At Looker, we believe in putting our customers' success first. It’s a way of life around here. As Margaret pointed out in her latest blog, caring for our customers is baked into the way we operate.
At Looker, we provide highly technical support over chat, and as you might suspect, we use data - and Looker - to evaluate our efforts. The way we measure success, however, might be a little different than what you would see elsewhere.
For example, a normal chat KPI is chat duration, and many organizations are looking for ways to get that duration down. At Looker on the other hand, chat duration is not an immediate indication of success or failure. We see each chat as a fresh opportunity to teach our customer something more about how they can use Looker, and we’re prepared to take all the time needed to help that person become a Looker expert.
We quantify what we can, but we realized that just looking at the numbers wasn’t enough. To truly evaluate a chat, it needed to be read by a person who could provide qualitative feedback, as well.
Back when our support team consisted of just a handful of us handling support, it was easy for each of us to monitor or review all chats. But as the Department of Customer Love grew we realized we needed a more structured method of tracking growth in more tangible, measureable metrics.
To ensure our level of service scaled as our customer base grew, we adopted a mentorship process to help train and monitor our progress as a team. “Mentors” are assigned newer analysts as “Mentees” and they systematically review random selections of chats, providing detailed feedback as needed. Graded on a “1 to 4” scale, Mentors evaluate the chat based on the overall success of the exchange as well as the Three T’s, our standard touchstone of must-do’s in each and every support encounter.
Accumulating this scaled data each week allows us to use Looker to run our own analysis to reveal how we’re performing as a team, but numbers can only tell us so much. The Mentors’ comments are key to helping Mentees to improve their skills, so detailed notes are used to reflect both their excellent and “not yet excellent” performance. Feeding this information into BigQuery allows us to use Looker to drill into and explore this data in any number of ways that help benefit the team.
With Looker, we can easily slice and dice this data to help us to:
We also find that Peer-to-Peer evaluations keep even the most seasoned support analyst fresh and up to speed on any new insights or Looker innovations.
Sure, we monitor our chat metrics in Looker, but we’re not just looking at dashboards that show chat load and duration to measure success.
At the end of the day we know a chat's success is still quite nuanced by what truly matters: Ensuring the customer feels they received the help they needed and feel they’ve learned something new they can do with Looker on their own.
Helping customers do more with their data, resolving issues and making people happy is what we do in the Department of Customer Love… and we love every minute of it.