Cuebiq Footfall Attribution Benchmarks Trends and Benchmarks for Marketers Leveraging Location Data

IntroductionBenchmarking Success Amid Increased Location Adoption

In 2017, Cuebiq released the industry’s first report analyzing footfall attribution benchmarks, providing marketers with a resource to gauge how their advertising campaigns were performing when it came to driving offline activities.

By providing new metrics and industry averages, Cuebiq gave advertisers the tools needed to understand how their campaign performed when compared to their industry vertical and to adapt future campaigns to better direct consumers to a desired point of interest (POI).

As both location intelligence and attribution became even more widely adopted throughout 2017, with marketers’ investment in cross-channel measurement doubling* compared to the previous year, Cuebiq continued to partner with clients to measure footfall attribution of cross-channel campaigns. The data and benchmarks shared in this report are derived from campaigns measured throughout 2017.

As a media agnostic player, Cuebiq functions as a third-party measurement partner, independently of media sales. All of the benchmarks contained in this report are derived independently, for the sole purpose of helping the industry

better measure its success.

2

As with the very first Cuebiq Footfall Attribution Benchmark report released in 2017, this report provides updated benchmarks for the key metrics of uplift, visit rate, dwell time, and time of visit. In addition, we’ve analyzed campaign data to include:• New verticals• Benchmarks by platform• New metrics such as Cost Per Incremental Visit (CPV)

*Source: eMarketer “Study: Marketers Renew Interest in Cross-Channel Attribution,” April 10, 2017.

https://www.emarketer.com/Article/Study-Marketers-Renew-Interest-Cross-Channel-Attribution/1015595

WHAT’S NEW

Introduction

3

Methodology

Data collection and analysisBy teaming up with apps using precise location and Bluetooth technology, Cuebiq’s data collection methodology via proprietary SDK leverages GPS, WiFi, and Bluetooth signals to understand anonymous offline behaviors at scale. Cuebiq collects over 100 data points per user/per day, which we analyze to determine users’ dwell time and visit frequency at locations to create the biggest and most accurate geo-behavioral visitation data available in the marketplace today. As a result, Cuebiq is able to truly map and measure the offline consumer journey.

Methodology

AttributionCuebiq determines visit uplift by comparing the group of users exposed to an ad campaign to a control group. The control group is composed of users in the Cuebiq database who match the targeting criteria of the campaign but have not been exposed to its ads. Cuebiq then compares the visits of the exposed group to the control group to determine footfall uplift. For example, you may be running a campaign to bring foot traffic into your new store location. That campaign brought in 10% more people from the exposed group compared to the control group; therefore, the campaign had a lift of 10%.

Privacy complianceCuebiq does not collect any personally identifiable information, meaning that all of its collected information is completely anonymous. Its privacy-compliant methodology has earned the company membership status with the Network Advertising Initiative (NAI), the leading self-regulatory industry association dedicated to responsible data collection and its use for digital advertising.

Over the past several years, marketers have placed a great deal of emphasis on attribution and understanding how digital touchpoints can drive consumers into stores. With growing interest in footfall attribution, marketers are eager to compare their campaign results with others and gauge whether or not they were truly successful.

Fortunately, high quality location data at scale enable reliable footfall attribution measurement, enabling brands to see how their audience is reacting to a campaign in the physical world and arming marketers with actionable insights.

This section provides benchmarks broken down by vertical, platform, and quarter. Derived from footfall measurement of campaigns across a number of industry verticals, these benchmarks give marketers much-needed context to determine if their campaign results are good, bad, or average, helping them measure ROI and determine strategies for future campaigns.

Footfall Attribution Benchmarks

4Footfall Attribution Benchmarks

Uplift

5Footfall Attribution Benchmarks

Uplift, which measures the impact of ad exposure in driving in-store visits, is a key metric to help marketers better understand their advertising performance.

Using footfall attribution reports run for Cuebiq clients between Q1 and Q4 2017, footfall uplift benchmarks were determined by advertising category, platform, and quarter in which the campaigns ran.

The benchmarks are provided as a range. Uplift lower than the lower bound indicates low campaign performance in driving consumers to store. Uplift between the two thresholds indicates average performance. Uplift higher than the upper threshold indicates high performance.

AUTOMOTIVE

12-38%BIG BOX RETAILERS

8-48%C-STORES

25-49%

CASUAL DINING

11-44%DISCOUNT STORES

13-23%ELECTRONICS

40-94%

ENTERTAINMENT

13-54%FINANCIAL SERVICES

31-86%GAS STATIONS

35-68%

GROCERY STORES

20-46%HOME APPLIANCES

27-64%HOME IMPROVEMENT

13-48%

PET STORES

30-60%PHARMACIES

54-81%QSR

13-38%

RETAIL

3-21%SPORTING GOODS

12-43%TELCO STORES

31-85%

Uplift by PlatformWith all attribution analyses, it’s important to understand how each channel is contributing to the overall campaign success. As more marketers plan and execute campaigns across multiple channels, it’s even more important to understand how these channels contribute to driving footfall traffic uplift.

When broken down by platform, these benchmarks show that on average the success bar is higher for in-app advertising, with the highest percentages for both the upper and lower thresholds. This makes sense, considering consumers spend 89.2% of their time on smartphones in apps, and 76.8% of their time on tablets in apps*. Advertisers also have more control over the ad experience in these environments, because in-app ads integrate seamlessly into the design and the experience of the app. As a result, in-app advertising tends to see higher uplifts. As the benchmarks indicate, web ads alone rank third in uplift averages, and they derive a boost from mobile when used in

cross-device campaigns.

6Footfall Attribution Benchmarks

Uplift by QuarterUplift rate varied throughout the year, with campaigns running in Q2 and Q4 seeing the highest rates of the year.

Note: Due to the campaign conversion window needed to measure footfall attribution, several Q1 campaigns were omitted from the 2016 Cuebiq report because the results became available mid-Q2, after the report was released. Those not included in the original report have contributed to the uptick seen here for Q1 2017 vs. the benchmarks released in last year’s report.

*Source: eMarketer “Comparing Responses to In-App and Mobile Web Ads” June 19, 2017

https://www.emarketer.com/Article/Comparing-Responses-In-App-Mobile-Web-Ads/1016037

Q1Q2Q3Q4

20%median uplift by quarter

web

in-app

cross-device

10-33%average uplift range

19-49%average uplift range

17-46%average uplift range

63%37%57%

7Footfall Attribution Benchmarks

Cost Per VisitMarketers looking to accurately measure their ROI can leverage Cuebiq’s attribution analysis to measure their cost per incremental visit (CPV). This metric represents the budget spent to receive one incremental visit from the exposed group, compared to the visits from the control group. As marketers experiment more with location data to target consumers, CPV will play a major role in determining just how much value they receive from their investments.

Based on the analysis of campaigns measured by Cuebiq throughout 2017, the median CPV across all verticals was $23.05. General retail had the highest median CPV, $54.51, followed by home appliances, at $52.29, indicating that a higher investment is typically needed to drive consumers to visit these stores. Auto followed, with a CPV of $36.64, again illustrating how changing consumer habits have impacted dealership visits in the path to purchase. Meanwhile, convenience stores ($1.81), finance ($4.48), and electronics ($8.70) all had median CPVs below $10.

Auto

C-Stores

Casual Dining

Electronics

Entertainment

Financial Services

Gas Stations

Grocery Stores

Home Appliances

Pharmacies

QSR

Retail

Sporting Goods

Telco

$36.64

$1.81

$26.01

$8.70

$12.75

$4.48

$16.49

$22.14

$52.29

$18.38

$21.83

$54.51

$23.37

$28.61

median cpv uplift

8

Location data has quickly emerged as a powerful solution to tie together signals across channels, because it helps marketers measure in-store footfall. The more marketers leverage location intelligence, the easier they can answer broader questions to inform their marketing strategies and better understand real-world, in-store experiences.

Cuebiq’s data collection methodology and intelligence platform allow brands to go much deeper when evaluating their campaigns. In order to map and measure the offline consumer journey, Cuebiq applies proprietary machine-learning algorithms that distill the vast amount of location data collected into “visit data.” This is possible thanks to the platform’s ability to understand if multiple signals originated from the same location over time and, based on certain parameters, determine if they should be classified as one visit. For example, Cuebiq may collect six data points over three hours for the same anonymous user, all from one movie theater, and can determine that this was one visit. Conversely, if Cuebiq only detects one data point over 10 minutes at a movie theater, the intelligence platform will determine that this was not a visit.

These kinds of insights are leveraged in each Cuebiq Attribution report, giving brands benchmarks to get a sense of engagement for consumers who visit their locations after being exposed to an ad.

Cuebiq analyzed data across all campaigns it ran in 2017, identifying specific metrics to help marketers understand how customers engaged with their brand in the offline world and how that behavior can be properly attributed to advertising. Those key metrics are visit rate, dwell time, and time of visit.

Visit Rate, Dwell Time, and Time of Visit Benchmarks

VISIT RATEidentifies the relationship between exposed users

and in-store visits. It is calculated by the number

of exposed users who went to the POI versus the total

number of users reached.

DWELL TIMEis the amount of time spent at a specific POI.

TIME OF VISITrefers to the most popular time(s) of day to

visit a POI.

Visit RateVisit rate identifies the relationship between exposed users and in-store visits. Based on analysis of campaigns that Cuebiq measured throughout 2017, we identified daily average visit rates for several verticals. These values can be used as a baseline when evaluating how campaigns are performing in driving consumers to store.

When looking across all categories, Q4 2017 saw the lowest overall average visit rate for the entire year. Automotive, telco, entertainment, QSR, and casual dining all experienced the lowest quarterly visit rates of the year in Q4, possibly indicating that consumers had different priorities during the busy holiday season. However, brands shouldn’t necessarily be alarmed heading into 2018, as several of these verticals experienced their highest average quarterly visit rates of the year in Q1 2017.

As with our uplift analysis in the previous section, it’s also important for marketers to understand the difference in visit rate by platform. Once again, mobile in-app has the highest average visit rate, with web benefiting from the addition of in-app in cross-device campaigns.

Auto

Big Box

C-Stores

Casual Dining

Discount Stores

Electronics

Entertainment

Financial Services

Gas Stations

Grocery Stores

Home Appliances

Home Improvement

Pet Stores

Pharmacies

QSR

Retail

Sporting Goods

Telco

9Visit Rate, Dwell Time, and Time of Visit Benchmarks

0.73%

5.45%

6.97%

3.32%

4.47%

1.52%

3.90%

6.34%

2.83%

4.50%

0.85%

4.12%

5.65%

3.13%

6.02%

5.03%

2.21%

3.78%

3.09%web avg.

4.21%in-app avg.

4.03%cross-device avg.

Q1 Q2 Q3 Q4median visit rate by quarter

2.45% 2.92% 4.31% 2.51%

visit rate by advertising category visit rate by platform

10Visit Rate, Dwell Time, and Time of Visit Benchmarks

Dwell TimeWhile uplift and visit rate are the foundation of footfall attribution analysis, they only scratch the surface on the possible use cases for precise location-based data. Cuebiq’s data collection methodology and intelligence platform allow brands to look at how long consumers spend at POIs and, by measuring the average time spent at locations by advertising category (dwell time), brands can measure how long consumers are spending in their stores compared to the industry average, and if they are achieving their on-location goals.

A high visit rate is certainly appealing, but dwell time can indicate a number of different factors. QSR locations pursue shorter visits, ensuring that customers can get in and out – longer dwell times indicate long lines and wait times, leading to a bad in-store experience. Meanwhile, short dwell time in a retail environment may signal that a consumer found what they wanted quickly and made a purchase, but it can also signal that they left quickly without making a purchase. Advertisers will always need to compare their dwell time against their goals to ensure that they are spotting any potential problems with the in-store experience. In high-competition verticals, understanding dwell time can make all the difference between growing revenue or watching it slip away to the competition.

Time of VisitAnother valuable metric for measuring campaign success is time of visit. For example, if a fast food chain ran a campaign to promote a breakfast item and found that the top time of visit was 1 p.m., it’s clear that the campaign was not successful at driving to store for the specific time-window it was targeting, even if it drove a higher visit rate overall.

Use cases like this don’t apply to every vertical, but monitoring the most popular times to visit a POI can help the brand determine the best times of the day to purchase media for reaching consumers. It should also influence staffing decisions for POIs, addressing any potential on-location issues and thereby increasing the likelihood of a higher dwell time.

Auto

:35

DW

ELL

TIM

ETI

ME

OF

VISI

T

Big Box

C-Stores

CasualDining

DiscountStores

Electron-ics

Entertain-ment Finance

GasStations

Grocery Stores

HomeAppliances

HomeImprove-

ment

Pet Stores

Pharmacies QSR Retail SportingGoods

Telco

:32

:22

:40

:32:36

:48

:24:22

:34 :30

:36:32

:21

:33

:18

:30 :31

9-10AM

12-1PM 12-1PM 12-1PM 12-1PM 12-1PM 12-1PM

1-2PM 1-2PM

5-6PM6-7PM 6-7PM 6-7PM 6-7PM 6-7PM 6-7PM

7-8PM 7-8PM

11Beyond Digital

*Source: Winterberry Group “2018 Outlook for Data Driven Marketing”

Beyond Digital:Footfall Attribution for Traditional Media Channels

Over the past 12 to 18 months, we have seen a growing trend as traditional media players and advertisers started testing and adopting emerging technologies to help address the challenges of a fast-changing, data-driven, zero-based budgeting world where marketers are pressed to prove substantial ROI for their activations.

Spending on digital advertising surpassed expectations in 2017, hitting $89.2 billion, while mobile accounted for 70% of the market. Meanwhile, new tools and data sources, such as location data and footfall attribution, have been breathing new life into traditional media, helping both marketers and their media counterparts better plan and measure their investments.

As marketers double down on cross-channel media strategies and measurement, it’s important to do more than simply measure how each channel performs separately. Marketers want to understand how each form of media affects store visits, and how different combinations drive ROI, or fail to do so. By using location data to attribute the role each channel plays in the campaign outcome, brands can ensure that they are developing the best media mix for their future endeavors.

As new technologies unlock the ability to make better-informed decisions on the media mix and optimize ad spend based on real-time performance, we expect to see this trend gain incredible momentum in 2018.

Let’s look at how marketers are putting location analysis into practice across channels.

12Beyond Digital

“

24 Hour Fitness Uses OOH to Drive New Memberships in Key Markets

The fitness center chain 24 Hour Fitness wanted to measure the impact of its OOH advertising campaign geared towards driving visits and increasing new club memberships amongst women 25-54 in key markets.

The OOH campaign was managed by Clear Channel Outdoor RADAR, which enabled the client to understand campaign performance in the physical world through Cuebiq as their media-agnostic measurement partner. Cuebiq ran its footfall attribution analysis to understand campaign effectiveness, providing insights on footfall uplift, day and time of visits, dwell time, and an audience analysis to understand offline interests of those consumers who visited a brand location upon being exposed to the campaign.

The findings showed that the OOH campaign was extremely successful in driving consumers to 24HF locations, generating a 240% increase in guest traffic among customers exposed to the advertising. In addition, the combination of data accuracy, density, and scale used for its footfall analysis allowed 24 Hour Fitness to review their results at an even more granular level, including: by DMA; how frequency of exposure affected visitation rates; dwell time of exposed devices; and by daily comparison of visitations between the exposed and control groups.

We worked with Cuebiq to augment our Clear Channel Outdoor campaign to create dynamic ads to target SF and LA commuters at different times of the day.

-Mike Carney, Marketing VP, 24 Hour Fitness

13Beyond Digital

Dining Chain Measures Impact of OOH in Driving Location Visits

A regional casual dining brand wanted to understand the effectiveness of its OOH campaign in driving consumers to store. The late summer campaign targeted regions of Texas with OOH advertising from July 10 to September 3, 2017. The OOH campaign was managed by Clear Channel Outdoor RADAR, which enabled the client to understand campaign performance in the physical world through Cuebiq as their media-agnostic measurement partner. Cuebiq ran its footfall attribution analysis to provide insights on footfall uplift, day and time of visits, and dwell time.

Overall, the campaign generated a visit uplift of 149% among customers exposed to the billboards. A deeper dive into the footfall data found that peak visit time for consumers who saw the ad was midday, Tuesdays through Fridays, indicating that the campaign was successful in engaging clients during lunchtime. Almost half of the consumers spent 15 minutes or less in the POIs and an additional 28% between 15 and 30 minutes, indicating that the locations were operating efficiently for quick lunches and/or food pick-ups during the peak visit times. The analysis also showed that consumers exposed to the ad at least five times drove higher visit rates, showing that repeated messaging ultimately drove campaign success.

VISIT UPLIFT

149%

14Beyond Digital

QSR Brand Looks for the Media Mix That Delivers Maximum ROI

A leading QSR brand running both mobile and OOH ads wanted to understand how each channel impacted visit to its locations, with the ultimate objective of identifying the media mix that would yield the maximum ROI. The OOH campaign was managed by Clear Channel Outdoor RADAR, which enabled the client to understand campaign performance in the physical world through Cuebiq as their media-agnostic measurement partner. Cuebiq and Clear Channel Outdoor tested channel effectiveness by analyzing three consumer groups: those targeted via OOH advertising only, mobile advertising only, and a third set exposed to a combination of OOH and mobile retargeting.

The findings showed that while each channel had positive performance independently, the combination of OOH and mobile retargeting led to skyrocketing lift, proving the immense value of this strategy. By combining mobile targeting capabilities with a rich location data set, the brand can continue to leverage this tactic across future campaigns.

QSR brand visitation rates

Unexposedunexposed to OOH and unexposed to

mobile

Mobileexposed to mobile

only

OOHexposed to OOH only

OOH+Mobileexposed to OOH and exposed to mobile

2.39% 3.61% 11.12% 19.81%

51% Lift

365% Lift

729% Lift

15Beyond Digital

CPG Brand Leverages Mobile to Drive Grocery Store Visits

A leading CPG brand wanted to measure the impact of its mobile advertising campaign in driving consumers to Kroger stores in Q4 2017. The ability to measure advertising effectiveness in driving to retail locations is particularly critical for brands in the CPG vertical, which often face pressure to prove that they are driving store traffic in order to grow relationships with retailers and secure in-store advantages. The campaign leveraged Aki Technologies’ moment marketing science platform to target consumers during the most receptive and relevant moments, with Cuebiq serving as their media-agnostic measurement partner.

Cuebiq ran its footfall attribution analysis to understand campaign effectiveness, looking into footfall uplift, day and time of visits, dwell time, and offline interests of consumers who visited Kroger locations upon being exposed to the campaign. The findings helped the CPG brand prove a visit uplift of 70% among customers exposed to the moment-targeted advertising and a 12% visit rate, both significantly higher than the grocery store vertical benchmarks.

Cuebiq’s attribution analysis also allowed the client to gain a deeper understanding of consumer behaviors at Kroger locations and gather insights on consumers’ offline interests and visitation patterns. For example, the advertiser discovered that 32% of users visited a Kroger location within three days of being exposed to the campaign. The brand was also able to learn that Saturday and Sunday afternoons were the most frequented days and times to visit, and that 83% of consumers spent between three and 32 minutes in the store.

SPENT UP TO 32 MIN. IN STORE

83%

16Beyond Digital

Leading Retail Brand Measures Cross-Device Campaign Effectiveness

A leading retail brand wanted to measure the impact of its cross-device campaign running from May to December 2017. Cuebiq ran its footfall attribution analysis to provide insights on footfall uplift, day and time of visits, and dwell time, as well as an audience analysis to better understand offline interests of those exposed to the campaign message.

The findings showed that the campaign was very effective in driving consumers to stores, generating a 52% uplift on exposed users. Almost two-thirds of users visited the store within one week of being exposed to the campaign. Saturdays were the most popular day to visit, and 68% of users spent between 20 and 30 minutes in store, on par with the retail dwell time average. The best day of the campaign was December 23rd, tied to holiday shopping.

Finally, through the audience analysis, Cuebiq identified a strong affinity with healthy lifestyle lovers and frequent gym goers. These insights can now be used to better target audiences across the web and in-app, and possibly help shape future creative and messaging.

VISIT UPLIFT

52%

17Brand Affinity

*Source: Winterberry Group “2018 Outlook for Data Driven Marketing”

Brand Affinity:Additional Insights into Consumers’ Offline Behaviors

Because foot traffic is based on real-world data, it helps paint the full customer journey picture, which includes multiple interests and habits that consumers exhibit in the offline world. In addition to understanding how often consumers visit brands’ own POIs, additional insights into their overall brand affinity may open new and previously unexplored marketing strategies, creative messaging, and targeting opportunities.

For example, brands can also look at foot traffic analysis for their competitors, providing a better understanding of market share and customer loyalty. They can also identify unique and unexpected brand affinities that extend to other industries, and factor that knowledge in to both their ad creative and media buying plans.

The more connections a brand can make between its customers and other, similar behaviors, the easier it is to build a cohesive marketing strategy that will reach these high-value consumers. In the following page are a series of examples of brand affinities and behaviors derived from the footfall attribution analysis of campaigns measured by Cuebiq from Q1 2017 to Q4 2017.

18Brand Affinity

AUTOMOTIVE Consumers in market for a new car also love Burger King and frequently visit Shell

BIG BOX shoppers also love Payless and dining at Olive Garden

CONVENIENCE STORES frequentvisitors also love McDonalds and frequently visit BP gas stations

CASUAL DINING aficionados also shop at Sunglass Hut and JCPenney

DISCOUNT STORES loyalists also love Subway and shop at Walmart

ELECTRONICS frequent shoppers also frequently visit Bank of America

and love Starbucks

ENTERTAINMENT JUNKIES alsoshop at Best Buy and frequently visit CVS Pharmacy

FINANCIAL SERVICES frequent branch visitors also love Wendy’s and Walmart

GAS STATIONS frequent visitors also shop at Family Dollar and visit Subway

19Brand Affinity

GROCERY STORES shoppers also love Tim Hortons and frequently shop at Simon Malls

HOME APPLIANCES stores frequent visitors also shop at Macy’s and

Bath & Body Works

HOME IMPROVEMENT frequentshoppers also shop at Zumiez and frequently visit Pep Boys

PET STORES frequent shoppers also love McDonalds and shop at Walgreens

PHARMACY frequent shoppers alsolove Best Buy and Starbucks

QSR loyalists also shop at 7-Elevenand Dollar Tree

RETAIL frequent shoppers also love Dunkin Donuts and Williams-Sonoma

SPORTING GOODS shoppers also frequently use The UPS Store and are

customers of Edward Jones

TELCO stores frequent visitors also bank at Chase and shop at Foot Locker

About

Cuebiq is a next generation location intelligence and measurement company, leveraging the largest database of accurate and precise location data in the U.S. to help marketers map and measure the consumer journey. Its leading data intelligence platform analyzes location patterns of 61 million monthly active U.S. smartphone users on over 180 mobile

apps, allowing businesses to glean actionable insights about real-world consumer behaviors and trends. Cuebiq’s SaaS platform provides clients offline location analytics, real-time campaign optimization and footfall attribution, and geo-behavioral audiences for cross-platform ad targeting. Cuebiq is headquartered in New York with offices in San

Francisco, Chicago, Italy, and China.

To learn more, please contact us at cuebiq.com/contactFind us on Twitter: @cuebiq