Context Management: The Key to Personalized Customer Service at Scale

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Jan 28, 2021
Bonnie Bailly
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With nearly 2,000 self-proclaimed chatbot providers on the market, finding the right one for your business may seem to be a difficult and time-consuming task. In reality, of these 2,000 providers, only a few are actually doing things right. 

The ability to properly manage context is one area you must be sure a chatbot platform handles well. Context management and personalization is key to creating truly useful chatbots as context-aware bots are able to provide your customers with faster, more personalized experiences.

What is context management?

Context management is the ability for bots to understand, store, and use relevant data from user messages at the right time and in the right context in order to deliver personalized, fluid conversations at scale.

Many bot solutions simply use predefined user flows with decision trees or keyword detection to converse with users. Meaning the bot can only accurately answer a user’s question if she stays within the same intent or use case in which she began the conversation. 

However, real conversations are messy. Your customers often start by asking one question and then switch to another mid-conversation. The ability for the bot to switch between different intents or use cases rather than following a single one to its logical conclusion is what context management is about. A good chatbot should be able to not only switch gears on a dime, but also identify and store important pieces of contextual information for later use in the conversation. 

Chatbots that maintain the context of a user conversation are able to much more efficiently resolve your customers’ inquiries 

Examples of effective contextual conversations

Examples of contextual data include things like your customers’ names, email addresses, physical addresses, booking numbers, payment information, seat numbers, times, and locations.

Mindsay chatbots capture and store contextual data for many different use cases. Using APIs, we pull information from third party systems, store it as context, and use it to mark where a customer left off, pre-fill order information, and generally create more personalized, seamless customer experiences. 

Here is one example of a contextual conversation: 

Chatbot conversation about tracking an order

This is just a quick example of how our bots remember and store information so your customers don’t have to keep submitting the same information over and over again. 

Good context management can allow your chatbots to resolve your customers’ requests without any qualifying questions or conversations. For example, a public transportation company could display QR codes at different stations with all of the relevant parameters (name of station, train line, date, time, etc.). Then, if a passenger wanted to send feedback or file a complaint, she would simply need to scan the QR code which would immediately open the chatbot asking her to give her feedback via text, voice, and/or images.

Reporting an issue to a chatbot via QR code

Context management can be used in the same way to resolve customer requests about insurance policies, bank account balances, phone plans, utility bills, order statuses, and more.

If you’re ready to start creating your own powerful context-aware chatbots, get started here!

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