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Intent analysis and filtering

The Pega 7 Platform adds intent analysis to its Text Analytics functionality. Through intent detection, you can determine a writer's intent (in posts, comments, messages, and so on) and determine whether the writer is likely to subscribe to your services or buy your products. The intent analysis can also help you prevent churn by reacting to customers who complain or express dissatisfaction about your offerings.

The intent analysis is available only for English language content.

The Pega 7 Platform includes the default pzDefaultIntentModel rule, which detects the following intent types:

Primary Secondary Examples
direct cancel
  • I canceled my service in April but continue to get bills.
  • Please cancel the contract as soon as you can.
direct confirmation, yes
  • It's been resolved on my end. Thx.
  • I've reordered and have confirmation.
direct confirmation, no
  • No thanks, the deposit & the required payments are done now.
  • I refused because I will be out of town from Thursday till Monday.
direct escalation
  • I told him I wanted to speak to his manager.
  • In uPlusTelco, the staff & the manager are to help with any type of problem.
direct greeting
  • Hello, sir!
  • Thanks & Regards
direct stall
  • Requested the team to switch back to that plan.
  • I want to switch to another plan which is 1099 rental with 1mbps speed and unlimited download.
inbound apologize
  • I am very sorry that the package was not delivered on time.
  • Let me apologize for the inconvenience.
inbound compare
  • Much better and easier communication than with the old provider!
  • uPlusTelco's phones are cheaper and more functional than their competitors'.
inbound complain
  • There's been no response so far!
  • I just filed my complaint.
inbound praise
  • Thanks for your help!
  • Best customer support ever.
objective N/A
  • A bank is a place that looks after people's money and keeps it safe.
  • A budget is a plan on how you will spend the money you earn.
outbound churn
  • I'll move over to another provider.
  • We switched back to uPlusTelco yesterday.
outbound inquire
  • Why didn't he like the bank?
  • Phishy emails from uPlusTelco?
sale purchase
  • I bought a brand new laptop and it's working great!
  • I can change my plan to online on their website.
sale sell
  • Poor service uPlusTelco and poor and cheap selling methods
  • Besides that they are selling their data packs at such high rates!
sale wish
  • Two incidents I would like to inform you about:
  • I hope this happens only occasionally.

Use cases

Intent detection can be demonstrated in scenarios for chatbot applications:

Scenario 1: Case creation

Case creation based on detected entities and user intention

Case creation based on the detected entities and user intention

In this scenario, based on the text input, the text analyzer that is used by the chatbot can detect all of the information that is required to create a case for booking a flight.

Scenario 2: Incomplete request

Incomplete request that requires more information

Incomplete request that requires further inquiry

If the information that is provided in the input text is insufficient, for example, if the flight destination is not provided, the chatbot might request that the user provide the missing details for booking a flight.

Scenario 3: Escalation to human operator

Escalation to human operator

Escalation to a human operator

In situations in which the user is unable to get assistance, or is annoyed or irritated, the chatbot can escalate and transfer the conversation to a human operator.

For more information, see Intent analysis.

Expression for filtering intent types

You can configure a data flow to filter text analysis content (posts, comments, messages, and so on) based on the detected intent. For example, you can create a data flow that filters out records that the text analyzer associated with the churn intent type. This type of data flow must contain a Filter shape in which the following expression is defined:

.pyOutcome.pyIntents(1).pyName = "outbound > churn"

The text analyzer can detect multiple intent types that characterize the analyzed content. The pyIntents property must be associated with a subscript value (n), where n is the position in the sequence of detected intents. The detected intents are ordered by their confidence score, starting with the highest score.

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