Correlating geographic positions of mobile devices with point-of-sales device transactions
Embodiments are disclosed for a method that may include accessing events in a field-searchable data store. The events may include raw machine data associated with a timestamp. The raw machine data may represent interactions between a mobile device and one or more network devices at a locale. The method may further include determining, based on the interactions, one or more geographic positions of the mobile device, and calculating a metric for the locale using the geographic positions.
1. A method, comprising:
accessing a plurality of events in a field-searchable data store, the plurality of events comprising raw machine data associated with a timestamp, the raw machine data representing a plurality of interactions between a mobile device and at least one network device at a locale;
determining, based on the plurality of interactions, at least one geographic position of the mobile device;
calculating a metric for the locale using the at least one geographic position;
obtaining a plurality of sales transactions from a point of sale (POS) device at the locale;
correlating the at least one geographic position and at least one time interval determined from the plurality of interactions with the plurality of sales transactions;
determining, in response to the correlating, an impact on the metric by a promotion used in the plurality of sales transactions; and
predicting, using the impact on the metric, a value of the metric in a periodic time interval using a trend in a plurality of previous values of the metric in the periodic time interval.
2. The method of claim 1 ,
wherein the metric is a wait time in a line at a register at the locale.
3. The method of claim 1 , further comprising:
obtaining, in response to the correlating, at least one correlation comprising a first correlation of a first geographic position and a first time interval;
identifying the first correlation as an anomaly; and
excluding, in response to identifying the first correlation as an anomaly, the first correlation from the at least one correlation.
4. The method of claim 1 ,
wherein the metric is a walk-by conversion rate at the locale.
5. The method of claim 1 , further comprising:
adjusting, in response to predicting the value of the metric, an operating parameter of the locale.
6. The method of claim 1 , further comprising:
obtaining sensor data from at least one sensor at the locale; and
correlating the at least one geographic position and at least one time interval determined from the plurality of interactions with the sensor data.
7. The method of claim 1 , further comprising:
obtaining sensor data from at least one sensor at the locale; and
correlating the at least one geographic position and at least one time interval determined from the plurality of interactions with the sensor data, wherein the metric is a spike in a size of walk-by traffic at the locale.
8. The method of claim 1 , wherein the at least one network device comprises a wireless access point.
9. The method of claim 1 , wherein the plurality of interactions comprises a request from the mobile device to connect to a network at the locale via the at least one network device.
10. The method of claim 1 , wherein the metric is a processing time to redeem the promotion.
11. A computer system, comprising:
a field-searchable data store comprising raw machine data associated with a timestamp, the raw machine data representing a plurality of interactions between a mobile device and at least one network device at a locale; and
circuitry configured to:
access the plurality of events;
determine, based on the plurality of interactions, at least one geographic position of the mobile device;
calculate a metric for the locale using the at least one geographic position;
obtain a plurality of sales transactions from a point of sale (POS) device at the locale;
correlate the at least one geographic position and at least one time interval determined from the plurality of interactions with the plurality of sales transactions;
determine, in response to the correlating, an impact on the metric by a promotion used in the plurality of sales transactions; and
predict, using the impact on the metric, a value of the metric in a periodic time interval using a trend in previous values of the metric in the periodic time interval.
12. The system of claim 11 , wherein the circuitry is further configured to:
obtain, in response to the correlating, at least one correlation, the at least one correlation comprising a first correlation of a first geographic position and a first time interval;
identify the first correlation as an anomaly; and
exclude, in response to identifying the first correlation as an anomaly, the first correlation from the at least one correlation.
13. The system of claim 11 , wherein the circuitry is further configured to:
obtain sensor data from at least one sensor at the locale; and
correlate the at least one geographic position and at least one time interval determined from the plurality of interactions with the sensor data.
14. The system of claim 11 , wherein the at least one network device comprises a wireless access point.
15. The system of claim 11 , wherein the plurality of interactions comprises a request from the mobile device to connect to a network at the locale via the at least one network device.
16. The system of claim 11 , wherein the metric is a wait time in a line at a register at the locale.
17. The system of claim 11 , wherein the metric is a walk-by conversion rate at the locale.
18. The system of claim 11 , wherein the circuitry is further configured to:
adjust, in response to predicting the value of the metric, an operating parameter of the locale.
19. The system of claim 11 , wherein the circuitry is further configured to:
obtain sensor data from at least one sensor at the locale; and
correlate the at least one geographic position and at least one time interval determined from the plurality of interactions with the sensor data, wherein the metric is a spike in a size of walk-by traffic at the locale.
20. The system of claim 11 , wherein the metric is a processing time to redeem the promotion.
21. A non-transitory computer-readable medium comprising instructions, execution of which in a computer system causes the computer system to:
access a plurality of events in a field-searchable data store, the plurality of events comprising raw machine data associated with a timestamp, the raw machine data representing a plurality of interactions between a mobile device and at least one network device at a locale;
determine, based on the plurality of interactions, at least one geographic position of the mobile device;
calculate a metric for the locale using the at least one geographic position;
obtain a plurality of sales transactions from a point of sale (POS) device at the locale;
correlate the at least one geographic position and at least one time interval determined from the plurality of interactions with the plurality of sales transactions;
determine, in response to the correlating, an impact on the metric by a promotion used in the plurality of sales transactions; and
predict, using the impact on the metric, a value of the metric in a periodic time interval using a trend in a plurality of previous values of the metric in the periodic time interval.
22. The non-transitory computer-readable medium of claim 21 , wherein the instructions, upon execution, further cause the computer system to:
obtain sensor data from at least one sensor at the locale; and
correlate the at least one geographic position and at least one time interval determined from the plurality of interactions with the sensor data.
23. The non-transitory computer-readable medium of claim 21 , wherein the at least one network device comprises a wireless access point.
24. The non-transitory computer-readable medium of claim 21 , wherein the plurality of interactions comprises a request from the mobile device to connect to a network at the locale via the at least one network device.
25. The non-transitory computer-readable medium of claim 21 , wherein the metric is a wait time in a line at a register at the locale.
26. The non-transitory computer-readable medium of claim 21 , wherein the instructions, upon execution, further cause the computer system to:
obtain, in response to the correlating, at least one correlation comprising a first correlation of a first geographic position and a first time interval;
identify the first correlation as an anomaly; and
exclude, in response to identifying the first correlation as an anomaly, the first correlation from the at least one correlation.
27. The non-transitory computer-readable medium of claim 21 , wherein the metric is a walk-by conversion rate at the locale.
28. The non-transitory computer-readable medium of claim 21 , wherein the instructions, upon execution, further cause the computer system to:
adjust, in response to predicting the value of the metric, an operating parameter of the locale.
29. The non-transitory computer-readable medium of claim 21 , wherein the instructions, upon execution, further cause the computer system to:
obtain sensor data from at least one sensor at the locale; and
correlate the at least one geographic position and at least one time interval determined from the plurality of interactions with the sensor data, wherein the metric is a spike in a size of walk-by traffic at the locale.
30. The non-transitory computer-readable medium of claim 21 , wherein the metric is a processing time to redeem the promotion.