Embodiments relate to systems, devices, non-transitory computer-readable storage media, and computer-implemented methods for performing behavioral analytics using short identifiers by obtaining historical event data for multiple events and multiple short identifiers, where each event is associated with a short identifier, selecting a first event location associated with a first event, selecting a second event location associated with a second event that is geographically dispersed from the first event location, determining identifier collisions based on short identifiers associated with the first event location and short identifiers associated with the second event location in the historical event data, determining an average percentage of identifiers collisions based on the identifier collisions that were determined, performing behavior analytics on the short identifiers to generate behavioral models associated with the short identifiers, and generating adjusted behavioral models based on the average percentage of identifier collisions.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a processing system of a device comprising one or more processors; and a memory system comprising one or more computer-readable media, wherein the one or more computer-readable media contain instructions that, when executed by the processing system, cause the processing system to perform operations comprising: obtaining historical event data comprising a plurality of events and a plurality of short identifiers, wherein each event of the plurality of events is associated with a short identifier of the plurality of short identifiers; selecting a first event location associated with a first event of the plurality of events; selecting a second event location associated with a second event of the plurality of events that is geographically dispersed from the first event location; determining identifier collisions based on short identifiers associated with the first event location and short identifiers associated with the second event location in the historical event data; determining an average percentage of identifier collisions based on the identifier collisions that were determined; performing behavior analytics on the plurality of short identifiers in the historical event data to generate behavioral models associated with the plurality of short identifiers; generating adjusted behavioral models based on the average percentage of identifier collisions; obtaining a short identifier from a user device; determining, based on the short identifier from a user device, an adjusted behavioral model of the adjusted behavioral models; determining an expected event frequency based on the adjusted behavioral model; determining an actual event frequency; and transmitting an indication of a new event value to the client device based on a determination that the expected event frequency is less than the actual event frequency, whereby the client device displays a notification corresponding to the indication of the new event value. 2. The system of claim 1, wherein the plurality of short identifiers are integers. 3. The system of claim 2, wherein the integers are stored as short integer variables. 4. The system of claim 2, wherein the integers are four digit integers. 5. The system of claim 1, wherein the determining the average percentage of identifier collisions comprises determining identifier collisions between multiple sets of geographically dispersed event locations. 6. The system of claim 1, wherein performing the behavior analytics comprises performing a cohort analysis for a base time window by assigning each identifier of the plurality of identifiers to a cohort of a plurality of cohorts associated with an event type based on a number of events associated with the identifier and the event type. 7. The system of claim 6, wherein the behavior analytics are performed on each cohort of the plurality of cohorts. 8. The system of claim 6, wherein performing the behavior analytics further comprises: assigning a subsequent time window as a new base time window; and performing a cohort analysis for the new base time window. 9. The system of claim 1, the operations further comprising determining the new event value based on an actual number of events, an expected number of events, a minimum value, and a weighting value. 10. The system of claim 1, the operations further comprising: obtaining a second short identifier from a second user device; determining that the second short identifier is not one the plurality of short identifiers; and transmitting an indication of a second new event value associated with an event to the second client device based on a determination that a total value of the event is less than a baseline value of the event. 11. A method comprising: obtaining historical event data comprising a plurality of events and a plurality of short identifiers, wherein each event of the plurality of events is associated with a short identifier of the plurality of short identifiers; selecting a first event location associated with a first event of the plurality of events; selecting a second event location associated with a second event of the plurality of events that is geographically dispersed from the first event location; determining identifier collisions based on short identifiers associated with the first event location and short identifiers associated with the second event location in the historical event data; determining an average percentage of identifier collisions based on the identifier collisions that were determined; performing, using one or more processors, behavior analytics on the plurality of short identifiers in the historical event data to generate behavioral models associated with the plurality of short identifiers; generating adjusted behavioral models based on the average percentage of identifier collisions; obtaining a short identifier from a user device; determining, based on the short identifier from a user device, an adjusted behavioral model of the adjusted behavioral models; determining an expected event frequency based on the adjusted behavioral model; determining an actual event frequency; and transmitting an indication of a new event value to the client device based on a determination that the expected event frequency is less than the actual event frequency, whereby the client device displays a notification corresponding to the indication of the new event value. 12. The method of claim 11, wherein the plurality of short identifiers are integers. 13. The method of claim 12, wherein the integers are stored as short integer variables. 14. The method of claim 12, wherein the integers are four digit integers. 15. The method of claim 11, wherein the determining the average percentage of identifier collisions comprises determining identifier collisions between multiple sets of geographically dispersed event locations. 16. The method of claim 11, wherein performing the behavior analytics comprises performing a cohort analysis for a base time window by assigning each identifier of the plurality of identifiers to a cohort of a plurality of cohorts associated with an event type based on a number of events associated with the identifier and the event type. 17. The method of claim 16, wherein the behavior analytics are performed on each cohort of the plurality of cohorts. 18. The method of claim 16, wherein performing the behavior analytics further comprises: assigning a subsequent time window as a new base time window; and performing a cohort analysis for the new base time window. 19. The method of claim 11, the operations further comprising determining the new event value based on an actual number of events, an expected number of events, a minimum value, and a weighting value. 20. The method of claim 11, the operations further comprising: obtaining a second short identifier from a second user device; determining that the second short identifier is not one the plurality of short identifiers; and transmitting an indication of a second new event value associated with an event to the second client device based on a determination that a total value of the event is less than a baseline value of the event.