Leveraging geographic positions of mobile devices at a locale
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;
correlating the at least one geographic position and at least one time interval determined from the plurality of interactions to obtain at least one correlation, the 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.
2. The method of claim 1 , further comprising:
calculating a metric for the locale using the at least one geographic position,
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 a sales transaction from a point-of-sale (POS) device at the locale; and
correlating the at least one geographic position and the at least one time interval determined from the plurality of interactions with the sales transaction.
4. The method of claim 1 , further comprising:
calculating a metric for the locale using the at least one geographic position;
obtaining a plurality of sales transactions from a POS device at the locale; and
correlating the at least one geographic position and the at least one time interval determined from the plurality of interactions with the plurality of sales transactions, wherein the metric is a walk-by conversion rate at the locale.
5. The method of claim 1 , further comprising:
calculating a metric for the locale using the at least one geographic position;
obtaining a plurality of sales transactions from a POS device at the locale;
correlating the at least one geographic position and the at least one time interval determined from the plurality of interactions with the plurality of sales transactions; and
determining, in response to the correlating, an impact on the metric by a promotion used in the plurality of sales transactions.
6. The method of claim 1 , further comprising:
calculating a metric for the locale using the at least one geographic position; and
predicting a value of the metric in a periodic time interval using a trend in previous values of the metric in the periodic time interval.
7. The method of claim 1 , further comprising:
calculating a metric for the locale using the at least one geographic position;
obtaining a plurality of sales transactions from a POS device at the locale;
correlating the at least one geographic position and the 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 previous values of the metric in the periodic time interval.
8. The method of claim 1 , further comprising:
calculating a metric for the locale using the at least one geographic position;
obtaining a plurality of sales transactions from a POS device at the locale;
correlating the at least one geographic position and the 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;
predicting, 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; and
adjusting, in response to predicting the value of the metric, an operating parameter of the locale.
9. 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 the at least one time interval determined from the plurality of interactions with the sensor data.
10. 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 the 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.
11. The method of claim 1 , wherein the at least one network device comprises a wireless access point.
12. 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.
13. 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;
correlate the at least one geographic position and at least one time interval determined from the plurality of interactions to obtain 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.
14. The system of claim 13 , wherein the circuitry is further configured to:
obtain a sales transaction from a point-of-sale (POS) device at the locale; and
correlate the at least one geographic position and the at least one time interval determined from the plurality of interactions with the sales transaction.
15. The system of claim 13 , wherein the circuitry is further configured to:
calculate a metric for the locale using the at least one geographic position;
obtain a plurality of sales transactions from a POS device at the locale;
correlate the at least one geographic position and the at least one time interval determined from the plurality of interactions with the plurality of sales transactions; and
determine, in response to the correlating, an impact on the metric by a promotion used in the plurality of sales transactions.
16. The system of claim 13 , wherein the circuitry is further configured to:
calculate a metric for the locale using the at least one geographic position;
obtain a plurality of sales transactions from a POS device at the locale;
correlate the at least one geographic position and the 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.
17. The system of claim 13 , 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 the at least one time interval determined from the plurality of interactions with the sensor data.
18. The system of claim 13 , wherein the at least one network device comprises a wireless access point.
19. The system of claim 13 , 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.
20. 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;
correlate the at least one geographic position and at least one time interval determined from the plurality of interactions to obtain 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.
21. The non-transitory computer-readable medium of claim 20 , wherein the instructions, upon execution, further cause the computer system to:
obtain a sales transaction from a point-of-sale (POS) device at the locale; and
correlate the at least one geographic position and the at least one time interval determined from the plurality of interactions with the sales transaction.
22. The non-transitory computer-readable medium of claim 20 , wherein the instructions, upon execution, further cause the computer system to:
calculate a metric for the locale using the at least one geographic position;
obtain a plurality of sales transactions from a POS device at the locale;
correlate the at least one geographic position and the 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.
23. The non-transitory computer-readable medium of claim 20 , 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 the at least one time interval determined from the plurality of interactions with the sensor data.
24. The non-transitory computer-readable medium of claim 20 , wherein the at least one network device comprises a wireless access point.
25. The non-transitory computer-readable medium of claim 20 , 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.