Unlocking the Power of Passive In-Store Data Analytics | Street Fight

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Unlocking the Power of Passive In-Store Data Analytics

0 Comments 02 July 2014 by

Data digital flowAll indoor location data is not created equal. In the world of indoor location analytics there are two types for data collection: active and passive. The business objectives and data sets for these two types are different. Retailers have embraced active data via apps and are now testing beacons — but they should also consider the value that passive location data brings to the overall understanding of consumer behavior in-store.

Differences Between Passive and Active Data
While “passive” indoor location data captures natural behavior and intent of the general shopper population, “active” indoor location data captures only those shoppers who chose to directly engage via apps or log-in to access wifi in-store. This data set exclusively captures smartphone user behavior excluding any other devices.

Active indoor data can capture demographic information of consumers via opt-in such as: age, zip code and gender. Specific activities performed on an app or via wifi can also be tracked: websites visited, device type, coupons endorsed, etc. This user data gives visibility into identifying specific shopper profiles and their shopping behaviors.

Passive indoor data collection, on the other hand, requires no action on the part of the consumer. This data is collected most commonly through the passive detection of anonymous wifi and/or bluetooth signals emitted from mobile devices and video camera solutions. The data is anonymous aggregated information of shoppers, including those that may have downloaded an app or checked-in. The resulting data sets are much larger than those of active data.

Passive Observation vs. Active Engagement
Passive data measures shopper behavior through observation rather than app dependent active engagement or consumer initiated actions. The passive observation of shopper behavior measures natural shopper intent. By measuring natural intent, we can then identify opportunities to make improvements, influence behavior and drive results based on how most of the in-store shoppers engage with the environment. The objective of passive measurement is to gain an accurate view of shopper behavior as it is happening naturally. Passive analytics captures behaviors of shoppers whether or not there is any app or log on activity. The passive data set is a larger shopper population allowing for a more representative sample of shopper in-store behavior.

Active data captured via beacon-activated apps engages in-store shoppers connecting them with geo-targeted messaging. Beacons influence behavior and intent. The actions can be measured as to which messages had most activity and where. In passive measurement, shopper behavior can also influenced to drive to action and results, however, it is done through data analysis, making changes to the environment and then measuring results without disrupting intent. This addresses the large segment of the shopping population who are not smartphone users or active with apps and beacons. Passive data looks at stores from the vantage point of measuring how the shoppers behavior across the in-store environment. This view shows how areas within the environment work together and how they interact influencing behavior throughout the store.

Active and Passive Data Have Different End Users
Each of these data sets resonates with different functional areas within retail organizations. Marketing, consumer insights, market research benefit from demographic insights captured with active data information. How customers engage, what messages they engage with and who of the customer base is responding informs effectiveness of their communications with their customer segments. Passive data is utilized cross-functionally across multiple lines of business.  In addition to the aforementioned groups, operations, merchandising and finance use passive data measurement to optimize performance of their respective areas.

Passive data sets are larger than active data sets, anonymized and used across multiple lines of business for holistic understanding of store performance. Active data sets are much smaller, identified data sets delivering insights for marketing and research teams.

Data Context for Passive and Active Data Sets
Active and passive data sets used in combination, give context and deeper insights. Active data can be an overlay to the larger passive aggregate data set. Passive data gives context to active data sets because we have visibility into overall in-store shopper behavior. By overlaying active data onto the passive data set, retailers will be able to get a clear measure of how much influence and how effective they are with active engagement. For example, of the people who endorsed a coupon (active) how many shoppers visited the product/department, how long did they stay and which products/departments did they stop at before they endorsed the coupon (passive).

While individual-shopper-level data has value in understanding shopper profiles, the data is set is a subset of a larger population of all shoppers visiting stores. Passive data gives context to active data and helps retailers understand the complete picture of in-store shopper engagement and behavior. Each in a silo of measurement is interesting and has value. Used in combination, these data sets have context completing a picture of shopper behavior and engagement in the offline environment.

Anne Marie StephenAnne Marie Stephen is a passionate evangelist for innovation in retail technology and analytics. She is currently Vice President of Sales and Customer Development at iinside, location based mobile technology, analytics and engagement for offline environments.

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