Blockchain-based technologies for hyper-personalized interactions across enterprises

1. A method for executing hyper-personalized interactions across enterprises using interactions added to blockchains as transactions, the method comprising:

determining, by a first enterprise system, an intent from a first interaction added to a blockchain via a blockchain transaction, wherein determining the intent from the first interaction added to the blockchain comprises processing data stored on the blockchain using self-executing computer-executable code stored on a block of the blockchain;

determining, by the first enterprise system, a correlation between the intent and a set of subsequent related interactions with one or more enterprise systems different from the first enterprise system; and

generating, by the first enterprise system, a second interaction with a second enterprise system of the one or more enterprise systems different from the first enterprise system, wherein the second interaction is within the set of subsequent related interactions.

2. The method of claim 1 , wherein determining the correlation between the intent and the set of subsequent related interactions comprises applying machine learning to data associated with the first interaction.

3. The method of claim 2 , wherein applying the machine learning to the data associated with the first interaction comprises applying a random forest classifier to the data associated with the first interaction.

4. The method of claim 2 , further comprising training a machine learning model associated with the first interaction.

5. The method of claim 1 , wherein determining the intent from the first interaction added to the blockchain comprises analyzing the first interaction in conjunction with an associated end user's past interactions stored to the blockchain.

6. The method of claim 1 , wherein determining the intent from the first interaction added to the blockchain comprises analyzing at least one of global positioning system (GPS) data, wearable device data, Internet of Things (IoT) data, or social media data of an end user.

7. The method of claim 1 , further comprising generating, by the first enterprise system, a third interaction with a third enterprise system of the one or more enterprise systems different from the first enterprise system, wherein the third interaction is within the set of subsequent related interactions.

8. The method of claim 1 , wherein each of the first enterprise system and the second enterprise system is part of a permissioned blockchain network that stores the blockchain.

9. The method of claim 1 , wherein determining the correlation between the intent and the set of subsequent related interactions with the one or more enterprise systems different from the first enterprise system comprises clustering a plurality of intents of an end user.

10. An enterprise system for executing hyper-personalized interactions across enterprises using interactions added to blockchains as transactions, the enterprise system comprising:

at least one processor; and

at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the enterprise system to:

determine an intent from a first interaction added to a blockchain via a blockchain transaction, wherein to determine the intent from the first interaction added to the blockchain comprises to process data stored on the blockchain using self-executing computer-executable code stored on a block of the blockchain;

determine a correlation between the intent and a set of subsequent related interactions with one or more other enterprise systems; and

generate a second interaction with a second enterprise system of the one or more other enterprise systems, wherein the second interaction is within the set of subsequent related interactions.

11. The enterprise system of claim 10 , wherein to determine the correlation between the intent and the set of subsequent related interactions comprises to apply machine learning to data associated with the first interaction.

12. The enterprise system of claim 11 , wherein to apply the machine learning to the data associated with the first interaction comprises to apply a random forest classifier to the data associated with the first interaction.

13. The enterprise system of claim 11 , wherein the plurality of instructions further causes the enterprise system to train a machine learning model associated with the first interaction.

14. The enterprise system of claim 10 , wherein to determine the intent from the first interaction added to the blockchain comprises to analyze the first interaction in conjunction with an associated end user's past interactions stored to the blockchain.

15. The enterprise system of claim 10 , wherein to determine the intent from the first interaction added to the blockchain comprises to analyze at least one of global positioning system (GPS) data, wearable device data, Internet of Things (IoT) data, or social media data of an end user.

16. The enterprise system of claim 10 , wherein the plurality of instructions further causes the enterprise system to generate a third interaction with a third enterprise system of the one or more other enterprise systems; and

wherein the third interaction is within the set of subsequent related interactions.

17. The enterprise system of claim 10 , wherein the blockchain is stored to nodes of a permissioned blockchain network.

18. The enterprise system of claim 10 , wherein to determine the correlation between the intent and the set of subsequent related interactions with the one or more other enterprise systems comprises to cluster a plurality of intents of an end user.