalike Modeling – The Best Way to Build Lookalike Audiences

Modern marketers are always looking for ways to grow their successful campaigns and reach new audiences. Lookalike modeling is an effective way to identify customer attributes and use these to build new and larger lookalike audiences to expand the reach of marketing activity.

There are several ways to do this and marketers focus on these attributes and behaviors as the core identifiers of their target audience.

But what if there was a better kind of attribute to identify similar audiences. What if this behavioral data was a better indicator of similarity that just having visited the same product page?

And what if these datasets were underutilized in lookalike modeling – allowing you to build more relevant audiences for your campaigns?


The issues with lookalike modeling

Current data on lookalike is, for the most part, a valid way to build lookalikes. But often these datasets are for individuals that look like others in the seed audience.

This might appear obvious but do you want to build your audience based on looks? Wouldn’t it be better to focus on how consumers behave, rather than outdated demographics – such as a page like that occurred years ago?

Well, this is possible when the focus is placed more on act alike audiences, rather than lookalike.


Using location to create behavioral based lookalikes

Act alike audience is better than lookalike modeling because you are using more recent data and you are using data which signifies intent. A great example of this is location data. It’s current and traveling to a specific location is a much better signifier of consumer intent.

Behavioural based lookalike modeling is more effective because you can provide narrowly defined attributes and use this to build new and highly relevant audiences to expand your marketing activity.


Example – the current way

Let’s look an example, in this case, city gym going customers. This is currently how lookalike modeling works:

We take the existing attributes from our data set of ideal target customers. These might have the following traits:

  • Age: 24-49
  • Male 60%
  • Social profile matches sport interests
  • Mobile-focused

Using this information you could quickly build a lookalike audience that had similar characteristics. The problem is that this same audience profile might overlap with men who are merely interested in watching football matches at home.

This is the problem with focusing on what customers look like, rather than what they do and how they behave.


Using location and actions

With action-based lookalike modeling, marketers can rely on dynamic behavior to identify attributes. These attributes can then be used to build more effective lookalike audiences.

Let’s imagine we are still trying to target the same consumers – city going gym goers

We might have customers in our database that exist in our target group but share none of the characteristics discussed above. But they have still converted and carry potential value when building a lookalike audience.

Let’s use location to illustrate this example.

We can identify where the seed lookalike goes and then identify other devices that exhibit similar behaviors.

In this case, we can map our customers, and we can see that a high percentage of them visits both whole foods and a high-end drug store within a three month period.

We can then build a lookalike audience that consists of every other device that enters both of these locations within three months. This can be done anywhere in the world, and we can even use categories of locations (health stores) to make this work in across several different regions.

Our lookalike audience, in this case, would contain people that were demographically different from our customers. They wouldn’t necessarily look like our audience, but they would behave like our customers.

This can ever be extended to build new audiences based on visits to you or your competitor’s real-world locations, which means that you can create competitive lookalikes based on your competitor’s customers.


A better way – that can also be combined with your current lookalike modeling

Of course, these attributes can be combined with your current lookalike modeling. A good balance between demographic info and behavioral data is more likely to identify customers that will improve your lead generation.

With the rise of DMP solutions that now have ready to activate location data, it’s the perfect time to use behavior as a building block for lookalike audiences.

Moving to a behavioral-based advertising model with less weight placed on demographics marketers can build more effective audiences and maximize their KPIs.


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Marketing & Advertising

What Is Lookalike Modeling? All You Need To Know in 2021

One significant challenge marketers face is how they can grow their audiences once they want to achieve scale.

Growing targets will always mean that marketers need to reach more people. The problem that marketers encounter is how to grow these audiences while keeping them relevant to their product or proposition.

Expanding your audience beyond your current database is crucial to achieving future growth. What digital tools for marketers are there to reach new audiences? How can you ensure that a bigger audience doesn’t mean fewer conversions and less relevant consumers?


What is lookalike modeling?

This is where lookalike modeling comes in. Marketers need to find new customers and ensure that these new audiences are relevant to their businesses goals.

Lookalike modeling is the process of identifying new customers that look and behave like your current audience.

It involves taking a seed audience and defining key characteristics which differentiate these. From here smart modeling and other processes will help to identify a new larger, audience that is similar to your current customers.


What do you need to start building lookalike audiences?

As with many forms of digital advertising, lookalike modeling works using data. Data comes in many forms, and it’s really up to you to decide on which datasets are the most effective at identifying your target customer.

The most successful lookalike audiences are based on unique first-party data. This needs to encompass a range of first, second and third party datasets that cover both online and offline behavior.

That’s an awful lot of data to process, notwithstanding the process of collecting processing and managing that comes along with it. Luckily there are several solutions to help.

DMP for lookalike audiences

This data is combined with a program that can quickly identify other consumers who exhibit similar behavior. This process usually occurs inside a DMP (data management platform). It can also be done in some demand-side platforms (DSP) as well as in house.

in a little box – a Data management platform is a tool that aggregated and unifies data from many different sources to create a clear, holistic view of your data.


How does lookalike modeling work?

If that sounds slightly complicated, do not worry. Lookalike modeling is simple as long as you have the right dataset to work from.


Choosing datasets

First party, second party, third party, online, offline CRM, purchase, location – data comes in many different forms and comes from many different places.

You need to pull these datasets into a single place to maximize the effectiveness of your lookalike audiences.

This data is essential to get right. The more information you have, the more likely you are to build a better lookalike audience.


Define attributes

Next up you’ll need to identify the attributes or behaviors that identify your most valuable customers.

This will look different depending on the type of data sets you’re using. You can combine attributes from different datasets to create more specific seed audiences.

The more specific your look-alike model, the more likely you will find your target audiences. The stricter your seed audience, the more likely it will help you to realize your goals.

Of course, this will affect the size of your lookalike audiences. The more attributes you select, the more likely you are to filter out potential customers.

Ultimately it depends on the goals of your campaigns and what you want to achieve by building lookalike audiences. If you need to target specific people with a high-value proposition, then it might make sense to use more narrowly defined behaviors.

However, if you are looking to focus on reach and awareness then being less strict with your attributes will generate a larger audience that will most likely drive more awareness.


Some examples of datasets and attributes

Location-based lookalike audience

Purchase data

frequency and amount

Browsing history

Interest in specific products


Building the lookalike audience

This is done in the DMP or DSP and will look slightly different depending on the type that you use.

For external lookalikes, this might be done via a third party. For example, location-based lookalikes will usually be done by the provider.

The process is similar depending on where it occurs and look like the following.


  1. Analyze the seed audience
  2. Apply algorithms to find profiles that match
  3. The result is a lookalike audience


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What can you use lookalikes for?

The primary use for lookalike modeling is to find new prospects for your business.

Building lookalike audiences allow marketers to scale their campaigns to relevant consumers. With the instant reach available to marketers via digital targeting platforms, lookalike modeling can instantly help a business scale their key metrics and improve their bottom line.

Lookalike targeting can also help to extend the reach of specific campaigns. All campaigns eventually run dry, no matter how effective they are. Using lookalike audiences, these high performing campaigns can be extended to reach new audiences that will hopefully have a similar level of conversion.

Audience modeling is part of every successful media buying strategy. All media buyers should be aware of how lookalikes work in order to make informed decisions concerning their ad campaigns.


Best practices to build lookalike audiences

  • Find the line between reach and conversion – you need to focus on the number of attributes that you select. Too many might reduce the reach of your lookalike. Too few and your lookalike audience will not be closely related to your seed audience to produce the desired results.
  • The more data, the better the lookalike modeling will be
  • Think about new datasets that your competitors aren’t using. This will give you an advantage and allow you to build better lookalike audiences.
Marketing & Advertising

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



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

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

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


Why is this important?

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

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

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


1st party data

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

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

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

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

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

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


What you can do with first party data

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


Engagement, personalization

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

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


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



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


2nd party data

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

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

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

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

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


What can you do with second party data

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

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


New audiences, new business

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

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



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

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


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3rd party data

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

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

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

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


What you can do with third-party data

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

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


Expand your audience

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


Tips for marketers and advertisers

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

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



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

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


Accuracy and transparency

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

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

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

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