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.

Cuebiq’s incrementality methodology provides a powerful measurement tool so you can understand how and where your dollars are increasing footfall to your stores. Even better, it allows you to understand if your campaign is driving in loyal or casual customers, how well the campaign performed for each of them, and a breakdown of campaign effects by sociodemographic cut. Did we mention retargeting?

When it comes to advertising to increase footfall, few questions are as important as: Are my campaign dollars increasing visits to my store? And if so, how many additional visits am I looking at, and from whom? A good response to these questions unlocks the power for marketers and agencies to understand how budget is really moving the needle in terms of additional visits. 

A Small Pizza Shop in Brooklyn

Picture this: you own a small pizza shop in Brooklyn called Rocco’s Pizza. One day, you are standing outside handing out flyers for a 2-for-1 promotion (buy one slice, get one free!). How would you measure the effectiveness of your campaign? In other words, how would you know which customers who saw the flyer would have gotten a slice anyway, and which ones were convinced by your promotion?

Measuring The Flyer Campaign

The answer is tricky, and science provides good guidance: you’d compare the exposed group (people who saw the flyer) with a similar, unexposed group (people who look like the exposed but didn’t see the flyer), and then check on average the difference in visits for these two groups.

However, wouldn’t it be better to compare the behavior of people who saw your flyer with themselves, instead of with other people that are similar, but not exactly, like them? Well, this is obviously physically impossible, since a single person cannot be both exposed and unexposed to your campaign. 

With the help of machine learning, it is possible. Cuebiq’s solution to measure incremental visits leverages the power of statistics to generate an artificial version of the devices in the exposed group (called a counterfactual) after learning how exposed and control devices behave. If we look at everyone who received a flyer at Rocco’s Pizza and analyze the behavior of exposed and control devices, we can calculate incremental visits. For this example, our methodology determined that the flyer campaign at Rocco’s Pizza directly contributed to a 15% increase in visits to its pizza shop compared with the visits that would have happened without the flyer campaign. Cuebiq’s incrementality solution goes beyond simply comparing exposed and control groups, allowing you to measure incremental visits by comparing each exposed customer with themself –had they not been exposed! Pretty powerful stuff.

Device-level Insights: A Door to Your Customers Behavior.

One particularly unique and highly useful feature of our methodology (which separates us from our competitors) is our ability to calculate incrementality for the entire campaign as a sum of device-level incrementalities. For example, in a simple campaign with 3 devices, device 1 shows an incrementality score of 0.7 incremental visits, device 2 has a score of 0.5, and device 3 a score of 0. Then, for the entire campaign we simply take all individual scores and add them, so that this campaign would yield 0.7+0.5+0 = 1.2 incremental visits. Our solution therefore provides a campaign-level metric as a sum of individual metrics.

Now, this unique feature of our methodology opens up incredible possibilities for further dissecting your campaign results: think about the kind of insights you can get when you know additional features about a device, like how often a device usually visits your store, or its socio-demographic features. This helps to understand if your campaign is moving the needle with customers that are regulars to your business,  or if it is increasing visits among people who would not otherwise usually go. Knowing whether your message resonated with loyal or casual customers, or with a specific socio-demographic group means retargeting now becomes a data-driven procedure!

Different campaigns have different goals. Some of your campaigns will be focused on attracting new customers, while other campaigns will be geared towards rewarding loyal customers. Our methodology allows you to know which group your campaign is resonating with most and exactly how it is driving additional visits. This provides you with the flexibility to utilize that information to assess your current campaign and optimize your next campaigns by targeting the specific group of people you need.

These detailed analyses are only possible with Cuebiq’s solution, and they provide a valuable starting point for the optimization of future campaigns. By generating customer segments from the behavioral analysis you can use it as a starting point to retarget future campaigns. 

How powerful is that?

Want to learn more about incrementality and how it can work for you? Book a demo with one of our specialists, or dive deeper into the methodology with our white paper on incrementality.

The post Why Incrementality? Put Your Money Where Your Growth Is. appeared first on Cuebiq.

Millions of Americans have been planning end-of-summer trips to get away. According to AAA, booking trends reveal that Americans are currently making travel arrangements, though cautiously and more last-minute. As for mode of travel, road trips are accounting for 97% of summer trips, enabling travelers to tailor their schedule and identify rest stops based on their comfort level, as well as capitalize on low gas prices.

However, the CDC is continuing to caution against travel, and some states have issued restrictions for incoming travelers such as mandatory testing and required quarantine. New York, New Jersey, and Connecticut launched a Tri-State Quarantine. Earlier this month, New York City set up quarantine checkpoints along main bridges and tunnels to intercept travelers from “high-risk” states, warning them to isolate for 14 days or risk paying fines up to $10,000.

In order to understand whether incoming out-of-state travelers are complying with various state restrictions, we conducted an analysis.

Traveler Analysis Methodology

In our analysis, we focused on incoming travelers to new states within the past 14 days. For each state, we analyzed incoming travelers to calculate the percentage of those travelers that were sheltering in place within a 14-day period.

• If a device only moved within 330ft from its home location, we considered that device to be sheltering in place. 
• Taking into account the total population and ratio of residents and travelers in each state, we also provided an estimated value of the total number of absolute travelers.

And the Groundbreaking Results Were...

The data revealed some interesting findings, especially with regard to travelers from high-risk states. From August 8–21, travelers from Florida to New York only sheltered in place at a rate of 19.6% upon arrival, indicating that about 80% of those travelers did not shelter in place for the 14-day quarantine required upon entering New York. Travelers from Virginia, another state on New York’s mandatory quarantine list, sheltered in place at an even lower rate of 15.7%.

It will be interesting to see whether low-risk states like New York will ramp up enforcement of restrictions, in light of the number of travelers coming from states with higher COVID-19 confirmed cases.

If you’re interested in getting more customized insights about self-quarantine compliance of travelers, be sure to check out Cuebiq’s Traveler Analysis.

By combining these insights with Cuebiq’s full suite of Mobility Insights, you can also analyze the types of places visited by non-compliant travelers, such as bars.

The post To Travel or Not To Travel: Americans Grapple With State Restrictions appeared first on Cuebiq.

Hopes of the United States following a linear path to recovery from the coronavirus have been quashed in recent weeks. When the country began to reopen after Memorial Day weekend, COVID-19 cases started to rise. Over the Fourth of July weekend, a CNN article referencing Cuebiq data showed that even more people traveled to 10 cities identified as coronavirus hot spots. States in areas across the U.S. are seeing cases rise to new heights, with Florida shattering the record for new single-day COVID-19 cases last week. 

As states around the country grapple with coronavirus outbreaks that came after reopening, you may be asking the question: “Does reopening bars correlate with coronavirus spikes?”

The COVID Tracking Project and Cuebiq’s Visit Index

At Cuebiq, we also wanted to answer this question, so we dug into the data. In order to understand which types of venues — like bars — are truly “high-risk,” we analyzed whether there was a correlation between foot traffic to certain venues and “test positivity rates,” the percentage of positive tests by state.

In this analysis, we looked at the positive test data from The COVID Tracking Project and Cuebiq’s Visit Index to bars, full-service restaurants, and department stores, focusing on three states: Florida, Arizona, and Georgia. We selected these states based on clean test positivity data and their identification as potential hot spots for an increase in cases in the month of June, 2020.

We focused our analysis on positive test data for June 1 — June 30 and compared it to Cuebiq’s Visit Index to bars for eight days earlier, May 23 — June 22. In the case of Georgia, where bars started to reopen on June 1, we limited the analysis for test positivity rates starting on June 9.

Positivity Rates and Correlation With Foot Traffic to Bars

Our findings were revelatory: There were strong correlations for all three hotbed states between positivity rates and foot traffic to bars.

Meanwhile, states such as New York and New Jersey, which delayed reopening of bars, have seen far lower levels of coronavirus spikes. We found that visitation to full-service restaurants and department stores did not show a correlation with positivity rates.

The correlation with bars and test positivity rates is not entirely surprising. A recent Time article featuring Cuebiq data cites Gerardo Chowell-Puente, an epidemiology and biostatistics professor at Georgia State University, in saying that “bars likely pose a greater transmission risk than other indoor venues, like retail stores or movie theaters, because their patrons tend to have more close interactions with one another.” Not to mention, when people are inebriated they are much more likely to disregard social distancing protocols.

As more states begin to evaluate — and reevaluate — their criteria for reopening, it will be interesting to see how they include bars in those plans, in light of these new findings.

Learn more about Cuebiq’s Mobility Insights and how they can help brands understand the impact of COVID-19 in our blog.

The post Data Tells All: Reopening Bars Correlates With New Coronavirus Cases appeared first on Cuebiq.

The marketing landscape has shifted in a major way since the beginning of the COVID-19 pandemic. Shelter-in-place restrictions have begun to lift in states across the U.S., and marketers need to adjust their strategies accordingly. Let’s take a look at how the reductions in shelter-in-place restrictions have affected consumer mobility, and how you can use those insights to plan your marketing spend strategically.

Consumers Are Proceeding With Caution

More than half of the country has reduced COVID-related restrictions at this point, and more states are on track to reopen by the end of May. While you might expect consumers to rush to stores after being on lockdown for weeks on end, that has not necessarily proven to be true. Using Cuebiq’s COVID-19 Mobility Insights, we saw that on Wednesday, May 6, the national mobility index was still down -20% when compared to last year's average.

That said, we have indeed started to see pockets of increased mobility, predominantly in states that have reopened. In Nebraska, which reopened recently, the CMI (Cuebiq Mobility Index) is down -13%, indicating that people are moving more than the national average but still not as much as last year in the same region. Zooming in, we see differences in mobility at the county level, with many counties seeing positive weekly mobility trends. 

The path to normalcy has begun, but the process will likely be slower than expected. Even in states where almost all business types are allowed to reopen, many local businesses continue to stay closed, especially in major metropolitan areas, as owners weigh how and when to return to business as usual.

How to Use This Insight:

It’s important to align your marketing spend with consumer mobility, reaching consumers on the platforms they’re using with messages that resonate: 

• Don't shift your marketing dollars away from social, mobile, and other marketing tactics that align with low consumer mobility just yet! Although states have started to reopen, many consumers are still opting to stay indoors.
• As consumer mobility increases, which you can monitor using Cuebiq’s Mobility Insights, you can begin to shift your marketing tactics toward pre-COVID strategies.

Consumers Are Turning to DIY Projects

Consumers are not only proving to be more cautious than expected, but they are also turning toward a new hobby: DIY projects. While springtime usually brings increases in foot traffic from DIYers to stores, you might expect those increases to be tempered by the pandemic this year. 

However, the data shows that this has not been the case. Using offline intelligence, we saw that home improvement stores had a 5% positive gain the week of 4/27 when compared to the prior week. Auto parts stores also saw a 6% gain, indicating that consumers are not only starting home projects but also opting to service their own vehicles. Why might DIY projects be on the rise? With the unemployment rate at 14.7%, an all-time high, many consumers may have more time on their hands while planning to cut costs by doing their own house or car work.

How to Use This Insight:

There are a few ways you can leverage this insight to refine your marketing strategy:

• Collaborate with brands that cater to DIYers, such as home improvement retailers.
• Create video content that will inspire DIYers to not only shop at your store but also follow tutorials for their DIY projects.
• Advertise on platforms that DIY consumers might use as an inspiration board, such as Pinterest.

As more restrictions lift, marketers will need to be nimble and consistently reevaluate their strategies to ensure they’re aligning with changing state regulations and consumer behaviors. 

Subscribe to our blog to get the latest tips on how to manage your marketing strategies during this time of reopening.

The post How to Use Mobility Insights to Prepare for Reopening appeared first on Cuebiq.

During this uncertain time, it’s important to make use of tools designed to manage brand health through the COVID-19 crisis. From a strategic standpoint, understanding exactly how your business is performing on a day-to-day basis is critical, as is gaining insights that you can use to inform your business strategy during this time. There’s one powerful tool you can use to accomplish both of these things: offline intelligence.

Use Cases for Offline Intelligence Insights

Offline intelligence can help marketers monitor business performance and inform their ad strategies to align with the current environment. Below are some of the main ways you can use offline intelligence to mitigate the effects of the crisis on your business.

Brand Impact Analysis 

Offline intelligence provides mobility insights that can help you understand the impact the crisis is having on your brand in terms of mobility levels. For essential businesses that remain open, gaining a window into the mobility levels at your locations and in the surrounding areas can offer valuable insights.

For example, grocery stores can adjust their store operating hours based on mobility levels, to ensure they have ample time for cleaning and restocking of shelves. What’s more, they can use location-based audience segments to reach grocery shoppers and keep them abreast of the changes they are making to their store operations to keep everyone safe as they remain open.

Market Share Analysis

Another use case for offline intelligence is market share analysis. It’s increasingly important that brands be able to compare the impact the coronavirus is having on their own brand versus their competitors, so that they can assess loss of market share and develop strategies to cope.

QSRs, for example, can benefit from analyzing location data to win back market share by prioritizing customer needs. In fact, Cuebiq’s Mobility Insights found that while the QSR category decreased in visitation by 5% the week of 3/23 vs. the previous week, one fast-food brand actually saw a 2% increase in visitation. They achieved this by promoting limited-time offers that appeal to consumers facing financial hardships during the crisis and protecting customer and employee health through new restaurant policies.

Emergency Media Planning

Additionally, marketers can use offline intelligence to move media budgets to geographies where the offline impact of COVID-19 is significantly lower. During this trying time, it’s more important than ever to be nimble with your ad dollars, to ensure you are reaching your intended audience on the right platforms with messages that resonate.

For example, when it comes to running ads on OOH during this time, it’s necessary to take a local approach. County-level mobility insights can help you strategically place OOH campaigns where mobility is high and population density is low — targeting essential workers and family members who are using their cars. To resonate with this audience, it’s important to use tailored messaging to support essential workers and consumers whose focus is on performing critical tasks for themselves or family members.

Real-Time COVID-19 Mobility Insights

If you’re looking for a tool that offers the above types of insights, you might consider using Cuebiq’s COVID-19 Mobility Insights. Recently launched to illustrate how mobility patterns are changing throughout the country during the crisis, these insights are publicly available. They are also updated daily and can help you understand how population behaviors are evolving, so you can modify your national and local business strategies accordingly.

In addition to these publicly available insights, Cuebiq is also offering brand and vertical-level insights directly within our platform to help you monitor brand health with real-time offline intelligence. 

To access these vertical and brand-level visitation insights, simply request credentials to log in to our platform.

The post How to Use Offline Intelligence to Manage Brand Health During the COVID-19 Crisis appeared first on Cuebiq.