Testing Tech Savviness of Your End Users

The incoming generation of workers, the Millennials, expect more from employers in terms of employee benefits. They also expect more from how they interact with service providers, like financial advisors (“FA”) and 401k managers. This trend is part of a larger movement of data-driven customer experience (“CX”). Data-driven CX in general allows businesses to target to specific customers.

FAs may meet a few obstacles in meeting this trend. The first is that they have to send the same message to different generations across a variety of platforms, which we addressed previously. The second is that to ensure a data-driven CX operates it has to be simple. The Corporate Executive Board’s recent surveys of 7,000 customers showed that customers want the simplest experience possible with trustworthy information tailored to their individual needs. A data-driven CX is a two way street: you have to deliver individualized information and also plan to receive information back from the customer in a systematic way that can be collected and analyzed.

One way to balance the need for simplicity with the need for data is to focus on how tech savvy your customers or employees of your customers are. To do so, focus on where the employees or end-users are struggling. And, do so in a way that allows you to both test the customer, while also providing an opportunity for them to learn.

Once you have more information about the tech savvy-ness of your end users, you can shape the customer experience with that data to create a centralized communication platform, like a dashboard, across a variety of distribution channels so that you can send the same message to multiple populations.  This will allow you to individualize enrollment materials, support tools for decision making and even presentations and videos.

What areas should be tested? Key technologies involved in delivering the employee benefit experience include: email; the benefits website and portals; presentation software like web-ex and Citrix; social media, including the messenger apps in Facebook; and online chat for help and specific questions.  Trends in how customers and companies use these technologies can help understand why you need to be aware of how savvy your end user may be with the technology.

Social media savvy-ness is a two way street in terms of understanding your customer. Some businesses have started to use the messenger app to deliver information to customers (like when a product has shipped), creating a familiarity among customers and your end users with this system, something that could transfer into how your software interacts with your end user.  Some companies confirm and receive orders only via text, another messaging system. While this trend is a developing one, many in the tech industry expect that the messaging functions of apps for customer service will become more refined in the near future.  

On a more advanced level, some businesses use artificial intelligence to push individualized information to customers. For example, Pypestream has created a new messaging app that allows businesses to securely chat with customers. This system allows businesses to send secure messages to customers through specific channels. If the technology is secure enough, it could allow FAs to send individualized messages on specific trigger dates to customers (for example, days to retirement, or need for rebalancing).  On a less advanced level, many companies are moving to automated responses, called chatbots. This allows customers to receive quick information on general topics, like the bots in Slack allow for quick setting of deadlines and reminders.

Trends in customer service also include using an omnichannel system for delivering customer service. Those systems allow a help desk to track previous interactions with customer service by an employee or end-user. For example, an employee first sends a general message about inability to access their account, from a drop down box. They then follow up with a specific email. By the time they then contact your online chat, they may have reported the same problem to three different people in three different ways. An omnichannel system integrates this data so that whomever responds understands that the customer or employee’s history.

Similarly, predictive models collect information from the Internet, through Twitter or Facebook or other social media sites, to see what your customer has been discussing. True predictive modeling systems would allow your customer service team to know that an employee has been discussing retirement in nearly every tweet, post or Instagram picture which would enhance the customer service representatives understanding of the issue (or importance of timing) behind the contact. However, most predictive modeling systems are priced beyond where they might be useful for FAs.

Key questions to ask your end users are:

• General feelings about new apps and technology platforms, including, whether they are hesitant to try a new platform, app or device or whether they are confident in trying new technology.
  
  
• Approaches to how end users learn new technology generally, including whether they prefer to learn on their own, with manuals and tutorials, or look to have one-on-one training.
  
  
• The end user’s preferred technology, including cell or smart phones versus desk tops or tablets.  This may help determine use of mobile messaging or chatbots.
  
  
• How your end users are using their mobile devices.

While technology literacy isn’t the same as health literacy (understanding how much a patient understands medical language), FAs could take a cue from the health care industry and find a system that allows for measurements of technology literacy to be noted in an employee or end-user’s files. That system or database field could allow for specific or targeted proactive efforts to raise the technology literacy of the end user prior to rolling out new apps, platforms or making major changes to your benefits website.