SecuRita
A conversational INTERFACE design to enhance customer support experience for Securitas Technology
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Securitas Technology provides a range of services allowing access control, remote monitoring, intrusion systems, and so on for both commercial and residential spaces. One of their most used security management applications is HQ that allows users to view, remotely monitor their building, and respond to notifications.
The onset of the Covid-19 pandemic caused a steep raise (600%) in the number customer service calls creating a problem for the business because of increased customer waiting times, abandoned calls and extensive load on the customer care representatives. In order to navigate this problem we worked on a conversational user interface bot that we saw as an effective and feasible solution to capture user needs and help the business in maintaining the relations with their customers.
Post the pandemic, the customer service experience at Securitas Technology suffered because of the surge in support calls and shortage of labor. The UX product manager and our key stakeholder wanted to make this experience more seamless for them by providing a quick and hassle free alternative.
The UX writing of this bot was made to empathize with customers in a way that it understood their problem and provided relevant solutions and replies
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Adapts to user behavior and queries quickly. As it is used more and asked a broad array of questions, it becomes easier for the bot to recognize intents and is able to direct users in the right direction
The bot, after getting an input from the user regarding a particular task, always asks for a confirmation before performing it. This way, the user is able to build trust with the bot and take negative feedback into consideration.
To overcome hight wait times to perform simple tasks like putting your system on test, raising a new ticket, or checking the status of an existing ticket, we enabled Rita to perform these tasks on its own, so the users can avoid waiting on a 1800-xxx-xxx line for long time.
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We generated a user persona and user flow based on data gathered from the interviews and insights from our affinity mapping exercise.
• The Persona helped us empathize and understand the user more deeply
• The user flows allowed us to define a structured start-to-finish flow of how the user might interact with our chatbot for the two scenarios
With the final user flow in place, we decided to go forward with VoiceFlow to create a prototype of our chatbot. VoiceFlow allowed us to design and create a real conversation of the chat flow that was finalized.
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We decided to test this prototype with internal stakeholders to get an idea of how users will behave while interacting with the bot. Think aloud sessions were conducted with 5 internal users to narrow down and understand how the prototype can be reiterated based on
1. How the participants perceive the language of the bot?
2. Is the conversation flow as expected?
We tested users on 3 task scenarios: putting their system in test, checking status of a ticket, and creating a new ticket
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In the final re-iteration of our prototype from the user testing feedback, our focus was to improvise the key experiences. Creating the final experience in Figma enabled us to handle some edge cases to overcome the constraints of VoiceFlow. We embedded the following insights to our Figma prototype:
• More human-like and empathizing language from the chatbot.
• Some action needs to be taken by the bot on negative feedback.
• Specific addresses should be easier to input.
• Service Ticket Status should include - Ticket ID, Date of service, date due to be serviced, system category, service technician.
• Service tickets should be categorized.
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