Detection of focused vs. wanderer customers based on in-store movement trackings
Publication Date: 2014-Jan-12
The IP.com Prior Art Database
Understanding and classifying shoppers in physical stores is a challenge, since it requires analyzing data about unique individuals. Most in-store analytics provide traffic pattern analysis in the aggregate, since identifying unique individual has been a challenge. With the broad use of smartphones, it is now easier to identify unique individuals in stores by tracking signals such as Bluetooth, WIFI, RFID, etc emitted from their mobile devices. In-store tracking solutions such as IBM Presence Zones and others, provide location tracking for unique individuals based on the mobile device carried by the user. This provides new sources of data for analyzing customer behavior, which can be used to improve store decision making and marketing strategies.
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-store movement trackings
store movement trackings
We assume the input of customer in-store movements data. Such data can be obtained by means of wi-fi monitoring, RFIDs, etc and other technologies that provide information about unique individuals.
We assume that each tracking event is associated with a unique customer id, zone id (e.g., Mens cloths), and enter and exit times.
The following are the various analytics steps for learning focused vs. wanderer
customer segments using various machine learning techniques.
Step 1: Data processing:
Raw zone event data records were transformed into customer visit records
A visit is all events in which the exit of event N is at the same time (or at most 20 seconds before) as the enter in event N+1
Excluded "spotty" data - i.e. where mobile device is not captured consistently
throughout the store visit (such as with devices that don't ping the network often)
A visit is valid if it starts with a store event, includes at least one zone event, and its last zone event exit time agrees with the store event exit time
A co-visit is group of visits, each at most 5 seconds apart. i.e. People shopping together
A returning visit is a visit of same customer within 20 days of her last visit
Zones were further grouped based on their type, e.g.: Mens Zone: Mens-T-Shirts, Mens-Shoes, Mens-Pents
Step 2: Feature Extraction:
Store visit time (sec)
Visit start time of day (hour)
GroupSize A visitor is in a group if entered and left the store exactly the same
time as at least an additional customer. A size of 0 means that the user is not in a group.
WeekDay 1..7, 1 for Sunday, 7 for Saturday.
IsWeekend 1 if so, 0 otherwise. Set to 1 for Saturday or Sunday.
ReVisitStrength20 0 for non returning customer, 1 if returned after 20 days, 2 if
returned after 19 days, … 19 if returned after 1 day, 20 if returned at the same day.
IsReturningCustomer 1 if ReVisitStrength20 is greater than 0.
CashiersTime time (seconds) spent in cashier zones in the last event of the
IsPurchase 1 if CashiersTime is at least 60 seconds, 0 otherwise.
Areal Features (Zone and Zone Group)
Num. area visits
Avg. area visit time
Stdev. area visit time
Area change rate = #area visits / total visit time in seconds
Num. unique area visits
Num area visits diversity - based on Entropy, computed as: − Pa · log(Pa)
wanderer customers based on in
wanderer customers based on in -
Detection of focused vs . .
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,when: Pa= num−visits−in−area−a / num−area−visits
Diversity of accumulated area visit time - Similar to previous diversity, just that
here:Pa = total−time−spent−in−area−a / total−visit−time Step 3: Cluster Analysis (Customer segments detection)
A K-Means with k=2 provided the most coherent clusters (Silhouette ~ 0.8, 14 features)
Manual cluster exploration reveals clear behavioral differences among the two
customer segments, labeled hereinafter "focused" and "wanderer" customers.