AUTOMATED GENERATION OF SERVICE ITEM RECOMMENDATIONS
The present invention relates to systems and methods for generating price book service item recommendations. A process of the disclosed technology includes steps for generating an estimate comprising one or more service items, based on selections at a user interface, analyzing the one or more service items to identify at least one recommended service item, generating a recommendation for the at least one additional service item, and presenting the recommendation on a display associated with the user interface. In various embodiments, a neural network can be applied to determine an association between the one or more service items and at least one additional service item based on a set of past invoices. Systems and machine-readable media are also provided.
1 . A system for generating price book service item recommendations, comprising: one or more processors; and
a computer-readable medium comprising instructions stored therein, which when executed by the processors, cause the processors to perform operations comprising:
generating an estimate comprising one or more service items, based on selections at a user interface;
analyzing the one or more service items to identify at least one recommended service item;
generating a recommendation for the at least one additional service item; and
presenting the recommendation on a display associated with the user interface.
2 . The system of claim 1 , wherein analyzing the one or more service items comprises: applying a neural network to determine an association between the one or more service items and at least one additional service item based on a set of past invoices.
3 . The system of claim 2 , wherein the association identifies service items commonly sold together.
4 . The system of claim 2 , wherein the association identifies a manually-defined relationship between service items.
5 . The system of claim 2 , wherein each invoice identifies at least one sold service item.
6 . The system of claim 2 , wherein the neural network utilizes a unique identifier associated with each service item.
7 . The system of claim 2 , wherein the set of past invoices represent at least one of: a time period, a season, a service region, and a type of service.
8 . The system of claim 1 , wherein the one or more service items are selected from a price book comprising a plurality of service items for purchase.
9 . The system of claim 1 , wherein the user interface is at least one of: a mobile computing device, a smartphone, a tablet, and an interactive display.
10 . The system of claim 1 , further comprising adding the at least one additional service item to the estimate in response to a selection on the user interface.
11 . A computer-implemented method for generating price book service item recommendations, comprising:
generating an estimate comprising one or more service items, based on selections at a user interface;
analyzing the one or more service items to identify at least one recommended service item;
generating a recommendation for the at least one additional service item; and
presenting the recommendation on a display associated with the user interface.
12 . The computer-implemented method of claim 11 , analyzing the one or more service items comprises: applying a neural network to determine an association between the one or more service items and at least one additional service item based on a set of past invoices.
13 . The computer-implemented method of claim 11 , wherein the association identifies at least one of: service items commonly sold together and a manually-defined relationship between service items.
14 . The computer-implemented method of claim 12 , wherein each invoice identifies at least one sold service item, and the neural network utilizes a unique identifier associated with each service item.
15 . The computer-implemented method of claim 12 , wherein the set of past invoices represent at least one of: a time period, a season, a service region, and a type of service.
16 . The computer-implemented method of claim 11 , wherein the user interface is at least one of: a mobile computing device, a smartphone, a tablet, and an interactive display.
17 . The computer-implemented method of claim 11 , further comprising adding the at least one additional service item to the estimate in response to a selection on the user interface.
18 . A non-transitory computer-readable storage medium comprising instructions stored therein, which when executed by one or more processors, cause the processors to perform operations comprising:
generating an estimate comprising one or more service items, based on selections at a user interface;
analyzing the one or more service items to identify at least one recommended service item;
generating a recommendation for the at least one additional service item; and
presenting the recommendation on the user interface.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein the instructions for analyzing the one or more service items further comprises: applying a neural network to determine an association between the one or more service items and at least one additional service item based on a set of past invoices.
20 . The non-transitory computer-readable storage medium of claim 18 , wherein the association identifies at least one of: service items commonly sold together and a manually-defined relationship between service items.
22 . The system claim 1 , further comprising:
receiving, from at least one remote computing device, sale information comprising a service request requiring labor, and a profile about a customer;
applying a first machine learning module to the sale information and a variable data set to generate a set of customer insights; and
generating a set of recommended service items associated with the service request and the set of customer insights.
23 . The system of claim 22 , wherein the variable data sets are continuously refreshed, in real-time.
24 . The system of claim 22 , wherein the variable data sets comprise at least one of a set of external information and technician insights.
25 . The system of claim 24 , wherein the set of external information comprises at least one of a weather prediction, a seasonality, a service item availability, technician availability, supply chain information, and cost information.
26 . The system of claim 24 , wherein the technician insights relate to at least one of technician expertise, completed service requests associated with a technician, a set of service items associated with completed service requests.
27 . The system of claim 22 , further comprising applying a second machine learning module to generate the set of recommended service items.
28 . The system of claim 22 , wherein customer insights comprise at least one of: a likelihood to purchase a type of product, a budget range, a propensity to finance, and an incentive for the customer.
29 . The system of claim 22 , wherein the variable data set comprises at least one of: information previously collected about the customer and information previously collected about the service request.