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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Ziff Media Group, a publisher encompassing a portfolio of leading digital properties in tech, culture, and shopping, decided to run an attribution campaign for their client, Amazon Ring. Amazon wanted to drive purchase conversions and Ziff wanted to measure the campaign’s efficacy at driving consumers into select retail partners. With the help of Cuebiq, a leading consumer insights and measurement company, they took a data-driven approach. 

This campaign ran from November to December 2022 across ZMG tech and shopping sites featuring the Ring sales for the Holiday season, leading consumers in store at Walmart, Best Buy, and Target for their holiday shopping needs. Cuebiq pixeled the inventory and through their data supply of 15MM Daily Active Users was able to track 8% of offline users. From there, Cuebiq populated data daily to optimize in-flight and provide live reporting to Ziff and their client. This allowed their team to understand the campaign performance in respect to store visits.

The Results

The campaign was extremely successful, and Cuebiq uncovered key metrics like impressions, visit rate, and incrementality effect, allowing the Ziff team to understand how and why the campaign was so successful at driving consumers to the desired stores.

With Cuebiq’s incrementality methodology, Amazon Ring successfully saw an increase in visits directly or related to exposure from Amazon Ring ads. 48% of store visits were made by people who had not visited these locations 90 days prior to media exposure. This means that 48% of visitors were most likely visiting these locations to purchase the Amazon Ring product as their visits occurred only after exposure to the ad.

Incrementality is the count of new visits from people who hadn’t visited these stores until viewing an ad.

Incremental projected visits are the total number of projected visits that can be attributed to a campaign’s influence.

Understanding Key Metrics: Uplift & Visit Rate

Measuring uplift and visit rate for Big Box and CPG can often prove difficult, as the locations measured are highly trafficked regardless of ad exposure and the control group will likely be visiting the locations as well. Even a low uplift for CPG is strong, as the likelihood for control group visitation is very high.

According to Cuebiq’s measurement metrics, the average incrementality effect was 23.1%, with some regions like Boston, MA, seeing that number as high as 58.72%. Amazon Ring also saw a 24.59% visit rate on 5.8M impressions, which is well above the industry average. 

“The Ziff media drove nearly 25% of exposed users into stores where Amazon Rings are sold. This falls in the high-performance threshold of Big Box brands,” said Jon Friedman, EVP of Revenue at Cuebiq. “We are also able to tell that Ziff media drove around 216k visits that would not have occurred if not for their media based on our incrementality neural network. We count this as a huge success.”

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It goes without saying that location data is a powerful tool in effective brand marketing—but the role of location data in the future of digital advertising is uncertain. New technologies and laws around data privacy have reshaped consumer expectations about how brands should use location data in digital advertising campaigns. And while brands understand the importance of using location data responsibly, it’s challenging to keep up with evolving privacy standards. That’s why the NAI, or Network Advertising Initiative, hosted a Privacy Meetup with various industry leaders to discuss the use of precise location data in the future of digital advertising at Brand Safety Week in New York City.

Experts from Google, The Trade Desk, IAB, MediaPost, and our very own EVP of Privacy, Gerald Smith, discussed how companies can safely use location data in brand campaigns to reach geographically relevant audiences. After hours of insightful conversation, the key takeaway from these panels gives brands a clear direction for how to prepare for the future of location data in digital advertising. But before we get there, it’s important to understand some context around the NAI.

The NAI

The NAI is a non-profit organization dedicated to promoting the health of online ecosystems by maintaining and enforcing high standards for data collection and use in online and mobile advertising. The NAI has been a champion of privacy protection and established The Enhanced Standards for Precise Location Information Solution Providers, which prohibit the collection, use, and transfer of precise location data from sensitive places, such as those tied to religious worship, sensitive healthcare services, military bases, and LGBTQ+ identity. They also restrict companies from using, selling, or sharing, in the absence of a legally-binding request, precise location data for law enforcement or national security purposes. 

These standards have set limitations on the use of data about sensitive locations and put consumer privacy first without cues from legislation, meaning all companies that abide by the standards are doing so voluntarily – which enables the crucial and continued role of location data in brand marketing.

“For location data to continue playing a pivotal role across industries, it is vital not only to adopt, but advocate for standards that put consumer privacy first,” said Gerald Smith. “We firmly believe that the NAI Enhanced Standards is the path forward for location data and are proud to be leaders supporting this initiative.”

Key Takeaway: Know Your Partners

The key takeaway from the conference was to take accountability in your partnerships. 

This year brought the industry some of its most pressing privacy-related challenges and opportunities, and it’s never been more critical for advertisers and agencies to work with location data partners that share a focus on data integrity and consumer privacy. Lots of companies claim to be “privacy-safe”, but don’t actually commit the necessary resources toward research and development. And the reality is that brands and partners without robust privacy protections won’t have the same longevity as those who weave privacy enhancing technologies into the fabric of their business.

The Road Ahead

What can you do to prepare for the future? Ask what companies are doing with sensitive locations. 

“Cuebiq is a leader in using location data responsibly for advertising, as demonstrated by their commitment to abide by our industry-leading enhanced standards for precise location data,” said Leigh Freund, President and CEO of the NAI. “They’re showing companies can leverage actionable mobility insights without undermining consumer privacy.”

Cuebiq was among the first to voluntarily commit to the Precise Location Information Solution Provider Voluntary Enhanced Standards. We’ve also published our Sensitive Points of Interest Policy, which states that any location deemed legally or culturally sensitive will not be used for targeting or attribution. We’ve been investing in privacy enhancing technologies for years–in fact, they’ve been fundamental to our business from its inception through every product update. 

For brands to continue leveraging location data in digital advertising, they need to bring consumer privacy to the forefront of their business proactively — not only because it’s the right thing to do — but because it’s what must be done to compete in the rapidly evolving market of tomorrow.

To learn more about Cuebiq’s commitment to privacy, visit our Privacy Center.

The post Brand Safety Week and The State of Privacy appeared first on Cuebiq.

Tourism is a massive industry, with millions of dollars spent to promote local, national and international travel. Governments and tourism boards all over the United States (and the world) spend a large percentage of their marketing budgets to promote tourism in their territory. Some campaigns are focused on bringing in visitors from surrounding states or on a specific landmark of a region (a national park, a beach town, museums, etc.), while others promote an entire state or country to potential national and international travelers.

Yet despite its prominence, decision-makers in the industry have a quite limited set of tools to understand how effective their campaign dollars are. Governments and marketers have a number of tools to assess how their campaigns are doing—that is, whether indeed people exposed are visiting. For example, they can match online purchases of airfare to the destination, use data from hotels and other tourist destinations where the origin of guests is logged, official data from industry reports, etc.

While these methods are useful in their own way, they lack immediacy because they rely on external sources with multiple layers in between the exposure and the marketing insight—multiple data sources need to be cleaned, processed, and stitched together to provide some insight about the campaign effectiveness.

At Cuebiq, we have a powerful new solution that allows marketers and governments to answer this question in a much more immediate fashion with the use of geolocation data.

The Power of Geolocation

Imagine the following: a state’s tourism board runs a campaign to attract visitors from surrounding states and wants to understand if the people who are being exposed are visiting within a specific conversion window. Instead of relying on any of the aforementioned methodologies, wouldn’t it be much more powerful and immediate to have privacy-safe, real-time information about the devices exposed and their behavior after the exposure?

This is exactly the core logic driving our new solution for tourism campaigns. We take an impression log of a tourism campaign and are able to determine which exposed devices live in the target locations of the campaign (in this example, the surrounding states). We are then able to understand which of these exposed devices are later in the locality of interest post-exposure, and generate useful metrics based on their mobility—which brands they are visiting, which verticals are the most popular, and which specific areas of a city or state tourists are traveling to.

Beyond these already valuable insights about where exposed visitors are coming from—and where they go—we are also able to generate aggregate, privacy-safe metrics about the general income of the visitors by using highly granular census data paired with the approximate home location of a device. This allows you to parse where your visitors are coming from and understand their general income profile. You can then slice and dice the data as needed to understand if there are different patterns of visitation according to income, and if there are differences in brands or verticals different groups are visiting.

A New Jersey Tourism Campaign

Let’s look at an example of a campaign with the goal of promoting tourism to New Jersey at large. 

The video above shows an interactive heatmap of all the visits to brands in New Jersey from devices that live outside of the state and were exposed to the tourism campaign. You can see a visual representation of where in NJ the exposed visitors are visiting brands, and can change the geographic aggregation parameters if you need a more detailed view of where people are visiting. Since these are branded visits users can also filter visits by vertical (in this example we looked at banks) or even by specific brands (here we use Bank of America and Citibank).

Another layer of the map (shown above) shows a different set of insights: the geographic distribution of the campaign exposures and which states are actually being served impressions from this campaign. Of the surrounding states, most impressions are being served in New York, Pennsylvania, and Maryland, while fewer impressions are being served in areas further away from NJ like Vermont, North Carolina, and a small percentage in Florida. This campaign is mostly targeting tourism from the surrounding states and geographies bordering NJ.

The last layer of the interactive map above shows yet another set of insights: the county of origin of those converted visitors. While the previous map showed where devices were exposed, this map shows the geographic distribution of the home county of all the converted visitors (darker blue means higher proportion of devices that come from that county). We can see that most of the visitors come from counties that border NJ, which is expected since visitors likely drive into the state, so geographic closeness increases visit density. Interestingly though, not all of the counties with high density of origin directly border NJ. The shades of darker blue around Cleveland, Pittsburgh, Columbus, Albany, and even all the way to Miami, mean the campaign is generating conversions in non-obvious places marketers should be paying more attention to.

Beyond the geographic analysis explained above, our methodology allows us to quickly show insights about which verticals (or economic areas) the converted devices are visiting. The video above shows over 70 verticals and the relative proportion of visits to each one out of all registered visits. The top three verticals are retail banks, malls and quick-service restaurants. You can also see the geographic distribution broken down by the top 4 states of origin, which account for approximately 90% of all visits. This allows a direct breakdown by state of origin and vertical in a visually immersive way.

Finally, a very powerful feature of our tourism solution (shown above) is the breakdown of visitors by their income–specifically, we use the official Census Block Group (BG) data to associate a device’s approximate home location with the median household income of their BG. In the video above you can see on the horizontal axis the median income of the home BG, and on the vertical axis you can see the number of devices that fall within an income range. As a useful summary of the histogram, above it you can see the min/max range and median income for visitors by state of origin. We can see that visitors from Virginia come from BGs with the highest median income at about 108k, then visitors from Maryland at about 86k, New York at about 73k, and finally visitors from Pennsylvania have the lowest median income at about 69k.

The non-obvious insight here is that there are fewer visitors from higher income regions, and the bulk of visitors comes from relatively lower income areas. The goals of the campaign dictate which set of visitors are most important: for this campaign, if marketers or governments want to bring in tourists from higher income areas then they should focus their campaigns on Virginia and Maryland, and if not, on New York and Pennsylvania. This is an important result because if a campaign’s goal is to bring in higher income visitors, then simply looking at volume is not enough.

Just imagine how powerful these insights can be for your own campaigns. 

The amount of depth and detail that our tourism solution has will surely spark insights and discussions between marketers, agencies, and tourism boards that will help understand who the campaign is driving, and where to, with a level of detail that no one else in the market can offer. To learn more about how Cuebiq can measure tourism campaigns, book a demo.

The post Come Back Soon! Measuring the Effectiveness of Tourism Campaigns with Geolocation Data appeared first on Cuebiq.

Is it me?  Or is Q4 starting to feel a bit like the good ‘ole days?  Sure, the uncertainty brought on by the last few years is still wreaking havoc on  marketing strategy and budgets (of course), but the potential for another strong holiday season is real. According to Deloitte's 2023 Holiday retail survey, consumers plan to spend an average of $1,652 this season, surpassing pre-pandemic figures for the first time.

People are also expected to start their holiday shopping early this year. According to eMarketer, half of consumers expect to start their holiday shopping before November.

Don’t Fall Into The Same Old Trap

The unprecedented “unknowns'' combined with the elevated consumer optimism that comes with the holiday season means the competition to engage in-market shoppers will be as fierce as ever.  Add a fragmented media landscape to the mix and marketers are justifiably feeling intense pressure to allocate all dollars to working media.  Given these dynamics, I would argue that this year, perhaps more than any other, it is vital for brands to understand how their media and marketing efforts perform in real time, and to be able to optimize accordingly and report results in real time. Prioritizing measurement and analysis during your campaign can drive explosive ROAS, while dismissing it as a “nice to have'' will put your brand at an incredible disadvantage. 

Measurement To The Rescue

Location-enabled measurement in particular can improve a campaign’s efficiency and effectiveness by ensuring media is targeted at consumers (and regions) most receptive to messaging, and optimizing the tactics and channels that are performing in real time. Specifically, measurement will help you:

1. Understand Incrementality:  Gain access to incrementality at the consumer level and understand whether your brand’s advertising is actually changing consumer behavior by driving additional footfall and adjust accordingly.
2. Improve Media Mix:  Fine-tune budget allocation by first understanding which network, program, or creative is performing best with respect to driving store visits.
3. Lower Cost per Visit:  Compare costs of attracting new vs. returning customers, for a data-driven approach to decreasing CPV/CPIV.
4. Gain Customer Insights:  Reveal behavior of new and returning customers to evaluate campaign success and refine your targeting and messaging strategies. 

It’s been a long year, and the weight of the decisions we make as marketers feels overwhelming in the face of predictions and unprecedented challenges to our respective business.  Cuebiq has spent the past several years helping our clients understand real world behaviors to inform these types of decisions.  Let us help you—contact your Cuebiq sales representative or email me at jfriedman@cuebiq.com.

The post Maximize Your Media Budget with Measurement appeared first on Cuebiq.

Cuebiq explores how marketers are planning for a future without cookies, what techniques and technologies will fill the void, and how first-party data will play a crucial role in growing and understanding your consumers.

The post Is MTA Dead? The Future of Attribution appeared first on Cuebiq.

This note has been on my desk for over a year now, and the concept of “incrementality” has come up over and over again as one of the biggest problems marketers face. They are looking to measurement platforms for a solution — in Cuebiq’s case, an offline measurement platform. As a measurement company, we need to meet the increasingly sophisticated demands of an industry that is embracing data-driven solutions.

Introducing Campaign Incrementality for Marketers

Marketers haven’t fully embraced incrementality because attribution companies have not taken selection bias seriously. For those unfamiliar, selection bias refers to the bias introduced when researchers select a sample for analysis in such a way that the conclusions are driven by the selection process itself. In advertising terms, marketers can be subject to selection bias when they only analyze data on consumers who were already planning to visit their stores — therefore, they are not seeing the true or real effect of their advertising on influencing new visitors and changing consumer behavior. 

At Cuebiq, we take this issue seriously and have developed new and exciting tools to help marketers understand campaign incrementality at the consumer level, so their ad dollars can be spent more efficiently. A few weeks ago I read an article on the Correspondent in which the authors reported, "The brightest minds of this generation are creating algorithms which only increase the effects of selection." We are essentially trying to debunk this myth that marketers are promoting selection bias. Our mission is to provide data-driven tools that minimize bias and help marketers  understand incrementality and make decisions upon it so they can increase advertising performance with maximum efficiency.

Learn More Now

Cuebiq’s Enhanced Platform: Answering What Marketers Need to Know 

Cuebiq has invested heavily in state-of-the-art methodologies and algorithms that enhance our existing solutions to provide new capabilities that help our customers keep their competitive edge in the market. This innovative mindset is at the core of our company and reflects our commitment to shaping the industry by using the latest advancements in causal machine learning that help bring revolutionary tools to our clients. 

With the new enhancements to our platform, which features a real-time dashboard, marketers can decouple organic store visits from incremental  store visits. They can now calculate the cost per incremental visit much more easily, giving them the ability to optimize their strategies to lower this metric. 

To achieve these results, we teamed up with some of the brightest researchers in this field, implementing state-of-the-art developments in causal machine learning. The result? We deployed a scalable environment to bring our enhanced offline measurement solution to life.

Schedule a Demo

Consumer-Level Metrics to Measure Advertising Impact

This means marketers and analysts can now integrate data-driven activations into their strategies to increase their return on advertising spend (ROAS) by lowering their cost per acquisition (CPA). Post-campaign analytics can determine which subgroups in the exposed group were more sensitive (the audience that is generating the most incremental visits) to the campaign message in terms of visitation patterns, competitive brand analysis, mobility patterns, hyper-detailed (but privacy-compliant) demographic breakdowns, and so forth. These insights, accumulated campaign after campaign, provide an evidence-based framework for audience building, campaign targeting, and brand insights that focuses on increasing the incremental advertising effect by lowering the cost per incremental visit.

Establishing New Industry Standards 

Speaking personally and for the entire team at Cuebiq, we’re not only excited to help marketers but also proud to be at the forefront of deploying incrementality solutions the industry needs. There were many challenges we had to overcome as our product evolved; not only did we have to turn complex theories into practice, but we also had to address practical challenges such as the fact that random ad exposure is currently not done at scale, in a cross-channel setting.

Ultimately, we want to help marketers better understand how their campaigns are driving visits. The gold standard to understanding a causal relationship of this sort would be to create a control group by randomizing exposed and control conditions in the target audiences. The lack of random assignment to A or B groups presents a downstream challenge to companies like us because it introduces bias in the causal inferences we make about campaign effect. Minimizing this bias was the biggest problem we had to address in creating this solution, which we did using the latest developments in causal machine learning.

Get a Sneak Peak and Schedule a Demo 

In my next article, I will be sharing details about our upcoming white paper that provides a deep dive into our methodology, also known as C.A.T.E. (Conditional Average Treatment Effect) or Individual Treatment Effect (ITE). This paper will include the results of the experiments we have run so far and more details around how they can help marketers make the most out of their cross-channel advertising investments. 

Schedule a Demo

The post Revolutionizing Campaign Measurement: Unlocking Incrementality With Offline Intelligence appeared first on Cuebiq.

As a retailer, you’re probably well aware that Amazon Prime Day is quickly approaching. And you're probably fed up with hearing about how Amazon has monopolized back-to-school season with Prime Day, stealing more and more market share from brick-and-mortar retailers over the last few years.

But what if we told you that you could actually benefit from Prime Day, if you played your cards right? Read on to learn what you could do differently this year to actually capitalize on the success of Prime Day — which you can apply to your marketing efforts to outsmart Amazon.

Understand the New Back-to-School Paradigm

Before you can start strategizing on how to outsmart Amazon, however, it’s important to understand how they completely changed back-to-school season with Prime Day. By creating a one-day shopping promotion during the second week in July — that sold over 100 million products last year (according to eMarketer) — Amazon has effectively hijacked back-to-school season and moved it up to July. And it makes sense, right — if consumers can get insane deals online in July, why would they wait until August or September to get traditional back-to-school deals from brick-and-mortar retailers?

As follows, it’s important for retail marketers to acknowledge that Amazon Prime Day is the new back-to-school kick-off point. And in fact, 84% of retailers surveyed by RetailMeNot believe back-to-school shopping began on or before Prime Day last year… but now they need to capitalize on this knowledge.

Run Deals Relative to Prime Day

Instead of letting Amazon Prime Day hinder them, retailers should use the day to their advantage to increase back-to-school sales. There are three main ways retailers can run deals relative to Prime Day:

1. Target shoppers in the weeks leading up to Prime Day
2. Target shoppers actively shopping on Prime Day 
3. Target shoppers who may have missed Prime Day with deals once it’s over

Several savvy retailers have capitalized on one or more of these strategies to ride the waves of Prime Day.

• Macy’s promoted “Black Friday in July” for the entire week last year, offering massive deals both in-store and online, plus free shipping.
• Kohl’s held a one-day sale event ahead of Prime Day, called “It’s A Big Deal.”
• Target offered discounts on Prime Day itself, emphasizing that there’s “no membership required” to benefit from their various deals, in direct opposition to Amazon Prime.

With proper planning, retailers can take full advantage of Prime Day and offer consumers not only great deals, but also a strong user experience that Amazon may not always be able to provide.

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Invest in New Technology

Once you’ve determined the timeline for when you’d like to run deals relative to Prime Day, you need to make sure you’re maximizing those marketing strategies. One way to do so is by investing in new technology.

By leveraging new data sets, you can target consumers based on their profile, measure the effectiveness of your marketing campaigns, and then optimize them in-flight. For example, you can leverage offline intelligence to strategize and execute more efficient, better-performing marketing campaigns that drive in-store visits.

You can also use offline intelligence to increase brand loyalty — a metric that many retailers cite along with customer engagement as key to measuring success. If retailers can capitalize on new data sets, they will be able to leverage consumer insights for more effective loyalty programs, better co-branding initiatives, and evaluating new partnerships. To find out more about how offline intelligence can help retail marketers, check out our white paper.

As retailers look to capture market share and compete with Amazon for back-to-school season, they must invest in technology that will help them enhance their marketing campaigns. At the end of the day, if they don’t make their marketing campaigns count, they will be left behind.

The post How Brick-and-Mortar Retailers Can Reap the Benefits of Amazon Prime Day for Back-to-School Season appeared first on Cuebiq.

If you work in marketing, you may have heard about offline analytics. But if you’ve never dabbled in location data before, then you’re probably wondering why it’s such a hot topic right now and more importantly, how it can help you as a marketer. Read on to learn how offline intelligence can reveal valuable insights about your brand’s consumers, which will help you improve the ROI of your marketing efforts.

What Are Offline Analytics?

Let’s start with the basics. For those unfamiliar, offline analytics enable you to gain insight from the location or geographic component of data. Businesses can use offline analytics to gain actionable insights about their consumers and better understand the offline consumer journey. Through analytics, brands can see what their consumers like, where else they visit, how they react to different types of messaging, and a multitude of other behavioral preferences.

Ultimately, this knowledge is fundamental to establishing a better connection with consumers and their interests. Even more importantly, as a brand marketer, it’s essential to understand your consumers’ preferences. By providing insights into these consumer preferences, analytics don’t just improve a brand’s story. They can also inform your marketing strategies and help you connect with your target audience, ensuring you drive maximum ROI.

Key Insights You Can Gain From Offline Analytics

In order to better understand the offline consumer journey, you can use analytics to identify areas of growth and activate those insights to drive performance in real time. Offline analytics reveal five key insights:

1. Footfall Patterns

Through footfall analysis, you can measure footfall, offline trends, and share of visits to your brand and competitors. This means you can see how your brand stacks up against your competition in terms of how many consumers are actually spending time at your brand locations.

2. Brand Loyalty

Loyalty analysis allows you to gain deeper insights into consumer loyalty, which you can then activate in real time. You can see how loyal consumers are to your own brand versus to your competitive set, enabling you to identify potential opportunities for conquesting.

3. Time Analysis

Time analysis lets you discover share of visits by day of week and day-part, as well as time spent at your brand vs your competitors’ locations. By understanding the busiest time of day and day of week, you can modify your marketing strategies accordingly.

4. Geographical Insights

Through geographical analysis, you can see brand and competitive insights at the national, state, and DMA level. Knowing which branches of your stores are the most highly trafficked can help you plan for future store openings and closings.

5. Brand Affinity

Finally, brand affinity analysis enables you to gain insight into consumers’ offline interests and their cross-shopping activities. This information is crucial, as it can reveal opportunities for co-marketing initiatives and help you better understand your consumers’ complete offline journey.

The possibilities and use cases for offline analytics are endless. By taking advantage of these insights, you can get one step closer to understanding your consumers and maximizing the success of your marketing efforts.

Learn more about how offline analytics can drive ROI in our next blog, “How Brand Refinement Through Analytics Can Impact Your Bottom Line.”

The post How to Use Offline Analytics appeared first on Cuebiq.

In an age of personalized subscription boxes and customizable AR experiences, consumers are increasingly expecting a shopping experience hyper-tailored to their interests. It’s more important than ever that your brand be consumer compatible. But what exactly does that entail?

Consumer compatibility is the overall metric that measures the value consumers place on your brand. It shows how effective your consumer-brand relationship is. This is not a new concept; for years loyalty programs have helped measure consumer compatibility.

However, at Cuebiq we believe consumer compatibility comprises more than just loyalty. For you to really understand how much your consumers value your brand, you need to consider additional factors such as time spent in store, visit frequency, and brand affinity. It’s essential that you measure each of these, because they’ll lead to meaningful insights that you can implement to improve your consumer-brand relationship and drive ROI.

Gain Insights

Visits + Dwell Time

First, it’s important to measure footfall traffic to understand where your stores fall into the offline consumer journey. Are consumers spending significant time in your stores, or are they leaving seconds after walking in the door?

“Dwell time,” or how long consumers spend in store, is the key to understanding this. Through dwell time, you can tell the difference between real versus fake visits to a location.

Just because a user is pinged near a movie theater does not mean they actually saw a movie — they could have been just walking by. In order to determine whether they saw the movie or not, you need to consider how long they spent at that location.

At Cuebiq, we verify all visits to locations using dwell time, to distinguish actual visits from non-relevant data points.

Visit Frequency and Time of Visit

In addition to dwell time, you need to evaluate how frequently consumers are visiting your stores. Are they one-time shoppers or are they returning to your stores time and time again?

You can gain insights like these through location data, which reveals not only how often consumers visit your stores but also which times are most popular for visits. This information can inform marketing activations and promotions for your brand, especially when you pair it with demographic attributes of your consumers.

If you see that students are visiting your stores the most on Saturday afternoons, you might offer a back-to-school special in stores at that time to increase sales.

Brand Loyalty

As you most likely know, brand loyalty is essential to measure. While you probably already measure the loyalty of consumers to your brand through loyalty programs, you might not know that you can use brand loyalty for competitive intelligence as well.

Through location data, you can measure how loyal consumers are to your competitive set versus your own brand. Then, you can identify the least loyal competitor consumers — or the most likely to switch loyalties — and target them for competitive conquesting.

If you’re a marketer at Ford, and a top competitor of yours is Toyota, you can use location data to identify the segment of Toyota consumers that is most vulnerable and then target them with ads.

In this way, brand loyalty can reveal new audiences for targeting and enable you to develop a stronger conquesting strategy.

Brand Affinity

Finally, it’s important to consider the complete offline consumer journey in your consumer compatibility analysis. In addition to understanding how consumers are interacting with your own brand, it can be helpful to know where else your consumers shop and what their offline interests are.

If you see that your consumers are avid Dunkin' fans and often visit the Dunkin' near your brand location, you could develop an ad campaign with messaging that plays on that shared interest. Brand affinity can reveal opportunities for co-marketing like this that you might not have considered before.

Ultimately, gaining a more complete picture of where your consumers shop and what their preferences are can help you close the loop on the offline consumer journey.

Get started analyzing your consumer compatibility with our Location Analytics solution today.

The post What Is Consumer Compatibility and Why Is It Important for Your Brand? appeared first on Cuebiq.