METHOD AND SYSTEM FOR STATISTICAL ANALYSIS OF CUSTOMER MOVEMENT AND INTEGRATION WITH OTHER DATA
Movement patterns for customers in a retail environment are quantified using a set of movement traces. The quantifications are correlated with other retail metrics to determine which patterns are conducive to positive results for the retailer. In an implementation, first and second distributions are generated using the movement traces. One of the first or second distributions is compared to another of the first or second distributions. A value is calculated indicating a degree of difference between the distributions. In another implementation, a set of node sequences representing paths of customers in the retail environment are obtained. The node sequences are associated with consumer behavior patterns. A target customer is tracked and a target node sequence representing a current path of the target customer is generated. The target node sequence is compared with the set of node sequences to make a prediction about the target customer.
1 . A method comprising:
obtaining a plurality of node sequences that represent paths of customers in a store, each node sequence comprising a sequence of node indices, each node index identifying a node placed on a floor plan of the store, a point on a path of a customer having been correlated to the node;
associating the plurality of node sequences with a plurality of consumer behavior patterns;
tracking a target customer in the store and generating a target node sequence that represents a current path of the target customer in the store;
comparing the target node sequence with the plurality of node sequences to determine a consumer behavior pattern associated with the target node sequence; and
based on the consumer behavior pattern associated with the target node sequence, making a prediction about the target customer.
2 . The method of claim 1 comprising:
calculating a first string edit distance between the target node sequence and a first node sequence associated with a first consumer behavior pattern;
calculating a second string edit distance between the target node sequence and a second node sequence associated with a second consumer behavior pattern;
if the first string edit distance is less than the second string edit distance, associating the first consumer behavior pattern to the target customer; and
if the second string edit distance is less than the first string edit distance, associating the second consumer behavior pattern to the target customer.
3 . The method of claim 1 wherein a first consumer behavior pattern of a first node sequence is associated with shoplifting and the method comprises:
calculating a string edit distance between the target node sequence and the first node sequence;
comparing the string edit distance to a threshold value;
if the string edit distance is less than the threshold value, associating the first consumer behavior pattern associated with shoplifting to the target customer; and
upon the associating, generating a security alert to prevent the target customer from shoplifting.
4 . The method of claim 1 wherein a first consumer behavior pattern of a first node sequence is associated with not making a purchase and the method comprises:
calculating a string edit distance between the target node sequence and the first node sequence;
comparing the string edit distance to a threshold value;
if the string edit distance is less than the threshold value, associating the first consumer behavior pattern associated with not making a purchase to the target customer; and
upon the associating, generating an alert for a salesperson to assist the target customer in making the purchase.
5 . The method of claim 1 wherein the comparing the target node sequence with the plurality of node sequences comprises:
calculating a Levenshtein distance between the target node sequence and a node sequence of the plurality of node sequences.
6 . The method of claim 1 wherein the making a prediction about the target customer comprises:
predicting that the target customer will shoplift.
7 . The method of claim 1 wherein the making a prediction about the target customer comprises:
predicting that the target customer will leave the store without making a purchase.
8 . The method of claim 1 wherein the store comprises a grocery store or a clothing store.
9 . The method of claim 1 wherein the making a prediction about the target customer comprises:
predicting that the target customer will purchase a specific item in the store.
10 . The method of claim 1 wherein the making a prediction about the target customer comprises:
predicting that the target customer will purchase a specific quantity of an item in the store.
11 . A method comprising:
obtaining a plurality of node sequences that represent paths of customers in a store, each node sequence comprising a sequence of node indices, each node index identifying a node placed on a floor plan of the store, a point on a path of a customer having been correlated to the node;
associating the plurality of node sequences with a plurality of consumer behavior patterns;
tracking a target customer in the store and generating a target node sequence that represents a current path of the target customer in the store;
comparing the target node sequence with the plurality of node sequences to determine a consumer behavior pattern associated with the target node sequence; and
based on the consumer behavior pattern associated with the target node sequence, making a prediction about the target customer before the target customer leaves the store.
12 . The method of claim 11 wherein the comparing the target node sequence with the plurality of node sequences comprises:
calculating a Levenshtein distance between the target node sequence and a node sequence of the plurality of node sequences.
13 . The method of claim 11 wherein the prediction comprises the target customer will shoplift.
14 . The method of claim 11 wherein the prediction comprises the target customer will leave the store without making a purchase.
15 . The method of claim 11 comprising:
generating an alert based on the prediction made about the target customer.
16 . The method of claim 11 wherein the comparing the target node sequence with the plurality of node sequences comprises:
calculating a first distance between the target node sequence and a first node sequence of the plurality of node sequences;
calculating a second distance between the target node sequence and a second node sequence of the plurality of node sequences;
if the first distance is less than the second distance, identifying a consumer behavior pattern associated with the first node sequence as being associated with the target node sequence; and
if the second distance is less than the first distance, identifying a consumer behavior pattern associated with the second node sequence as being associated with the target node sequence.
17 . The method of claim 11 wherein the comparing the target node sequence with the plurality of node sequences comprises:
calculating a first distance between the target node sequence and a first node sequence of the plurality of node sequences;
calculating a second distance between the target node sequence and a second node sequence of the plurality of node sequences;
if the first distance is closer to zero than the second distance, identifying a consumer behavior pattern associated with the first node sequence as being associated with the target node sequence; and
if the second distance is closer to zero than the first distance, identifying a consumer behavior pattern associated with the second node sequence as being associated with the target node sequence.
18 . A method comprising:
obtaining a plurality of node sequences that represent paths of customers in a store, each node sequence comprising a sequence of node indices, each node index identifying a node placed on a floor plan of the store, a point on a path of a customer having been correlated to the node;
associating the plurality of node sequences with a plurality of consumer behavior patterns;
tracking a target customer in the store and generating a target node sequence that represents a current path of the target customer in the store;
calculating a Levenshtein distance between the target node sequence and at least a subset of the plurality of node sequences to determine a consumer behavior pattern associated with the target node sequence;
identifying a smallest Levenshtein distance as being between the target node sequence and a first node sequence of the at least a subset of the plurality of node sequences; and
predicting a first consumer behavior pattern for the target customer, wherein the predicted first consumer behavior pattern is associated with the first node sequence.
19 . The method of claim 18 wherein the prediction is made before the target customer leaves the store.