Datasets

Sense360 collects proprietary data from several different consumer panels, totaling over 10 million consumers across the United States. These data sources are organized in the datasets used among the different dashboards:

Transaction

Transaction insights are collected from consumers' debit cards, credit cards, and banks accounts. The data is collected in real-time from financial institutions and personal finance services that allow consumers to track and monitor their financial status. Subsequently, Sense360 undergoes the processing and cleaning of the raw transaction data and converts it into the brand/retailer-level spend, assigning transactions to the different purchase geographies and channels. The proprietary weighting models ensure a complete view of all transactions; the weighting methodology controls for income and geographical representativeness to be as close as possible to the overall US population.

When the underlying data is processed, Sense360 converts those transactions into usage metrics such as Panel sales, Wallet share, Check size, and Penetration. These metrics are used together with longitudinal analyses such as Guest frequency and Migration over time.

Used in Competitive performance, Market performance, Customer profile, Customer migration, and KPI tracking.

Metrics

These metrics apply to Transaction dataset:


Metric	Description	Usage
Panel sales	Total sales for a brand or market among the panel of 6 million U.S. consumers.	Proxy for total growth or decline in brand performance. Shown for either the overall population or recent brand guests, which can inform spend migration or stealing versus competition. These metrics can be broken out for different demographics, which may help understand drivers of growth or softness. Looking at the metrics by market speaks to total market trends; cutting by designated market area (DMA) may speak to local market growth or contraction.
Spend per capita	Average amount of spend per panelist on the specified brand or market.
Transactions per capita	Average number of transactions per panelist on the specified brand or market.
Penetration	Percent of total panelists that have one or more transactions with the specified brand or market in the period.	Understand success of acquisition efforts. Understand how growth or contraction in customer base is driving performance changes. Understand if the total market is growing/contracting in terms of total customers
Wallet share	Percent of spend that a brand or category receives out of all the spend in the market	Proxy for market share. Understand if a brand is over or under-performing the other brands in the market. Understand the proportional size of brands to one another in an intuitive way.
Frequency	Average number of transactions per customer on the specified brand or market. A customer must have at least one transaction with the specified brand or market within the time period to be included in the metric.	Uncover customer transaction and spend frequency on a per customer basis and its contribution to performance. Understand customer value compared to one of a competitor. Understand if customers are transacting or spending more on the brand or competitors. Understand how much the average market consumer is transacting or spending and if it is increasing or decreasing.
Spend per customer	Average amount that a customer spends on the specified brand or market.
Average check size	Average amount of dollars spent per transaction of all transactions made for a brand.	Comparing brands to one another, as well as trending over time, may indicate performance in driving sales through improvements in price, quantity, and/or product mix. Understand how ticket size is contributing to performance. Understand how the brand's ticket size compares to market average and competitors. Understand if total market average check is growing or shrinking over time and if the brand's check size growth is lagging/ or exceeding the market.
Median check size	Midpoint check size among all transactions, that is, the equal number of transactions above and below amount.

Collection and methodology

Collecting Transaction data consists of three steps:

Collection — Consumer spend data is collected from a panel of around 10 million consumers across their debit, credit, and banks cards. However, Sense360 filters the panel down to only the highest quality and most consistent users (around 5 million consumers).
The data is collected directly from financial institutions (banks) and from personal finance services that allow consumers to track and monitor their financial state.
Processing — Processing and cleaning of the raw transaction data. Sense360 receives the data five days after the transaction and requires a 10-day lag to ensure the bulk of the data has been received and to stable metrics.
Processing includes: mapping charge line items into the brands spent at, assigning transactions to the geographies of purchase, and applying a variety of models to the data to select only the highest quality panelists, correct for bias, and ensure stability.
Reporting and insights — Sense360 converts transaction data into usage metrics like Spend, Share of wallet, Check size, and Migration and stealing to report on those metrics.

Transaction data set collection process.

Biases

These biases affect the Transaction data set:


Bias	Description	Impact
Demographic	Under-representation in the low income (< USD 30,000) and extremely high income (> USD 300,000) ranges. Ultimately, banked consumers who use debit and/or credit represents a slightly more high-spending, urban, and affluent portion of the population.	Check Size and Spend-per-customer are higher. Brands with customer bases made of higher income and more urban show stronger metrics.
Credit/debit transactions only	Cash, check, wire transfer, B2B invoicing, and other transaction types are not included.	The more of a brands’ transaction mix comes from non-credit/debit transactions, the more underrepresented their performance is. For restaurants, Check size and Spend-per-Customer are higher. For retailers, Check size and Spend-per-Customer may be higher or lower depending on the dynamics of the business. While traditional business credit cards are represented, B2B invoices and account transfers are not.
Returns	Negative transactions (returns) are ignored in Sense360 KPI metrics as it is impossible to know in which period the original transaction occurred and/or whether the transaction location remains consistent.	This slightly inflates numbers across retailers. However, this is consistently applied across the board and still results in "apple-to-apple" comparisons. The most impacted retailers are those that experience a higher degree of returns and time periods where more returns happen.
Larger Check sizes for restaurants	Includes catering and native digital payments (such as order ahead, delivery, and pick up) which have larger check. While it includes 1st-party digital payments, 3rd-party payments are tracked separately (for example, DoorDash).	Check Sizes and Spend-per-Customer are higher. Businesses with strong catering and digital businesses show stronger performance. This is the reason why both Median Check size (closer to a typical, in-restaurant transaction) and Average Check size (pulled up by large orders and catering) are provided.
1st-party digital transactions	Transaction data set provides billions of transaction data points similar to a credit card statement. The way the information comes through may allow, in some but not all cases, to separate digital versus physical transactions.	Not every brand/retailer has digital transactions identified. For some brands, only a subset of digital transactions can be identified, which results in the distribution of digital being smaller than expected, though trends and insights are still directionally insightful.
Fees, taxes, and tips	All consumer spending is included in Sense360 metrics.	Frequency and Avg.check size are higher than actual numbers. Numbers do not align with financial data since they do not include marketplace fees (such as DoorDash, Instacart), taxes, and tips.
3rd-party delivery	Sense360 exposes data on what specific businesses customers are ordering on 3rd-party platforms, such as DoorDash. However, the raw data does not have full coverage of all platforms (for example, UberEats does not expose this information) and not all businesses are mappable within the mappable platforms due to POS integrations.	Businesses that are not mappable may have their performance understated. Comparisons between a business that can be mapped and one that cannot should remove 3rd-party delivery for "apples-to-apples" comparisons.
Buy now, Pay later	These transactions are assigned to the Buy now, Pay later provider (such as Klarna, AfterPay) and Sense360 unable to discern which retailer the purchase is for.	Retailers that receive significant purchase volume through the Buy now, Pay later channel are understated. The most impacted retailers and/or occasions are those that are skewed towards a higher usage of Buy now, Pay later.
DMA assignment and fixed geo- weights	Transaction locations are assigned to the panelist home zip code since transaction location is often unavailable (such as in digital purchases). Additionally, geographical representation in our models is locked to the most recent census.	DMA cuts do not always match real-world sales in terms of the performance of physical locations (especially if it is a particular business that attracts customers from outside of their zip code). Spending within a DMA where a business does not operate may be available if a customer from that DMA shopped while traveling. Spending could be over or understated in a location where the population is rapidly changing, though directional trends remain accurate and consistent.
Check size and Frequency for hospitality	All transactions are included, such as room service and spa treatments at hotels, as well as baggage fees and in-flight WiFi for airlines.	Frequency is inflated. Check sizes are smaller than expected when focusing solely on average room booking charges or average flight charges.
Aggregator data for hospitality	Sense360 to identify which hospitality brand a consumer is transacting with when purchasing through an aggregator (for example, Travelocity, Kayak, etc.)	Sales are understated. The most impacted brands are those that receive a higher proportion of business through aggregators.

Foot traffic

Foot traffic data is collected from a panel of approximately 400 thousand opted-in consumers using SDKs placed within apps stored on their mobile devices. The data is collected in real-time by mobile applications that send location-based surveys or other types of location-based services to consumers.

Subsequently, Sense360 undergoes the processing and cleaning of the raw visit data and converts it into the visited brands, as well as assigns a geography, daypart, and duration to visits.

Advanced machine learning models and phone sensors are then used to accurately identify visits to a brick and mortar location. The proprietary weighting models ensure a complete view of all all visits and account for natural smartphone adoption biases and natural panel churn; the weighting methodology also controls for demographics and geographical representativeness to be as close as possible to the overall US population.

Used in Customer profile.

Warning: Sense360 does not collect personal identifiable information nor resells individual user-level data. Additionally, sensitive information is obfuscated and data is encrypted both during transmission and at rest.

Metrics

These metrics apply to Foot traffic dataset:


Metric	Description	Usage
Visit share	Percent of visits the brand or retailer receives out of all the visits in the market. It similar to Market share, but it measures visits instead of revenue.	Provides a high-level read into performance benchmarked against the market and competitors. Tracking market share over time shows the percent of captured visits compared to the rest of the market. This can be broken out for different day-parts, week-parts, and geographies, which may help understand drivers of growth or softness.
Brand draw	Percent of visits that a brand or retailer receives out of all of the visits in the market. As opposed to Visit share, it only looks at visits within 1 mile of a brand's locations.	Understand how often a person chooses a brand or retailer versus a competitor in the same market when they are within striking distance of a brand or retailer location.
Head-to-head brand draw	Percent of visits that the brand or retailer receives when its stores are within 1 mile of a competitor's stores.	Understand how often a person chooses the brand or retailer versus the competitor when the two are within striking distance of each other.

Collection and methodology

Collecting Foot traffic data consists of three steps:

Collection — Foot traffic data is collected from a panel of around 5 million opted in consumers using SDKs placed within apps stored on their mobile devices. Advanced machine learning models and phone sensors are used to accurately identify visits.
Processing — Processing and cleaning of the raw transaction data. Sense360's proprietary weighting models ensure a more complete view of all visits and account for natural smartphone adoption biases and natural panel churn. The weighting methodology also controls for demographics and geographical representativeness to be as close as possible to the overall US population.
Reporting and insights — Sense360 converts visits into usage metrics like Visit share and Brand draw that are available through a variety of lenses including Daypart, Weekpart, 1-mile Visit share, and Visit share head-to-head against nearby competitors.

Foot traffic data set collection process.

Biases

These methodology-driven biases affect the Foot traffic data set:


Difference	Description	Example impact	Takeaway
Check size	Foot traffic data does not account for check size	Check size has been the biggest driver of growth and drive a 5 - 7% divergence from foot traffic in recent years (NRN and MillerPulse, March 2019).	Check size is crucial to monitor and can be analyzed through Transaction data.
Market and competitor changes	Visit share is relative and factors in market/competitor changes in Foot traffic.	Visit share can increase even when internal transactions decrease, in the case where competitors transactionsdecrease even further.	While Visit share moves differently than internal transactions, it provides valuable competitive and market context.
Party size	Foot traffic is influenced by party size, but transactions do not account for Party size.	In 2x1 promotions, transactions may remain flat but could drive a significant increase in Visit share.	Visit experience survey can be used to understand party size changes.
Digital and delivery	Foot traffic data does not capture delivery purchases.	Delivery has become a significant portion of revenues, though this is not be captured and Visit share understates their total growth (for example, Dominos, Chipotle).	Delivery is crucial to monitor and can be analyzed through Sense360 Delivery IQ solution.
Short and close visits	Short (2.5 minutes or less) and close (less than 100 meters from previous destination) visits are not captured.	Short visits are often mobile pick-up orders; brand performance that relies heavily on this is understated.	Short visits are often mobile pick-up orders; brand performance that relies heavily on this is understated.

Surveys

Survey data is collected by delivering surveys directly to customers' mobile phones and desktops, and are tied to consumers' real-world behavior through Sense360's integrated foot traffic and digital survey panels.

For visit-based surveys, a proprietary SDK sits inside mobile applications that reward panelists for completing surveys. When a visit is detected to a location of interest, Sense360 uses a targeting system to select users based on certain behaviors, such as a visit to specific locations or some demographic criteria. If the panelist fulfills the survey requirements, an alert is triggered on a mobile phone to display an in-app survey when opening the alert notification.

For digital surveys, consumers are targeted based on their digital behavior, collected through a browser plugin that monitors all the URLs that a panelist visits on their browser. The plugin tracks the page domain, time spent on the website, and the pages they were at before and after. Subsequently, Sense360 targets consumers that have visited specific sites or have specific demographic criteria. If the panelist fulfill the survey requirements, an alert is triggered to display the survey in the browser when opened.

Used in Brand perception, Restaurant customer experience, and Retail customer experience.

Collection and methodology

Collecting Survey data consists of three steps:

Collection — Sense360 directly integrates its proprietary SDK technology into survey partner mobile applications. Through this integration, it observes foot traffic behavior of respondents and directly target surveys to respondents based on their real world behavior.
In certain situations, Sense360 augments sample from other sources.
Processing — Processing and cleaning of the raw transaction data. Typical steps can include removing poor quality responses, weighting the data to be representative of the U.S. population, and calculating a variety of metrics and cross-tabs.
Reporting and insights — Sense360 exposes the data in a variety of self-service dashboards as well as custom reports prepared by client service teams.

Survey data set collection process.

Biases

These biases affect the Surveys data set:


Bias	Description	Impact
Mobile panelists	Sense360 visit-based panel primarily consists of mobile panelists — respondents who are taking surveys on their mobile phones through survey apps.	Mobile panelists tend to skew younger than desktop panelists.
Demographic skews	Survey panels almost universally skew towards lower-income respondents (the population that is most motivated by survey incentives). Additionally, Sense360 panel skews younger as a result of being primarily mobile.	While representative sampling and weighting may correct for this bias to an extent, it is unavoidable and results in an over-representation of this demographic's opinions, thus brands that resonate with this group getting more favorable responses.
Self-perception and recall error	Regardless of source, all survey data is subject to a variety of well-documented human biases.	Larger brands are more top-of-mind and tend to be endorsed higher across the board, regardless of true perceptions. It is recommended to normalize data when comparing large brands to small brands. Survey respondents tend to answer in a way that reflects more desirably on themselves.