DATA-DRIVEN SALES RECOMMENDATION TOOL
One example method includes receiving a quote for provision of goods and/or services, and the quote concerns a particular account, receiving information concerning characteristics of the account identified in the quote, receiving information concerning characteristics of the goods and/or services specified in the quote, generating a probability that the quote will be approved by the account, and the probability is generated based on the characteristics of the account and the characteristics of the goods and/or services specified in the quote, when the probability is below a threshold, generating an adjusted quote based on input received, and generating an updated probability based on the adjusted quote.
1 . A method, comprising:
receiving a quote for provision of goods and/or services, and the quote concerns a particular account;
receiving information concerning characteristics of the account identified in the quote;
receiving information concerning characteristics of the goods and/or services specified in the quote;
generating a probability that the quote will be approved by the account, and the probability is generated based on the characteristics of the account and the characteristics of the goods and/or services specified in the quote;
when the probability is below a threshold, generating an adjusted quote based on input received; and
generating an updated probability based on the adjusted quote.
2 . The method as recited in claim 1 , wherein part of the method performed by a machine learning model.
3 . The method as recited in claim 1 , wherein the probability and/or updated probability are generated based on one or both of: a first deal cluster comprising a plurality of deals and identifying common quote characteristics among respective quotes that are associated with the deals; and/or a second deal cluster comprising a plurality of deals and identifying common account characteristics among respective accounts associated with the deals.
4 . The method as recited in claim 3 , further comprising assigning the updated quote to one of the deal clusters, and updating a ratio of approved deals associated with the deal cluster to which the updated quote is assigned.
5 . The method as recited in claim 1 , further comprising:
presenting, to a user, possible modifications to goods and/or services specified in the quote;
receiving, from the user, the input, and the input indicating selection of one or more of the modifications;
generating the adjusted quote based on the input received from the user; and
providing, to the user, the updated probability.
6 . The method as recited in claim 5 , wherein the modifications are presented in serial form, or hierarchical form.
7 . The method as recited in claim 1 , wherein the updated probability and/or the updated quote are generated almost immediately after receipt of the input from the user.
8 . The method as recited in claim 1 , further comprising:
generating a first deal cluster comprising a plurality of deals and identifying common quote characteristics among respective quotes that are associated with the deals; and/or
generating a second deal cluster comprising a plurality of deals and identifying common account characteristics among respective accounts associated with the deals,
and generation of the updated probability is based on the characteristics identified in the first deal cluster and/or the characteristics identified in the second deal cluster.
9 . The method as recited in claim 1 , wherein a margin associated with the quote is maintained in the updated quote.
10 . The method as recited in claim 1 , wherein the updated probability is higher than the probability.
11 . A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
receiving a quote for provision of goods and/or services, and the quote concerns a particular account;
receiving information concerning characteristics of the account identified in the quote;
receiving information concerning characteristics of the goods and/or services specified in the quote;
generating a probability that the quote will be approved by the account, and the probability is generated based on the characteristics of the account and the characteristics of the goods and/or services specified in the quote;
when the probability is below a threshold, generating an adjusted quote based on input received; and
generating an updated probability based on the adjusted quote.
12 . The non-transitory storage medium as recited in claim 11 , wherein one or more of the operations are performed by a machine learning model.
13 . The non-transitory storage medium as recited in claim 11 , wherein the probability and/or updated probability are generated based on one or both of: a first deal cluster comprising a plurality of deals and identifying common quote characteristics among respective quotes that are associated with the deals; and/or a second deal cluster comprising a plurality of deals and identifying common account characteristics among respective accounts associated with the deals.
14 . The non-transitory storage medium as recited in claim 13 , wherein the operations further comprise assigning the updated quote to one of the deal clusters, and updating a ratio of approved deals associated with the deal cluster to which the updated quote is assigned.
15 . The non-transitory storage medium as recited in claim 11 , wherein the operations further comprise:
presenting, to a user, possible modifications to goods and/or services specified in the quote;
receiving, from the user, the input, and the input indicating selection of one or more of the modifications;
generating the adjusted quote based on the input received from the user; and
providing, to the user, the updated probability.
16 . The non-transitory storage medium as recited in claim 15 , wherein the modifications are presented in serial form, or hierarchical form.
17 . The non-transitory storage medium as recited in claim 11 , wherein the updated probability and/or the updated quote are generated almost immediately after receipt of the input from the user.
18 . The non-transitory storage medium as recited in claim 11 , wherein the operations further comprise:
generating a first deal cluster comprising a plurality of deals and identifying common quote characteristics among respective quotes that are associated with the deals; and/or
generating a second deal cluster comprising a plurality of deals and identifying common account characteristics among respective accounts associated with the deals,
and generation of the updated probability is based on the characteristics identified in the first deal cluster and/or the characteristics identified in the second deal cluster.
19 . The non-transitory storage medium as recited in claim 11 , wherein a margin associated with the quote is maintained in the updated quote.
20 . The non-transitory storage medium as recited in claim 11 , wherein the updated probability is higher than the probability.