Raise.com Help Center

Creating a global Help Center for all consumer-facing products that allowed customers to find the answers to their pressing questions without contacting service.

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tools

Sketch, Excel, Mural.ly

employer

Raise

role

UX Designer / IA

 

problem statement

With an average of 24 tickets cropping up per 1,000 orders placed on Raise, the UX and data teams collaborated to analyze all help tickets entering the Zendesk system over a three month time period. Ultimately, it was found that many of our users shared seven common questions, which, when cross referenced with call and chat times, were costing each Member Services agent roughly 4.6 hours of each 8 hour shift.

Furthermore, the Zendesk support platform was found to be a costly support and ticketing solution. In an effort to increase efficiency and cut costs, Raise instead implemented with Desk.com (a Salesforce subsidiary) to create a brand new Help Center from the ground up with the goal of allowing customers to find the information they needed without Member Service intervention.

kpis

  • Reduction of Member Services ticket volume
  • Decreased ticket resolution time

process

 To begin, I scraped Raise's website to identify any content or copy that could fit within a Help Center, adding items to a Google Sheet. I then preformed a gap analysis based on the content that already existed vs. a pull of our queries that customers were contacting our Member Services team to resolve. From this point, I worked with copywriters to provide a rough suggestion for content, which was then edited by copywriters for final approval and inclusion in our Help Center. Before the matrix was final, keywords were written for each individual Help Center topic to correlate with our search bar to provide live recommendations as a customer typed a query in an email form (see below).   

To begin, I scraped Raise's website to identify any content or copy that could fit within a Help Center, adding items to a Google Sheet. I then preformed a gap analysis based on the content that already existed vs. a pull of our queries that customers were contacting our Member Services team to resolve. From this point, I worked with copywriters to provide a rough suggestion for content, which was then edited by copywriters for final approval and inclusion in our Help Center. Before the matrix was final, keywords were written for each individual Help Center topic to correlate with our search bar to provide live recommendations as a customer typed a query in an email form (see below).

 

 Once our copy was finessed, I began to assess each content piece in order to preform affinity mapping. Through this process, we had created our final information architecture for the Help Center. I used the online tool Mural so that these clusters were easily sharable digitally across multiple stakeholder groups.   

Once our copy was finessed, I began to assess each content piece in order to preform affinity mapping. Through this process, we had created our final information architecture for the Help Center. I used the online tool Mural so that these clusters were easily sharable digitally across multiple stakeholder groups.

 

 As copy and IA came together, I turned to sketching to ideate quickly.   

As copy and IA came together, I turned to sketching to ideate quickly.

 

Due to the tight nature of this project, and our contract with Salesforce which outlined development dates, I moved very quickly from sketches to high fidelity designs. The designs above highlight the Help Center index page, our brand page (i.e. top-level content), and search results page. Each page also contained robust annotations that were passed along to our development partners.

 

A right rail was used to highlight our brand pages globally for quick navigation across a wide array of topics. The right rail also contained all of our Contact Us functionality wherein customers could reach out to our Member Services team via Live Chat, Email, or Phone. The Live Chat button is dynamic, only appearing when the Member Services team members are on shift and at their desks to triage incoming chats (see below for Live Chat UI).

 

 Lastly, as customers type a message to a Service agent, we detect keywords from their text input in order to surface helpful content in the form of Related Answers -- again, back to the notion of encouraging customers to find the information they are looking for without needing to contact a Member Services agent.   

Lastly, as customers type a message to a Service agent, we detect keywords from their text input in order to surface helpful content in the form of Related Answers -- again, back to the notion of encouraging customers to find the information they are looking for without needing to contact a Member Services agent.