The surge in home networking and accompanying challenges for call centre operations has exposed Internet connections and speeds as leading issues for troubled homeworkers during the Covid-19 pandemic.
A survey by Sweepr of 600 consumers in the United States and UK has found that of those who experienced technical issues, 58% could not connect to the Internet; 59% experienced slow broadband speeds; 27% had a router/modem issue; and nearly 20% could not connect to a service over a recent 60 day period.
In response, 82% first tried to fix the problem themselves, citing speed and convenience as their top two motivators. However, 72% of those who relied on their provider’s self-service tools found them not easy to find, and 71% said those instructions were also not easy to understand.
Alan Coleman, Founder and CEO of Sweepr, says the proliferation of devices and networks is creating a greater complexity in our homes. “My son was playing Fortnight on his Xbox and then complaining bitterly to me about how poor our Wi-Fi was. And then me trying to do some portion of self-diagnosis to figure out what was going on and realising there were seven or eight different interdependent products, networks and services contributing to his gameplay. And I was meant to figure out where the the failure was in that ecosystem.”
The ultimate result was Sweepr that aims to make self-service simple for the connected home through a white-label, cloud-based platform that enables service providers and connected device manufacturers to respond to consumers’ support requests. This could be through a website, an app, or a skill attached to a smart speaker.
“Problems are complex because homes are complex. And then the attribution of the problem falls more often on the CSP or the ISP than might otherwise be fair, because it might be an issue at a fortnight. It might be a problem with the backslides servers and they’ll get the call and they’ll get the blame,” says Coleman.
Sweepr combines information from home networks, connected devices, and service diagnostics to build context for a user’s problem, using a combination of data analytics, natural language processing and machine learning techniques.
The objective is not only to identify the problem, but if it later requires human intervention to have customer services armed with the right information, or potentially the news that the technical department has already solved the problem.