Francesco Guglielmino, CEO of Cuebiq, sat down with Sima Vasa on her weekly podcast, Data Gurus. Together, they covered all things location data and consumer privacy, from the state-of-the-art transparency policies at Cuebiq, to the power of personalized ads and data sharing. Guglielmino also voices his views on his how to balance privacy and profits, why proactive consumer privacy is always worth the cost, and how we've progressed from the "Wild West"—the early days of mobile data collection. Watch the interview below, or listen to the podcast on Spotify, Apple Podcasts, or Google Podcasts.

[embed]https://youtu.be/6yx57MDhCtw?si=bvAqlFTSUF2ykXAh[/embed]

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We sat down with Jon Friedman, who leads the Marketing Solutions team at Cuebiq. Learn about Jon’s vision for the team, what he sees as Cuebiq’s biggest differentiator, and how he envisions the company evolving over the next year.

1. Can you tell us a little about your role and what you’re working on?

I am the SVP, Marketing Solutions, and my job is to help our clients solve business problems. Location is an extremely powerful data point, and by understanding the dynamics of a client’s category and its specific challenges, we can empower brands and agencies to leverage real-world behaviors to inform all aspects of media/marketing efforts, from targeting through campaign optimization and measurement, as well as the analysis of consumer mobility and visitation trends.

2. What is your vision for leading the Cuebiq Marketing Solutions team this year?

The location space is incredibly complex, made even more complicated by broader marketplace forces including industry consolidation, an increasingly aggressive regulatory environment, and operating-system changes that threaten data supply. Since Cuebiq’s inception, our focus has always been to help our partners navigate through the “noise,” and my vision for the Marketing Solutions team is that we will continue to develop as solution sellers, super-focused on understanding how location can help our clients win.

3. What is the biggest opportunity you see for data right now?

I think the biggest opportunity is for companies to (finally) prioritize privacy compliance and data quality over vanity metrics. Companies on all sides need to demand transparency with respect to data sourcing and to insist partners and suppliers maintain a consumer-first focus.

A prime example is that our platform is 100% informed by first-party data (focused on a named-consent framework) to ensure not only the industry’s most stringent compliance to current/future privacy regulations but also that consumer rights and sensitivities are considered beyond the mandates of the law.

As another example, the location business was built on “scale,” defined as “how many devices a platform ‘sees’ over a given month.” At Cuebiq, we’re convinced that “seeing” a device sporadically with no context renders it useless and we have been working hard to change the conversation from MAUs to DAUs. It’s necessary to understand an opted-in device’s behavior over time to gain any real insight from offline analytics and attribution.

4. What is the biggest challenge you anticipate in the coming years for the advertising industry? 

It’s well documented that the past few years have accelerated the changes traditional media companies needed to make in response to the consumption trends we’ve been anticipating and starting to see for years. An even greater complicating factor of the past few years has been the pandemic’s substantive impact on business models across almost every vertical. For example, e-commerce, curbside delivery, and BOPIS have become integral to many business models during the pandemic. I think the biggest challenge of this year will be finding a common ground between the resulting “new” business needs of brands, and the models being developed by media suppliers and the rest of the adtech ecosystem. Complicating matters further, consumers will be emerging from the pandemic, which will create yet another “new normal,” which, of course, promises more change.

5. In your opinion, what is Cuebiq’s biggest differentiator as a company in the location-data space?

I think Cuebiq’s biggest differentiator is how our commitment to our principles manifest in our business practices. We hold our partners to the same standards to which we hold ourselves. For example, one of our key tenets is privacy, and all of our partners not only agree to strict contractual terms that prohibit any attempt to merge our data with personally identifiable information, but we also require that many undergo an annual third-party audit of their compliance with those terms.

Social responsibility is another key principle of ours, exemplified by our Data for Good program. During the pandemic, we provided free access to our Mobility Insights dashboards so that brands, journalists, and organizations including the New York City’s Mayor’s office could illustrate mobility patterns to help save lives.

6. How do you see Cuebiq evolving over the course of the next year? 

Over the next year, we will continue to innovate to solve problems. We have dedicated resources and processes in place and we are excited to help our partners understand how to unlock the power of location data to address their challenges.

7. If you were to summarize how you feel about the new year ahead in three words, what would they be?

Hopeful, persevering, and unifying

Want to chat with Jon or a member of his team to learn more about how Cuebiq is innovating this year? Set up a time to talk.

Cuebiq's Data for Good initiative is being continued with support from the Spectus.ai data cleanroom and their Social Impact professionals. Their commitment to positive social impact through the ethical and responsible use of location-based data makes further insights possible. We invite you to visit https://spectus.ai/social-impact/ for more on contributions to academia and research partners.

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Originally posted by LiveRamp on 12/08 here.

As 2020 draws to a close, we’re looking to the year ahead, especially with major industry changes going into effect in the coming year. We sat down with Antonio Tomarchio, Founder of Cuebiq, a leading consumer insights and measurement company, to talk about upcoming industry opportunities. Cuebiq provides brands and marketers with a trusted, high-quality, and transparent currency for offline visitation data to map and measure the consumer journey. Marketers can leverage Cuebiq’s available audiences spanning retail, automotive, and QSR to expand their campaigns.

Read more of the conversation between LiveRamp’s VP of Data Partnerships, Kaitie Coghlan, and Cuebiq’s Antonio Tomarchio below:

Kaitie Coghlan: We’ve faced quite a year in 2020, with incredible challenges, but we also see opportunities in the year ahead. What do you see as the biggest opportunity for data in the industry next year?

Antonio Tomarchio: In my opinion, the biggest opportunity is the ability to leverage and monetize first-party data. According to Forrester Research, a majority of businesses are focusing on this type of strategy. They are looking for new and innovative ways to develop their data so others can also benefit. Yet, in order to unleash the full potential of first-party data, it’s paramount to have strong investments in privacy-compliant technology. Marketers need easy access to safe, externally sourced data to build and enrich audiences, as well as build applications like attribution.

KC: We’ve also seen many of our partners realize the importance of leveraging and monetizing first-party data. What can media owners do in 2021 to get more use of their unique first-party data?

AT: Media owners can start using their unique first-party data to direct their ad dollars, build solutions, and ultimately monetize it. As the amount of first-party data increases, the industry will see the rise of the platform-as-a-service (PaaS) model. Advertisers are becoming more data-savvy and sophisticated, and they have more custom needs. A one-size-fits-all SaaS solution is not the way anymore. Advertisers need the flexibility to build solutions quickly and unleash the full potential of their data by building custom KPIs, analytics, and applications. The PaaS model enables media owners to build those solutions in a quick and cost-effective way.

KC: That’s a good call-out. Reframing software-as-a-service as platform-as-a-service is a helpful way to think about how a first-party strategy can guide existing data efforts. When it comes to audience data, we’ve seen that achieving accuracy can come at the expense of scale. How do you advise clients to think about this, and what are you doing to ensure accuracy?

AT: In order to ensure accuracy, clients have to be able to enrich audience data with their own first-party data. Then, they need to find the right external data sources that can guarantee high-quality additional enrichment without compromising on scale. Advertisers should invest in having data handlers inside the organization who can find the right data sources. Clients must make first-party data more available inside the enterprise organization so marketers can use it.

KC: Great point. Here at LiveRamp, we place a high importance on data privacy and security. Privacy by design is a framework we abide by and implement throughout all our teams at LiveRamp and it’s great to hear Cuebiq is also helping further that approach. As you look toward 2021, what do you think the key opportunities will be to make an impact with consumers?

AT: The combination of first-party data and the rise of the PaaS model can open endless opportunities for innovation in Mar Tech. According to an IDC report, there will be over 500 million enterprise applications in the cloud by 2023 that will leverage data to produce insights. With this in mind, the combination of taking advantage of one’s own first-party data, in addition to privacy-centric data sourcing for external data in PaaS environments, can really open the road to actionable customer insights.

KC: What new or existing challenges will there be in 2021 and how do you think advertisers should navigate them?

AT: There are challenges coming in 2021 from the demise of identifiers, specifically the Apple mobile identifier, IDFA. This could be a big problem for the ecosystem, and could be an impediment for innovation. We know LiveRamp’s ATS solution, which focuses on a value exchange in the ecosystem, was designed with these industry shifts in mind. Cuebiq also has been prepared for IDFA opt-in enforcement for months.

The lack of third-party cookies as an identifier also raises a challenge, especially with regard to identifiers. However, it is possible to adjust to a cookieless future and a change in advertising infrastructure through a privacy-conscious approach rooted in first-party data. There is an opportunity to create identifiers and ID graphs that can go beyond the IDs provided by the operating systems.

KC: Thank you for sharing your thoughts and insights with us, Antonio. If you were to summarize how you feel about the new year ahead in three words, what would they be?

AT: First-party data, PaaS, and privacy.

To learn more about our partnership with Cuebiq, visit the partner page on our website.

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The best APIs are simple and clean, predictable and discoverable. But to achieve your business goals, APIs also must be built on best-in-class data quality and meet the highest levels of privacy compliance to protect your brand’s safety. After all, an API is only as useful as the data it is piping in for your brand.

Cuebiq recently launched our own APIs, and you can rest assured that we only use the best quality data for your performance data. In fact, Cuebiq is the only location insights company that can provide the formidable combination of five key data-quality elements to ensure you’re measuring performance from the best data available. Keep reading to find out what they are. 

1. First-Party Data 

At Cuebiq, our commitment to privacy is at the core of everything we do, from establishing an industry-leading Named Consent and privacy framework, to setting the standard when it comes to ethical and responsible data collection. What is Named Consent? Well, Named Consent means that the user knows the name of the company or companies with which their information may be shared PRIOR to providing their consent. In a nutshell, Named Consent is when users are establishing a direct, first-party relationship with those companies through their consent.

Because Cuebiq has direct relationships with our app partners and users through Named Consent, we’ve been able to build the industry’s leading source of first-party location data. A great example of Cuebiq’s Named Consent can be found on our Privacy Center (under the “Consent” tab). First-party data is extremely important when it comes to APIs, because you need to make sure the data is not only of the highest quality but also meets the highest levels of privacy compliance, to protect your brand’s safety.

2. Accuracy at Scale

Cuebiq maintains direct relationships with 100+ app partners that reach a diverse base of 20 million Daily Active Users (DAU), which creates a strong foundation for realistic and actionable insights. Through persistent background collection, we map de-identified users’ mobility every day — an average of 100 points per day. When combined with over two years of historical data, Cuebiq is best positioned to understand consumers’ paths to purchase and measure changes in behavior. Daily counts matter because if you do not see users’ patterns every day, then you can’t have reliable measurement of visitation patterns and offline behaviors. 

3. Representative Data 

You can be sure that Cuebiq’s panel reaches a diverse and statistically relevant sample of the U.S. population — which is key for scalable, actionable insights. Third parties have analyzed our user base confirming the statistical significance of the panel in representing the overall population. In one such study, the University of Washington wanted to validate Cuebiq data as compared to cellular network and in-vehicle GPS data. With a sample of 500k devices, researchers found that Cuebiq data is highly demographically representative, based on a 91% correlation with census population data at the census-tract level.

4. Double-Verified Visits

Because of the scale of our data, 100+ data points per device per day, Cuebiq can understand how much time consumers spend at various locations — what we call dwell time. Using dwell time, we are able to parse actual visits from non-relevant data points, thus, verifying visits. By comparing time spent to the type of POI visited (convenience store vs. movie theater), we are able to distinguish a real visit vs. a person simply walking by.

5. Media-Agnostic Measurement

While many companies claim to be media-agnostic, we believe that when a company both sells and measures media there is a conflict of interest. Cuebiq does not sell media and is not affiliated with a media company. We are 100% media, DMP, and DSP agnostic, with the sole purpose of helping clients understand their brand’s performance.

If you’re interested in learning more about Cuebiq’s APIs, be sure to visit our new developer site.

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For developers, time is of the essence — and Application Programming Interfaces are major time-savers. With APIs, developers can automate manual processes and eliminate the need for human involvement, saving them valuable time. They can also lay the groundwork to enable different ecosystem players to innovate independently and develop specialized solutions. Learn more about the specific benefits of APIs for developers below. 

Routine Task Automation

One of the key challenges developers face is building uploading tools. Having to go into s3 to download files and then porting them into their own platforms is very time-consuming. APIs can help streamline that process by automating manual processes and extracting performance data in real time, thus eliminating repetitive and time-consuming tasks.

For example, APIs enable developers to autofill information into forms and tables. They also allow developers to gain real-time access to information rather than accessing the data on a scheduled cadence.

Ecosystem Innovation

We know that it’s often not ideal to have many different platforms with different logins. With APIs, developers can combine data from multiple parties into their platform of choice to develop specialized solutions and enable independent innovation. Having multiple players in the ecosystem come together enables developers to provide a more complete solution that is interoperable.

For example, developers can use APIs to build specialized solutions for their customers or partners. With an API strategy, developers can partner more flexibly with multiple vendors to build a more cohesive solution. 

Audience Extraction

Finally, developers can use APIs to simplify workflows with turn-key extractions of custom audiences and data feeds. Alternatively, when it comes to audience creation, they can integrate the Cuebiq Audience Builder functionality into their own platform. With the Cuebiq Audience Builder, brands and agencies can create custom audience segments based on real-world visitation patterns. 

These are just some of the many benefits APIs offer developers in terms of saving them time and streamlining their data integration efforts. Stay tuned for more content around APIs on the Cuebiq blog, and check out this blog if you’d like to learn more about location data quality.

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These days, it’s all about artificial intelligence — especially when it comes to the advertising ecosystem. Yet, as AI becomes more and more integral to advertising, the ecosystem needs to adjust accordingly to keep up with technological advancements. Specifically, several best practices need to be implemented in order for an AI-driven ecosystem to truly thrive.

Before I dive into that, let’s backtrack to how exactly AI is affecting the advertising ecosystem. In my last blog, I wrote about two highly beneficial applications of AI in advertising: predictive and prescriptive analytics. From choosing the right media mix allocation, to optimizing audience strategy by channel, AI can help marketers dramatically improve their strategies, saving them valuable time and money.

In order for AI to function as effectively as possible in these ways, it is paramount to follow some best practices for an AI-driven ecosystem. Below are the key best practices we at Cuebiq have identified to make a prolific and ethical AI-driven advertising ecosystem a reality.

Creating a Healthy Big Data Ecosystem

For AI to be effective, there needs to be a healthy underlying data ecosystem. Even though each brand will have its own data stack, based on their needs and resources available, all will need high-quality, privacy-compliant data at scale.

You may be wondering, what makes data “high-quality”? First, the data cannot be biased — it needs to be representative of all groups of people, not limited to a certain pool. To avoid bias, the data must have scale and breadth. What’s more, the data must be collected in a privacy-compliant manner, ensuring transparency, consent, and control for the end user, and accountability for the company managing the data.

As follows, defining best practices for the algorithms behind AI will be critical. This goes hand in hand with establishing guidelines for which data should be taken into account to fuel the AI, both ethically and effectively. 

Shifting From an Audience Taxonomy Mindset to a Data-Driven, Prescriptive Mindset

Once a healthy big data ecosystem is established, there needs to be a tectonic shift toward a data-driven, prescriptive mindset. What does that mean? Well, beyond fueling the programmatic ecosystem, AI can also enable a shift to longer-term KPIs and consumer lifetime value. Through predictive analytics for example, algorithms can determine which consumer segments will become high lifetime value consumers, which is more accurate than choosing audiences “a priori.”

As an example, by leveraging machine learning, Cuebiq discovered that high lifetime value consumers of a leading retail brand don’t visit locations as often as might be expected. How could that brand use this insight? Once they know that these consumers don’t visit as often, they can then use offline intelligence to analyze if the consumers go somewhere else between visits. They might learn, say, that these consumers come to their store for big-ticket items but visit another retailer for smaller purchases. With this information, they can then identify specific tactics to drive this segment to their store more often. This is just one of many ways a brand can turn insights from offline intelligence into actions that improve their marketing strategies. 

AI-powered insights like this will enable the shift from what I refer to as the current “audience taxonomy mindset” to a real-time, data-driven prescriptive mindset. The beauty of a data-driven, prescriptive mindset for segmentation and targeting is that the AI constantly updates its analysis according to audience behaviors and refines its predictions.

In order to achieve this paradigm shift from the “audience taxonomy mindset” to longer-term KPIs and consumer lifetime value potential, a framework to guide advertisers and publishers would be helpful.

Collaborating With the Walled Gardens

Additionally, for an AI-driven advertising ecosystem to be truly effective, brands will need to have a complete picture and unified measurement for all of their efforts, including the walled gardens. A unified metric for campaign performance will give advertisers the ability to measure consistently and effectively across all platforms, bringing the power of AI-driven recommendations to their full potential.

To realize this, an open dialogue with the walled gardens will be crucial. By collaborating with the walled gardens, those in the rest of the advertising community will be able to unlock the full potential of comprehensive and unified measurement.

If we can successfully implement these best practices, we will be on a win-win path for both end consumers and the advertising industry, empowering advertisers to engage in more relevant conversations and serve better ad experiences, while preserving consumers’ desires and their right to privacy and transparency.

You can learn more about Cuebiq’s commitment to privacy here.

The post Establishing Best Practices for an AI-driven Ecosystem appeared first on Cuebiq.

New partnership makes subscribing to high-quality location data a breeze.

Amazon’s AWS Data Exchange is a new service that makes it easy for millions of AWS customers to securely find, subscribe to, and use third-party data in the cloud. Cuebiq is thrilled to be among the first certified partners available within the AWS Data Exchange. This partnership makes Cuebiq’s industry-leading Visit Intelligence easily accessible to AWS customers. The initial phase of the partnership makes visitation insights available to customers for marketing and non-marketing use cases in automotive, QSR, retail, entertainment, grocery, financial services, hotels, and transportation verticals. 

All Cuebiq data that is available through AWS Data Exchange is compliant with our Future-Proof Privacy approach. This approach to privacy meets not only existing privacy regulations but is also focused on setting higher standards and industry best practices, to ultimately be prepared for future regulation that might come into effect. This stance is made possible by the fact that Cuebiq is the only location intelligence company that collects 100% first-party data. We have a direct link to the app publishers, which ensures compliance with the four key privacy principles of consent, control, transparency, and accountability. This also allows us to maintain a direct link to the end users, ensuring that we can disclose the terms of our data collection and enable opt-in and opt-out. 

Currently, the data available from Cuebiq on AWS Data Exchange is exclusively aggregate visitation data providing insights on visitation patterns at commercial locations. When being utilized for marketing use cases, these types of aggregated insights are ideal for campaign planning, audience targeting and segmentation, and cross-channel measurement. When being utilized in non-marketing use cases, such as transportation use analysis or real-estate evaluation, visitation insights can be used to understand the impact of strategic investments based on how consumers are moving through the world. For example, a large retail brand may want to evaluate a new location for a storefront. By utilizing visitation insights the brand would be able to quantify the performance potential of a prospective location, run due diligence for upcoming investments based on historical footfall trends, quantify cannibalization risk of opening a new store, as well as identify in-fill opportunities within existing markets. Alternatively, if the same retailer prefers to focus on optimizing their current locations, they can use Cuebiq’s Visit Intelligence to evaluate underperforming locations for potential closure or relocation by measuring visits at designated locations, building audience segments to improve store traffic for existing locations, and accelerate the ramp-up of new locations.

For more information on use cases that apply to marketers as a whole, as well as industry-specific use cases such as transportation and real estate, you can download the solutions below.

• Cuebiq for Marketers
• Cuebiq for Real Estate
• Cuebiq for Transportation

About Cuebiq:

Cuebiq is a leading consumer insights and measurement company, providing brands and marketers with a trusted, high-quality, and transparent currency for offline visitation data to map and measure the consumer journey. Cuebiq is at the forefront of industry privacy standards, follows a privacy-compliant framework in its data collection, and was one of the very first location providers certified by leading privacy association, NAI.

Cuebiq gives brands and marketers access to the largest database of anonymous and accurate location data in the United States. Its AI-driven platform offers audiences as well as cross-channel and TV attribution capabilities, which empower brands and marketers to make better, more-informed business decisions and marketing strategies. 

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Originally posted by IAB on 4/30 here.

The recently released IAB guide “Defining the Data Stack” provides a framework for both advertisers and publishers to build or enhance their data stack, depending on where they are in their data capabilities journey. As addressed in the guide, choosing the best data sets for your stack is first and foremost an exercise in prioritizing needs. If you want to grow your second and/or third-party data, you’ll find yourself with a crucial task at hand: evaluating data providers.

While it often feels overwhelming, the evaluation process is crucial. Once you select a data source, you associate your brand not only with the quality of the product you are using but also with the integrity of the partner you choose. As not all data is created equal — whether you are a brand, an agency, or a publisher interested in vetting new data sources — it is paramount to thoroughly assess vendors and data sets for both quality and accountability.

Below are some key questions to ask in your assessment. It is helpful to structure your outreach in a formal RFI and ask prospective partners to provide as much detail as possible so that you can compare and contrast each provider’s offering and effectively score them.

How was the data collected?

Methodology matters. Good data produces accurate and actionable insights, while unreliable data can generate misleading insights — which are often more dangerous than no insights at all. Depending on the type of data you are evaluating, you may find that the data collection methodology, which impacts the overall quality and consistency of the data, varies by vendor.

Understanding the pros and cons of each data collection methodology will yield valuable insights into the quality and scale of the data sets you are evaluating. Throughout your evaluation process, make sure to compare results, screen for inconsistencies, and seek additional information to help you along in the process.

As part of your methodology analysis, it is also valuable to understand what the original purpose of the data collection was — think of survey data, for example — in order to screen for potential bias.

What sources were used?

You may find that certain data sets were created by combining multiple data sources, often to achieve scale. In this case, you’d want to understand what “multiple sources” really means. Scale is typically one of the drivers for securing second and third-party data, but it is important to determine the following:

• Whether you are getting a diverse and large universe representative of the US population — in which case, great!
• Whether the data was collected using different methodologies, thus generating inconsistencies that require cleansing and manipulation, and/or precluding you from being able to ascertain the origin, quality, and compliance of the data itself.

This is why it is also important to ask, “Who collected the data?” Was it the partner you were evaluating or was it brokered from other parties? The closer you are to the origin of the data, the more control you’ll have regarding its quality and integrity.

Is the data accurate?

The term “accuracy” carries different meanings based on the specific type of data, but in its simplest form, it should do what it purports to do. For example, if you are evaluating demographic data, a segment classified as Male 18-34 would have to contain users that fit that demo in order to be “accurate.” However, in the location data space, an “accurate” representation of “visits to an AMC theater” would take into account only users who spend 60+ minutes at the movie theater — rather than any data point seen in proximity of the theater, regardless of time spent.

Accuracy often goes hand in hand with scale, because you’ll likely want high-quality data with great reach. You might find it helpful to set clear benchmarks for your evaluation, based on both your specific needs and industry best practices.

Is the data fresh?

The longevity of the data depends on the specific data set. Some data sets, such as certain demo characteristics, can be considered “static” because they remain constant or rarely change. In contrast, other data sets, such as location data, are more “dynamic” in nature because they constantly evolve based on user behavior. When evaluating “dynamic” data sets, you can ask questions such as “when was the data collected?” and “how often is the data refreshed?” This will ensure that you get access to the most accurate and relevant information available.

What is the vendor’s privacy framework?

Does the data contain personally identifiable information (PII)? Did the users, from whom the data was collected, opt into the data collection? How is the vendor ensuring privacy compliance? These are only a few of the many questions on this very important topic, and based on the specific type of data you are looking for, you’ll want to clearly understand the vendor’s framework to ensure that your partner stands by solid principles.

As I mentioned in a recent post, user privacy has moral and ethical implications, which should be key drivers for all players in the ecosystem. Yet it is also apparent that user privacy has become a business imperative for brands, agencies, and publishers as they identify the data sets and data partners for their stacks. In fact, in today’s data-driven landscape, brand safety is no longer just about the environment in which ads run, but it is also tied to the origin of the data utilized. For this reason, it is paramount to be aware of and screen for partners’ data collection practices, to ensure that they themselves are in a safe position. And in today’s landscape, users are asking — rightfully so — for practices that may go beyond existing regulations and grant them the transparency, control, and access to data that they deserve, along with data provider accountability.

Can I evaluate a data sample?

Trying is believing. If you have the in-house resources, evaluating a data sample is a powerful way to get a better sense of what you should expect after you sign the contract.

Want to learn more about offline intelligence? Subscribe to Cuebiq’s newsletter to receive all our latest blogs!

The post How to Evaluate Data Partners: 6 Top Questions to Ask appeared first on Cuebiq.

You probably already know how important it is to back up your marketing decisions with data. But you might not know that the quality of the data can make or break the success of the decisions you make — and that the quality of location data in particular can differ greatly between providers.

So, if you want to evaluate a location data provider yourself, how can you determine the quality of the data? Below, we’ve compiled four key factors to help you evaluate the quality of location data, whether you’re choosing a data provider for the first time or reevaluating an existing provider.

1. Scale of the Data

When considering the quality of location data, scale is essential because it allows you to get granular in your analysis without losing statistical relevance. For example, if you are interested in understanding how a specific DMA or store of yours is performing, scalable data will ensure that the performance is not based on the analysis of only a handful of visits.

2. Accuracy of the Data

Accuracy goes hand in hand with scale because it helps you make sure that the data is accurately identifying visits to real locations (or POIs, as we call them). After all, there isn’t too much value in having vast scale if the data is not accurate.

For instance, if you’re launching a marketing campaign targeting consumers who are loyal to McDonald’s, you need to make sure that the audience segments you’re activating are built on accurate data. Otherwise, you won’t be spending your media dollars efficiently — and you’ll also be providing your consumers with a bad user experience.

3. Density of the Data

Data density adds a third dimension to this picture by allowing you to understand whether offline consumers visited a location, and if so, how much time they spent there (which we call “dwell time”). Your data could be highly accurate, but if you cannot tell apart consumers who spent 20 minutes at your store vs. those who were just driving by, then you cannot truly understand how consumers are interacting and engaging with your brand. Through dwell time, you can distinguish actual visits from non-relevant data points.

4. Privacy and Transparency of Location Data Collection

And last, but definitely not least, are privacy and transparency. It is paramount that all location data be collected anonymously and only from users who’ve opted in to sharing their location data. Cuebiq has embraced privacy as a core value from the very start, and our commitment revolves around four key principles: Consent, Transparency, Control, and Accountability.

Ultimately, using high-quality data that exhibits these qualities will help you gain meaningful consumer insights, which can be instrumental in enhancing your advertising campaigns and helping you achieve your marketing goals.

The post How to Determine the Quality of Location Data: 4 Key Metrics to Consider appeared first on Cuebiq.

Over the past few years, companies like Facebook and Google have released their attribution tools for marketers. Even Snap acquired location-based startup Placed, to prove brands that advertising on Snapchat drives store visit (among other things). While one could argue that the more attribution solutions available in the marketplace the better - to drive some good old-fashioned competition - all three announcements were received with growing concern by marketing executives, worried about the lack of separation between the entity running the media and the entity measuring its effectiveness. Ultimately, how trustworthy can you be if you run the media and give yourself great reviews on how it performed?

The importance of media agnostic, third-party verification is a serious topic of conversation in the industry right now, extending beyond attribution to all measurement areas. In 2017, Marc Pritchard, P&G’s chief brand officer, made it clear that platforms need to implement MRC-accredited third-party viewability measurement or risk losing P&G’s business. In 2021, Nielsen's local and national TV ratings services lost its accreditation, which set the tone for what's next in 2022. According to a ANA survey on Walled Gardens, 89% of ANA members want independent MRC audits for the big digital platform players.

As a marketer, a media planner, or a media buyer this should come as great news: the industry at large is moving in the right direction, recognizing the potential conflict of interest of a media company also powering verification and measurement.

As a media company, embrace this powerful trend and partner with (not buy) independent measurement companies, from viewability to attribution.

To quote Crossmedia’s CEO Kamran Asghar, who in a Digiday article said his agency would never use attribution services from Google or Facebook, “We do our best to avoid any vendors — be it media or tech — that pose a conflict of interest. Google is a media company, and, therefore, clients should monitor it — and all channels — with credible third parties who are independent of selling media.”

Learn more about Cuebiq's measurement capabilities here, or set up a chat with a member of our team.

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