Greater User Satisfaction
with contextual conversations
Effective context management enables virtual assistants to interact with users more quickly and intuitively; it means less robotic and scripted. Contextual data helps assistants complete tasks faster and creates a more natural, human-like back and forth conversations. Personalize experiences to gain higher user satisfaction.
The platform allows you to capture and reuse contextual data for a large variety of scenarios to create more complex use cases and redefine the user experience.
Context Management at the Framework Level
Manage data and contextual details at the framework level – data that can come from user inputs, API responses, sessions, and more – with little coding required. It enables you to create context-aware assistants that can follow conversation history, harvest and harness information from 3rd-party systems, and accurately predict and populate the appropriate task or response.
Variable Memory
When you first create tasks, you can access session variables provides by the platform, or custom variables that you define, as well as the context that defines the scope of the variable.
This allows you to store and retrieve data that can be used for a variety of things, such as personalized greetings, determining where prior conversations left off, or pre-filling order details.
You determine how long these variables are stored and accessible by your assistants – so you can create more intuitive conversations with users on your terms.
Context Switching
Humans tend to switch back and forth between intents. To facilitate this, the platform allows you to determine the conditions that must be met to enable or disable context switches, and add conditional exceptions between tasks with the ability to pass contextual data between them.
The platform handles virtually all complex and diverse content switching scenarios in an effective and efficient manner.
Unparalleled Flexibility
With multiple context types
Create virtual assistants that remember user inputs and answers – and automatically modify their behavior based on sentiment and context. Connect to your backend business systems to get and store the data you need – data that is otherwise often static, contained within a single location, and rarely remembered in context. The platform allows you to create dynamic conversational experiences with the ability to customize, categorize, and apply contextual information as you see fit.
What: Individual user information and preferences can be stored and used in varying tasks. This information can be shared with all enterprise assistants that the user interacts with.
Why: Transactions and interactions are made shorter and easier, as users no longer have to provide the same basic information again and again.
Example: Information like a user’s home address, payment information, airline seat, or baggage preference can be stored and remembered for future interactions.
What: Information that represents company-wide rules and standards that apply to all users and assistants.
Why: Allows developers to ensure assistants keep company requirements in context and that they are enforced when needed.
Example: A company travel or expense policy could be applied that overrides an employee’s preferences when using a travel assistant to book a flight or hotel.
What: User or task information dynamically captured at the assistant level that can be used with some or all of the users of that assistant.
Why: Allows you to use information captured by your assistants – such as the user or task counts and transaction values – to design additional logic that is specific to that assistant.
Example: A customer-facing shopping assistant could collect information about the number of users that purchase a product on a given day. You could design and apply the logic that says to offer every 100th user a special promotion or discount.
What: Information shared by the user that the assistant must remember during a session.
Why: Keeps the user from having to repeat information that’s relevant for completing multiple tasks in a single session.
Example: A bank customer says, “how much do I have in checking?” and then asks to “transfer $500 to savings.” The assistant could recognize “checking” as session context and thus make the transfer without asking the user, “from which account?”.
- User Context
What: Individual user information and preferences can be stored and used in varying tasks. This information can be shared with all enterprise assistants that the user interacts with.
Why: Transactions and interactions are made shorter and easier, as users no longer have to provide the same basic information again and again.
Example: Information like a user’s home address, payment information, airline seat, or baggage preference can be stored and remembered for future interactions.
- Enterprise Context
What: Information that represents company-wide rules and standards that apply to all users and assistants.
Why: Allows developers to ensure assistants keep company requirements in context and that they are enforced when needed.
Example: A company travel or expense policy could be applied that overrides an employee’s preferences when using a travel assistant to book a flight or hotel.
- Bot Context
What: User or task information dynamically captured at the assistant level that can be used with some or all of the users of that assistant.
Why: Allows you to use information captured by your assistants – such as the user or task counts and transaction values – to design additional logic that is specific to that assistant.
Example: A customer-facing shopping assistant could collect information about the number of users that purchase a product on a given day. You could design and apply the logic that says to offer every 100th user a special promotion or discount.
- Session Context
What: Information shared by the user that the assistant must remember during a session.
Why: Keeps the user from having to repeat information that’s relevant for completing multiple tasks in a single session.
Example: A bank customer says, “how much do I have in checking?” and then asks to “transfer $500 to savings.” The assistant could recognize “checking” as session context and thus make the transfer without asking the user, “from which account?”.
Custom Code Logic
Manipulate API responses, promote additional data to the user context, and pull data from the user context with support for custom code logic. This allows you to seamlessly extend the existing functionality of the platform and create advanced, custom conversational experiences that are driven by virtually any form of context.
